presentation neurobehavioral systems stimulus

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

• PMC3907162.1 ; 2013 Jan 23
• ➤ PMC3907162.2; 2013 Jun 6

A flexible user-interface for audiovisual presentation and interactive control in neurobehavioral experiments

Christopher t noto.

1 Department of Neurology, Georgetown University, Washington DC, 20057, USA

2 Department of Physiology and Biophysics, Georgetown University, Washington DC, 20057, USA

Suleman Mahzar

3 Current address: Faculty of Computer Science and Engineering, GIK Institute, Topi, 23640, Pakistan

James Gnadt

4 Current address: NINDS/NIH, Systems and Cognitive Neuroscience, Neuroscience Center, Bethesda MD, 20892, USA

Jagmeet S Kanwal

CTN: Participated in experimental design, surgical procedures, writing scripts, data acquisition and analysis, and writing the manuscript.

JG: Designed original experimental set up, participated in scientific discussions, animal acquisition, care and surgeries.

JSK: Conceptualized and participated in experimental design, data analysis and writing the manuscript.

All authors read and agreed to the final version of the manuscript.

Associated Data

Version changes, updated. changes from version 1.

We thank the referees for their reviews of our manuscript and we have addressed the issues raised in the revision. Principally, we address issues relating to sampling rate for specific analog channels collected during our experiments and hardware specifications (ADC conversion, sequencer stepping rate), and the general pitfalls inherent in using video monitoring system in neurophysiology

Peer Review Summary

A major problem facing behavioral neuroscientists is a lack of unified, vendor-distributed data acquisition systems that allow stimulus presentation and behavioral monitoring while recording neural activity. Numerous systems perform one of these tasks well independently, but to our knowledge, a useful package with a straightforward user interface does not exist. Here we describe the development of a flexible, script-based user interface that enables customization for real-time stimulus presentation, behavioral monitoring and data acquisition. The experimental design can also incorporate neural microstimulation paradigms. We used this interface to deliver multimodal, auditory and visual (images or video) stimuli to a nonhuman primate and acquire single-unit data. Our design is cost-effective and works well with commercially available hardware and software. Our design incorporates a script, providing high-level control of data acquisition via a sequencer running on a digital signal processor to enable behaviorally triggered control of the presentation of visual and auditory stimuli. Our experiments were conducted in combination with eye-tracking hardware. The script, however, is designed to be broadly useful to neuroscientists who may want to deliver stimuli of different modalities using any animal model.

Introduction

In neurophysiological research, correlating neural signals driven by stimulus presentation and behavioral response needs to be completed within a limited time frame, generally less than 2 hours when conducted with non-human primates. This requires effective and efficient control of presentation of stimuli, acquisition of data, and monitoring of behavior for reward and task progression. Behavioral neuroscientists have to continuously struggle to both keep up with technological advances to accelerate data throughput and to customize stimulus delivery and data acquisition systems to do cutting-edge research. This adds to the burden of labor-intensive electrophysiological recordings from single or multiple neurons in awake-behaving animals, which nevertheless continues to be one of the most reliable and useful ways to understand neural computations and function. Stimulus presentation paradigms may also need to be routinely modified to conform to the goals of an experiment. All of this has to be accomplished with the constraint of maintaining the experimental animal in a healthy condition until the experiment has run its course, which may take from weeks to months. Moreover, user requirements, dictated by the scientific data and state of knowledge, are a moving target that makes it difficult for for-profit vendors to meet all the needs of their customers. Laboratory heads are frequently faced with the task of either hiring a permanent programmer at the cost of tens of thousands of dollars in annual salary to create and maintain a new program, or abandoning a particular line of experiments that scientifically may be the right direction in which to proceed. Even the choice of hardware and software packages that laboratory personnel could interface with and manipulate easily largely depends upon the available expertise of those working in the laboratory and frequently shifts with the departure of key personnel.

To effectively meet our own needs for the study of gaze control in response to the presentation of audiovisual stimuli, we developed a user-interface that provides a template for others facing a similar challenge. Specifically, we describe an experimental design that uses a custom-written script for controlling communication between Presentation software (Neurobehavioral Systems, Inc., Albany CA) package and data acquisition hardware (Cambridge Electronic Design, Ltd., Cambridge, UK) along with vendor-provided Spike2 software. Each package runs independently on separate personal computers ( Figure 1A ).

A . The system is divided into three levels: Data Acquisition and Behavioral Monitoring, Stimulus Presentation Control, and Stimulus Presentation. B . Typical flow of experiment based on performance of subjects. C . Logical flow chart for behavioral tasks used in the training and testing paradigms.

Rapid eye-movements, or saccades, channel important visual information into the association and prefrontal cortex where it is integrated with previous knowledge to take decisive action 1 – 3 . Much research effort has gone into validating the role of the superior colliculus (SC) 4 – 7 and cortical areas, such as the frontal eye fields 2 and lateral intraparietal areas 1 , in the control of eye movements during visual saccades. Less is known about the kinematic properties and control of auditory priming of saccades 8 , 9 and virtually nothing is known about the modulation of visual saccades by contextual auditory information. Clearly, sensory recall of auditory objects as well as error-correction and decision mechanisms underlying memory-guided saccade initiation or head orientation need to be invoked 9 – 11 . It is less clear, however, where in the brain these two functionally distinct mechanisms might converge 12 , 13 .

We used our newly developed audiovisual presentation and control scripts to acquire new data on responses to auditory and visual stimuli in a reward-driven behavioral task that involved tracking eye movements in a nonhuman primate. Our goal was to facilitate the exploration of neurons that integrate multimodal sensory information from naturalistic stimuli to elicit adaptive behavior. As a first step, we trained monkeys to associate relatively novel sounds, including animal vocalizations, with images that were also considered novel for monkeys maintained in a captive environment. To begin to explore the pontine circuitry creating such associations as well as eye movements, we first used species-specific calls to identify complex auditory stimulus-driven neurons in the IC, and naturalistic images to identify visual and saccade-driven neurons in the SC. This narrowed our search space for finding audiovisual neurons, located potentially at the boundary region between the IC and the SC, and testing if reward modulated their activity. We focused on eye movements as the adaptive behavior since these can be accurately tracked, provide a rapid response, and are controlled by neural activity within the SC 4 – 7 .

Materials and methods

We used two software packages, Presentation (Neurobehavioral Systems, Inc., Albany CA) and Spike2 (Cambridge Electronic Design, Ltd.) in conjunction with data acquisition hardware (Power1401 plus , Cambridge Electronic Design, Ltd.), to control stimulus presentation based on our subject’s behavior. This required communication between Presentation and Spike2 software via serial and parallel ports to either advance or terminate a subject’s task in real-time based on either correct or incorrect behavioral responses, respectively. Data acquisition at a relatively high sampling rate (0.1 ms resolution) by the Power1401 was performed concurrently with stimulus presentation and behavioral monitoring. Our design integrates hardware that is either routinely available in a neurophysiology laboratory or commonly available from vendors ( Table 1 and Figure 1A ). Presentation software is readily available from Neurobehavioral Systems for on-line download ( http://www.neurobs.com ). We chose Presentation because of its large, comprehensive scripting language and intuitive user interface, and because the software allowed a simple method to communicate via both serial and parallel ports of a personal computer running a Windows operating system. The software itself is easy to use and numerous example scripts make the language easy to learn. Spike2 and the Power1401 plus are available for purchase from Cambridge Electronic Design ( http://www.ced.co.uk/indexu.shtml ). We chose Spike2 and the Power1401 plus because of its extensive scripting capabilities, ease of use, and inherent control of data acquisition (here, an analog-to-digital converter cycled through the incoming signals at 1 MHz), independent sequencer control, and straightforward manipulation of both parallel and serial ports (see http://www.ced.co.uk/pru.shtml for hardware specifications). Each software package runs independently on its own personal computer to avoid compromising processor resources. Under Presentation control, video is output by a Radeon 9250 video card on a 55” Visio flat panel HD TV and sound is output using a SoundBlaster audio card by a Bose speaker system. A 16 ms error is inherent in the presentation of visual stimuli due to the 60 Hz refresh rate of the LCD monitor. For experiments designed to perturb subconscious elements of the visual system, display delays could be accounted for by additional code written into the scripts discussed below, or an LCD display may be substituted with some other form of imaging, e.g. a fast stepping motor turning a vertically oriented circular slide tray.

Animal care and preparation

Three Rhesus monkeys ( Macacca mullata ; 2 males and 1 female) acquired from a research facility at Wake Forest University, were available during various stages of testing and data acquisition for the development of protocols described here. Compatible animals were housed in paired grooming/contact cages (~2.5 cubic meters), in a room with a light and dark cycle set by an automatic day/night timer (light from 6AM to 6PM daily) and with full view of colony mates in a large open room. Cages were continuously equipped with swings, mirrors, foraging devices and/or small toys. Daily care and medical maintenance of the animals, including a balanced diet of dry food formula, vegetables and fruit, were routinely provided. Environmental enrichment for the monkeys included playing of natural sounds, radio or TV and daily handling, mock grooming and socialization by laboratory personnel.

Surgical procedures: eye coil implantation and neural recordings

Animals were prepared for participation in experiment by performing two surgeries. For the first surgery, we implanted a head restraining device and one scleral eye coil. With the head secured in a stereotaxic device, a 5 cm midline incision was made in the scalp. Periosteum and muscle was retracted using blunt techniques and the calvarium scraped free of soft tissue. A 3 cm stainless steel bar, which fits a head restraining apparatus of the primate chair, was attached vertically to the calvarium using surgical stainless steel screws and a stainless steel recording chamber anchored to the skull using screws and a mound of acrylic bone cement 14 , 15 . The screws are mounted into small burr holes in the bone and buried in the bone acrylic along with the head post and electrical connectors. A scleral eye coil was implanted on one eye. Briefly, the conjunctiva was cut near the limbus and reflected to expose the sclera. A coil made of three turns of Teflon-insulated wire was sutured to the sclera using 6-0 Vicryl, and the conjunctiva was sutured back over the coil. The ends of the coil wire were led out of the orbit subdermally to the acrylic cap where they were attached to a small electrical connector. One week post-surgery, we began a daily task-specific training regimen. Once training proceeded to an acceptable level, generally within a few months, another aseptic surgery was performed to implant an eye coil on the second eye and one or two stainless steel recording chamber(s) were mounted into the head cap under stereotaxic guidance. The acrylic overlaying the appropriate portion of the skull was removed using dental burrs in a hand drill and a 15 mm craniotomy was made. Stainless steel recording cylinders were placed over the craniotomy and cemented into place with bone or dental acrylic. The sterile interior of each cylinder was secured with a threaded Teflon cap having a pressure-release vent.

Post-surgical maintenance included prophylactic antibiotics for 7 days (Baytril, daily 2–5 mg/kg) and 2–5 days of narcotic analgesics (buprenorphine, 0.05–0.1 mg/kg BID) followed by 3–5 days of acetaminophen (5–10 mg/kg). Flunixin, a non-steroidal anti-inflamatory agent, was administered for 1 to 3 days (0.5–1 mg/kg). We also monitored body weight and food/water intake daily, and performed maintenance of the skin margin and cleaned the recording cylinders.

Behavioral training

During the behavioral training, the monkeys sat in the Plexiglass primate chair within a cube of magnetic field coils. To avoid recording of eye movements being confounded with head motion and to stabilize the head while electrodes are inserted in the brain, the head was restrained painlessly by clamping the head post to a device on the chair. To motivate the subjects to perform adequately, for five days per week they received their daily fluids as reward for proper behavior. When daily training or experiments are terminated prematurely, fluids are supplemented up to the normal daily level for that subject. Fluid intake was monitored and recorded daily. Additionally, pulpy fruit or vegetables were used to reward good behavior when returning the animal to the home cage.

Using standard behavioral shaping procedures, the animals were trained to fixate and to follow small visual or auditory stimuli by rewarding them with a drop of fruit juice from a gravity-fed “straw” for successfully completing each series of eye movements defined by the presentation of the stimuli. Training and experimental procedures were performed for no longer than 5 hours per day, usually for 1–3 hours. Animals exhibiting discomfort were readjusted within the chair or returned to their home cage. The daily manipulations for the animals did not produce pain or distress. The cooperative demeanor of the monkeys gives us reason to believe that they find the laboratory situation stimulating and the social interaction with the investigators satisfying.

All surgical and experimental procedures were performed in accordance with federal and institutional guidelines on the care and use of laboratory animals as part of protocols approved by the Georgetown animal care and use committee (protocol # 09-025).

Stimulus display and trial design

Figure 1B shows what is displayed on the screen and the actions of the subject in response to the presentation of a visual stimulus. Figure 1C is a logical flow diagram to show the various steps listed as 4 tasks in the experimental scheme. The tasks are described as follows:

1. Association task:

A sound is played and the associated target image is simultaneously presented at the center of the screen. In our experiments, short (1 s) tone bursts and natural sounds (communication calls) were presented at stimulus levels of ~80 dB SPL (decibels of sound pressure level).

2. Left-right-association task:

A sound is played and an associated target image is simultaneously presented centered at a horizontal location a user-selected distance from the center of the screen, either on the left or on the right side (the decision to present left or right is decided randomly at run-time).

3. Single distracter task:

(a). A sound is played and at the same time a “green dot” is presented at the center with simultaneous presentation of the associated target image and a distracter image on either sides of the circle. The position of images is decided randomly at run-time.

(b). The target image and distracter image are retained on screen and eye-focus is monitored.

4. Multiple distracters task:

(a). A sound is played and at the same time, a “green dot” is presented at the center with simultaneous presentation of an associated “target image” and multiple (user selected) distracter images at user-specified locations on the screen. The position of the images is deliberately kept fixed in this task.

(b). The target image and distracter image are retained on the screen and eye-position is monitored.

Experimental design

Running the script described in Figure 2A provides a user-interface in Spike2 that begins a cascade of dialog boxes that request information relevant to the experiment (e.g. subject name) and the basic parameters needed to monitor the behavior of the subject (e.g. detection window size, reward duration). After supplying the basic information ( Figure 2B ), a list of experimental scenarios is presented to the user in order to select the condition a subject will face. We have programmed a number of saccade-related tasks that use one (or more) of eight audio stimuli to direct our subject’s behavior to learned associations of visual images. A check box arrangement indicates a combination of stimuli the user intends to use in the experiment. As well, a number of timing variables (‘Time to get on Target’, ‘Initial Fixation Time’, ‘Fixation Time for Reward’) are adjustable by the user. Clicking the ‘OK’ button collapses the association-training dialog box allowing the user to hit the ‘Run’ button to initiate the scenario or to select a different scenario. From this point forward, the parameters dialog, the experimental scenario dialog, and a quit option are always available as buttons to the user on the Spike2 program interface. Selecting another experimental scenario automatically names and saves the current data file while initiating data collection into a new file for the newly selected scenario. Clicking on the ‘quit’ button saves the current data file, terminates the presentation of the ongoing stimulus to the subject and ends execution of the Spike2 script.

A . Flow chart showing the design of experiment control sequence shared between the four scripts. B . The initial interactive user-interface used to collect basic information about the experiment set-up. C . User-interface used to collect the initial parameters for behavioral monitoring of subjects during experiments.

The association-training paradigm

Although quite simple, our ‘Association Training Paradigm’ allowed us to illustrate the inner workings of 1) the “Spike2 control” script, 2) the “sequencer script”, and 3) the “presentation script” as they operate across all the current scenarios available to the user. Before going on, we should discuss what we expect from the program and subject, so we can better discuss the interweaving functions of these three scripts. Figure 1B shows the progression of stimuli if the subject succeeds across all phases of the trial or fails at any time in the trial. This task has three phases: 1) an initial black screen or timeout screen, 2) an initial fixation target, and 3) test stimulus presentation. During phase 1, behavior is not actively monitored. The duration of the timeout is set to 2 seconds in the presentation script. At the inception of phase 2, the sequencer acting through the script loaded to the Power1401 memory begins monitoring eye position. The subject must first acquire the target and maintain gaze on the target within a small “forgiveness” window for the user-defined epoch of time. Successful fixation of the target advances the scenario to phase 3 by a command issued first from the sequencer to the Spike2 ‘control’ script and then from the ‘control’ script to the presentation script. Failure results in a reset to the black screen and a brief timeout using the same flow from sequencer to presentation script. A response token is sent directly back from the presentation script to both the ‘control’ script and sequencer ensuring that all three scripts remain synchronized. Phase 3 consists of the presentation of our test stimuli, here the co-presentation of an image and sound. Successful fixation of the image within a forgiveness window, equal to the size of the image and for the user-defined time, initiated by a dialog box shown in Figure 3A , results in the delivery of a reward to the subject as commanded by the sequencer. Successful fixation or failure to look at the image commands a reset of the experimental process to the black screen for a 2 second refresh period.

A . The list of proposed paradigms users may select from to start a session; currently available are Calibration and the Association Tasks, and include “walk” tasks that were not implemented in the present version. “Walks” are saccade tasks designed to use a single target that appears on a black screen in various locations, moving in patterns ascribed by the selection buttons and subsequent dialogs boxes that may be added by the user. They can be used to train the animal and record metrics of their eye movements. B . User-interface that allows for selection of the auditory-visual pairing used during the association training paradigm and the timing criteria necessary for successful completion of the task.

Script components

Our stimulus delivery and data acquisition package consisted of four primary components that operate in conjunction with one another. A “sequencer” script written from within Spike2 is loaded to the Power1401 for real-time monitoring of eye position and saccades. Sequencer scripts (included in the “Sequencer Files” file below) are ultimately responsible for issuing commands that direct progression through a task and to reward the subject. Two scripts (included in the “Spike2 Control Scripts” file below) operate in the Spike2 software environment. The first script controls the interfaces into which a user inputs relevant control parameters. The second script provides the functional control between the scripts running on the Spike2 computer and script running on the Presentation computer. One script (included in the “Presentation Scripts” file below) runs in the Presentation software environment commanding the output of stimuli and communicating by connections of the parallel line (to the Power1401) and the serial line (to the computer running Spike2) a time-stamp indicating when the presentation script commands the presentation of a stimulus.

Sequencer script

The sequencer script downloaded to the Power1401 module runs using an independent clock ticking at 1 µs from the Spike2 computer, but communicates with it through a high-speed USB port. The sequencer script consists of two parts: 1) the initialization section and 2) the monitoring section. The initialization sections load the user-defined variables set while interacting with the dialog boxes created by the ‘interface’ script. In our example of the association-training scenario, the variables loaded are the edges of the forgiveness window, the three timing criteria (time to get on target, initial fixation time, and fixation time for reward), and reward pulse duration. The sequencer cannot act on these values directly so we convert them to sequencer-relevant values. The edges of the forgiveness window are converted from the user-defined values in degrees to digital-to-analog converter (DAC) values. The timing criteria and reward pulse duration are converted from milliseconds to sequencer steps per ms. The monitoring section is made up of the same number of sections as the scenarios or situations (here three), each with specific tasks. The first task checks that the subject acquires the fixation target after it is presented within the user-defined epoch of time. The state of fixation, success or failure, is sent to the Spike2 ‘control’ script. If the sequencer determines the subject has worked within the task bounds, the sequencer steps to the next phase of the monitoring section and waits for a confirmation that the scenario has advanced from the ‘control’ script. The second task checks that fixation is maintained on the target for the specified time. Once again, information about the state of fixation, either success or failure, is sent to the Spike2 ‘control’ script. If the sequencer determines the subject has worked within the task bounds, the sequencer steps to the final phase of the monitoring section and waits for a confirmation that the status has advanced from the ‘control’ script. The final phase operates exactly as the second phase except that if the subject complies, a reward pulse is sent from the Power1401 to a reward delivery system though one of the digital I/O ports. Regardless of whether the trial is deemed a success or failure, the sequencer returns to the initialization section and resets the variables to their initial user-defined state. The process loops with each trial.

Multiple sequencer files necessary for monitoring and rewarding correct animal behavior during the various tasks

The Spike2 ‘control’ script

The ‘control’ script runs in the background on the Spike2 computer and uses the first bit of the COM-1 port input to communicate with the Presentation computer and controls advancement through the Presentation script. The serial line conveys the hexadecimal representation of the words (descriptors and terminators) used to call images and sounds and response tokens between acquisition and presentation computers, respectively. This bit was opened, written to, and closed by the respective portions of the scripts running on the acquisition or presentation computers. Each task consists of a looping “do case” function with progress through the function determined by the fixation state passed from the sequencer. In our example of the association-training task, there exist three fixation failure situations and three successful fixation situations. The first failure scenario occurs when the screen is black and the subject has no target to fixate. The script simply calls for the presentation of the fixation target by issuing a command to the computer running Presentation. The second and third failure situations are similar and initiate a command for the presentation of the black screen to the subject. The first success case assumes that the subject’s gaze is directed toward the fixation target’s location when there is none present. In this situation, the script calls for the fixation target to be presented, just as in the first failure case. The second success situation calls for the Presentation script to display the test stimulus. The third success scenario initiates a reset of the screen to a blank (black) display by the Presentation script. Following each call to the Presentation computer, the ‘control’ script listens for a reply on bit 1 of the COM-1 port. Upon receipt of the response the “do case” state is returned to the sequencer to allow advancement through the monitoring sections of the script ensuring proper stepwise alignment of all three scripts throughout the task.

The two control scripts used to interact between users and the presentation script as well as the configuration used to collect data.

The presentation script

The Presentation script running on the Presentation computer acts as a slave to the Spike2 ‘control’ script receiving instructions and replying through the first bit of the COM port. This script has three primary sections: 1) the video monitor setup, 2) image and sound object creation, and 3) the experimental loop. The first section of this script requires the user to predefine the current display properties (resolution and color depth) including the height and width of the monitor, and distance of the subject to the screen. In this way, Presentation calibrates itself so that target and image positions may be stated in degrees and drawn at the appropriate size. The second section predefines all the potential objects that may be called during the experiment after selection via a dialog box ( Figure 3B ) and their association, if any. For example, our fixation target is a small green dot. We have created an object (e.g. named ‘greendot’) that holds all the relevant information about how presentation draws our fixation target (e.g. dot size, color of the background, etc.) when a call is made to the object. In the third section, the Experimental Loop monitors the COM-1 port for communication from the Spike2 computer. This loop is largely comprised of “if, then, else” statements. Each communication from the ‘control’ script is pre-defined so that when the ‘control’ script shunts words and terminators (e.g. ‘grendot\n’) to the Presentation computer, the Experimental Loop recognizes the word (grendot) and terminator (\n) and falls into the appropriate “if” statement. In the case of the ‘grendot\n’ combination, the “if” statement calls for our object ‘greendot’ so that the fixation target is displayed on the monitor, and at the same time triggers a reply to the ‘control’ script on the COM-1 port and to the Power1401 on bit 8 of the parallel port (a 1 ms low-high-low transistor-transistor logic (TTL). The script then returns to the loop, listening for the next command from the Spike2 computer. In this way, each object may be called in any sequence as commanded by the Spike2 ‘control’ sequence. In the case of our example, the next word that the loop would receive would be ‘SndPICn\n’. Similarly, the loop falls into the appropriate “if” statement, displays the test stimulus, replies to the Spike2 computer and Power1401, and returns to the loop.

The single necessary presentation script needed to display images and sounds upon command from the Spike2 control script.

Paired sound and image files necessary for running all association tasks, frequency tone ranges used for testing frequency tuning of neurons (not discussed in the text) and species-specific communication calls.

Data acquisition and analysis

Up to five days a week, a two-hour neural recording period occurred between 10AM and 4PM to ensure overlap with veterinary staff hours. Animals were moved from their home cage to an adjacent room for neural recording sessions while seated comfortably in a primate chair. In the recording room, the animal’s head is fixed facing forward, in full view of the LED (light emitting diode) monitor set 48 inches in front of them with the center of the screen at the approximate height of the animals straight ahead gaze. Extracellular neuronal recordings were made using standard electrophysiological methods in behaving subjects using fine wire tungsten microelectrodes (31 gage, Microprobe, Inc.) mounted in a guide tube of stainless steel hypodermic tubing 16 . Transdural penetrations were made by a hydraulic microdrive (FHC, Inc.) advancing a tungsten electrode through the bore of a 21 gage hypodermic needle mounted in a micropositioner that attaches to the outside of the chronic recording cylinders on the animal’s head. Neuronal activity was recorded on the hard drive of the data acquisition computer running the Spike2 control scripts via a high impedance amplifier system (AMC Systems, Inc.). We collected one channel of raw neural signal at either 25kHz or 50kHz, four channels corresponding to horizontal and vertical eye position at 1kHz, one auditory channel at 25kHz, and one channel of timestamps at 10kHz, generated on-the-fly during acquisition of data using an adjustable threshold set on the channel collecting the neural signal, for spike times. Digitizing the raw neural signal allowed for post-hoc analysis using the Spike2 software that provides software window discriminators and level detectors as well as various forms of waveform analysis including template matching and spike sorting, using PCA algorithms.

Data analysis and recording was conducted using Spike2 software (Cambridge Electronic Design, Ltd.). Custom-written scripts were used to build raster plots and peristimulus – time histograms (PSTHs) for display of processed data for well-isolated single units whenever possible. Only sample data from single or few-unit activity are provided here to demonstrate feasibility for the purposes of this project, which was designed for development of experimental control procedures.

Results and discussion

As proof of concept, we present here a number of behavioral and neural responses from various brain structures that are activated in response to naturalistic stimuli presented within our experimental set up. To reiterate, we were primarily concerned with capturing 4 basic types of neural responses: 1) visual, 2) auditory, 3) saccade, and 4) reward-driven. This analysis utilizes the timestamps placed in the data files by the presentation script’s 1 ms TTL pulse sent to the Power1401 during data acquisition. Neurons were recorded from the midbrain in the putative inferior and superior colliculi (IC and SC, respectively) of one of our nonhuman primate subjects. Figure 4 describes typical neural responses in the IC following the presentation of complex communication sounds or “calls” that contained acoustic features preferred by the neuron 17 – 20 . Of the ten neurons from which electrophysiological activity was recorded, all responded to at least one of the seven sounds presented. As an example, Figure 4 shows the response of two neurons from the same animal to the same three sounds. We found that each sound produced a distinct temporal response pattern. These patterns could range from no or transient increases in the overall firing rate (upper left panel) to intense phase-locked responses to acoustic features within a call (lower right panel).

Amplitude envelops (top) and raster and PSTH (10 ms bins) plots superimposed on call spectrographs of three different call types (grunt, harmonic, noisy) to show the response of 2 neurons (neuron 1 is from a female and neuron 2 is from a male) in the monkey inferior colliculus. Each call presentation was repeated 40 times per histogram. Grey vertical dotted lines indicate sound onset. Note the response build-up to the third predominant amplitude modulation in the last call. Average first peak response latency to calls was 20.9 +/- 3.5 ms (n = 10). Responses with a potential for temporal facilitation are enclosed by ellipses, although response enhancement may also depend on the basic acoustic patterns within complex sounds or on amplitude tuning. Calls were downloaded from the following web site: http://www.soundboard.com/sb/Rhesus_Monkey_sounds.aspx .

The neuron shown in Figure 5A and 5B illustrates the characteristic visual activity one expects to find while recording from rostral-superficial layers of the SC 21 . Once gaze was directed to position the eyes within the receptive field of this neuron we observed steady, low-rate firing within ~20 ms. In this example, the subject was required to make a saccade to capture a sound-associated image. After the fixation target was extinguished and the target image was presented in the peripheral field of vision, the neural response declined and resumed only when the eyes were positioned again on the target image. The neuron shown in Figure 5C and 5D fits the characteristics attributed to neurons of the intermediate layers of the SC 22 , 23 . Namely, a 60–80 ms build up in activity followed by a burst of spikes just prior to the initiation of direction-dependent saccades to our visual stimulus. Examination of the neural data collected during “spontaneous” eye movement behavior shows that this neuron preferred saccade vector (>20 degrees amplitude, 137 degrees angle), which is well off the axis of our stimulus.

A . Summed histogram from multiple trials triggering visual stimulus-induced activity in a “fixation” neuron located at the rostral pole and superficial layers of the superior colliculus (SC). Electrophysiological responses (timestamps for spikes) were aligned to the time at which the subject acquired the fixation target to begin the trial. B . Saccade-triggered transient suppression of neural activity in a different neuron located within intermediate layers and caudal to the fixation-neuron in the same animal. Dashed vertical line is at time zero for stimulus presentation in “A” and for target fixation in “B”. The width of the grey bar indicates the neuron’s visual delay (>20 ms). C . Raster plots (above) and binned profile of summed response (below) to compare neural activity during rightward (top panel) and leftward (bottom panel) saccades. Grey bins indicate the build-up phase while the black bins indicate the burst phase of the neuron. Dashed vertical line indicates saccade onset. D . Heat map of saccade-related neural activation. Black box enclosed by dashed lines indicates position of the target image relative to central gaze. Solid white arrow represents the vector for the preferred saccade as indicated by firing rate of the neuron.

Example data collected in the IC and SC showing auditory, visual and saccade-related activity, as well as reward-related activity.

Of the total population of neurons studied in the SC by Jay and Sparks 12 , 79% showed saccade-related bursts prior to eye movements to either visual or auditory evoked target stimuli suggesting that saccades evoked by either stimulus share a common efferent pathway to generate the movement. Meredith et al. 13 recorded 113 neurons in the SC (82/113 were auditory-visual neurons) of anesthetized cats during presentation of single and temporally overlapping sensory stimuli. Peak response in neural firing to multisensory signals occurred when stimuli were presented concurrently, with the second stimulus starting <100 ms from the first. Since then, research has shown that in the deep layers of the SC, most neurons respond to both visual and auditory stimuli; 99 of 121 SC neurons showed significant alteration in firing rates due to eye position 12 .

Approximately 60% of neurons within the IC have been shown to respond to not only sound 17 , but to some extent visual- and saccade-related activity 24 , 25 . Inputs from the lateral nucleus of the IC and the nucleus of the brachium of the IC to the SC also exist 26 . This pathway may be responsible in part for the auditory activity observed in the deep layers of the SC 27 and is one route via which auditory information can influence saccadic eye movements. The response of IC neurons to visual stimulus and during eye movements is much less robust than the activity observed following visual stimulation and during saccades in the SC. The use of natural stimuli is expected to boost the responses of IC neurons in an audiovisual recall task to reveal multi-sensory integration that can influence saccade-related activity. Figure 6 illustrates a neuron’s activity that is putatively considered reward-dependent 28 , 29 . The neuron was located rostral to the IC and deep to the region known to contain neurons controlling saccade-related activity in the SC. The neural activity was clearly phase-locked to the task, but was less obviously linked to auditory stimuli ( Figure 6A ), contrary to what one would ordinarily expect in IC neurons (compare with Figure 4 ). This activity was not strictly linked to visual stimuli, nor was it saccade-related in terms of SC activity. The activity of this neuron seemed to indicate an expectation of reward that builds up based on successfully meeting task-related milestones ( Figure 6B ). During the task and especially following the onset of the sound, very distinct differences existed in the firing pattern of this neuron compared to between the two conditions.

A . Single experimental trial illustrating stimulus presentation and related neural activity. During these trials, the subject was rewarded for successful discrimination of a sound-associated (target) image and a distractor image. B . Spike density waveform averaged from 20 trials aligned on fixation target onset. All other behavioral and stimulus markers are centered at their average time of occurrence and grey boxes indicate the first standard deviation in event time.

Both auditory and visual activity in space is read out in the SC in a manner that is appropriate for generating accurate saccades to sounds and images, respectively, although visually evoked saccades have high velocity, greater precision and shorter reaction times than auditory evoked saccades 4 , 11 , 30 . These and many other findings clearly indicate an extensive auditory input to the SC. Briefly, visual information from the retina drives the development of and maintains a spatial representation of auditory space in the IC 31 – 33 . This has been demonstrated in owls 33 , 34 and is believed to be true in mammals. We presume that retinal inputs pass through the optic tectum and the superficial layers of the superior colliculus (SCs) before converging on auditory areas in the IC ( Figure 7 ). A pathway from the retina to SCs to IC is known to exist in mammals 35 . Over the long term, the convergence of visual and auditory signals reinforces an enduring spatial map in the IC. Recently, many neurons within the IC (the brachium of the IC. The external capsule of the IC, and the core of the IC), have been shown to respond to not only sound 17 , but to some extent visual- and saccade-related activity and in some cases responses are modifiable by reward 24 , 25 .

The superior (SC) and inferior colliculi (IC) receive direct visual (shades of green) and auditory (shades of red) projections, respectively and have reciprocal connections with each other. The IC also receives emotive inputs from the amygdala (AM) either directly 43 or via reward circuitry in the ventral striatum 44 , and has reciprocal connections with the auditory cortex (AC) for cognitive processing 48 . Saccadic eye movements are controlled by outputs from the SC via local burst generators (BG) driving motor neurons innervating extraocular muscles (EOM). The SC receives information from the visual cortex and premotor neurons in the frontal cortex, particularly the frontal eye fields. AC = Auditory Cortex; CN = cochlear nucleus (VIIIn); MN = motor neurons (nuclei of cranial nerves III, IV and VI); PM = Pre-motor Cortex; VC = Visual Cortex.

In many species, including humans, who rely predominantly on vision for their survival, auditory cues may trigger eye movements either for interaction with the environment or for communication with conspecifics. Many researchers point to the SC and IC as components of a multi-modal sensory integration system, where visual and auditory signals within the brain merge into a co-dependent representation of the world 3 – 5 , 36 , 37 . Neurophysiological and anatomical data support the idea that this linkage occurs only two or three synapses beyond the retina and auditory nerve. Signals sent out of the IC and SC are also fed-back onto their independent systems helping to modulate behavior (see Figure 7 ).

In summary, collecting behavioral and neural data using our suite of scripts and hardware together with subsequent analysis yielded new insights, providing strong evidence for the advantage of using a novel and customized paradigm. Our scripted user-interface demonstrated that pairing auditory and visual stimuli caused modest changes in activity throughout the trial period in a receptive neuron located deep within the SC. This was in contrast to the response of the same neuron presented with the same stimuli when the animal listened to them passively. The SC appears to be the site where sensory signals encoded in different frames of reference converge, and are translated into a common coordinate system commanding movement execution 11 (e.g. retinotopic-centered commands to resolve motor error). Integration of auditory and visual information also appears to occur at this site. A major cortico-collicular auditory projection suggests that the cortex may direct this integration via the IC, particularly during the learning phase 38 , 39 . After that, subcortical circuits may function autonomously for computing a reaction.

Conclusions

In conclusion, we have developed a simple and relatively straightforward user-interface that directs and monitors subject behavior as well as acquires data. This particular set-up and the customized paradigms used in this experiment may be impossible for vendors of commercial stimulus presentation and data acquisition software and hardware to develop for the general neuroscience community due to the specific needs of each research laboratory. Our experimental design and custom scripts, however, are flexible to meet virtually all experimental control and data acquisition needs of those interested in conducting behaviorally controlled, response-based experiments. We have used a modified design to run psychophysics experiments on human subjects and these can be combined with dense array EEG recordings in response to the presentation of auditory and visual stimuli 40 . In essence, our template can be used to build any type of subject-interactive experiments. There is high potential for applying our pragmatic design to control neurobehavioral experiments using readily available hardware and software. Our studies, using earlier methodologies, showed that arousal has a role in bottom-up modulation of thalamic activity in the control of eye-movements 41 , 42 . Our new methodology allowed us to discover the location of audiovisual neurons at which reward-based, and possibly anxiety-driven, influences may converge to modulate behavior 43 – 47 . Studying these circuits in intact, normal animals is important to decipher the interplay of excitation and inhibition between different neural circuits for dynamic control of eye movement and gaze control.

Acknowledgements

Mr. Dolphus Truss was especially helpful in assisting with animal care issues.

v2; ref status: indexed

Funding Statement

Work supported in part by grant EY015870 to JSK from the National Eye Institute (NEI). We also thank the Biomedical Graduate Research Organization (BGRO) of Georgetown University for financial support to JSK during the later phase of this project. The content is sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Referee response for version 2

Bruce cumming.

1 Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health (NIH), Bethesda, MD, USA

This would be a much stronger contribution if the authors added measurements of the critical parameters, including quality of video synchronization and loop delay. Their responses place some theoretical limits, but a measurement would be much better.

Nonetheless, as a forum for publishing code that can be used by people not concerned with these details, this may be useful.

I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Farrel Robinson

1 Department of Biological Structure, University of Washington, Seattle, WA, USA

The amendments made to the article have improved the manuscript.

Referee response for version 1

This title is appropriate. The content of this article clearly describes the authors’ interface, components, and connections and the behaviour of this system. The authors support their assertion that they have developed a flexible and useful system for controlling and recording behaviour experiments. The authors show enough appropriate examples to support their assertions.

Vincent Ferrera

1 Columbia Neuroscience, Columbia University Medical Center, New York, NY, USA

Minor comments

• Not clear about the hardware for neural signals. Was neural data stored on a third computer? Or on the spike2 computer? How many channels, what sampling rate, etc? How were spikes detected – window discriminator, waveform analysis?

Jagmeet Kanwal

Georgetown University, USA

Thank you for taking the time to review our manuscript. Below we provide our detailed response to specific comments per changes made in the updated version of the manuscript. 

• The technical specifications can be found at:  http://www.ced.co.uk/pru.shtml . We now cite this resource in the revised version. We specifically choose the Power 1401 because the hardware is capable of converting analog signals at high (1MHz) rates while simultaneously reading the ADC channels to allow the user to monitor subject behavior. Thus, the behavioral monitoring script loaded into the 1401’s sequencer at a 1 µs tick rate; i.e., one line of code was read with each tick of the clock. Combining these two capabilities gave us practically instantaneous monitoring of our subject and the ability to respond to their behavior with sub-millisecond precision. In the examples presented, we simply rewarded the animal for maintaining its eye position within a defined spatiotemporal window. One could extend this monitoring by defining a threshold on eye velocity, using that threshold to output a TTL pulse to trigger an external stimulator with similar precision.
• This statement now reads: “which nevertheless continues to be  one of  the most reliable and useful ways to understand neural computations and function. ”
• Bit-1 of the serial line, com port, conveys the hexadecimal representation of the words (descriptors and terminators) used to call images and sounds and response tokens between acquisition and presentation computers, respectively.  This bit was opened, written to, and closed by the respective portions of the scripts running on the acquisition or presentation computers. The signal passed between computers in less than a millisecond.  We are unclear on which “signal timing diagram” is referred to by the reviewer since we do not use this term in the manuscript.
• We agree that LCD displays include an inherent error in the timing of the display of images because of their refresh rates (at 60Hz, the error could be up to ~16ms per frame). The error can be exacerbated by slow video rendering by the video card, which nowadays is less of an issue than in the past. We did not determine the exact error of our system. However, the response time of the display was 8 ms, which is half the refresh rate meaning that the system carries a maximum error of approximately 16 ms to refresh the entire image. For computing absolute perceptual or behavioral response delays, the refresh rate can be subtracted from the timing of the behavioral response, e.g., in our case eye-movements, though we were not interested in these particular parameters, only in using eye movements to control stimulus presentation and reward delivery.
• The neural signal passed through a filter and amplifier before undergoing analog-to-digital conversion by the 1401. We stored the data on the same computer running the acquisition script. We typically collect one channel of raw neural signal at either 25kHz or 50kHz, four channels corresponding to horizontal and vertical eye position at 1kHz, one auditory channel at 25kHz, and one channel of timestamps at 10kHz, generated on-the-fly during acquisition of data using an adjustable threshold set on the channel collecting the neural data on spike timestamps. Digitizing the raw neural signal allowed for post-hoc analysis using the Spike2 software that provides software window discriminators and level detectors as well as various forms of waveform analysis including template matching and spike sorting using PCA algorithms.

Smaller questions

• An important problem is how precisely video events are synchronized with the neurophysiology. The moment at which a display computer requests an image change and the moment at which the first pixels of the new image are actually displayed on their LCD are two different things, and may well not even have a fixed delay (depending on the details of both the display and the rendering). How is this achieved? What is the delay? How variable is it? These crucial parameters are not reported.
• What is the total loop delay from detecting some event in the A/D stream (eye movement, Spike) and the change in some output (electrical stimulus, image refresh with a new image)? If detecting these events depends upon the Spike2 control script, then delays can be quite long. Implementing them in the Sequencer is much harder. This loop delay potentially places fundamental limits on the range of applications that might be possible (whether gazed contingent displays are possible, or performing cancellation tests with antidromic stimulation). 
• Since the paper objective is to describe a software/hardware system, the details about surgery and training, and most of the results, seem irrelevant. This space would be better used describing measures of the system performance. 
• In principle one might do something similar combining other separate systems e.g. any other commercial electrophysiology system and psychophysics toolbox for the display. What are the merits of the different possibilities? Without comparing the available options, this description is of limited use.

I have read this submission. I believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

We thank the referee for taking out the time to review our manuscript and have addressed the issues raised in the revision. Below we respond to each comment. 

Smaller questions :

• We agree that LCD displays include an inherent error in the timing of the display of images because of their refresh rates (at 60Hz the error could be up to ~16ms per frame). The error can be exacerbated by slow video rendering by the video card, which nowadays is less of an issue than in the past. The delay in our system was ~16ms, but the exact error (undetermined) is likely much less. We now include information on these issues in the revised version of the manuscript.
• The analog-to-digital converter (ADC) cycles through the analog signals at 1MHz (providing a read rate of 1 µs for the behavioral monitoring script loaded to the 1401’s sequencer); one line of code is acted upon with each tick of the clock. The sequencer has direct access to the ADC signals. The monitoring of events in this way is dependent on the length of the sequencer script loop. In our example, we sampled the position of the eye every 9 µs while in the loop. This means reading even a couple of hundred lines of code takes an order of magnitude less time for loop delays, allowing implementation of cancellation tests with antidromic stimulation, if needed.  As indicated earlier, the delays of concern originate more with display issues for visual stimulation using naturalistic stimuli (simpler stimuli can be presented by other means), but these are still much smaller than perceptual delays, which are on the order of a couple of hundred milliseconds. For perturbing subconscious perception, either display delays could be accounted for in a stimulation paradigm or an LCD display may be substituted with a motorized slide projector. 
• We describe relevant details regarding system performance as well as offer the results obtained in our study as a proof of concept, highlighting a potential scientific advance that may not be possible with a more cumbersome system. 
• We agree that there are many options available for conducting neurophysiological studies and sometimes making the best choice can be difficult. A comparison between specific options would be helpful, but is somewhat arbitrary in the absence of knowledge of all available equipment and experimental goals. Therefore, we simply provide one example for a specific set of experiments and leave it to the reader to make the necessary comparisons given their objectives and available equipment.  For this reason, we also believe it is important to illustrate the usage of the proposed setup in generating new findings and describe them adequately as well as provide animal protocols, as indicated by the editors/publishers. To that end, this manuscript accomplishes a clear and specific methodological goal.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 01 December 2020

Control of response interference: caudate nucleus contributes to selective inhibition

• Claudia C. Schmidt   ORCID: orcid.org/0000-0002-1296-577X 1 ,
• David C. Timpert 1 , 2 ,
• Isabel Arend 3 ,
• Simone Vossel   ORCID: orcid.org/0000-0002-6351-8849 1 , 4 ,
• Gereon R. Fink 1 , 2 ,
• Avishai Henik 3 &
• Peter H. Weiss   ORCID: orcid.org/0000-0002-5230-9080 1 , 2  

Scientific Reports volume  10 , Article number:  20977 ( 2020 ) Cite this article

2201 Accesses

20 Citations

1 Altmetric

Metrics details

• Cognitive control
• Human behaviour

While the role of cortical regions in cognitive control processes is well accepted, the contribution of subcortical structures (e.g., the striatum), especially to the control of response interference, remains controversial. Therefore, the present study aimed to investigate the cortical and particularly subcortical neural mechanisms of response interference control (including selective inhibition). Thirteen healthy young participants underwent event-related functional magnetic resonance imaging while performing a unimanual version of the Simon task. In this task, successful performance required the resolution of stimulus–response conflicts in incongruent trials by selectively inhibiting interfering response tendencies. The behavioral results show an asymmetrical Simon effect that was more pronounced in the contralateral hemifield. Contrasting incongruent trials with congruent trials (i.e., the overall Simon effect) significantly activated clusters in the right anterior cingulate cortex, the right posterior insula, and the caudate nucleus bilaterally. Furthermore, a region of interest analysis based on previous patient studies revealed that activation in the bilateral caudate nucleus significantly co-varied with a parameter of selective inhibition derived from distributional analyses of response times. Our results corroborate the notion that the cognitive control of response interference is supported by a fronto-striatal circuitry, with a functional contribution of the caudate nucleus to the selective inhibition of interfering response tendencies.

Similar content being viewed by others

Cingulate cortex morphology impacts on neurofunctional activity and behavioral performance in interference tasks

Early response competition over the motor cortex underlies proactive control of error correction

Parsing the Flanker task to reveal behavioral and oscillatory correlates of unattended conflict interference

Introduction.

Control of response interference refers to a subprocess of cognitive control that demands to resolve a response conflict by inhibiting a prepotent yet inappropriate response tendency in favor of selecting a task-appropriate response 1 . Conceptually, response interference control is a selective inhibition process that serves to suppress the activation of one response (but not another) and thus prevents an incorrect response selection due to interfering response tendencies 2 . Other conceptually different cognitive components of conflict-driven (inhibitory) control are cognitive flexibility, working memory, and (global) response inhibition 3 , 4 . Note that the term (global) response inhibition is used in the current study to describe the control processes of withholding a prepotent or canceling an already initiated yet inappropriate action, which may imply globally suppressing any ongoing responses 5 .

There is general agreement that the diverse cognitive control (sub)processes (i.e., response interference control, response inhibition, cognitive flexibility, and working memory) rely on functionally connected (pre)frontal cortical regions 6 , 7 . More specifically, the anterior cingulate cortex (ACC) has been implicated in the detection of (response) conflicts and the monitoring of control demands 8 , 9 . In contrast, the dorsolateral prefrontal cortex (DLPFC) is thought to subserve the active maintenance of (internal) goals and/or task representations in order to implement cognitive control 10 , 11 . Other cortical areas that are commonly recruited by the diverse cognitive control processes include the (right) inferior frontal gyrus (IFG), the pre-supplementary motor area (pre-SMA), the insula as well as (posterior) parietal cortices 12 , 13 .

The functional interaction between the frontal (and parietal) brain regions during the execution of cognitive control is considered to be modulated by subcortical structures, such as the basal ganglia (BG) and thalamus 14 , 15 . While recent neuroimaging meta-analyses reported increased activity in the dorsal striatum (the main input structure of the BG comprising the caudate nucleus and putamen) and thalamus across a wide range of cognitive control demands 16 , 17 , 18 , only (global) motor response inhibition consistently activated the bilateral thalamus 16 . Besides the latter finding, previous work has emphasized the role of the subthalamic nucleus (STN) in (global) response inhibition 19 , 20 , 21 . In contrast, the dorsal striatum (particularly the caudate nucleus) has mainly been associated with working memory 22 , 23 , cognitive flexibility/task-switching 24 , 25 , and (associative) control-learning mechanisms in response to changing control demands 26 , 27 . Besides, there is growing evidence for relevant contributions of the dorsal striatum to the control of response interference (including selective inhibition). In particular, the electrophysiological recording of neural activity in the caudate nucleus of non-human primates performing an anti-saccade task has revealed evidence for the dorsal striatum’s functional relevance for selective inhibitory control 28 , 29 . Moreover, these studies proposed that striatal neurons projecting to the BG’s direct pathway promote volitional saccades away from a visual stimulus (i.e., anti-saccades). In contrast, striatal neurons projecting to the BG’s indirect pathway suppress reflexive saccades toward the stimulus in favor of an anti-saccade 30 , 31 . Likewise, drawing upon models of BG functions, recent neuro-computational studies provided further support for the notion that the dorsal striatum might mediate selective inhibition via the indirect pathway of the BG, as studied with anti-saccade, saccade-override, and Simon tasks 32 , 33 . In patients with neurodegenerative disorders affecting the striatum, such as Parkinson’s disease (PD) and Huntington’s disease (HD), experimental-psychological studies found impairments in resolving response interference in Simon and arrow-versions of the Eriksen flanker tasks 34 , 35 , 36 , including deficits in the selective inhibition of interfering response tendencies 37 , 38 . Finally, some previous functional neuroimaging findings in healthy subjects also point to the dorsal striatum’s involvement in response interference control in (variants of) the Simon task 39 , 40 . Notably, these latter studies do not allow to infer whether the dorsal striatum specifically contributed to selective inhibition or whether it was involved in other processes required for interference control.

Accordingly, the current study examined the neural mechanisms subserving the control of response interference further. Using event-related functional magnetic resonance imaging (fMRI) in healthy subjects, we specifically focused on the role of the dorsal striatum in the selective inhibition of interfering response tendencies.

To investigate the control of response interference (including selective inhibition), we here applied the Simon task in which participants are asked to respond to a predefined stimulus feature (e.g., color or shape) based on an arbitrary mapping to left or right manual responses, irrespective of the spatial location of the stimulus 41 . In each trial, the stimulus randomly appears either on the left or on the right side of a fixation point and hence the stimulus location either spatially match or mismatch the task-assigned (correct) response. A mismatch between the spatial location of the stimulus and the relative position of the assigned response (i.e., an incongruent condition) is typically associated with longer response times (RTs) and increased error rates, as compared to when both positions correspond (i.e., a congruent condition). Accordingly, even though the stimulus location is irrelevant for the task, it facilitates the response to the task-relevant stimulus feature in congruent trials but interferes with the correct response selection in incongruent trials 42 . The RT difference between incongruent and congruent conditions is termed the Simon effect 43 and used as a measure of the ability to resolve stimulus–response conflicts due to interfering response tendencies in incongruent trials 44 .

In the present study, a unimanual version of the Simon task was applied in which left or right responses were given with the index and middle fingers of the same hand (see below). A unimanual response set-up was used here to relate the current functional imaging results to our previous study in stroke patients that investigated response interference control processes after unilateral lesions of the striatum 45 . Specifically, by using a unimanual task in that previous study we aimed to avoid confounding effects of potential paresis of the contralesional hand or arm following stroke. Motion direction of (coherently) upward or downward moving dots was defined as the task-relevant stimulus feature since motion stimuli might be more sensitive to engage subcortical structures 46 , 47 .

Resolving stimulus–response interference has been discussed in terms of demanding the selective inhibition of interfering response tendencies during response selection 48 , 49 . While there are also other approaches used to explain how stimulus–response interference is controlled (e.g., via attentional control processes) 50 , we here relied on the activation-suppression hypothesis that accounts for the temporal dynamics of a proposed inhibitory control process in conflict tasks and particularly in the Simon task 51 . According to this hypothesis, an (incorrect) response activation (i.e., a response tendency evoked by the task-irrelevant stimulus location) is followed by selective inhibition in incongruent trials to execute the correct response. Since the process of (selective) inhibition gradually builds up within a trial, its impact is more apparent in slower than in faster responses. Given these dynamics, the Simon effect is affected by selective inhibition more in trials with relatively long (intra-individual) RTs than in trials with shorter RTs and consequently decreases with slower responses. The process and individual efficiency of selective inhibition can be revealed by RT distributions in which the Simon effect is analyzed as a function of response latency 49 . Specifically, a (more) decreasing Simon effect across the RT distribution (i.e., with slower responses) is associated with (more efficient) selective inhibition of interfering response tendencies 52 . Previously, the RT distribution parameter of selective inhibition (i.e., the decrease of the Simon effect across the RT distribution) has already been proven applicable for relating inter-individual differences in selective response inhibition to the underlying neural mechanisms 53 , 54 as well as for examining group differences in the efficiency of selective inhibition between neurological patients with assumed impairments in inhibitory control and healthy subjects 37 , 45 .

As outlined above, models and studies of basal ganglia function propose that—at the subcortical level—response selection and selective inhibition may be modulated by the dorsal striatum through a balance of activity in the direct and indirect BG pathways, respectively 33 , 55 . Accordingly, the dorsal striatum (i.e., caudate nucleus and putamen) should be involved in the control of response interference by putatively mediating the selection of task-appropriate responses and/or the selective inhibition of interfering response alternatives. In line with this assumption, our previous study in stroke patients indicated less efficient selective inhibition to resolve response interference after unilateral lesions of the striatum when taking into account the temporal dynamics of the interference control processes across the RT distribution 45 . Hence, we here hypothesized that the individual efficiency of selective inhibition as indexed by the decrease of the Simon effect across the RT distribution 49 is associated with (increased) activation of the dorsal striatum.

Material and methods

Participants.

Initially, 16 healthy subjects participated in the study. All participants were screened for factors contraindicating magnetic resonance imaging (MRI) scanning, provided written informed consent to participate in the study, and received financial compensation for their participation.

Three participants had to be excluded from further analyses due to extensive head movement (rotation > 3°, n  = 1) or technical problems ( n  = 2) during scanning. Thus, the final sample included 13 subjects (9 female) with a mean age of 25.5 years ( SD  = 4.2 years). All subjects were right-handed, according to the Edinburgh Handedness Inventory (laterality quotient [LQ]: M  = 92.4, SD  = 11.0) 56 , and reported normal or corrected-to-normal visual acuity. None of the participants suffered from any neurological or psychiatric diseases.

The local ethics committee of the Faculty of Medicine of the University of Cologne had approved the study, which was conducted following the ethical principles of the World Medical Association (Declaration of Helsinki; revised version, October 2013).

Stimuli and task

The software Presentation® (Neurobehavioral Systems, Inc.) was used for stimulus presentation and response logging.

The task was presented on a screen (screen width: 65 cm) mounted on the wall at the back of the magnet bore. Participants viewed the monitor via a movable mirror system attached to the MR head coil (viewing distance: 245 cm).

The current unimanual Simon task resembles the task design used in our previously published study on response interference control and striatal lesions 45 . The target stimulus consisted of a square involving a flow field of dots coherently moving either upward or downward. The squares (subtending 2° × 2° of visual angle) were displayed on a black background either to the left or to the right side of a white centrally presented fixation cross and positioned such that their boundaries were 2° left or right of the fixation point.

For the current version of the Simon task, participants were instructed to respond to the moving dots’ motion direction by giving left or right finger responses (see below), irrespective of the location at which the stimulus was presented. Based on the spatial correspondence between the position at which the stimulus appeared (i.e., left or right visual field) and the relative side of response according to the task instruction (i.e., left or right finger), two trial types were defined. A stimulus whose spatial location matched the side of response assigned by the task represented a congruent trial (e.g., a stimulus with upward-moving dots requiring a left finger response was presented on the left side of the fixation cross). Conversely, a stimulus whose spatial location and task-assigned response side did not match defined an incongruent trial (e.g., a stimulus with upward-moving dots requiring a left finger response appeared on the right side of the fixation cross; Fig.  1 ).

Schematic of the unimanual Simon task. Participants were required to discriminate the motion direction of a moving dots stimulus by responding with the index or middle finger of the same hand, irrespective of the spatial location (i.e., left or right side of the fixation cross), at which the stimulus was presented. Each participant completed two experimental blocks by successively using the left and right hand. The example here shows a left-hand response condition in which upward-moving dots are mapped to a left response (i.e., the middle finger) and downward-moving dots are mapped to a right response (i.e., the index finger). Based on the spatial correspondence between the stimulus position (left or right side of the fixation cross) and the task-assigned (relative) response side (left or right finger), a stimulus with upward-moving dots that is presented in the left visual field represents a congruent trial. Conversely, a stimulus with upward-moving dots that appears in the right visual field is defined as an incongruent trial. Please note that the arrow was not presented to the participants during the experiment but is shown here to illustrate the motion direction of the dots. Further note that the stimulus itself did not move in position (i.e., up or down) but stayed fixated.

Design and procedure

Before the functional imaging, all participants practiced the task on a laptop computer outside the MR scanner to get familiarized with the task instructions and the stimulus–response mapping.

For the mapping between the task-relevant stimulus feature (i.e., upward or downward moving dots) and the relative side of response, participants were required to use the index and middle fingers of one hand, i.e., of either the left or the right hand. In other words, when responding with the left hand, left responses were given with the middle finger, and right responses were given with the index finger. Conversely, participants gave left responses with the index finger and right responses with the middle finger when responding with the right hand. During the functional imaging session, each participant successively responded with the left and right hand. For response registration, MR compatible LUMItouch response keypads were used. The response keypads were always positioned to the left side (for the left-hand response condition) or the right side (for the right-hand response condition) of the participants.

The mapping between the task-relevant stimulus feature (i.e., upward or downward motion) and side of response (left or right finger) was counterbalanced across subjects. However, it was held constant within participants throughout the functional imaging experiment, i.e., independent of whether they responded with their left or right hand in the respective functional run. Half of the participants were instructed to respond to upward-moving dots by using the left finger (i.e., the middle finger of the left hand or the index finger of the right hand) and to downward-moving dots by using the right finger (i.e., the index finger of the left hand or the middle finger of the right hand). Conversely, the other half of the participants were instructed to respond to upward-moving dots with the right finger and downward-moving dots with the left finger.

The duration of the trials was jittered between 3400 and 5000 ms. More precisely, at the beginning of each trial, a central fixation cross, which remained present throughout the task, changed its size after a variable period of 800, 1200, 1600, or 2000 ms. The change in the size of the fixation cross served as a cue (for 500 ms) to signal the target stimulus’s start. Subsequently, a patch of moving dots (with either upward or downward motion) was randomly displayed at the left or right side of the fixation cross for a fixed duration of 1500 ms. Following a variable inter-trial interval (ITI) of 600, 800, or 1000 ms, the next trial started (Fig.  1 ). Throughout the experiment, participants were asked to maintain central eye fixation and respond as quickly and accurately as possible to the target stimuli.

Within the scanning session, each participant completed two experimental runs, separated by a short break in which participants were instructed to change their responding hand. Each run contained 160 experimental trials (i.e., 80 congruent trials and 80 incongruent trials) presented in the left or right visual field in a pseudo-randomized order. To increase the statistical efficiency of the event-related design 57 and avoid (too) long inter-trial intervals that might reduce the magnitude of the (overall) Simon effect 58 , null trials were not included.

The duration of the fMRI experiment (both runs) amounted to approximately 35 min in total.

Functional imaging data acquisition and preprocessing

Imaging data were acquired on a 3-T MRI system (Magnetom Trio; Siemens, Erlangen, Germany).

Functional T2*-weighted blood oxygenation level dependent (BOLD) signal sensitive images were obtained from a gradient-echo planar imaging (EPI) sequence (echo time [TE] = 30 ms; repetition time [TR] = 2200 ms; flip angle = 90°; field of view [FOV] = 200 mm × 200 mm; matrix size = 64 × 64; voxel size = 3.1 mm × 3.1 mm × 3.1 mm; bandwidth = 2232 Hz/pixel). Two functional runs were consecutively conducted within one scanning session. A total of 330 EPI volumes, each consisting of 36 axial slices covering the whole brain (slice thickness: 3.1 mm; interleaved slice acquisition, 0.3 mm gap), were collected for each subject in each of the two runs.

Additional high-resolution anatomical images (176 slices, voxel size = 1 mm × 1 mm × 1 mm) were acquired for registration purposes using a standard T1-weighted 3D magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence.

Imaging data were preprocessed and analyzed using the Statistical Parametric Mapping software package (SPM12, revision 6906, Wellcome Department of Imaging Neuroscience, London; https://www.fil.ion.ucl.ac.uk/spm/ ).

The first nine volumes of each run were discarded before the analysis to allow for T1 equilibration effects. The remaining volumes (2 × 321) were spatially realigned to the new first image and subsequently re-realigned to the mean of all images using a six parameter (three translations, three rotations) rigid-body transformation to correct for residual inter-scan head movements. Motion parameters were estimated for each run separately. The obtained mean EPI images for each subject (per run) were then spatially normalized to the Montreal Neurological Institute (MNI) template using the segmentation function (as implemented in SPM12). This algorithm enables combined image registration, tissue classification, and bias correction based on a probabilistic framework. Subsequently, the resulting deformation fields of the mean EPIs were applied to the individual EPI volumes and the T1 scan, which was coregistered to the mean of the realigned EPIs of the first run. Thereby, all volumes were transformed into standard stereotaxic (i.e., MNI) space, and the functional images were resampled to a 2 mm 3  × 2 mm 3  × 2 mm 3 voxel size. Finally, the normalized functional images were spatially smoothed using a Gaussian kernel of 8 mm full-width at half-maximum (FWHM) to accommodate inter-participant anatomical variability.

Statistical analysis of behavioral data

Statistical analyses of the behavioral data acquired during the fMRI scanning were performed using the software IBM SPSS Statistics (Statistical Package for the Social Sciences, Version 25, SPSS Inc., Chicago, Illinois, USA).

Trials with incorrect or missing responses were discarded before the analysis. First, error rates and mean response times (RTs) from all correct trials in each condition were analyzed separately using repeated measures analyses of variance (ANOVAs) with the within-subject factors responding hand (left-hand response, right-hand response), stimulus location (left visual field, right visual field), and stimulus–response congruency (congruent, incongruent). Please note that in the context of the current unimanual Simon task, the factor ‘congruency’ is defined by the intended finger response of one hand (i.e., left or right finger of a given hand) in relation to the stimulus location (i.e., left or right visual field).

Moreover, to assess the sequence-dependent modulation of the Simon effect 59 , mean RTs were analyzed as a function of previous and current trial congruency (after dropping the first trial in each condition) using a repeated measures ANOVA with responding hand (left-hand response, right-hand response), previous stimulus–response congruency (congruent, incongruent), and current stimulus–response congruency (congruent, incongruent) as within-subject factors.

Finally, to examine the process (and efficiency) of selective inhibition, RTs were further examined by distributional analyses (for each run, i.e., left-hand response condition and right-hand response condition, separately) 51 , 60 . First, for each participant, correct RTs were separated into congruent and incongruent conditions and sorted in ascending order. The individual RT distributions were then partitioned into four quantile bins with a roughly equal number of trials (about 20 trials per quartile), ranging from the fastest to the slowest RTs. Finally, the mean RTs were computed separately for each of the quartiles for congruent and incongruent conditions, and were analyzed using a repeated measures ANOVA with the within-subject factors responding hand (left-hand response, right-hand response), stimulus–response congruency (congruent, incongruent), and quartile (Q1, Q2, Q3, Q4). To further characterize a significant congruency x quartile interaction effect, a polynomial contrast was used that tests for the change/pattern (over time) of more than two group means of a dependent variable (here: for the time course of the RT difference between incongruent and congruent condition (i.e., the Simon effect) across the RT distribution) 61 . For visual depiction, the Simon effect was obtained for each quartile and, averaged across subjects, plotted against the mean RT per quartile (Vincentizing procedure) 62 .

A significance level of p  < 0.05 was applied for all behavioral analyses.

Statistical analysis of functional imaging data

The functional imaging data were analyzed in an event-related design using a general linear model (GLM) implemented in SPM12 63 .

At the single-subject level, the two runs (i.e., left-hand response condition and right-hand response condition) were included as separate sessions in the model (i.e., concatenated). For each run, four conditions of interest were defined, i.e., trials of upward and downward moving dots appearing in the left and right visual field, respectively (resulting in a total of eight conditions for both runs). Error trials (incorrect responses and misses) were modeled separately and included in the design matrix as a condition of no interest. The trials were modeled as events at the onset of the target stimuli, that is as delta functions with zero duration. Please note that in SPM, an ‘event’ is defined as having a stimulus duration of 0 s 64 . The resulting stimulus functions were then convolved with a canonical hemodynamic response function (HRF) and its first-order temporal derivatives to model the BOLD responses associated with the task. Additionally, the six head movement parameters derived from the (rigid-body) realignment were entered in the model as nuisance regressors to account for signals correlated with head motion. Data were high-pass filtered at 1/128 Hz to remove low-frequency signal drifts. Subsequently, parameter estimates were calculated for each voxel using a restricted maximum likelihood (ReML) approach with adjustment for serial autocorrelation by a first-degree autoregressive [AR(1)] model. Finally, eight condition-specific contrast images (four conditions of interest per run) were created for each subject.

Congruency effects

The individual contrast images of the first-level analysis were then used for the second-level group statistics (random-effects analysis) in a flexible factorial design with the within-subject factors responding hand (left-hand response, right-hand response), stimulus location (left visual field, right visual field), and stimulus–response congruency (congruent, incongruent). Correlations amongst errors and inhomogeneity of variances (non-sphericity) were estimated with restricted maximum likelihood (ReML) and adjusted for by modeling non-independence of conditions across subjects and assuming unequal variances both between conditions and between subjects.

The factorial analysis focused on the main effect of congruency using a planned t -contrast. In particular, to identify brain regions related to stimulus–response conflicts in the Simon task, both left and right incongruent trials were contrasted against congruent trials, collapsed across responding hand (i.e., averaged across the two runs). Imaging results from voxel-wise analyses of the factorial design are reported for activations that were significant at a statistical threshold of p  < 0.05, family-wise error (FWE) whole-brain corrected for multiple comparisons at the cluster level using an uncorrected voxel-level threshold of p  < 0.001 65 , 66 , 67 . Brain regions were identified by using the SPM Anatomy toolbox 68 and the Automated Anatomical Labelling (AAL) atlas 69 .

Additionally, the putatively differential effect of stimulus location on congruency was investigated with planned interaction contrasts. This analysis did not reveal any significant activation clusters. To further examine the pattern of neural activity in response to incongruent versus congruent stimuli as a function of stimulus location, beta estimates from the peak voxels of the activated clusters in the main contrast of congruency were extracted for each subject. Subsequently, the mean beta estimates were subjected to separate repeated measures ANOVAs with the within-subject factors responding hand (left-hand response, right-hand response), stimulus location (left visual field, right visual field), and stimulus–response congruency (congruent, incongruent). As with the behavioral results (see below), there were no significant main effects of responding hand and no differential interaction effects for the left and right responding hands (all p -values > 0.135). Therefore, the mean beta estimates were collapsed across responding hands, and the factor stimulus location was (re)coded as ipsilateral hemifield (visual field on the side of the responding hand) and contralateral hemifield (visual field opposite to the responding hand). The mean beta estimates for congruent and incongruent trials as a function of hemifield (collapsed across responding hands) were then tested for interaction effects using a 2 × 2 repeated measures ANOVA. Again, the interaction effects of stimulus location (ipsilateral hemifield, contralateral hemifield) and congruency (congruent, incongruent) were not significant (all p -values > 0.181), and the extracted beta estimates are reported only descriptively (see below and Supplementary Fig. S1 ).

Individual differences in selective inhibition

Finally, to identify brain areas associated with individual differences in the efficiency of selective inhibition engaged in controlling response interference, a second-level regression analysis was performed. For this purpose, differential first-level contrast images for the main effect of congruency (incongruent > congruent) were created for each subject and entered into an fMRI regression analysis with the individual RT distribution parameter of selective inhibition (i.e., the course of the Simon effect across the RT distribution) as a covariate. That is, for each participant, the slope accounting for the course of the Simon effect across the entire RT distribution (i.e., the Simon effect values at each RT bin) was computed based on ordinary least squares (OLS) regression and included as a covariate in the second-level regression analysis. Significance testing was then performed on the covariate using a directed t -contrast to identify brain regions whose activation patterns co-varied with the individual’s Simon effect slope values. Based on the activation-suppression hypothesis 49 , the process (and efficiency) of selective inhibition is reflected in a decrease of the Simon effect with slower responses (i.e., a negative-going slope). Accordingly, significant activations in the fMRI regression analysis should indicate brain regions that co-varied with the individual efficiency of selective inhibition. To further probe the hypothesis that neural activation in the dorsal striatum is associated with selective inhibition (i.e., to specifically test for co-variations between activation in the dorsal striatum and the individual Simon effect slope values), a small volume correction was applied using a binary mask of the dorsal striatum (i.e., caudate nucleus and putamen, left and right combined). The mask was anatomically defined based on the AAL atlas 69 using the WFU PickAtlas toolbox (version 3.0) 70 available with SPM. Results from this region of interest (ROI) analysis are reported at a significance level of p  < 0.05, FWE-corrected for the search volume. Finally, for each participant, beta estimates (for the contrast between incongruent and congruent conditions) were extracted from the peak voxels of the activated clusters in the striatum and correlated with the individual Simon effect slopes across the entire RT distribution using Pearson correlation.

Behavioral data

Overall, participants made few errors (error rate: M  = 1.9%, SD  = 1.3%). The ANOVA of error rates showed a statistical trend towards a main effect of congruency [ F (1,12) = 3.69, p  = 0.079, η p 2  = 0.24], indicating a tendency for making more errors in incongruent trials (2.4%) than congruent trials (1.5%).

Since the analyses of mean RTs did not reveal significant main effects of responding hand nor differential interaction effects for the left and right responding hand (all p -values > 0.229), mean RT data were collapsed across responding hands for all further analyses. Note that to this end the factor stimulus location was (re-)coded as ipsilateral hemifield (i.e., left visual field when responding with the left hand and right visual field when responding with the right hand) and contralateral hemifield (i.e., right visual field when responding with the left hand and left visual field when responding with the right hand).

There was a significant main effect of congruency [ F (1,12) = 14.0, p  = 0.003, η p 2  = 0.54], with longer RTs for incongruent trials (609 ms) than congruent trials (584 ms). Moreover, the interaction effect of stimulus location and congruency reached significance [ F (1,12) = 11.21, p  = 0.006, η p 2  = 0.48]. Paired-samples t -tests showed that the RT difference between incongruent and congruent conditions was significant for the contralateral hemifield [624 ms vs. 578 ms; t (12) = 4.43, p  = 0.001, d  = 0.41], but not for the ipsilateral hemifield [594 ms vs 591 ms; t (12) = 0.48, p  = 0.641, d  = 0.03]. Figure  2 shows the mean RTs as a function of stimulus location and stimulus–response congruency (collapsed across responding hands).

Mean response times (RTs) as a function of stimulus location and stimulus–response congruency (collapsed across responding hands) for the healthy young subjects (n = 13). Results illustrate an asymmetrical Simon effect that was more pronounced in the contralateral hemifield (for both left- and right-hand responses). Error bars indicate standard errors of the mean (SEM). *Paired-samples t -test: t (12) = 4.43, p  = 0.001, d  = 0.41.

A second repeated measures ANOVA showed that the main effect of congruency was further modulated by a significant interaction effect of previous and current trial congruency [ F (1,12) = 16.18, p  = 0.002, η p 2  = 0.57]. Post-hoc t -tests revealed that the RT difference between (current) incongruent and congruent conditions was absent when the preceding trial was incongruent [593 ms vs. 594 ms; t (12) = 0.03, p  = 0.977, d  = 0.002] compared to when the preceding trial was congruent [622 ms vs. 572 ms; t (12) = 5.32, p  < 0.001, d  = 0.48], reflecting a significant post-conflict behavioral adjustment as an act of (proactive) interference control 59 .

The distributional analysis of RTs furthermore yielded a significant interaction effect of congruency and quartile [ F (3,36) = 3.61, p  = 0.022, η p 2  = 0.23], which, however, did only survive at a trend level after Greenhouse–Geisser correction accounting for non-sphericity [ F (1.169,14.024) = 3.61, p  = 0.074]. The subsequent polynomial contrast showed that the difference in RTs between incongruent and congruent conditions linearly decreased across the RT distribution [ F (1,12) = 5.45, p  = 0.038, η p 2  = 0.31]. A further RT distributional analysis including ipsi- and contralateral stimuli as a separate factor revealed a significant three-way stimulus location x congruency x quartile interaction effect [ F (1.720,20.639) = 6.74, p  = 0.007, η p 2  = 0.36; Greenhouse–Geisser corrected]. Post-hoc polynomial contrasts for ipsi-and contralateral conditions separately showed that for stimuli in the ipsilateral hemifield the difference in RTs between incongruent and congruent conditions (i.e., the Simon effect) linearly decreased with slower (intra-individual) responses and even reversed for the slowest part of the RT distribution [ F (1,12) = 11.64, p  = 0.005, η p 2  = 0.49]. In contrast, for stimuli in the contralateral hemifield the Simon effect did not significantly differ across the RT distribution [ F (1,12) = 0.14, p  = 0.712, η p 2  = 0.01].

Functional imaging data

Results of the second-level factorial analysis are summarized in Table 1 .

Regarding the effects of responding hand, the contrasts right-hand response (RH) versus left-hand response (LH) and vice versa (LH > RH) showed significant neural activation in the respective contralateral motor cortex and ipsilateral cerebellum. Additionally, left-hand responses induced significant activation in the right (secondary) somatosensory cortex. Note that the left hand was the non-dominant hand of the participants. The main contrasts of stimulus location (i.e., visual field), namely right visual field (RVF) versus left visual field (LVF) and vice versa (LVF > RVF), significantly activated clusters in the respective contralateral visual cortices.

The main effect of congruency (contrasting incongruent trials against congruent trials, incongruent  >  congruent , i.e., the overall Simon effect) revealed significant activation clusters in the right anterior cingulate cortex (ACC), the (head of the) caudate nucleus bilaterally, and the right posterior insula (Fig.  3 ). Beta estimates of this contrast were extracted from the peak voxels in the right ACC and bilateral caudate nucleus. Plots of the mean beta estimates for congruent and incongruent conditions as a function of stimulus location (i.e., ipsilateral and contralateral hemifield) suggest that the main effect of congruency was mainly driven by the differential pattern of neural activity in the contralateral hemifield. However, a similar activity pattern was also present for the ipsilateral hemifield—albeit to a lesser degree (see Supplementary Fig. S1 ). The reverse contrast, congruent  >  incongruent , did not yield any significantly activated clusters. Note that all interaction terms did not reveal any significant activations at the predefined statistical threshold.

Brain activation maps of the factorial analysis for the contrast incongruent  >  congruent (i.e., activations associated with the Simon effect) for the healthy young subjects (n = 13). Incongruent trials compared to congruent trials (i.e., the overall Simon effect) induced significant activation clusters in the right anterior cingulate cortex (ACC; panel A ), the caudate nucleus bilaterally (panel B ), and the right posterior insula (panel C ). Note that in panel A , the activation cluster in the right caudate nucleus is also visible. All activations are significant at p  < 0.05, family-wise error (FWE) corrected at the cluster level using an uncorrected voxel-level threshold of p  < 0.001. Thresholded statistical parametric maps are overlaid onto sections of the Montreal Neurological Institute (MNI) single-subject T1 template image provided by SPM. Coordinates are given in MNI space. Colors reflect the T -values of the corresponding voxels. A = anterior; P = posterior; L = left; R = right.

At the whole-brain level, the fMRI regression analysis for the contrast of incongruent versus congruent trials with the Simon effect slopes (i.e., the course of the Simon effect across the entire RT distribution) as a covariate did not reveal significant activations at the predefined threshold of p  < 0.05 FWE-corrected for multiple comparisons at the cluster level (cluster-forming threshold p  < 0.001 uncorrected). When the cluster-defining threshold was lowered to p  < 0.005, the regression analysis yielded significant activations (only) within the bilateral striatum (particularly the caudate nucleus) at p  < 0.05, FWE-corrected at the cluster level. The fMRI regression analysis showed a significant association between the Simon effect slope values and activity in the caudate nucleus bilaterally across subjects (Table 2 ; Fig.  4 B), after applying small volume correction for the dorsal striatum (i.e., caudate nucleus and putamen). This result was corroborated by negative correlations between the beta estimates for the contrast incongruent > congruent extracted from the peak voxels of the activated clusters in the left and right caudate nucleus and the individual Simon effect slopes for the entire RT distribution. That is, higher activations in the left and right caudate nucleus were associated with more negative Simon effect slopes across the entire RT distribution (Fig.  4 C,D). For further fMRI regression analyses on the separate Simon effect slopes between each of the four RT quantiles (i.e., for the fast, middle, and slow RT segments), please refer to the Supplement (Supplementary Analysis and Supplementary Fig. S2 ).

Magnitude of the Simon effect (i.e., the difference in RTs between incongruent and congruent conditions) as a function of response latency (collapsed across responding hands) and brain activation patterns for the corresponding fMRI regression analysis with the RT distribution parameter of selective inhibition (i.e., the course of the Simon effect across the RT distribution as indexed by the slope) as a covariate for the healthy young subjects (n = 13). ( A ) Behavioral results of the response time (RT) distributional analysis: The magnitude of the Simon effect linearly decreased across the RT distribution. For each of the four quantiles (Q1–Q4) the respective mean RT and standard error of the mean (SEM) are given (in parentheses). Error bars indicate standard errors of the mean Simon effects (SEM SE ). ( B ) Mean activation map of the fMRI regression analysis: Across subjects, neural activity in the bilateral caudate nucleus significantly co-varied with the Simon effect slope values (i.e., the course of the Simon effect across the entire RT distribution used as a behavioral index of selective inhibition). Activations are significant, based on small volume correction at p  < 0.05, family-wise error (FWE) corrected for the search volume using an anatomically defined mask of the dorsal striatum (i.e., caudate nucleus and putamen). The thresholded statistical parametric map is overlaid onto a section of the Montreal Neurological Institute (MNI) single-subject T1 template image provided by SPM. The coordinate is given in MNI space. Colors reflect the T -values of the corresponding voxels. L = left; R = right. ( C ) & ( D ) Scatterplots depicting the correlation between the individual Simon effect slopes across the entire RT distribution ( y -axis) and the beta estimates for the contrast incongruent > congruent extracted from the peak voxels of the clusters in the left caudate nucleus (C; x -axis) and right caudate nucleus (D; x -axis): Higher activations in the left and right caudate nucleus were associated with more negative Simon effect slopes. The numbers in square brackets indicate the respective x -, y -, and z -coordinates in MNI space.

The present study aimed to delineate further the neural mechanisms underlying the control of response interference. In particular, we probed the hypothesis that striatal activity is associated with individual differences in the efficiency of selective inhibition engaged in controlling response interference. For that purpose, healthy (young) subjects underwent fMRI scanning during the performance of a unimanual Simon task. To focus on the neural mechanisms of the selective inhibition process, a specific theory-derived behavioral index of the (individual) efficiency of selective inhibition (derived from RT distribution analysis; outlined above) was regressed with functional imaging data across subjects 71 .

In line with previous studies that used a similar version of the unimanual Simon task 45 , 72 , behavioral results indicated a significant asymmetrical Simon effect. Thus, the current unimanual Simon task successfully elicited a stimulus–response conflict, which was more pronounced in the contralateral hemifield (i.e., in the visual field contralateral to the responding hand). Furthermore, an RT distributional analysis showed a significant decrease in the Simon effect as individual RTs increased, reflecting the process of selective inhibition 51 .

Notably, the decrease in the (mean) Simon effects across the RT distribution seemed mostly be driven by ipsilateral stimuli (i.e., stimuli in the visual field on the side of the responding hand). According to the activation-suppression hypothesis 49 , the decreasing Simon effect with increasing (intra-individual) RTs for ipsilateral stimuli might indicate that selective inhibition was more efficient in resolving interference in the ipsilateral hemifield, resulting in a markedly reduced overall Simon effect (in contrast to the contralateral hemifield).

It has been proposed that factors such as the spatial position of the effectors (responding hand or finger), the (relative) stimulus location within a visual field, the individual’s handedness, and the hemispheric lateralization of processes involved in the Simon task (e.g., motor attention, response selection, interference processing) impact the resolution/magnitude of the Simon effect 73 . Indeed, left–right asymmetries in the mean Simon effect (i.e., more substantial Simon effects on one side than on the other) were shown to occur robustly in bimanual tasks 74 , 75 . In contrast, in unimanual experimental set-ups, the effects of response-related factors on asymmetries in the mean Simon effect have been less conclusive 76 . As noted above, asymmetrical Simon effects between hemifields were found in two previous behavioral studies that used a unimanual Simon task, with smaller mean Simon effects on the side of the responding hand 45 , 72 . Still, other fMRI studies using unimanual variants of the Simon task did not report asymmetries in task performance 39 , 77 , 78 , 79 . To our knowledge, there are currently no studies available that analyzed the time course of the Simon effect across the RT distribution as a function of lateralized visual stimuli (neither for bimanual nor unimanual response set-ups). The current asymmetric pattern of behavioral results might be accounted for by a processing advantage towards the side of the responding hand (i.e., here in the ipsilateral hemifield; for similar accounts refer to 72 , 76 ). In line with this assumption, previous research suggested that cognitive control is enhanced near the hands, as indicated by reduced interference effects for stimuli (re)presented close to the responding hand 80 .

Whole-brain functional imaging analyses revealed that the overall Simon effect (i.e., the contrast between incongruent and congruent conditions independent of responding hand and hemifield) induced increased neural activation in the (right) ACC, (right) posterior insula, and the caudate nucleus bilaterally. Crucially, an ROI-based fMRI regression analysis confirmed the notion that more efficient selective inhibition (reflected in a more pronounced decrease of the Simon effect across the entire RT distribution) was associated with increased activation in the caudate nucleus bilaterally.

A specific contribution of the dorsal striatum to the control of response interference has been emphasized by studies that reported impairments in resolving response interference in patients with neurodegenerative diseases affecting the striatum, such as Parkinson’s disease (PD) 37 . Furthermore, the current results add to previous findings in both brain lesion 38 , 45 and neuro-computational modeling studies 33 that applied (variants of) the Simon task by providing support for a (functional) role of the dorsal striatum (particularly the caudate nucleus) in selective inhibition. Several previous functional imaging studies have also implicated that the striatum is engaged during conditions that entail the anticipation 81 , 82 , 83 and (subsequent) preparation of selective inhibitory control 84 , 85 . Specifically, these studies applied a selective stop-signal task in which subjects were instructed to initiate two responses (which should be executed in go-trials) and to suppress one particular response while continuing the other in case of a stop-signal. Additionally, information on which particular response (out of the two) might need to be selectively inhibited was given at the start of each trial.

Beyond these findings, global (non-selective) response inhibition functions have frequently been related to the right IFG and its interconnections to subcortical structures, such as the STN, during withholding or canceling an inappropriate response in standard go/no-go and stop-signal tasks, respectively 19 , 20 . Notably, it has been suggested that the control process concerning (conflict-driven) inhibitory demands subtly differ between selective inhibition in response interference control (as assessed by the Simon task) and global inhibition in response inhibition (as assessed by go/no-go or stop-signal tasks) 86 . Consequently, both control processes (i.e., selective and global inhibition) are thought to be implemented by distinct, albeit partly overlapping, cortical and subcortical neural networks 87 , 88 . It has been proposed that the Stroop, Eriksen flanker, and Simon tasks share the need to control a (prepotent) response tendency activated by a task-irrelevant stimulus dimension to execute another response based on a task-relevant stimulus feature 2 . Still, there is an ongoing debate concerning the (shared) cognitive control processes that may underlie these interference tasks—and whether they share a common mechanism of selective inhibition (for detailed studies on this issue see for example 89 , 90 , 91 , 92 ). Latent-variable factor analyses across a variety of response inhibition/interference tasks implied closely related 93 but dissociable forms of inhibition in the Stroop, Eriksen flanker, and Simon tasks (among other paradigms) 94 , 95 .

Taken together, by utilizing a theory-derived behavioral index of selective inhibition in the Simon task 49 , 51 , the present results suggest a functional contribution of the dorsal striatum (particularly the caudate nucleus) to the efficiency of the selective inhibition process engaged in the control of response interference. It should be noted that the current findings explicitly rely on the assumption that controlling stimulus–response interference in the Simon task entails the selective inhibition of interfering response tendencies 96 , 97 and that this selective inhibition process is revealed by a decrease of the Simon effect magnitude across the individual RT distribution 51 . Indeed, decreasing Simon effects with longer individual RTs have been reported in several previous studies on response interference control in healthy subjects 98 , 99 and empirically tied to (efficient) selective inhibition 48 . Furthermore, even though the interference in the Simon task might also result from a failure in (selective) attentional control 100 , several findings suggest that its resolution mainly occurs during the response selection/inhibition process (i.e., at the level of the response execution) 101 , rather than at the perceptual level during stimulus encoding 86 , 102 .

However, an alternative account for the decrease in the magnitude of the Simon effect with longer (intra-individual) RTs proposed that the automatic response activation by the (task-irrelevant) stimulus location spontaneously decays over time, leading to reduced interference (and hence a reduced Simon effect) with slower responses 103 , 104 . More recently, an elaborated diffusion process model has been introduced to predict the commonly observed decreasing Simon effects across the RT distribution 105 . More specifically, this diffusion model for conflict tasks (DMC) formally specifies the mechanisms underlying the Simon effect by modeling the time course of the automatic (task-irrelevant) response activation as a brief pulse-like function (while assuming the controlled processing channel driven by the task-relevant stimulus feature to constantly input into a diffusion process as long as the stimulus is present). Consequently, the output of such a brief pulse-like activation should affect short RTs more strongly than longer ones, resulting in the typically more substantial Simon effect with relatively faster responses, which then becomes reduced across the RT distribution 105 , 106 . It should be noted here that the mathematical model does not specify whether a decrease in automatic response activation might be due to an active inhibition process 49 or spontaneous decay 103 .

On the other hand, decreasing Simon effect functions might reflect cognitive processes beyond what is expected based on a diminished impact of (task-irrelevant) response activation with slower responses—either due to an active process of selective inhibition or passive decay. To reveal (putative) alternative and/or additional explanations of decreasing Simon effects, in a recent study manipulations of different stages of cognitive processing (i.e., perceptual, decision, and motor execution) that might modulate the Simon effect were combined with distributional analyses 107 . For example, prolonging the duration of the decision process (by increasing the number of stimulus–response (S–R) pairs) resulted in smaller (overall) Simon effects with four S–R pairs (for which mean RTs were also longer) than with two S–R pairs. Importantly, within each of the two conditions, the Simon effect also decreased with slower (individual) responses. In other words, at any given time point of the RT distribution, the Simon effect was smaller with four S–R pairs than with two S–R pairs—even when controlling for the different overall RTs between conditions. Since the time was controlled for in which the impact of the (task-irrelevant) response activation diminished (either due to active inhibition or passive decay), the authors proposed that the strength of the task-irrelevant activation (i.e., the response tendency evoked by the stimulus location) may have been weakened a priori in the four-stimulus condition compared to the two-stimulus condition, which in turn reduced the (overall) magnitude of the Simon effect 107 . While processing the (task-irrelevant) stimulus location should not systematically vary in the current version of the Simon task, it might still be subject to individual variability. Consequently, the observed decrease in the magnitude of the Simon effect across the RT distribution might be attributed to person-related changes in the time course (i.e., onset, build-up rate) and/or strength of the (automatic) response activation, the controlled selective inhibition of that response activation, or both.

Moreover, the current ACC finding is consistent with other neuroimaging studies that commonly reported ACC activations related to (response) conflict detection during a wide range of (conflict-driven) inhibitory control tasks 16 . Since a pronounced stimulus–response conflict characterized the incongruent conditions (compared to the congruent conditions) in the current unimanual Simon task, our results provide further support for the involvement of the ACC in the detection and/or monitoring of response-related conflict.

Besides, the current functional imaging results revealed that response interference in incongruent conditions (compared to congruent conditions) activated a cluster in the (right) posterior insula. Although less often mentioned than prefrontal regions, the insular cortex has also been suggested to play a role in cognitive control processes 12 , 108 . In this regard, the insula is proposed to be (more generally) involved in the maintenance of task sets across trials 109 , the estimation of forthcoming control demands 27 , and the initiation and adjustment of appropriate attentional control mechanisms to external stimuli 110 . Moreover, there is converging evidence that the insula and the ACC are functionally related, forming an “attentional” network that initiates control processes in response to conflict 111 . In the same vein, the present results support a role of the insula (together with the ACC) in response conflict, albeit its (specific) function in the control of response interference warrants further investigation.

The relatively small sample size can be considered a limitation of the current study. However, our study does not seem to be underpowered since we observed significant activations at standard cluster-corrected thresholds for the contrast between incongruent and congruent conditions 67 . Also, the argument that our current findings might be difficult to be reproduced falls short, since the current activations nicely confirm (and also extend) previous studies that found similar activations in cortical (and subcortical) regions when investigating response interference control with the Simon task with comparable sample sizes 39 , 40 , 78 , 112 , 113 as well as with larger ones 79 , 114 , 115 , 116 . In contrast, some functional imaging studies examining similar-sized groups of healthy participants did not find significant results for the comparison of incongruent and congruent conditions in the Simon task 53 , 54 , 58 . These discrepant findings might be partly due to procedural differences between the studies (e.g., length of inter-trial intervals, presentation of a congruency cue) that potentially affected the perceived response interference and, in turn, the associated neural processes 117 .

Conclusions

The present functional imaging results revealed a fronto-striatal network supported by the insula during response interference control. Most importantly, the dorsal striatum (particularly the caudate nucleus) substantially contributed to the selective inhibition process engaged in resolving response interference.

Nigg, J. T. On inhibition/disinhibition in developmental psychopathology: views from cognitive and personality psychology and a working inhibition taxonomy. Psychol. Bull. 126 , 220–246. https://doi.org/10.1037/0033-2909.126.2.220 (2000).

Article   CAS   PubMed   Google Scholar  

Aron, A. R. From reactive to proactive and selective control: developing a richer model for stopping inappropriate responses. Biol. Psychiat. 69 , e55–e68. https://doi.org/10.1016/j.biopsych.2010.07.024 (2011).

Article   PubMed   Google Scholar  

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H. & Howerter, A. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cogn. Psychol. https://doi.org/10.1006/cogp.1999.0734 (2000).

Diamond, A. Executive functions. Annu. Rev. Psychol. 64 , 135–168. https://doi.org/10.1146/annurev-psych-113011-143750 (2013).

Aron, A. R. & Verbruggen, F. Stop the presses: dissociating a selective from a global mechanism for stopping. Psychol. Sci. 19 , 1146–1153. https://doi.org/10.1111/j.1467-9280.2008.02216.x (2008).

Duncan, J. & Owen, A. M. Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci. 23 , 475–483. https://doi.org/10.1016/S0166-2236(00)01633-7 (2000).

Niendam, T. A. et al. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn. Affect. Behav. Neurosci. 12 , 241–268. https://doi.org/10.3758/s13415-011-0083-5 (2012).

Article   PubMed   PubMed Central   Google Scholar  

Botvinick, M. M., Cohen, J. D. & Carter, C. S. Conflict monitoring and anterior cingulate cortex: an update. Trends Cogn. Sci. 8 , 539–546. https://doi.org/10.1016/j.tics.2004.10.003 (2004).

Kerns, J. G. et al. Anterior cingulate conflict monitoring and adjustments in control. Science 303 , 1023–1026. https://doi.org/10.1126/science.1089910 (2004).

Article   ADS   CAS   PubMed   Google Scholar  

MacDonald, A. W., Cohen, J. D., Stenger, V. A. & Carter, C. S. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science 288 , 1835–1838. https://doi.org/10.1126/science.288.5472.1835 (2000).

Swann, N. C., Tandon, N., Pieters, T. A. & Aron, A. R. Intracranial electroencephalography reveals different temporal profiles for dorsal- and ventro-lateral prefrontal cortex in preparing to stop action. Cereb. Cortex (New York, N.Y.: 1991) 23 , 2479–2488. https://doi.org/10.1093/cercor/bhs245 (2013).

Article   Google Scholar  

Nee, D. E., Wager, T. D. & Jonides, J. Interference resolution: insights from a meta-analysis of neuroimaging tasks. Cogn. Affect. Behav. Neurosci. 7 , 1–17. https://doi.org/10.3758/cabn.7.1.1 (2007).

Zhang, R., Geng, X. & Lee, T. M. C. Large-scale functional neural network correlates of response inhibition: an fMRI meta-analysis. Brain Struct. Funct. 222 , 3973–3990. https://doi.org/10.1007/s00429-017-1443-x (2017).

Alexander, G., DeLong, M. R. & Strick, P. L. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9 , 357–381. https://doi.org/10.1146/annurev.ne.09.030186.002041 (1986).

Heyder, K., Suchan, B. & Daum, I. Cortico-subcortical contributions to executive control. Acta Physiol. (Oxf.) 115 , 271–289. https://doi.org/10.1016/j.actpsy.2003.12.010 (2004).

Cieslik, E.-C., Müller, V. I., Eickhoff, C. R., Langner, R. & Eickhoff, S. B. Three key regions for supervisory attentional control: evidence from neuroimaging meta-analyses. Neurosci. Biobehav. Rev. 48 , 22–34. https://doi.org/10.1016/j.neubiorev.2014.11.003 (2015).

Guo, Y., Schmitz, T. W., Mur, M., Ferreira, C. S. & Anderson, M. C. A supramodal role of the basal ganglia in memory and motor inhibition: meta-analytic evidence. Neuropsychologia 108 , 117–134. https://doi.org/10.1016/j.neuropsychologia.2017.11.033 (2018).

Li, Q. et al. Conflict detection and resolution rely on a combination of common and distinct cognitive control networks. Neurosci. Biobehav. Rev. 83 , 123–131. https://doi.org/10.1016/j.neubiorev.2017.09.032 (2017).

Aron, A. R. & Poldrack, R. A. Cortical and subcortical contributions to stop signal response inhibition: role of the subthalamic nucleus. J. Neurosci. 26 , 2424–2433. https://doi.org/10.1523/JNEUROSCI.4682-05.2006 (2006).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Aron, A. R., Herz, D. M., Brown, P., Forstmann, B. U. & Zaghloul, K. Frontosubthalamic circuits for control of action and cognition. J. Neurosci. 36 , 11489–11495. https://doi.org/10.1523/JNEUROSCI.2348-16.2016 (2016).

Ray, N. J. et al. The role of the subthalamic nucleus in response inhibition: evidence from deep brain stimulation for Parkinson’s disease. Neuropsychologia 47 , 2828–2834. https://doi.org/10.1016/j.neuropsychologia.2009.06.011 (2009).

Lewis, S. J. G., Dove, A., Robbins, T. W., Barker, R. A. & Owen, A. M. Striatal contributions to working memory: a functional magnetic resonance imaging study in humans. Eur. J. Neurosci. 19 , 755–760. https://doi.org/10.1111/j.1460-9568.2003.03108.x (2004).

Jahanshahi, M. et al. Striatal contribution to cognition: working memory and executive function in Parkinson’s disease before and after unilateral posteroventral pallidotomy. J. Cogn. Neurosci. 14 , 298–310. https://doi.org/10.1162/089892902317236911 (2002).

Cools, R., Ivry, R. B. & D’Esposito, M. The human striatum is necessary for responding to changes in stimulus relevance. J. Cogn. Neurosci. 18 , 1973–1983. https://doi.org/10.1162/jocn.2006.18.12.1973 (2006).

Yehene, E., Meiran, N. & Soroker, N. Basal ganglia play a unique role in task switching within the frontal-subcortical circuits: evidence from patients with focal lesions. J. Cogn. Neurosci. 20 , 1079–1093. https://doi.org/10.1162/jocn.2008.20077 (2008).

Chiu, Y.-C., Jiang, J. & Egner, T. The caudate nucleus mediates learning of stimulus-control state associations. J. Neurosci. 37 , 1028–1038. https://doi.org/10.1523/JNEUROSCI.0778-16.2016 (2017).

Jiang, J., Beck, J., Heller, K. & Egner, T. An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands. Nat. Commun. 6 , 8165. https://doi.org/10.1038/ncomms9165 (2015).

Ford, K. A. & Everling, S. Neural activity in primate caudate nucleus associated with pro- and antisaccades. J. Neurophysiol. 102 , 2334–2341. https://doi.org/10.1152/jn.00125.2009 (2009).

Watanabe, M. & Munoz, D. P. Saccade suppression by electrical microstimulation in monkey caudate nucleus. J. Neurosci. 30 , 2700–2709. https://doi.org/10.1523/JNEUROSCI.5011-09.2010 (2010).

Watanabe, M. & Munoz, D. P. Neural correlates of conflict resolution between automatic and volitional actions by basal ganglia. Eur. J. Neurosci. 30 , 2165–2176. https://doi.org/10.1111/j.1460-9568.2009.06998.x (2009).

Watanabe, M. & Munoz, D. P. Probing basal ganglia functions by saccade eye movements. Eur. J. Neurosci. 33 , 2070–2090. https://doi.org/10.1111/j.1460-9568.2011.07691.x (2011).

Stocco, A. et al. Individual differences in the Simon effect are underpinned by differences in the competitive dynamics in the basal ganglia: an experimental verification and a computational model. Cognition 164 , 31–45. https://doi.org/10.1016/j.cognition.2017.03.001 (2017).

Wiecki, T. V. & Frank, M. J. A computational model of inhibitory control in frontal cortex and basal ganglia. Psychol. Rev. 120 , 329–355. https://doi.org/10.1037/a0031542 (2013).

Beste, C., Saft, C., Andrich, J., Gold, R. & Falkenstein, M. Stimulus-response compatibility in Huntington’s disease: a cognitive-neurophysiological analysis. J. Neurophysiol. 99 , 1213–1223. https://doi.org/10.1152/jn.01152.2007 (2008).

Beste, C., Moll, C. K. E., Pötter-Nerger, M. & Münchau, A. Striatal microstructure and its relevance for cognitive control. Trends Cogn. Sci. 22 , 747–751. https://doi.org/10.1016/j.tics.2018.06.007 (2018).

Wylie, S. A. et al. The effect of Parkinson’s disease on interference control during action selection. Neuropsychologia 47 , 145–157. https://doi.org/10.1016/j.neuropsychologia.2008.08.001 (2009).

van Wouwe, N. C. et al. Dissociable effects of dopamine on the initial capture and the reactive inhibition of impulsive actions in Parkinson’s disease. J. Cogn. Neurosci. 25 , 710–723. https://doi.org/10.1162/jocn_a_00930 (2016).

Wylie, S. A., Ridderinkhof, K. R., Bashore, T. R. & van den Wildenberg, W. P. M. The effect of Parkinson’s disease on the dynamics of on-line and proactive cognitive control during action selection. J. Cogn. Neurosci. 22 , 2058–2073. https://doi.org/10.1162/jocn.2009.21326 (2010).

Peterson, B. S. et al. An event-related functional MRI study comparing interference effects in the Simon and Stroop tasks. Cogn. Brain Res. 13 , 427–440. https://doi.org/10.1016/S0926-6410(02)00054-X (2002).

Sylvester, C.-Y.C. et al. Switching attention and resolving interference: fMRI measures of executive functions. Neuropsychologia 41 , 357–370. https://doi.org/10.1016/S0028-3932(02)00167-7 (2003).

Simon, J. R. Reactions toward the source of stimulation. J. Exp. Psychol. 81 , 174–176 (1969).

Lu, C.-H. & Proctor, R. W. The influence of irrelevant location information on performance: a review of the Simon and spatial Stroop effects. Psychon. Bull. Rev. 2 , 174–207. https://doi.org/10.3758/BF03210959 (1995).

Hedge, A. & Marsh, N. W. A. The effect of irrelevant spatial correspondences on two-choice response-time. Acta Physiol. (Oxf.) 39 , 427–439. https://doi.org/10.1016/0001-6918(75)90041-4 (1975).

Article   CAS   Google Scholar  

Hommel, B. The Simon effect as tool and heuristic. Acta Physiol. (Oxf.) 136 , 189–202. https://doi.org/10.1016/j.actpsy.2010.04.011 (2011).

Schmidt, C. C. et al. Preserved but less efficient control of response interference after unilateral lesions of the striatum. Front. Hum. Neurosci. 12 , 414. https://doi.org/10.3389/fnhum.2018.00414 (2018).

Smith, A. T., Cotton, P. L., Bruno, A. & Moutsiana, C. Dissociating vision and visual attention in the human pulvinar. J. Neurophysiol. 101 , 917–925. https://doi.org/10.1152/jn.90963.2008 (2009).

Zackon, D. H., Casson, E. J., Stelmach, L., Faubert, J. & Racette, L. Distinguishing subcortical and cortical influences in visual attention: subcortical visual processing. Invest. Ophthalmol. Vis. Sci. 38 , 364–371 (1997).

CAS   PubMed   Google Scholar  

Burle, B., Vidal, F., Tandonnet, C. & Hasbroucq, T. Physiological evidence for response inhibition in choice reaction time tasks. Brain Cogn. 56 , 153–164. https://doi.org/10.1016/j.bandc.2004.06.004 (2004).

Ridderinkhof, K. R. Micro- and macro-adjustments of task set: activation and suppression in conflict tasks. Psychol. Res. 66 , 312–323 (2002).

Stoffer, T. H. & Yakin, A. R. The functional role of attention for spatial coding in the Simon effect. Psychol. Res. 56 , 151–162 (1994).

van den Wildenberg, W. P. M. et al. To head or to heed? Beyond the surface of selective action inhibition: a review. Front. Hum. Neurosci. 4 , 1–13. https://doi.org/10.3389/fnhum.2010.00222 (2010).

Burle, B., Possamai, C.-A., Vidal, F., Bonnet, M. & Hasbroucq, T. Executive control in the Simon effect: an electromyographic and distributional analysis. Psychol. Res. 66 , 324–336. https://doi.org/10.1007/s00426-002-0105-6 (2002).

Forstmann, B. U., van den Wildenberg, W. P. M. & Ridderinkhof, K. R. Neural mechanisms, temporal dynamics, and individual differences in interference control. J. Cogn. Neurosci. 20 , 1854–1865 (2008).

Forstmann, B. U. et al. Function and structure of the right inferior frontal cortex predict individual differences in response inhibition: a model-based approach. J. Neurosci. 28 , 9790–9796. https://doi.org/10.1523/jneurosci.1465-08.2008 (2008).

Mink, J. W. The basal ganglia: focused selection and inhibition of competing motor programs. Prog. Neurobiol. 50 , 381–425. https://doi.org/10.1016/S0301-0082(96)00042-1 (1996).

Oldfield, R. C. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9 , 97–113. https://doi.org/10.1016/0028-3932(71)90067-4 (1971).

Dale, A. M. Optimal experimental design for event-related fMRI. Hum. Brain Mapp. 8 , 109–114 (1999).

Jahfari, S. et al. Effective connectivity reveals important roles for both the hyperdirect (fronto-subthalamic) and the indirect (fronto-striatal-pallidal) fronto-basal ganglia pathways during response inhibition. J. Neurosci. 31 , 6891–6899 (2011).

Egner, T. Congruency sequence effects and cognitive control. Cogn. Affect. Behav. Neurosci. 7 , 380–390 (2007).

Schwarz, W. & Miller, J. Response time models of delta plots with negative-going slopes. Psychon. Bull. Rev. 19 , 555–574. https://doi.org/10.3758/s13423-012-0254-6 (2012).

Grant, D. A. Analysis-of-variance tests in the analysis and comparison of curves. Psychol. Bull. 53 , 141–154. https://doi.org/10.1037/h0038479 (1956).

Ratcliff, R. Group reaction time distributions and an analysis of distribution statistics. Psychol. Bull. 86 , 446–461. https://doi.org/10.1037/0033-2909.86.3.446 (1979).

Friston, K. J. et al. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2 , 189–210 (1995).

Henson, R. N. A. & Friston, K. J. In Statistical Parametric Mapping: The Analysis of Functional Brain Images (eds Friston, K. J. et al. ) 178–192 (Academic Press, Cambridge, 2007).

Chapter   Google Scholar  

Forman, S. D. et al. Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magn. Reson. Med. 33 , 636–647. https://doi.org/10.1002/mrm.1910330508 (1995).

Poline, J.-B., Worsley, K. J., Evans, A. C. & Friston, K. J. Combining spatial extent and peak intensity to test for activations in functional imaging. NeuroImage 5 , 83–96. https://doi.org/10.1006/nimg.1996.0248 (1997).

Woo, C.-W., Krishnan, A. & Wager, T. D. Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. NeuroImage 91 , 412–419. https://doi.org/10.1016/j.neuroimage.2013.12.058 (2014).

Eickhoff, S. B. et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 25 , 1325–1335. https://doi.org/10.1016/j.neuroimage.2004.12.034 (2005).

Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15 , 273–289. https://doi.org/10.1006/nimg.2001.0978 (2002).

Maldjian, J. A., Laurienti, P. J., Kraft, R. A. & Burdette, J. H. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage 19 , 1233–1239. https://doi.org/10.1016/S1053-8119(03)00169-1 (2003).

Forstmann, B. U., de Hollander, G., van Maanen, L., Alkemade, A. & Keuken, M. C. Towards a mechanistic understanding of the human subcortex. Nat. Rev. Neurosci. 18 , 57–65. https://doi.org/10.1038/nrn.2016.163 (2017).

Arend, I., Weiss, P. H., Timpert, D. C., Fink, G. R. & Henik, A. Spatial coding as a function of handedness and responding hand: theoretical and methodological implications. PLoS ONE 11 , e0151979. https://doi.org/10.1371/journal.pone.0151979 (2016).

Tagliabue, M. et al. The measurement of left-right asymmetries in the Simon effect: a fine-grained analysis. Behav. Res. Methods 39 , 50–61 (2007).

Rubichi, S. & Nicoletti, R. The Simon effect and handedness: evidence for a dominant-hand attentional bias in spatial coding. Percept. Psychophys. 68 , 1059–1069 (2006).

Spironelli, C., Tagliabue, M. & Umiltà, C. Response selection and attention orienting: a computational model of Simon effect asymmetries. Exp. Psychol. 56 , 274–282. https://doi.org/10.1027/1618-3169.56.4.274 (2009).

Heister, G., Ehrenstein, W. H. & Schroeder-Heister, P. Spatial S-R compatibility with unimanual two-finger choice reactions: effects of irrelevant stimulus location. Percept. Psychophys. 42 , 195–201 (1987).

Cyr, M., Yang, X., Horga, G. & Marsh, R. Abnormal fronto-striatal activation as a marker of threshold and subthreshold bulimia nervosa. Hum. Brain Mapp. 39 , 1796–1804. https://doi.org/10.1002/hbm.23955 (2018).

Liu, X., Banich, M. T., Jacobson, B. L. & Tanabe, J. L. Common and distinct neural substrates of attentional control in an integrated Simon and spatial Stroop task as assessed by event-related fMRI. NeuroImage 22 , 1097–1106. https://doi.org/10.1016/j.neuroimage.2004.02.033 (2004).

Marsh, R. et al. Deficient activity in the neural systems that mediate self-regulatory control in bulimia nervosa. Arch. Gen. Psychiatry 66 , 51–63 (2009).

Weidler, B. J. & Abrams, R. A. Enhanced cognitive control near the hands. Psychon. Bull. Rev. 21 , 462–469. https://doi.org/10.3758/s13423-013-0514-0 (2014).

Pas, P., du Plessis, S., van den Munkhof, H. E., Gladwin, T. E. & Vink, M. Using subjective expectations to model the neural underpinnings of proactive inhibition. Eur. J. Neurosci. 49 , 1575–1586. https://doi.org/10.1111/ejn.14308 (2018).

Pas, P., van den Munkhof, H. E., du Plessis, S. & Vink, M. Striatal activity during reactive inhibition is related to the expectation of stop-signals. Neuroscience 361 , 192–198. https://doi.org/10.1016/j.neuroscience.2017.08.037 (2017).

Vink, M., Kaldewaij, R., Zandbelt, B. B., Pas, P. & du Plessis, S. The role of stop-signal probability and expectation in proactive inhibition. Eur. J. Neurosci. 41 , 1086–1094. https://doi.org/10.1111/ejn.12879 (2015).

Majid, D. S. A., Cai, W., Corey-Bloom, J. & Aron, A. R. Proactive selective response suppression is implemented via the basal ganglia. J. Neurosci. 33 , 13259–13269. https://doi.org/10.1523/JNEUROSCI.5651-12.2013 (2013).

Smittenaar, P., Guitart-Masip, M., Lutti, A. & Dolan, R. J. Preparing for selective inhibition within frontostriatal loops. J. Neurosci. 33 , 18087–18097 (2013).

Egner, T., Delano, M. & Hirsch, J. Separate conflict-specific cognitive control mechanisms in the human brain. NeuroImage 35 , 940–948. https://doi.org/10.1016/j.neuroimage.2006.11.061 (2007).

Sebastian, A. et al. Disentangling common and specific neural subprocesses of response inhibition. NeuroImage 64 , 601–615 (2013).

Egner, T. Brain mapping of control processes. Brain Mapp. Encycl. Ref. 2 , 581–587. https://doi.org/10.1016/B978-0-12-397025-1.00049-X (2015).

Burle, B., Spieser, L., Servant, M. & Hasbroucq, T. Distributional reaction time properties in the Eriksen task: marked differences or hidden similarities with the Simon task?. Psychon. Bull. Rev. 21 , 1003–1010. https://doi.org/10.3758/s13423-013-0561-6 (2014).

Hübner, R. & Töbel, L. Conflict resolution in the Eriksen flanker task: similarities and differences to the Simon task. PLoS ONE 14 , e0214203. https://doi.org/10.1371/journal.pone.0214203 (2019).

Mansfield, K. L., van der Molen, M. W., Falkenstein, M. & van Boxtel, G. J. M. Temporal dynamics of interference in Simon and Eriksen tasks considered within the context of a dual-process model. Brain Cogn. 82 , 353–363. https://doi.org/10.1016/j.bandc.2013.06.001 (2013).

Scerrati, E., Lugli, L., Nicoletti, R. & Umiltà, C. Comparing Stroop-like and Simon effects on perceptual features. Sci. Rep. 7 , 17815. https://doi.org/10.1038/s41598-017-18185-1 (2017).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Friedman, N. P. & Miyake, A. The relations among inhibition and interference control functions: a latent-variable analysis. J. Exp. Psychol. Gen. 133 , 101–135 (2004).

Friedman, N. P. & Miyake, A. Unity and diversity of executive functions: individual differences as a window on cognitive structure. Cortex 86 , 186–204 (2017).

Stahl, C. et al. Behavioral components of impulsivity. J. Exp. Psychol. Gen. 143 , 850–886. https://doi.org/10.1037/a0033981 (2014).

Verbruggen, F., Liefooghe, B., Notebaert, W. & Vandierendonck, A. Effects of stimulus–stimulus compatibility and stimulus–response compatibility on response inhibition. Acta Physiol. (Oxf.) 120 , 307–326. https://doi.org/10.1016/j.actpsy.2005.05.003 (2005).

Burle, B., van den Wildenberg, W. P. M., Spieser, L. & Ridderinkhof, K. R. Preventing (impulsive) errors: electrophysiological evidence for online inhibitory control over incorrect responses. Psychophysiology 53 , 1008–1019. https://doi.org/10.1111/psyp.12647 (2016).

Miller, J. & Roüast, N. M. Dissociations of spatial congruency effects across response measures: an examination of delta plots. Psychol. Res. 80 , 805–820. https://doi.org/10.1007/s00426-015-0694-5 (2016).

Proctor, R. W., Miles, J. D. & Baroni, G. Reaction time distribution analysis of spatial correspondence effects. Psychon. Bull. Rev. 18 , 242–266. https://doi.org/10.3758/s13423-011-0053-5 (2011).

Valle-Inclán, F., Hackley, S. A. & de Labra, C. Stimulus-response compatibility between stimulated eye and response location: implications for attentional accounts of the Simon effect. Psychol. Res. 67 , 240–243. https://doi.org/10.1007/s00426-003-0131-z (2003).

Mostofsky, S. H. & Simmonds, D. J. Response inhibition and response selection: two sides of the same coin. J. Cogn. Neurosci. 20 , 751–761. https://doi.org/10.1162/jocn.2008.20500 (2008).

Treccani, B., Cona, G., Milanese, N. & Umiltà, C. Sequential modulation of (bottom-up) response activation and inhibition in a response conflict task: a single-pulse transcranial magnetic stimulation study. Psychol. Res. 82 , 771–786. https://doi.org/10.1007/s00426-017-0863-9 (2018).

Hommel, B. Spontaneous decay of response-code activation. Psychol. Res. 56 , 261–268. https://doi.org/10.1007/BF00419656 (1994).

Vallesi, A. & Umiltà, C. A. Decay of stimulus spatial code in horizontal and vertical Simon tasks. J. Gen. Psychol. 136 , 350–373. https://doi.org/10.1080/00221300903266580 (2009).

Ulrich, R., Schröter, H., Leuthold, H. & Birngruber, T. Automatic and controlled stimulus processing in conflict tasks: superimposed diffusion processes and delta functions. Cogn. Psychol. 78 , 148–174. https://doi.org/10.1016/j.cogpsych.2015.02.005 (2015).

Ellinghaus, R., Karlbauer, M., Bausenhart, K. M. & Ulrich, R. On the time-course of automatic response activation in the Simon task. Psychol. Res. 82 , 734–743. https://doi.org/10.1007/s00426-017-0860-z (2018).

Mittelstädt, V. & Miller, J. Beyond mean reaction times: combining distributional analyses with processing stage manipulations in the Simon task. Cogn. Psychol. 119 , 101275. https://doi.org/10.1016/j.cogpsych.2020.101275 (2020).

Wager, T. D. et al. Common and unique components of response inhibition revealed by fMRI. NeuroImage 27 , 323–340. https://doi.org/10.1016/j.neuroimage.2005.01.054 (2005).

Dosenbach, N. U. F. et al. Distinct brain networks for adaptive and stable task control in humans. Proc. Natl. Acad. Sci. U.S.A. 104 , 11073–11078. https://doi.org/10.1073/pnas.0704320104 (2007).

Menon, V. & Uddin, L. Q. Saliency, switching, attention and control: a network model of insula function. Brain Struct. Funct. 214 , 655–667. https://doi.org/10.1007/s00429-010-0262-0 (2010).

Medford, N. & Critchley, H. D. Conjoint activity of anterior insular and anterior cingulate cortex: awareness and response. Brain Struct. Funct. 214 , 535–549. https://doi.org/10.1007/s00429-010-0265-x (2010).

Fan, J., Flombaum, J. I., McCandliss, B. D., Thomas, K. M. & Posner, M. I. Cognitive and brain consequences of conflict. NeuroImage 18 , 42–57. https://doi.org/10.1006/nimg.2002.1319 (2003).

Georgiou-Karistianis, N. et al. Decreased functional brain activation in Friedreich ataxia using the Simon effect task. Brain Cogn. 79 , 200–208. https://doi.org/10.1016/j.bandc.2012.02.011 (2012).

Kerns, J. G. Anterior cingulate and prefrontal cortex activity in an fMRI study of trial-to-trial adjustments on the Simon task. NeuroImage 33 , 399–405. https://doi.org/10.1016/j.neuroimage.2006.06.012 (2006).

Rubia, K. et al. Progressive increase of frontostriatal brain activation from childhood to adulthood during event-related tasks of cognitive control. Hum. Brain Mapp. 27 , 973–993 (2006).

Strack, G., Kaufmann, C., Kehrer, S., Brandt, S. & Stürmer, B. Anticipatory regulation of action control in a Simon task: behavioral, electrophysiological, and fMRI correlates. Front. Psychol. 4 , 47. https://doi.org/10.3389/fpsyg.2013.00047 (2013).

Goghari, V. M. & MacDonald, A. W. Effects of varying the experimental design of a cognitive control paradigm on behavioral and functional imaging outcome measures. J. Cogn. Neurosci. 20 , 20–35. https://doi.org/10.1162/jocn.2008.20.1.20 (2008).

Download references

Acknowledgements

This research was funded by the German Israeli Foundation (GIF) for Scientific Research and Development awarded to AH and PHW (GIF Grant No: 1110-93/2010). SV is supported by funding from the Federal Ministry of Education and Research (BMBF, 01GQ1401). GRF gratefully acknowledges additional support from the Marga and Walter Boll Foundation.

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and affiliations.

Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany

Claudia C. Schmidt, David C. Timpert, Simone Vossel, Gereon R. Fink & Peter H. Weiss

Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

David C. Timpert, Gereon R. Fink & Peter H. Weiss

Department of Psychology and Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel

Isabel Arend & Avishai Henik

Department of Psychology, Faculty of Human Sciences, University of Cologne, Cologne, Germany

Simone Vossel

You can also search for this author in PubMed   Google Scholar

Contributions

D.C.T., I.A., S.V., G.R.F., A.H., and P.H.W. conceived and/or designed the study; D.C.T., I.A., and P.H.W. acquired the data; C.S., S.V., and P.H.W. carried out statistical analysis; C.S., I.A., S.V., G.R.F., and P.H.W. analyzed and/or interpreted the data; C.S. drafted the manuscript; C.S., D.C.T., I.A., S.V., G.R.F., A.H., and P.H.W. revised the manuscript; G.R.F., A.H., and P.H.W. supervised the study; and S.V., G.R.F., A.H., and P.H.W. obtained funding.

Corresponding author

Correspondence to Claudia C. Schmidt .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Schmidt, C.C., Timpert, D.C., Arend, I. et al. Control of response interference: caudate nucleus contributes to selective inhibition. Sci Rep 10 , 20977 (2020). https://doi.org/10.1038/s41598-020-77744-1

Download citation

Received : 01 October 2019

Accepted : 03 November 2020

Published : 01 December 2020

DOI : https://doi.org/10.1038/s41598-020-77744-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Neural underpinnings of ethical decisions in life and death dilemmas in naïve and expert firefighters.

• Isabel C. Duarte
• Ana Dionísio
• Miguel Castelo-Branco

Scientific Reports (2024)

Neural substrates of continuous and discrete inhibitory control

• Jonathon R. Howlett
• Heekyeong Park
• Martin P. Paulus

Translational Psychiatry (2023)

Atrophy in subcortical gray matter in adult patients with moyamoya disease

Neurological Sciences (2023)

Sex and strategy effects on brain activation during a 3D-navigation task

• Isabel Noachtar
• Ti-Anni Harris
• Belinda Pletzer

Communications Biology (2022)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

ORIGINAL RESEARCH article

Stimulus intervals modulate the balance of brain activity in the human primary somatosensory cortex: an erp study.

• 1 Department of Psychology, Suzhou University of Science and Technology, Suzhou, China
• 2 Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan
• 3 Beijing Institute of Technology, Beijing, China
• 4 Department of Psychology, Soochow University, Suzhou, China

Neuronal excitation and inhibition occur in the brain at the same time, and brain activation reflects changes in the sum of excitation and inhibition. This principle has been well-established in lower-level sensory systems, including vision and touch, based on animal studies. However, it is unclear how the somatosensory system processes the balance between excitation and inhibition. In the present ERP study, we modified the traditional spatial attention paradigm by adding double stimuli presentations at short intervals (i.e., 10, 30, and 100 ms). Seventeen subjects participated in the experiment. Five types of stimulation were used in the experiment: a single stimulus (one raised pin for 40 ms), standard stimulus (eight pins for 40 ms), and double stimuli presented at intervals of 10, 30, and 100 ms. The subjects were asked to attend to a particular finger and detect whether the standard stimulus was presented to that finger. The results showed a clear attention-related ERP component in the single stimulus condition, but the suppression components associated with the three interval conditions seemed to be dominant in somatosensory areas. In particular, we found the strongest suppression effect in the ISI-30 condition (interval of 30 ms) and that the suppression and enhancement effects seemed to be counterbalanced in both the ISI-10 and ISI-100 conditions (intervals of 10 and 100 ms, respectively). This type of processing may allow humans to easily discriminate between multiple stimuli on the same body part.

Introduction

When spatial attention to auditory ( Alho et al., 1999 ; Karns and Knight, 2009 ) or visual stimuli ( Noesselt et al., 2002 ; Macaluso et al., 2005 ) was modulated, evoked potentials were generated in the primary auditory or visual cortices. Regarding the somatosensory system, studies have been conducted using fMRI and event-related potentials (ERPs) in humans ( Meador et al., 2002 ; Forster and Eimer, 2004 ; Schubert et al., 2008 ), and they found that attention enhances activity in the primary somatosensory cortex (SI) when using a single stimulus. Animal studies ( Pilz et al., 2004 ; Braun et al., 2005 ; Reed et al., 2010 ) used double stimuli to show that the second stimulus suppresses the response to the first stimulus. This suggested that spatiotemporal interactions modulate the response magnitude in human SI. However, it remains unclear how the balance between attentional enhancement and double asynchronous stimulation-induced suppression is maintained.

Many previous studies examining the effects of spatial-selective attention have found that attentional effects occur in the early stage, but they did not find modulation of somatosensory evoked potential (SEP) components generated in S1. Some ERP studies used a mechanical tactile stimulus and found a contralateral N80 component with sustained attention and a bilateral P100 component with spatial attention in the early stages ( Eimer and Driver, 2000 ; Eimer and Forster, 2003b ; Zopf et al., 2004 ). Other electroencephalography (EEG) studies using tactile spatial sustained attention to mechanical stimuli found that the earliest somatosensory component (P50) was significantly increased for attended stimuli ( Zopf et al., 2004 ). In a simultaneous EEG-fMRI study, Schubert et al. (2008) used Braille stimulation and found significant effects of spatial-selective attention on P50 and P100 with left tactile stimuli and on N80 with right tactile stimuli in SI. Other ERP and SEP studies of mechanical tactile stimuli ( Eimer and Forster, 2003a ; Eimer et al., 2004 ; Forster and Gillmeister, 2011 ; Katus et al., 2012 ) showed that amplitudes of mid-latency components such as N140 and P200 were enhanced in response to tactile stimuli presented to the attended hand.

In addition, an electrophysiological study in owl monkeys ( Reed et al., 2010 ) selected paired skin sites and delivered pulses simultaneously (0 ms delay) or with onset asynchronies of 10, 30, 50, 100, and 500 ms to investigate the effects of varying the temporal proximity of stimuli. This study indicated that maximal suppression of firing rates occurred when the stimulus onset times were 30–50 ms. The owl monkeys were sedated in this study, so a suppressed effect was observed under unattended conditions.

The underlying attention and temporal processes in the human somatosensory cortex remain unclear when paired mechanical stimuli are presented. Thus, we hypothesized that enhancement and suppression occur as follows in human somatosensory areas: (1) The enhancement effect of sustained spatial attention will be stronger than the suppression effect of paired stimulation. (2) The suppression effect of paired stimulation will be stronger than the enhancement effect of sustained spatial attention. (3) The enhancement effect of sustained spatial attention and the suppression effect of paired stimulation will exist at the same time.

The present experiment was designed to determine whether the enhancement from sustained spatial attention or suppression from paired stimulation affects neurophysiological responses in human SI. We extended the work of previous studies to investigate the temporal dynamics of neural responses when mechanical tactile stimulation is delivered to the left or right index finger at different interstimulus intervals with attention focused on one hand. Participants were asked to focus their spatial attention on tactile stimulation of one hand (on a finger), and we instructed them to detect rare tactile target stimuli on the index finger of the attended hand. To achieve this aim, ERPs were computed in response to tactile stimulation.

Materials and Methods

Participants.

Nineteen undergraduate students were recruited as volunteers. With further analysis, two participants were excluded from the statistical analysis because of low performance. Seventeen participants (age range: 21–25; mean age: 22.5) remained in the sample. All participants had normal or corrected-to-normal vision and were right-handed. They had no neurological/psychiatric disorders and no hearing problems. The experimental protocol was approved by the ethics committee of Okayama University.

Material and Procedure

The experiment was conducted in a dimly lit, sound-attenuated room, with participants facing a computer screen (17 inch, LG, FLATRON) at a viewing distance of 60 cm. Tactile stimuli were applied to the distal phalanx of the left or right index finger using a piezoelectric Braille stimulator (KGS, Saitama, Japan). Each stimulator had eight individually controllable plastic pins grouped in a 2 × 4 array. The diameter of each pin was 1.3 mm. The distance between pins was 2.4 mm. Using a custom-built electrical drive, pins could be elevated from the resting position by 0.7 mm with a tactile force of 0.177 N/pin. The mechanical onset from the trigger to the highest position was ~38 ms, as measured by a high-speed camera, so we set the tactile stimuli presentation time to 40 ms.

Tactile stimuli were included for the standard and target. The target was 8 pins and was presented only on the side indicated by the visual instructions. The standard was one pin in the lower left (or right) when stimuli were presented on the left (or right) index finger. The stimulus presentations were composed of single and double conditions. The temporal proximity of stimulus presentations in the double condition consisted of three different interstimulus intervals (10, 30, and 100 ms). The interstimulus interval (ISI) is the time interval between the first tactile stimulus offset and second tactile stimulus onset ( Figure 1A ).

Figure 1 . (A) The types of tactile stimulation: standard (1 pin) and target (8 pins). (B) Illustration of attended left hand. The visual instruction was presented for 300 ms, and participants were instructed to direct their attention to the left index finger until the next instruction appeared. Standard stimuli presented on the left hand as attended stimuli. Stimuli delivered to the other hand were unattended stimuli. After the 1,500 ms interval, tactile stimuli (including two target stimuli and eight standard stimuli per block) were presented unilaterally to the left or right hand within 600 ms (total 2,100 ms). The target was presented only on the left side, and the participant responded vocally when it was detected.

Visual and tactile stimuli were presented by using Presentation software (Neurobehavioral Systems Inc., Albany, California, USA) housed outside of the dimly lit room. A block design was used for this experiment in which the standard and target stimuli were randomized in blocks of 10 trials, with 15 blocks in one session (for a total of 150 trials). In summary, the experiment comprised 16 separate sessions, consisting of 240 blocks for a total of 2,400 trials. Visual instructions indicating the left or right index fingers (each instruction angle was 5 × 7° flat at 3.5° left or right from the fixation) changed to red and were presented for 300 ms at the beginning of each block. The instructions asked the participants to keep their attention on the left or right index finger for that block. A fixation (a white cross of 1.7 × 1.7° of visual angle) was located between both instructions ( Figure 1B ). Each session contained four experimental conditions: a single condition and three types of double conditions (ISI-10 condition, ISI-30 condition and ISI-100 condition).

Figure 1B illustrates the experimental stimulation procedure for the attended left hand. Each block began with the visual instruction, which was presented for 300 ms. Within the 300 ms, the index finger of the left hand turned red in the visual instructions, and the subjects kept their attention on the finger position indicated by red (i.e., left index finger) until the next block. They were required to respond vocally when the target stimulus was detected on the left index finger. Thus, the participants had to direct their attention to the attended hand. A standard stimulus presented to this hand was named the attended stimulus. In contrast, stimuli delivered to the other hand were named unattended stimuli. After a 1,500-ms interval, tactile stimuli (including two target stimuli and eight standard stimuli per block) were presented unilaterally to the left or right hand within 600 ms (for a total of 2,100 ms as indicated in Figure 1B ). Visual instructions and tactile stimulation were presented in pseudorandom order. During the entire experiment, the participants were also instructed to avoid movements of the body, in particular, the eyes and fingers.

EEG Recording and Data Analysis

An EEG system (Brain Amp MR plus, Germany) was used to record signals through 28 electrodes mounted on an electrode cap (Easy cap, Herrsching Breitbrunn, Germany) as specified by the International 10–20 System. All electrodes were referenced to the combined signals from the bilateral earlobes. A horizontal electrooculogram (HEOG) was recorded from the outer canthus of the left eye. Eye blinks and vertical eye movements were recorded from an electrode placed 1.5 cm below the left eye. The impedance of all electrodes was below 5 kΩ. The raw signals were digitized with a sample frequency of 500 Hz with a 60-Hz notch filter. The bandpass of the amplifiers was DC to 250 Hz.

Brain Vision Analyzer software (version 1.05, Germany) was used to analyze the ERPs, which were averaged separately for each stimulus type offline. To remove the target stimulus, we analyzed only ERPs elicited by standard stimuli. The continuous EEG signals were segmented offline from 100 ms before to 500 ms after tactile stimulus onset. Baseline corrections were made against the data from −100 to 0 ms. We rejected artifact trials in which the amplitude reached ±80 μν from −100 to 500 ms, and we filtered the data with a bandpass filter retaining frequencies between 0.01 and 30 Hz. The data from each electrode were then averaged, and a grand average ERP was computed across all participants for each stimulus type.

For further analysis, the mean amplitude data were computed within the following time windows relative to stimulus onset: P50 (34–62 ms), N80 (64–92 ms), P100 (94–122 ms), N140 (124–172 ms), P200 (174–242 ms), and P300 (244–342 ms). In each time window, the mean amplitude data were analyzed using repeated measures analyses of variance (ANOVAs) with two factors (attended vs. unattended) × 4 conditions (single, ISI-10, ISI-30 and ISI-100 conditions), and data from electrodes C3 and C4 were analyzed separately. RStudio (Version 1.1.383) was used for all statistical analyses.

Figure 2 shows the grand averaged waveforms for the single condition and double conditions (ISI-10 condition, ISI-30 condition and ISI-100 condition). The electrode sites were C3/4, approximately overlying the contralateral SI. The black solid line represents the attended state, and the black dotted line represents the unattended state. In the single condition, attended stimuli elicited more positive responses than the unattended state. The double conditions resulted in the following: in the ISI-10 condition, the attended stimuli elicited activity similar to the unattended state; in the ISI-30 condition, the unattended stimuli elicited more positive activity than the attended state; and in the ISI-100 condition, the attended stimuli elicited activity levels close to the unattended state once again.

Figure 2 . The grand averaged waveforms for the (A) single condition (a, b) and (B) double conditions: (c,d) ISI 10 ms; (e,f) ISI 30 ms; (g–f) ISI 100 ms. The electrode sites were C3/4 approximately overlying the contralateral SI. Black solid line: attended. Black dotted line: unattended. The red arrow marks the onset of the second stimulus. The shaded areas indicate the periods used for the pointwise running t -tests comparing attended to unattended for all participants ( p < 0.05).

The left column in Figure 2 shows the ERPs elicited in the four conditions by tactile stimuli presented on the right index finger at contralateral electrodes (C3, right hand). All subjects demonstrated a clear P45 component in their responses to tactile stimuli presented to the right index fingers. ANOVA of the mean amplitudes of P45 revealed a main effect [ F (1,16) = 4.740; p < 0.05] of attention at C3, which was not accompanied by an attention × condition interaction; ANOVA of mean amplitudes of P100 revealed a main effect [ F (1,16) = 6.175; p < 0.05] of attention at C3, which was not accompanied by an attention × condition interaction. There was a main effect of conditions [ F (3,16) = 3.230; p < 0.05] at C3 for the P300 component.

The right column of Figure 2 shows the ERPs elicited in the four conditions by a tactile stimulus presented to the left index finger at contralateral electrodes (C4, left hand). The analysis of the left side for P45 and N80 revealed no main effect or interaction between attention and conditions, and only a weak significant difference in the t -test was found between the attention states in the ISI-30 condition ( p < 0.05). There was a significant interaction between attention and conditions [ F (3,16) = 6.589; p < 0.001] for P100; paired t -tests found the most significant difference between the unattended and ISI-30 conditions ( p < 0.001). No main effects of attention and conditions were found for N140, P200 and P300.

Figure 3 shows the mean amplitudes for the P45, N80, and P100 components. This result represents the attended minus unattended conditions on the left hand and right hand. Three components showed the lowest amplitude in the ISI-30 condition with the left-hand stimulus. The main effect of attention on the mean amplitudes of the P45 component was significant [ F (1,16) = 6.14, p < 0.05]. Post hoc comparisons between the single and ISI-30 conditions showed that most activation occurred at the C4 electrode ( p < 0.05). Regarding the N80 component, the interaction between attention and ISI was clear [ F (3,48) = 5.88, p < 0.05], and the mean amplitudes in the single and ISI-10 conditions were significantly higher than that in the ISI-30 condition ( p < 0.05). ISI-30 and ISI-100 were also significantly different ( p < 0.05). These results were also limited to the C4 electrode (left hand). In the last component, P100, there was no main effect or interaction at the C3 electrode, although an effect similar to the attention main effect was found [ F (1,16) = 3.77, p = 0.07], but at the C4 electrode, an interaction effect between attention and ISI was clearly found [ F (3,48) = 6.6, p < 0.05]. The mean amplitude in the single condition was higher than that in the ISI-10 and ISI-30 conditions ( p < 0.01). Additionally, there was a significant difference between ISI-30 and ISI-100 conditions ( p < 0.05). For the right hand, there were no significant differences between conditions for P45, N80, and P100.

Figure 3 . Mean amplitudes of attended minus unattended conditions on the left hand and right hand. The analysis time window for (A) P50 was 34–62 ms; (B) N80 was 64–92 ms; and (C) P100 was 94–122 ms. Black line: left hand. Dotted line: right hand. * p < 0.05, ** p < 0.01, *** p <0.001.

This study used double asynchronous stimulation to investigate the relationship between spatial attention enhancement and double asynchronous stimulation-induced suppression of brain activity in human SI. The participants were asked to focus their spatial attention on tactile stimulation of one hand (on a finger), and we instructed them to detect rare tactile target stimuli at the index finger of the attended hand. In double stimulation conditions, as the interstimulus intervals increased, a V-shaped effect was observed. We suggest that this occurs through an attention enhancement and a double stimulation-suppression effect.

We found a suppression effect in the ISI-30 condition, supporting a hypothesis from previous studies that multisensory stimulation shortens the response latencies of neurons and that post-activation inhibition of neurons is stronger than single stimulation. Research in monkeys found that the suppressive effect of paired stimulation activation on the interphalangeal nerve was stronger than that on the adjacent phalanges. The inhibition of interphalangeal nerve activity was caused by the proximity of receptor-related nerve cells in area 3b, which leads to nerve post-activation inhibition. As was observed in monkeys, neural response intensity was generally suppressed by a preceding conditioning stimulus when the test stimulus occurred after a 30- or 50-ms delay ( Reed et al., 2010 ). Other similar studies ( Fanselow and Nicolelis, 1999 ) examining rat whisker nerve reflexes found nerve post-activation inhibition following paired stimulation in quiet and movement states. In addition, Christian 2017 used double visual stimuli to investigate repetition suppression and suggested that stimulus-specific expectations about objects modulated the LOC and propagated back to the earliest cortical station processing visual input ( Grill-Spector and Malach, 2001 ; Utzerath et al., 2017 ). In the present study, the visual input was equivalent to cues to improve sensitivity to the tactile input, and the stimulus was repeatedly presented in the same location of the fingers. It was more intuitive to find nerve post-activation inhibition in area 3b. This experiment extended previous studies in monkeys and verified that the paired stimulation suppression effect in human primary somatosensory cortex 3b is similar to that in monkeys. The time of nerve post-activation inhibition may be ~30–50 ms.

In the single condition, we found some significant ERP components in the contralateral hemisphere by comparisons with the unattended side. The P50 and P100 components at the C4 electrode were significantly stronger on the attended side than on the unattended side ( Figure 2 ). An fMRI-EEG study used braille stimulation to investigate attentional effects on S1, and it found that left tactile stimulation (P50) was significantly enhanced by spatial-selective attention, suggesting that attention enhances the sensory signal during its early passage in S1( Schubert et al., 2008 ). This study also showed that P50 was the earliest component to be modulated by spatial-selective attention using stimuli similar to braille stimulation. Thus, the asymmetric effects of spatial selective attention on the two sides could also be found in the early and middle processing stages. For stimuli on the left hand, P50, P100, and P300 were found when comparing the attended vs. unattended hand, but on the other side, only the P300 attentional effect was found in the attended vs. unattended hand comparison. These asymmetric hemispheric activations may be explained by Mesulam's modality-non-specific model of spatial attention ( Mesulam, 1999 ). That is, higher-order areas in the left hemisphere control attention for events only on the right side, whereas the right hemisphere controls attention for both the left and right sides. Both theories may explain the asymmetric attentional effects on the SEPs, leading to earlier attentional modulation for left stimuli (i.e., P50 and P100 only for left and not for right stimuli).

We found some attentional enhancement in the single condition only. In the double stimuli conditions, the attentional effect was partially decreased as the interstimulus interval increased. A previous study suggested that when two or more stimuli were presented, the inhibition effects in based on the preferred stimulus ( Reed et al., 2010 ). In the ISI-10 condition, we did not observe any enhancement or suppression effect. There are two possibilities that explain these results: the interval may be too short, such that the subject cannot recognize the double stimuli, and when the stimulus is changed to double, the suppression effect is activated much more strongly than the attentional enhancement effect. According to the interaction of spatial attention enhancement and double asynchronous stimulation-induced suppression, when the enhancement and suppression effects are equal, there was no difference between attended and unattended states in terms of the neurophysiological responses to double asynchronous stimulation ( Figures 2 , 3 ). We suggest a tentative explanation that may account for this finding: the attention enhancement and double asynchronous stimulation-induced suppression effects decreased as the interstimulus interval increased. The stimulatory effect of attention is mutually competitive with the inhibitory effect of double stimulation. Moreover, the enhancement of spatial attention may be modulated by double stimulation suppression.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by the Ethics Committee of Okayama University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

YL, BD, JY, JW, QW, and MZ designed experiments. YL, BD, JY, YE, JW, and QW conducted experiments. YL and BD analyzed data. YL, BD, QW, and MZ wrote manuscript. All authors approved the manuscript.

This research was supported by the Japan Society for the Promotion of Science KAKENHI (Grant nos. 17K18855, 18H05009, 18K12149, 18K8835, 18H01411, 19KK0099, and 20K04381), National Natural Science Foundation of China (Grant no. 31700939), and a Grant-in-Aid for Strategic Research Promotion from Okayama University. In addition, this research was supported by the Social Science project of Suzhou University of Science and Technology (Grant nos. 332012902, 341922905).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Alho, K., Medvedev, S. V., Pakhomov, S. V., Roudas, M. S., Tervaniemi, M., Reinikainen, K., et al. (1999). Selective tuning of the left and right auditory cortices during spatially directed attention. Brain Res. Cogn. Brain Res. 7, 335–341. doi: 10.1016/S0926-6410(98)00036-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Braun, C., Hess, H., Burkhardt, M., Wühle, A., and Preissl, H. (2005). The right hand knows what the left hand is feeling. Exp. Brain Res. 162, 366–373. doi: 10.1007/s00221-004-2187-4

Eimer, M., and Driver, J. (2000). An event-related brain potential study of cross-modal links in spatial attention between vision and touch. Psychophysiology 37, 697–705. doi: 10.1111/1469-8986.3750697

Eimer, M., and Forster, B. (2003a). Modulations of early somatosensory ERP components by transient and sustained spatial attention. Exp. Brain Res. 151, 24–31. doi: 10.1007/s00221-003-1437-1

Eimer, M., and Forster, B. (2003b). The spatial distribution of attentional selectivity in touch: evidence from somatosensory ERP components. Clin. Neurophysiol. 114, 1298–1306. doi: 10.1016/S1388-2457(03)00107-X

Eimer, M., Forster, B., Fieger, A., and Harbich, S. (2004). Effects of hand posture on preparatory control processes and sensory modulations in tactile-spatial attention. Clin. Neurophysiol. 115, 596–608. doi: 10.1016/j.clinph.2003.10.015

Fanselow, E. E., and Nicolelis, M. A. (1999). Behavioral modulation of tactile responses in the rat somatosensory system. J. Neurosci. 19:7603. doi: 10.1523/JNEUROSCI.19-17-07603.1999

Forster, B., and Eimer, M. (2004). The attentional selection of spatial and non-spatial attributes in touch: ERP evidence for parallel and independent processes. Biol. Psychol. 66, 1–20. doi: 10.1016/j.biopsycho.2003.08.001

Forster, B., and Gillmeister, H. (2011). ERP investigation of transient attentional selection of single and multiple locations within touch. Psychophysiology 48, 788–796. doi: 10.1111/j.1469-8986.2010.01147.x

Grill-Spector, K., and Malach, R. (2001). fMR-adaptation: a tool for studying the functional properties of human cortical neurons. Acta Psychol. 107, 293–321. doi: 10.1016/S0001-6918(01)00019-1

Karns, C. M., and Knight, R. T. (2009). Intermodal auditory, visual, and tactile attention modulates early stages of neural processing. J. Cogn. Neurosci. 21, 669–683. doi: 10.1162/jocn.2009.21037

Katus, T., Andersen, S. K., and Müller, M. M. (2012). Maintenance of tactile short-term memory for locations is mediated by spatial attention. Biol. Psychol. 89, 39–46. doi: 10.1016/j.biopsycho.2011.09.001

Macaluso, E., Driver, J., van Velzen, J., and Eimer, M. (2005). Influence of gaze direction on crossmodal modulation of visual ERPS by endogenous tactile spatial attention. Brain Res. Cogn. Brain Res. 23, 406–417. doi: 10.1016/j.cogbrainres.2004.11.003

Meador, K. J., Allison, J. D., Loring, D. W., Lavin, T. B., and Pillai, J. J. (2002). Topography of somatosensory processing: cerebral lateralization and focused attention. J. Int. Neuropsychol. Soc. 8, 349–359. doi: 10.1017/S.1355617702813169

Mesulam, M. M. (1999). Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrapersonal events. Philos. Transact. R. Soc. B Biol. Sci. 354, 1325–1346. doi: 10.1098/rstb.1999.0482

Noesselt, T., Hillyard, S. A., Woldorff, M. G., Schoenfeld, A., Hagner, T., Jancke, L., et al. (2002). Delayed striate cortical activation during spatial attention. Neuron 35, 575–587. doi: 10.1016/S0896-6273(02)00781-X

Pilz, K., Veit, R., Braun, C., and Godde, B. (2004). Effects of co-activation on cortical organization and discrimination performance. Neuroreport 15, 2669–2672. doi: 10.1097/00001756-200412030-00023

Reed, J. L., Qi, H. X., Zhou, Z., Bernard, M. R., Burish, M. J., Bonds, A. B., et al. (2010). Response properties of neurons in primary somatosensory cortex of owl monkeys reflect widespread spatiotemporal integration. J. Neurophysiol. 103, 2139–2157. doi: 10.1152/jn.00709.2009

Schubert, R., Ritter, P., Wustenberg, T., Preuschhof, C., Curio, G., Sommer, W., et al. (2008). Spatial attention related SEP amplitude modulations covary with BOLD signal in S1–a simultaneous EEG–fMRI study. Cereb. Cortex 18, 2686–2700. doi: 10.1093/cercor/bhn029

Utzerath, C., John-Saaltink, E., Buitelaar, J., and Lange, F. P. (2017). Repetition suppression to objects is modulated by stimulus-specific expectations. Sci. Rep. 7:8781. doi: 10.1038/s41598-017-09374-z

Zopf, R., Giabbiconi, C. M., Gruber, T., and Müller, M. M. (2004). Attentional modulation of the human somatosensory evoked potential in a trial-by-trial spatial cueing and sustained spatial attention task measured with high density 128 channels EEG. Cogn. Brain Res. 20, 491–509. doi: 10.1016/j.cogbrainres.2004.02.014

Keywords: traditional spatial attention paradigm, ERP, interstimulus interval, enhancement and suppression, primary somatosensory cortex

Citation: Liu Y, Dong B, Yang J, Ejima Y, Wu J, Wu Q and Zhang M (2021) Stimulus Intervals Modulate the Balance of Brain Activity in the Human Primary Somatosensory Cortex: An ERP Study. Front. Neuroinform. 14:571369. doi: 10.3389/fninf.2020.571369

Received: 10 June 2020; Accepted: 22 December 2020; Published: 27 January 2021.

Reviewed by:

Copyright © 2021 Liu, Dong, Yang, Ejima, Wu, Wu and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Qiong Wu, wuqiong@usts.edu.cn ; Ming Zhang, psychzm@mail.usts.edu.cn

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

JavaScript seems to be disabled in your browser. For the best experience on our site, be sure to turn on Javascript in your browser.

• AUD - Australian Dollar
• USD - US Dollar
• Create an Account
• Compare Products

Presentation - Code License

Presentation® (Neurobehavioral Systems) the leading software for stimulus delivery and experimental control program for neuroscience experiments. It runs on any Windows PC, and delivers auditory, visual and multimodal stimuli with sub-millisecond temporal precision. Presentation is powerful enough to handle almost any behavioral, psychological or physiological experiment using fMRI, ERP, MEG, psychophysics, eye movements, single neuron recording, reaction time measures, other performance measures, and more.

Presentation (Neurobehavioral Systems) is the world's most popular experimental control software with over 80'000 registrations and 200'000 downloads, and counting.

License Renewals:

Prices for license renewals are the same as for a new license of the same configuration.

License transfer:

All the below Code License variations (number of codes or number of years or a combination of both) will only be available for download (no freight on any of the orders below will apply). Once purchased, license code will be supplied by e-mail and download path.

Our Promise

Activation Recovery Request Forums "; } Forums                               : trigger timing Elisabeth Friedrich      Hello, I would like to send triggers from presentation via a parallel port for EEG recordings. I have a question regarding the timing: Does it make a difference at which point I specify the trigger code in the scenario-trial (e.g. before or ... Mike Blank NBS Tech Support      Please see Help Guide : Presentation : Hardware Interfacing : Ports : Port Output : Event Port Output, for more information. The port_code is a property of a stimulus event (e.g., sort_event). The time the port code is sent is bound ... Pia Brinkmann      Hello, I have a similar question. I am using different tones(and associated triggers)with a parallel port for EEG recording (BIOSEMI). When I run the paradigm on presentation and I record the signal I see that after the trigger is recorded ... Mike Blank NBS Tech Support      In general, auditory stimuli must contend with two types of delay/latency: playback latency (which refers to any difference between the time the sound card reports the sound started playing, and the time audio actually was delivered), and scheduling delay (the ... Elisabeth Friedrich      Thanks! I ckecked the documentation and my files again and everything seems to work. I still have one question -just to make sure- in respect to your comment about me not having defined a time onset for the stimulus event. In ... Elisabeth Friedrich      And one last question about the triggers, in the documantation it says: pulse_width = 20; # if using parallel port Can I also use a smaller one, like 6? I would like to send triggers very shortly one after the other. However, I ... Mike Blank NBS Tech Support      How low you can set the pulse width depends mostly on whatever is receiving the codes. Typically such hardware will have a minimum pulse width to ensure the code is read properly. 6 ms may or may not work. If ... Elisabeth Friedrich      Thank you! I played around with the trigger a bit more and in one case I need the "delay codes" option because right after the response, the feedback event occurs and so the onset of the feedback and the response are ... Elisabeth Friedrich      Thank you! I played around with the trigger a bit more and in one case I need the "delay codes" option because right after the response, the feedback event occurs and so the onset of the feedback and the response are ... sunil hirekhan      Hi, I am using 'Presentation' for EEG data recording and analysis. The data recording is complete and the data analysis is in progress. It is noticed that the Markers recorded in EEG .easy file and the same Markers loaded from .log file ( as observed in the EEGLAB tool) do not match. There is a time difference in these respective Markers, whereas the EEG data recording was done using the same PC (single time reference). This makes it difficult for data analysis. Will you please explain how to synchronize exactly these times ( i.e. .easy & .log file markers) ? .. Thanking You in anticipation. Mike Blank NBS Tech Support      Elisabeth: the pulse width in the .sce file will override the experiment settings if it is used. You can't force a specific code to be delayed, but you could do something like put the feedback event at time X of ... Elisabeth Friedrich      Thanks for the fast and comprehensive response! I delayed the feedback_event for 20 ms ($delay_time) (pulse width is 10 ms) within the trial. That means that the trial will still occur immediatley after the response and last exactly for the $break_duration. Only ... Mike Blank NBS Tech Support      Yes, the event/trigger will occur at time $delay_time of the trial. Note however that you are not in that case controlling what is happening from time 0 of the trial until that time (i.e., the previous picture might still be ... sunil hirekhan      Hello Sir, It's really enthusiastic and encouraging to receive your prompt reply, Thank You Very Much, for the same. My query is different: In this experiment , first of all, the EEG data recording is started. Then, after checking for signal quality of ... Mike Blank NBS Tech Support      I may be misunderstanding this, but it seems like you are recording the EEG signal, and running Presentation simultaneously, and then you want to take the events from the Presentation .log file and essentially paste them into the EEG timeline. ... Elisabeth Friedrich      Thank you! I use the start_delay now and it works perfectly. One last question: My Uncertainty for RT is always 7, my Uncertainity for Time is between 0 and 7 and my Uncertainty for Duration between 1 and 14. Is this ... Mike Blank NBS Tech Support      Times in the logfile (including uncertainties) are listed in tenths of a millisecond, so yes, 7 means .7 ms. The "normal" range really depends on the characteristics of the system (Presentation is just measuring that value, not producing it). I ... Amacfarlane      Hi my experiment runs just fine and creates all the logfiles i want it to however the timing of the logfiles it seems to send the trigger at the end of 2 stimuli opposed to at the beginning. This is an example ... Mike Blank NBS Tech Support      What is the desired timing/structure of the various events (sound, fixation), and what is the entire trial definition? Amacfarlane      the entire exp code is: scenario = " story"; active_buttons = 2; button_codes = 0,1; write_codes = true; default_background_color = 30, 30, 30; begin; picture {} default; picture {         text{             caption = "story 1 & 2";                                         font_size = 30;             font_color = 255, 255, 255;                 } Instruct_Text;                 x = 0; y = 0;             };              picture ... Mike Blank NBS Tech Support      It would probably be a lot less confusing to break this down into multiple trials. The current trial is so complicated that I'm looking at and not entirely sure of the timing of all the events (and I look at ... Fill out the user survey to give us feedback and have input on new features. Presentation log conversion to BIDS Dear community, we are working with fMRI experiments using the presentation software of neurobehavioral systems for the stimulus presentation. We would like to convert the presentation log file to the BIDS format. I would like to ask you whether you have a suitable automatized python- or shell-based solution for this conversion? Best and thanks for the help, Christian Was actually thinking about this last week. Not aware of anything like this but in python but I know that fieldtrip can do some of that in matlab. https://www.fieldtriptoolbox.org/getting_started/bids/#writing-data-to-a-bids-dataset https://www.fieldtriptoolbox.org/example/bids_behavioral/#presentation-log-files I have not tested it though. Hi Christian, In this notebook, there is a function (get_timings) that I wrote to parse timings from the logs. Part of the function is specific to my task, but removing a few things and changing some variables should do the job just fine. Let me know if you have any issues with it. Best Andrea Dear Andrea, dear Remi-Gau, thanks a lot for your help! Best wishes Christian Hi @christian.schmitz would you mind sharing your code with us? We wonder if you came up with a generic function that can handle the commonly used CIMH-paradigms? World Atlas / Moscow Oblast / Krasnogorskiy / Area around 55° 56' 31" N, 37° 28' 30" E / Krasnogorsk / Physical Map of Krasnogorsk This is not just a map. It's a piece of the world captured in the image. The flat physical map represents one of many map types available. Look at Krasnogorsk, Krasnogorskiy, Moscow Oblast, Central, Russia from different perspectives. Get free map for your website. Discover the beauty hidden in the maps. Maphill is more than just a map gallery. Free map Panoramic 4 Shaded Relief Maps of krasnogorsk. Maphill is a collection of map images. This physical map of Krasnogorsk is one of them. Click on the Detailed button under the image to switch to a more detailed map. See Krasnogorsk from a different perspective. Each map type has its advantages. No map style is the best. The best is that Maphill lets you look at each place from many different angles. Sure, this physical map is nice. But there is good chance you will like other map styles even more. Select another style in the above table. See Krasnogorsk and Central from a different view. What to do when you like this map? If you like this Krasnogorsk, Krasnogorskiy, Moscow Oblast, Central, Russia map, please don't keep it to yourself. Give your friends a chance to see how the world converted to images looks like. Share this map. Use the buttons for Facebook, Twitter or Google+ to share a link to this physical map of Krasnogorsk. Maphill is the largest map gallery on the web. The number of maps is, however, not the only reason to visit Maphill. Get map of Krasnogorsk for free. You can embed, print or download the map just like any other image. All Krasnogorsk and Central maps are available in a common image format. Free images are available under Free map link located above the map. Is there anything more than this map? Sure, there is. It has been said that Maphill maps are worth a thousand words. No doubt about that. But you can experience much more when you visit Krasnogorsk. Be inspired. Central has a lot to offer. Each place is different. Each place is worth a visit. It will never be possible to capture all the beauty in the map. Krasnogorsk hotel deals. If any of Maphill's maps inspire you to come to Krasnogorsk, we would like to offer you access to wide selection of nearby hotels at low prices and with great customer service. Thanks to our partnership with Booking.com you can take advantage of up to 50% discounts for hotels in many locations in the area of Central. Book hotels online and save money. Krasnogorsk hotels See the full list of hotels in or close to Krasnogorsk , the list of destinations in Krasnogorskiy , browse destinations in Moscow Oblast , Central , Russia , Asia or choose from the below listed cities. Krasnogorsk hotels » Hotels in Krasnogorskiy » Hotels in Moscow Oblast » Hotels in Central » Hotels in Russia » Hotels in Asia » Hotels in popular destinations in Krasnogorskiy Nakhabino hotels » Opalikha hotels » Barvikha hotels » Gribanovo hotels » Znamenskoye hotels » Usovo hotels » Isakovo hotels » Gorki Vtoryye hotels » Novinki hotels » Nikol'skoye-Uryupino hotels » Il'inskoye hotels » Borki hotels » Maloye Sareyevo hotels » Chernaya hotels » Learn more about the map styles Each map type offers different information and each map style is designed for a different purpose. Read about the styles and map projection used in the above map (Physical Map of Krasnogorsk). Physical map Physical map illustrates the mountains, lowlands, oceans, lakes and rivers and other physical landscape features of Krasnogorsk. Differences in land elevations relative to the sea level are represented by color. Green color represents lower elevations, orange or brown indicate higher elevations, shades of grey are used for the highest mountain ranges in the world. Underwater topography is represented by blues. Darker blues are used for the deepest water, lighter shades of blue represent shallower water such as the continental shelf. Geographic map projection A map projection is a way how to transform points on a Earth to points on a map. This map of Krasnogorsk uses Plate Carree projection. The Plate Carree projection is a simple cylindrical projection originated in the ancient times. It has straight and equally spaced meridians and parallels that meet at right angles. All projections from a sphere to a plane are distorted. The drawback of the Plate Carree projection is that it doesn't make an attempt to compensate for these distortions. For the general view of Krasnogorsk, this is not a significant problem. The detailed maps use the Mercator projection which preserves the shape of small areas better. Locations near Krasnogorsk Destinations close to Krasnogorsk sorted by distance. Pavshino 1.7 km Gol'yevo 1.7 km Znamenskoye-Gubaylovo 2.0 km Chernevo 2.5 km Penyagino 2.8 km Arkhangel'skoye 3.7 km Voronki 3.8 km Zakharkovo 4.0 km Rublëvo 4.3 km Novobratsevskiy 4.5 km Popular searches A list of the most popular locations in Russia as searched by our visitors. Novosibirsk Oblast Ural Mountains Kabardino-Balkarian Republic Kaliningrad Oblast Recent searches List of the locations in Russia that our users recently searched for. Kursk Oblast Udskaya Guba Amur Oblast Bagaevskaya Pskov Oblast Ladozhskoe Ozero The Maphill difference It's neither this physical map nor any other of the many millions of maps. The value of a map gallery is not determined by the number of pictures, but by the possibility to see the world from many different perspectives. We unlock the value hidden in the geographic data. Thanks to automating the complex process of turning data into map graphics, we are able to create maps in higher quality, faster and cheaper than was possible before. Forever free We created Maphill to make the web a more beautiful place. Without you having to pay for it. Maphill maps are and will always be available for free. Real Earth data Do you think the maps are too beautiful not to be painted? No, this is not art. All 2D maps of Krasnogorsk are created based on real Earth data. This is how the world looks like. Easy to use This map is available in a common image format. You can copy, print or embed the map very easily. Just like any other image. Different perspectives The value of Maphill lies in the possibility to look at the same area from several perspectives. Maphill presents the map of Krasnogorsk in a wide variety of map types and styles. Vector quality We build each map individually with regard to the characteristics of the map area and the chosen graphic style. Maps are assembled and kept in a high resolution vector format throughout the entire process of their creation. Experience of discovering Maphill maps will never be as detailed as Google maps or as precise as designed by professional cartographers. Our goal is different. We want to redefine the experience of discovering the world through the maps. Fast anywhere Maps are served from a large number of servers spread all over the world. Globally distributed map delivery network ensures low latency and fast loading times, no matter where on Earth you happen to be. Spread the beauty Embed the above physical map of Krasnogorsk into your website. Enrich your blog with quality map graphics. Make the web a more beautiful place. Maphill is the web's largest map gallery. Get a free map for your website. Explore the world. Discover the beauty hidden in the maps. Map graphics revolution.™ Activation Recovery Request "; } Programming Presentation FAQ Why are the durations of picture stimuli in my scenario not what I requested? Why can't I present two pictures simultaneously? Why is there flicker between picture presentations? Why do my picture presentations get delayed more and more each time? How do I set up a trial that lasts a specific duration but the picture stimulus is removed after a button press? How can I improve the playback of my videos? Why can't I reference an object using a string? How can I change the type of my value? For example, how can I add an integer to a (string) event code? Why are my event codes not showing up in the logfile? Why are the durations of picture stimuli in my scenario not what I requested? For example, I asked for a 100 ms picture duration, but the durations are 116.6 ms. This is likely due to the vertical refresh of the monitor, as all actual durations will be a multiple of the refresh rate of the monitor. You should ask for durations that are about half a refresh period shorter than the desired duration (typically, this means set your durations about 5-8 ms short of your desired duration). To learn more about this, please see the Presentation documentation on the Picture Timing Control page (Presentation Help : Help Guide : Stimuli : Visual Stimuli : Picture Stimuli : Picture Timing Control), and the tutorial on Picture Stimulus Timing (Presentation Help : Help Guide : Tutorials : Tutorial - Picture Stimulus Timing). Why can't I present two pictures simultaneously? I want to have two images on the screen at the same time. In Presentation, a picture is an entire screen of graphics. To show two images on the screen at the same time, create a picture that contains both images. Why is there flicker between picture presentations? If the last picture stimulus in a trial has a duration other than next_picture, Presentation will erase that picture by showing the default picture before ending the trial (unless the trial ends first because of a response.) If you want the last picture to show continuously until the next trial, use a duration of next_picture and control the duration by using the trial_duration and trial_type trial parameters. Please see the documentation at Presentation Help : Help Guide : Stimuli : Visual Stimuli : Picture Stimuli : Picture Timing Control for more information. Why do my picture presentations get delayed more and more each time? A frequent cause for this problem is that you are adding more and more picture parts to your picture in PCL, rather than using picture::set_part() to change out picture parts or picture::remove_part() to remove parts. Check your PCL code for any use of picture::add_part(), particularly in a loop, when you do not intend to continuously add more picture parts to the picture. How do I set up a trial that lasts a specific duration but the picture stimulus is removed after a button press? One way to do this is to create a trial with the specified duration, of trial type fixed (the default), and then have the first picture in the trial have a duration of response. How can I improve the playback of my videos? For video playback, we've found that encoding a video with the XviD codec and using ffdshow tryouts to decode the video seems to work well with Presentation. The XviD codec is particularly good because it is geared specifically toward video playback, while ffdshow improves upon XviD alone for decoding because it takes less CPU power. The XviD codec can be downloaded from: , and ffdshow tryouts can be downloaded from: . To encode videos with the XviD codec that were previously encoded in some other (non-proprietary) format or are uncompressed, you can use VirtualDub. VirtualDub is an open source video editing tool available at . Please see the for more information on video. Why can't I reference an object using a string? For example, I have a set of bitmaps that I have called bitmap1, bitmap2, etc. and I want to reference them in a loop by using a string "bitmap" + string(i). PCL is precompiled into a type of intermediate code, and has the semantics of a compiled language. You cannot refer to a variable using the value of a string variable at runtime. You can often get around this by putting the objects in question in an array. For example: How can I change the type of my value? For example, how can I add an integer to a (string) event code? Presentation includes built-in conversion functions, which can be found in the second list on the Built-In Functions page of the documentation (Presentation Help : Help Guide : References : PCL Reference : Built-in Functions). For example, to convert an integer "i" to a string, you could use: Why are my event codes not showing up in the logfile? In many cases, this will happen when you write the code to take advantage of the "description" property of a stimulus object as the event code, but refer to the wrong stimulus object when obtaining the description. For example, oftentimes people will set the description for a wavefile, but then try to use the description of the sound that contains the wavefile for the event code. You need to obtain the description from the appropriate object. The following example shows a sound where the wavefile has the description property defined and then a sound where the sound has the description property defined. In the PCL, the event code is set to the description of the sound. Therefore, if you run this code, you will see only the event code "sound description" in the logfile or analyzer after the run. If you are unable to find the source of your problem, please post to the forum with your code. Location City:    Vatulino, Moskovskaya, Russian Federation (Russia) Name: Vatulino Airport ICAO: Details Type: Airport (Aerodrome, Airfield) Latitude: 55�39'45"N (55.662500) Longitude: 36�08'18"E (36.138333) Variation: 11.72�E (WMM2020 magnetic declination) 0.14� annual change Related Locations Nearby: Farthest: Find:    beyond   but within    Copyright © 2004-2024 . All rights reserved. The Great Circle Mapper name and logo are trademarks of the . All other trademarks mentioned herein belong to their respective owners. Please see for attributions and further copyright information.       135 IMAGES 2.3 Nervous Coordination PPT How Does the Brain Determine the Location of a Stimulus Neurobehavioral Systems Presentation 類神經行為系統 Nervous system Stimulus Response Model of the Nervous System Diagram COMMENTS Neurobehavioral Systems Presentation® is a stimulus delivery and experiment control program for neuroscience that runs on any Windows PC and delivers auditory, visual and multimodal stimuli with sub-millisecond temporal precision. Presentation® is powerful enough to handle almost any behavioral, psychological or physiological experiment using fMRI, ERP, MEG ... Picture Stimuli A picture stimulus represents one complete screen of graphics. Conceptually a picture stimulus is a collection of graphic elements, called picture parts, that are displayed at different locations on the screen. While the name "picture" suggests a single graphic contained in a graphic file, a picture stimulus in Presentation covers the entire ... Neurobehavioral Systems Presentation... runs on Windows Vista/7/8/10. is optimized for behavioral, psychological and physiological experiments using fMRI, ERP, MEG, single neuron recording, reaction time measures, and other performance measures. is built for precise stimulus delivery and accurate event logging. delivers 2D visual, 3D visual, and auditory stimuli ... What is Presentation? Presentation runs neurobehavioral experiments. Presentation is a stimulus delivery and experimental control software system for neuroscience. Presentation runs on Windows and uses standard PC hardware. Presentation was designed for behavioral and physiological experiments that collect fMRI, ERP, MEG, reaction time, and electrophysiological (e.g ... Presentation Presentation runs neurobehavioral experiments - Presentation is a stimulus delivery and experimental control software system for neuroscience. Presentation runs on Windows and uses standard PC hardware. Presentation was designed for behavioral and physiological experiments that collect fMRI, ERP, MEG, reaction time, and electrophysiological (e.g. single neuron) data. Presentation (software) Presentation is a Windows software application for conducting psychological and neurobehavioral experiments, developed by Neurobehavioral Systems Inc. and first released in 2003. It supports auditory and visual stimuli creation and delivery, records responses from nearly any input device and allows control of parallel port, serial port, TCP/IP and Ni-DAQ for communication to and from fMRI ... Quantitative models reveal the organization of diverse cognitive The experiment was performed for 3 days, with six runs performed each day. Presentation software (Neurobehavioral Systems, Albany, CA, USA) was used to control the stimulus presentation and the ... Neurobehavioral Systems Presentation Website: Go to site: Description: Neurobehavioral System Presentation is a software for conducting psychological and neurobehavioral experiments. This software supports auditory and visual stimuli creation and delivery, records responses from nearly any input device and allows control of parallel ports, serial ports, Transmission Control Protocol/Internet Protocol (TCP/IP) and National ... A flexible user-interface for audiovisual presentation and interactive We used two software packages, Presentation (Neurobehavioral Systems, Inc., Albany CA) and Spike2 (Cambridge Electronic Design, Ltd.) in conjunction with data acquisition hardware (Power1401 plus, Cambridge Electronic Design, Ltd.), to control stimulus presentation based on our subject's behavior. This required communication between ... Neurobehavioral Systems NBS Tech Support. 2021-11-09 06:42 PST. The stimulus manager allows you to access runtime data about any stimuli that have been presented (counting only stimuli that were response active, or were assigned event codes). For each stimulus that was presented, you can access a stimulus_data object ... Post to: What does stimulus_manager do? Control of response interference: caudate nucleus contributes to The software Presentation® (Neurobehavioral Systems, Inc.) was used for stimulus presentation and response logging. The task was presented on a screen (screen width: 65 cm) mounted on the wall at ... Presentation Documentation Help Guide. This help file provides complete documentation for the Presentation stimulus delivery and experimental control software. We have divided the documentation into the following sections: What's New. Specific features that are new in this version of Presentation. Introduction. Frontiers Visual and tactile stimuli were presented by using Presentation software (Neurobehavioral Systems Inc., Albany, California, USA) housed outside of the dimly lit room. A block design was used for this experiment in which the standard and target stimuli were randomized in blocks of 10 trials, with 15 blocks in one session (for a total of 150 trials). Presentation Presentation® is a stimulus delivery and experimental control program for neuroscience. It runs on any Windows PC, and delivers auditory, visual and multimodal stimuli with sub-millisecond temporal precision. ... Presentation (Neurobehavioral Systems) is the world's most popular experimental control software with over 80'000 registrations and ... Neurobehavioral Systems NBS Tech Support. 2019-03-04 14:29 PST. Times in the logfile (including uncertainties) are listed in tenths of a millisecond, so yes, 7 means .7 ms. The "normal" range really depends on the characteristics of the system (Presentation is just measuring that value, not producing it). Physiological and neural synchrony in emotional and neutral stimulus Participant pairs will be seated in front of a 27-inch computer screen (distance = 1.5 m) and stimuli will be presented using Presentation Software (Version 23.1; Neurobehavioral Systems Inc., Berkeley, CA, USA). Presentation log conversion to BIDS Dear community, we are working with fMRI experiments using the presentation software of neurobehavioral systems for the stimulus presentation. We would like to convert the presentation log file to the BIDS format. I would like to ask you whether you have a suitable automatized python- or shell-based solution for this conversion? Best and thanks for the help, Christian Trial Timing If you do not define start_delay, Presentation uses the value of the default_trial_start_delay header parameter. An actual start_delay of zero will not occur if the trial has a stimulus at time = 0.In that case, a start_delay = 0 means that the trial will start as soon as possible. The delay will usually not be more than a few msec, unless a picture stimulus is shown at time = 0. Mytishchi Mytishchi has a humid continental climate, which is the same as Moscow but usually a few degrees colder due to significantly lesser impact of urban heat island.The city features long, cold winters (with temperatures as low as −25 °C (−13 °F) to −30 °C (−22 °F) occurring every winter and a record low of −43 °C (−45 °F)), and short, warm-hot summers (with a record high of 38 ... Physical Map of Krasnogorsk This is not just a map. It's a piece of the world captured in the image. The flat physical map represents one of many map types available. Look at Krasnogorsk, Krasnogorskiy, Moscow Oblast, Central, Russia from different perspectives. Sports Venues & Stadiums in Krasnogorsk, Moscow Oblast View about Sports Venues & Stadiums in Krasnogorsk, Moscow Oblast on Facebook. Facebook gives people the power to share and makes the world more open and... Programming Presentation FAQ PCL is precompiled into a type of intermediate code, and has the semantics of a compiled language. You cannot refer to a variable using the value of a string variable at runtime. You can often get around this by putting the objects in question in an array. For example: text { caption = "A"; description = "A"; } A; UUMV Airport information about UUMV - Vatulino [Vatulino Airport], MOS, RU essay homework paper phd project resume review speech study thesis