experiment fish psychology

Fish Cognition and Consciousness

• Published: 14 December 2011
• Volume 26 , pages 25–39, ( 2013 )

Cite this article

• Colin Allen 1  

2494 Accesses

32 Citations

Explore all metrics

Questions about fish consciousness and cognition are receiving increasing attention. In this paper, I explain why one must be careful to avoid drawing conclusions too hastily about this hugely diverse set of species.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Understanding fish cognition: a review and appraisal of current practices

Fish do not feel pain and its implications for understanding phenomenal consciousness

Inside the Fish Brain: Cognition, Learning and Consciousness

Allen, C. (2004). Animal Pain. Noûs, 38 , 617–643.

Article   Google Scholar  

Allen, C. (2006). Ethics and the science of animal minds. Theoretical Medicine and Bioethics, 27 , 375–394.

Allen, C. (2010). Animal consciousness. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Winter 2010 Edition). http://plato.stanford.edu/archives/win2010/entries/consciousness-animal/ . Accessed May 13, 2011.

Allen, C. (forthcoming). Private codes and public structures. In D. McFarland, K. Stenning, & M. McGonigle-Chalmers (Eds.), The complex mind: An interdisciplinary approach . London: Palgrave-Macmillan.

Allen, C., Fuchs, P. N., Shriver, A., & Wilson, H. (2005) Deciphering animal pain. In M. Aydede (Ed.), Pain: New essays on the nature of pain and the methodology of its study (pp. 352–366). Cambridge: MIT Press.

Allen, C., Grau, J. W., & Meagher, M. W. (2009) The lower bounds of cognition: What do spinal cords reveal? In J. Bickle (Ed.), The Oxford handbook of philosophy of neuroscience (pp. 129–142). Oxford: Oxford University Press.

Arai, T., Tominaga, O., Seikai, T., & Masuda, R. (2007). Observational learning improves predator avoidance in hatchery-reared Japanese flounder Paralichthys olivaceus juveniles. Journal of Sea Research, 58 , 59–64.

Baars, B. J. (1997). In the theatre of consciousness: Global workspace theory, a rigorous scientific theory of consciousness. Journal of Consciousness Studies, 4 , 292–309.

Google Scholar  

Babb, S. J., & Crystal, J. D. (2006). Episodic-like memory in the rat. Current Biology, 16 , 1317–1321.

Bering, J. M., & Bjorklund, D. F. (2005). The serpent’s gift: Evolutionary psychology and consciousness. In P. D. Zelazo, M. Moscovitch, & E. Thompson (Eds.), Cambridge handbook of consciousness . New York: Cambridge University Press.

Bitterman, M. E. (1975). The comparative analysis of learning: Are the laws of learning the same in all animals? Science, 188 , 699–709.

Block, N. (1995). On a confusion about a function of consciousness. Behavioral and Brain Sciences, 18 , 227–247.

Block, N. (2005). Two neural correlates of consciousness. Trends in Cognitive Sciences, 9 , 41–89.

Braithwaite, V. (2010). Do fish feel pain? . Oxford: Oxford University Press.

Brown, C., & Laland, K. N. (2003). Social learning in fishes: a review. Fish and Fisheries, 4 , 280–288.

Bshary, R., Hohner, A., Ait-el-Djoudi, K., & Fricke. H., (2006). Interspecific communicative and coordinated hunting between groupers and giant moray eels in the Red Sea. PLoS Biology, 4 (12), e431. doi: 10.1371/journal.pbio.0040431 .

Cabanac, M., Cabanac, A. J., & Parent, A. (2009). The emergence of consciousness in phylogeny. Behavioral Brain Research, 198 , 267–272.

Cabanac, M., & Laberge, F. (1998). Fever in goldfish is induced by pyrogens but not by handling. Physiology & Behavior, 63 , 377–379.

Cai, D. J., Shuman, T., Harrison, E. M., Sage, J. R., & Anagnostaras, S. G. (2009). Sleep deprivation and Pavlovian fear conditioning. Learning and Memory, 16 , 595–599.

Carruthers, P. (2000). Phenomenal consciousness: A naturalistic theory . Cambridge: Cambridge University Press.

Book   Google Scholar  

Clark, R. E., & Squire, L. R. (1998). Classical conditioning and brain systems: The role of awareness. Science, 280 , 77–81.

Clayton, N. S., & Dickinson, A. (1998). Episodic-like memory during cache recovery by scrub jays. Nature, 395 , 272–274.

Clayton, N. S., Emery, N., & Dickinson, A. (2006). The rationality of animal memory: Complex caching strategies of western scrub jays. In S. Hurley & M. Nudds (eds.) Rational animals? Oxford: Oxford University Press.

Dadda, M., Piffer, L., Agrillo, C., & Bisazza, A. (2009). Spontaneous number representation in mosquitofish. Cognition, 112 , 343–348.

Desjardins, J. K., & Fernald, R. D. (2010). What do fish make of mirror images? Biology Letters, 6 , 744–747.

Eichenbaum, H., Sauvage, M., Fortin, N., Robitsek, R. J., & Komorovski, R. (2012). A comparative analysis of episodic memory. In T. R. Zentall & E. A. Wasserman (Eds.), The Oxford handbook of comparative psychology. Oxford: Oxford University Press (in press).

Graves, L. A., Heller, E. A., Pack, A. I., & Abel, T. (2003). Sleep deprivation selectively impairs memory consolidation for contextual fear conditioning. Learning and Memory, 10 , 168–176.

Kirsch, I., Lynn, S. J., Vigorito, M., & Miller, R. R. (2004). The role of cognition in classical and operant conditioning. Journal of Clinical Psychology, 60 , 369–392.

Kotrschal, A., & Taborsky, B. (2010). Environmental change enhances cognitive abilities in fish. PLoS Biology, 8 (4), e1000351. doi: 10.1371/journal.pbio.1000351 .

Kuba, M. J., Byrne, R. A., & Burghardt, G. M. (2010). A new method for studying problem solving and tool use in stingrays ( Potamotrygon castexi ). Animal Cognition, 13 , 507–513.

Masseck, O. A., & Hoffmann, K.-P. (2008). Responses to moving visual stimuli in pretectal neurons of the small-spotted dogfish ( Scyliorhinus canicula ). Journal of Neurophysiology, 99 , 200–207.

Merker, B. (2007). Consciousness without a cerebral cortex: A challenge for neuroscience and medicine. Behavioral and Brain Sciences, 30 , 63–81.

Nagel, T. (1974). What is it like to be a bat? Philosophical Review, 83 , 435–450.

Nicolau, M. C., Akaârira, M., Gamundía, A., González, J., & Rial, R. V. (2000). Why we sleep: The evolutionary pathway to the mammalian sleep. Progress in Neurobiology, 62 , 379–406.

Nilsson, J., Kristiansen, T. S., Fosseidengen, J. E., Fernö, A., & van den Bos, R. (2008). Learning in cod ( Gadus morhua ): Long trace interval retention. Animal Cognition, 11 , 215–222.

Nilsson, J., Kristiansen, T. S., Fosseidengen, J. E., Stien, L. H., Fernö, A., & van den Bos, R. (2010). Learning and anticipatory behaviour in a “sit-and-wait” predator: The Atlantic halibut. Behavioral Processes, 83 , 257–266.

Nordgreen, J., Joseph, P., Garner, J. P., Janczak, A. M., Ranheim, B., Muir, W. M., et al. (2009). Thermonociception in fish: Effects of two different doses of morphine on thermal threshold and post-test behaviour in goldfish ( Carassius auratus ). Applied Animal Behaviour Science, 119 , 101–107.

Nordgreen, J., Janczak, A. M., Hovland, A. L., Ranheim, B., & Horsberg, T. E. (2010). Trace classical conditioning in rainbow trout ( Oncorhynchus mykiss ): What do they learn? Animal Cognition, 13 , 303–309.

Oates, J., Manica, A., & Bshary, R. (2010). The shadow of the future affects cooperation in a cleaner fish. Current Biology, 20 , R472–R473.

Rose, J. (2002). The neurobehavioral nature of fishes and the question of awareness and pain. Reviews in Fisheries Science, 10 , 1–38.

Schuster, S., Wöhl, S., Griebsch, M., & Klostermeier, I. (2006). Animal cognition: How Archer Fish learn to down rapidly moving targets. Current Biology, 16 , 378–383.

Skinner, B. F. (1984). The evolution of behavior. Journal of the Experimental Analysis of Behavior, 41 , 217–221.

Sneddon, L. U., Braithwaite, V. A., & Gentle, M. J. (2003). Do fish have nociceptors: Evidence for the evolution of a vertebrate sensory system. Proceedings of the Royal Society, 270 , 1115–1121.

Stotz, K., & Allen, C. (2011). From cell-surface receptors to higher learning: A whole world of experience. In K. S. Plaisance & T. A. C. Reydon (Eds.), The philosophy of behavioral biology (pp. 85–123). Berlin: Springer.

Tennant, W. A., & Bitterman, M. E. (1975). Blocking and overshadowing in two species of fish. Journal of Experimental Psychology: Animal Behavior Processes, 1 , 22–29.

Tulving, E., & Markowitsch, H. J. (1998). Episodic and declarative memory: Role of the hippocampus. Hippocampus, 8 , 198–204.

Vargas, J. P., López, J. C., & Portavella, M. (2009). What are the functions of fish brain pallium? Brain Research Bulletin, 79 , 436–440.

Varner, G. L. (2011) Review of Victoria Braithwaite, Do Fish Feel Pain? (Oxford University Press, 2010). Environmental Ethics, 33 , 219–222.

Whiteman, E. A., & Côte, I. M. (2004). Monogamy in marine fishes. Biological Reviews, 79 , 351–375.

Yokogawa, T., Marin, W., Faraco, J., Pézeron, G., Appelbaum, L., Zhang, J., et al. (2007). Characterization of sleep in Zebrafish and Insomnia in hypocretin receptor mutants. PLoS Biology, 5 (10), e277. doi: 10.1371/journal.pbio.0050277 .

Zhou, W., & Crystal, J. D. (2009). Evidence for remembering when events occurred in a rodent model of episodic memory. Proceedings of the National Academy of Sciences, 106 , 9525–9529.

Zhou, M., & Smith, G. T. (2006). Structure and sexual dimorphism of the electrocommunication signals of the weakly electric fish, Adontosternarchus devenanzii . Journal of Experimental Biology, 209 , 4809–4818.

Download references

Acknowledgments

I am grateful for comments to audiences at the University of Utrecht, the Ruhr University, Bochum, and at the Leibniz Institute for Inland Fisheries and Freshwater Ecology in Berlin, and for comments on the manuscript by Michael Trestman, Bernice Bovenkerk, members of the “Spackled” group at Indiana University, and two anonymous referees for the journal. I also gratefully acknowledge the support of the Alexander von Humboldt Foundation and Indiana University during the time that this paper was prepared, and the hospitality of the Ruhr University-Bochum during my sabbatical year.

Author information

Authors and affiliations.

Program in Cognitive Science and Department of History and Philosophy of Science, Indiana University, Bloomington, IN, 47405, USA

Colin Allen

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Colin Allen .

Rights and permissions

Reprints and permissions

About this article

Allen, C. Fish Cognition and Consciousness. J Agric Environ Ethics 26 , 25–39 (2013). https://doi.org/10.1007/s10806-011-9364-9

Download citation

Accepted : 29 November 2011

Published : 14 December 2011

Issue Date : February 2013

DOI : https://doi.org/10.1007/s10806-011-9364-9

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

• Consciousness
• Find a journal
• Publish with us
• Track your research

• Skip to Page Content
• Skip to Navigation
• Skip to Search
• Skip to Footer
• COVID-19 Health and Safety
• Admissions and Ticketing
• Temporary Hall Closures
• Accessibility
• Field Trips
• Adult Group Visits
• Guided Tours
• Transportation
• Davis Family Butterfly Vivarium
• Invisible Worlds: Immersive Experience
• The Secret World of Elephants
• Turtle Odyssey
• Worlds Beyond Earth: Space Show
• Extinct and Endangered: Insects in Peril
• Grounded by Our Roots
• Ice Cold: An Exhibition of Hip-Hop Jewelry
• Opulent Oceans
• What's in a Name?
• Children & Family Programs
• Teen Programs
• Higher Education
• Adult Programs
• Educator Programs
• Evaluation, Research, & Policy
• Master of Arts in Teaching
• Online Courses for Educators
• Urban Advantage
• Viruses, Vaccines, and COVID-19
• The Science of COVID-19
• OLogy: The Science Website for Kids
• News & Blogs
• Science Topics
• Margaret Mead Festival
• Origami at the Museum
• Astrophysics
• Earth and Planetary Sciences
• Herpetology
• Ichthyology
• Ornithology
• Richard Gilder Graduate School
• Hayden Planetarium
• Center for Biodiversity and Conservation
• Institute for Comparative Genomics
• Southwestern Research Station
• Research Library
• Darwin Manuscripts Project
• Microscopy and Imaging Facility
• Science Conservation
• Computational Sciences
• Staff Directory
• Scientific Publications
• Ways to Donate
• Membership FAQ
• Benefit Events
• Corporate Support
• Planned Giving
• What Rocks on Your Block?
• Video and Text Passages
• Analyze the Data
• Make an Ecosystem Diorama
• Nature Explorations
• Greening of the Arctic: Online Seminar
• Keeling's Curve: The Story of CO2 Google+ Hangout
• Ozone’s Slow Recovery Online Seminar
• Warm Forecast for Coral Reefs Online Seminar
• SunScapes: Our Magnetic Star
• Central Park Geology Field Guide
• A Conversation with Jacques Malavieille
• Forecasting Earthquakes Using Paleoseismology
• Looking For Life In Antarctica... and on Mars
• Mapping Hot Springs on the Deep Ocean Floor
• Mapping Mt. Rainier
• Retrieving a Stromatolite from the Sahara Desert
• Studying Tree Rings to Learn About Global Climate
• Ultra-High-Pressure Experimentalist Who Studies the Deep Earth
• Zircon Chronology: Dating the Oldest Material on Earth
• Earth: Inside and Out Glossary
• Harry Hess: One of the Discoverers of Seafloor Spreading
• Arthur Holmes: Harnessing the Mechanics of Mantle Convection to the Theory of Continental Drift
• James Hutton: The Founder of Modern Geology
• Inge Lehmann: Discoverer of the Earth's Inner Core
• Milutin Milankovitch: Seeking the Cause of the Ice Ages
• Case Study: Friedrich Bessel and the Companion of Sirius
• Case Study: Gerard Kuiper and the Trans-Neptunian Comet Belt
• Case Study: John Michell and Black Holes
• Case Study: Fossil Microbes on Mars?
• Case Study: Neutrino Observatories
• Case Study: The Cosmic Microwave Background Radiation
• Profile: Ernst Chladni and Rocks from the Sky
• Profile: Georges Lemaître, Father of the Big Bang
• Profile: Cecilia Payne and the Composition of the Stars
• Profile: Ole Roemer and the Speed of Light
• Profile: Vera Rubin and Dark Matter
• Profile: Carl Sagan and the Quest for Life in the Universe
• Profile: Lyman Spitzer and the Space Telescope
• Profile: Fritz Zwicky's Extraordinary Vision
• Profile: Harold C. Urey
• PROFILE: Penelope Bodry-Sanders
• Case Study: The Belize Ethnobotany Reserve Project
• The Biodiversity Crisis: Losing What Counts
• PROFILE: Amy Vedder
• PROFILE: Dolores R. Santoliquido
• PROFILE: Jaime A. Pinkham
• PROFILE: Jane Goodall
• PROFILE: Abebe Getahun
• PROFILE: Clare Flemming
• PROFILE: Kevin Browngoehl
• The Biodiversity Crisis: Brown-Eyed, Milk-Giving… and Extinct: Losing Mammals Since A.D. 1500*
• Profile: Michael Balick
• The Biodiversity Crisis: Lake Victoria
• Restoration of the Elwha River by Dam Removal, Washington
• The Biodiversity Crisis: St. Lucia Parrot Recovery
• Wilderness Preservation Act, U.S.A.
• The Green Guerillas, New York City
• The Biodiversity Crisis: Jaguars
• The Biodiversity Crisis: Reefs in Crisis
• Antarctica: The Farthest Place Close to Home
• Bacteria Evolving: Tracing the Origins of a MRSA Epidemic
• Biodiversity Counts
• Deep Sea Vents
• Be a Sleuth: How Dinosaurs Behaved
• Classifying Dinosaurs Based on Fossils
• Observe a Dinosaur
• Meet the Paleontologists: Jin Meng
• Dinosaur Names
• Grouping Dinosaurs
• How Big Were Dinosaurs?
• What is a Fossil?
• Bigger Than You Think
• Be a Trackway Detective
• Dinosaur Timeline
• Dinosaur Teeth
• Dinosaur Name Game for Students
• Create a Timeline of Earth
• Explore the Family Tree of Birds
• Plate Tectonics Puzzle
• Relative Speed of Dinosaurs
• Smaller Than You Think
• Functions of Feathers
• Flesh Out a Fossil
• Sedimentary Layers Puzzle
• Dinosaur Illustrations
• Understanding Geologic Time
• Understanding Cladistics
• Make Your Own Liaoning Diorama
• What Makes a Dinosaur a Dinosaur?
• What is a Dinosaur
• What Teeth Tell Us
• Pixel This!
• Calculate Planetary Distances
• Astronomy Books for Adults
• Space Shuttle Orbiter
• Space Games
• Gravity: It's Universal
• 3-D Model of the Big Dipper
• The 3-D Universe
• Filtering Light
• Light: Its Secrets Revealed
• Digital Images: The Universe Exposed
• Focal Point
• Building a Simple Refracting Telescope
• Gathering Light
• Telescopes: Super Views from Space
• Building a Spectroscope
• White Light and Colored Light
• Detecting UV Light
• Earthquake Risk in Bangladesh
• Ecology Disrupted
• Exploring Bolivia's Biodiversity
• Field Trip to the Moon Companion Guide (for classroom and general use)
• Field Trip to the Moon Companion Guide (for visitors to the American Museum of Natural History)
• Field Trip to the Moon Live Presenter Script
• Field Trip to the Moon Educator's Guide
• Field Trip to the Moon Informal Educator's Guide
• Field Trip to the Moon Student Worksheet (for visitors to the American Museum of Natural History)
• Five Tools and Processes for Translating the NGSS Into Instruction and Classroom Assessment
• GRACE Teaching Case
• Paraphrasing
• Summarizing in Science
• Interactive Read-Alouds
• Interactive Reading Guides
• Strategies for Vocabulary Instruction
• Writing a Scientific Explanation Using the Explanation Tool
• NCEP Educator Resources
• Patterns: Investigating Weather and Climate
• Teleconnections
• Why are the Glaciers Melting?
• Climate, the Cryosphere, and the Carbon Cycle
• As the Arctic Goes, So Does the Rest of the Planet
• Is it Getting Greener?
• Climate Change and Ecosystems
• How do Humans Impact Climate Change?
• Dissolved CO2, Acidification, and the Effects on Marine Biomes
• Impact of Increasing CO2 on the Food Web in the Arctic Ocean?
• Earth's Climate System
• The Carbon Cycle and its Effects on the Polar Regions
• Becoming a Cultural Researcher
• Analyzing Images of Culture
• Multiple Cultures, Multiple Identities
• When Cultures Travel
• Studying the Material Culture of Three Nomadic Cultures
• Looking at Our Own Cultural Artifacts
• Culture as Self-Expression
• The Effect of Water Temperature, Water Acidity, and Animal Age and Body Size on the Opercular Respiratory Rate of Brown Bullhead Catfish
• The Effectiveness of Botanical Extracts as Repellents Against Aedes aegypti Mosquitoes
• Facial Expression and its Relationship to Gesture in Western Lowland Gorillas
• My Fish-Shaped Home: A History of Changes
• The Secret of the Fibonacci Sequence in Trees
• Saguaro Cactus: From Life to Death
• An Analysis of Mockingbird Nesting Behavior in Residential Areas
• Butterfly Buffet: The Feeding Preferences of Painted Ladies
• Investigating the Effects of Water Pollution on Daphnia magna
• Are Dogs' Tongues Really Cleaner Than Humans'?
• Bacterial Activity in Different Water Sources: A Sequel Comparative Study
• Mango Skins: An Effective Organic Pest Deterrent
• Afpectus Lunae: Does the Moon Rotate on Its Axis?
• Plant Extracts as Natural Insecticides

Goldfish as a Model for Understanding Learning and Memory: More Complex Than You Think

• The History and Geology of Rocks and Fossils in Helena, Montana
• Using Plant-Based Biofilters to Purify Household Wastewater
• Explaining the Divergence of the Marine Iguana Subspecies on Espanola
• Shaped by Nature and Man: The Geological History of the Palisades
• From Uplift to Glaciation: The Geological History of the Pikes Peak Region

Part of the Young Naturalist Awards Curriculum Collection.

The United States and the European Union are pouring hundreds of millions of dollars into research on understanding how the human brain functions. As several key aspects of learning and memory are shared by all animals (Kandel, 2006), researchers are studying different kinds of animals, from simple organisms to humans, to help explain learning and memory in the human brain. Most research has occurred in mammals, but some attention has been placed on fish as an animal model to help delineate the types of memory needed to complete spatial tasks such as navigation. Spatial cognition, a form of explicit memory that involves navigating around spaces, is found in animals with higher-level brain function, specifically those animals with an area of the brain called the hippocampus, the part of the cortex that stores explicit memory (Kandel, 2006). Experiments on rats led scientists to conclude that rats, like humans, can create in their brains a “comprehensive map” of the room in which experiments are performed, called a cognitive map, in order to navigate to a goal (Tolman, 1946). Unlike rats and humans, the fish brain does not have a cortex and consequently no hippocampus in which to store explicit memory, leading scientists to conclude that fish cannot create a cognitive map. However, scientists have found that fish do use spatial cognition, relying on allocentric (extra-maze) cues to navigate, and that there are areas of the fish brain that are analogous to the hippocampus (Salas et al., 2006). These findings suggest that fish have a higher level of functioning than is commonly believed (Brown, 2015) and perhaps have the ability to form a cognitive map. Moreover, as the first group on the vertebrate branch from which mammals descended, this finding would indicate that complex brain functions began to develop hundreds of millions of years ago.

This science project, which is in its third year, attempts to add to the body of research on fish learning and memory in a unique way. First, the length of long-term memory in fish in general has not been delineated, and certainly not in goldfish, though there have been anecdotal reports of long-term memory in certain species of fish, such as salmon returning home to spawn. Second, although the use of allocentric (extra-maze) and egocentric (intra-maze) cues by goldfish after training is well documented(Salas et al., 1996), delineating the time frame during training when goldfish switch from allocentric to egocentric cues to learn and memorize a task, has not been done. This time frame would serve to indicate when learning is converted from short-term memory to long-term memory. Third, this experiment uses different and more difficult mazes than the T-mazes, plus mazes, or small square box mazes used by researchers. The design of the mazes gives the goldfish several options in deciding where to navigate, and allows for the observation of behaviors relevant to determining whether the fish use place strategy (spatial cognition) or response strategy (rote memory) during the learning process, to an extent not previously documented or researched.

The exciting impact of research on goldfish would be to rewrite the biology textbooks about the place of fish in evolution as the starting point for the development of a higher-functioning brain, in particular the use of spatial cognition. Moreover, the ultimate goal would be to use these findings to understand learning, memory, and memory loss in humans, with the hope of one day unlocking the mystery of Alzheimer’s disease, a disease of spatial cognition.

Research Questions

• How long is the long-term memory of goldfish?
• How do the goldfish learn a spatial task? Specifically, which navigational strategies—place strategy (spatial cognition) or response strategy (rote memory)—did the goldfish use to learn to find the food in the maze during training?

I have owned goldfish and other types of fish for many years and am always astonished that they seem to recognize me when I come to feed them. My research into goldfish learning and memory started with my first science fair experiment three years ago to determine whether goldfish could learn to find food in a maze. In my literature review, I was disappointed to find that goldfish memory was frequently and often deridingly characterized as lasting three seconds (Osborne, 2010). However, my initial research showed that not only did goldfish learn quickly, but their memory was certainly longer than three seconds. My second-year experiment confirmed that goldfish can learn quickly, and showed that goldfish can recall memories for at least one month. Several months passed while I thought about what my next experiment, during which time I retested the goldfish on their memory of the maze from my second-year experiment. I was impressed that the goldfish located the food in the maze faster than on their last day of training. As a result, I became intrigued with two questions: (1) What is the duration of goldfish memory? and (2) How are goldfish able to recall the location of food in the maze after not being in the maze for several months? 

A significant amount of research has been done on goldfish learning and memory. Researchers in Spain have found that certain areas of the goldfish brain, called the medial and lateral telencephalon, correlate to regions of the human brain that deal with memory—the amygdala and hippocampus, respectively—by ablating parts of the goldfish brain and assessing its recall of a learned behavior in a plus maze (Salas et al., 1996). They found that spatial cognition in goldfish was dependent on a functioning lateral telencephalon, but response strategy navigation could occur without a functioning telencephalon (Rodríguez et al., 1994; López et al., 2000; Salas et al., 2006). They concluded that dual, parallel learning systems exist in goldfish: namely, a higher-level spatial cognitive strategy using allocentric cues and a lower-level response strategy (López et al., 2000; Vargas, 2004). The purpose of their studies was to delineate the functions of the goldfish brain and to provide evidence for the evolution of spatial cognition among various animal species, starting with fish (Rodríguez et al., 2002).

Further review of the scientific literature did not reveal any studies addressing the duration of long-term goldfish memory, the use of a complicated maze, or what learning strategies goldfish use during the training process. As the type of memory experiments proposed here can take more than one year to complete, it is not surprising that studies characterizing goldfish memory have not been done. In addition, the uniqueness of evaluating how goldfish learn during training was not addressed in the literature from Spain, which focused on testing the goldfish after training was complete. For example, the previous research considered how well the goldfish performed on reversal testing, in which the goal was changed after training, and not on which navigational strategy they used during training. Their research did not evaluate if and when the goldfish switched from using allocentric to egocentric cues during training, or if goldfish were predisposed to using one strategy over another. Many of the experiments done on goldfish in the past used classical conditioning and dealt with implicit memory, which does not require conscious attention for recall because the behaviors are usually reflexes (Kandel, 2006). Not wanting to shock the fish in these experiments, I did not use classical conditioning; instead, I used positive reinforcement with food.

In thinking about how goldfish learn the route to the food in the maze during training, I hypothesized that, in the short term, the goldfish would use landmarks in the room, or allocentric cues, to remember the location of the food, using place strategy (spatial cognition) to navigate to the goal. As the goldfish would be trained daily for 28 days, I suspected that consistently turning left into the food cubicle would become routine, predictable, and rote in the long term, leading the goldfish to rely on response strategy (rote memory) to navigate to the goal. The use of allocentric cues during the learning process of a spatial task would indicate that goldfish do have a complex component to their brains, and that spatial cognition is not limited to higher mammals and humans simply because they have a cortex. Therefore, using goldfish ( Carassius auratus ) as an animal model to understand learning and memory is relevant and important to delineating the evolution of spatial cognition as well as to potentially understanding diseases of memory.

If goldfish have long-term memories lasting up to six months, then goldfish trained to find food in a maze will, after intervals as long as six months, be able to recall the location of the food in the maze significantly faster than on the first day of training. During training, goldfish will initially learn to find food in a maze by using place strategy (spatial cognition), noted by a greater number of right turns than left turns in the test mazes. By the end of the training period, goldfish will change to a response strategy (rote memory), noted by a greater number of left turns than right turns in the test mazes.  Goldfish in the short term will rely on place strategy (spatial cognition) and allocentric cues (extra-maze cues) to solve the task, but will shift to a response strategy (rote memory) once the task becomes repetitive and predictable.

Research Methods

• Forty large goldfish ( Carassius auratus ) of various breeds
• Omega 1 small fish food pellets
• Four clear plastic storage boxes (73.6 cm x 45.7 cm x 15.2 cm; 38.8 liters) for mazes
• Several large aluminum baking pans, used as partitions in the maze
• Five-foot-tall cardboard box for a surround
• Flat white and dark brown spray paint

I made four different types of mazes, cutting the corners off the aluminum pans to make partitions for the cubicles and attaching them to the clear plastic storage boxes with a hot glue gun.

Clear Training Maze

The first type of maze was the clear maze used for training goldfish in last year’s experiment (Figure 1). The clear maze was used to determine the duration of long-term memories in goldfish in Experiment 1 and to train the new goldfish for Experiment 2, called the “clear maze group.”

The goldfish trained in the clear maze were exposed to allocentric (extra-maze) cues in the room used to conduct the experiment (Figure 4, room layout). One salient allocentric cue was the five-foot-tall cardboard box, with the right half painted dark brown and the left half painted flat white (Figure 5), that faced the clear training maze and two of the three test mazes (Figures 3b, 3c).

White Training Maze (Used by the Control Group)  

The maze for the control group (Figure 2) had the same design as that used to train goldfish in Experiments 1 and 2, except that it was painted flat white and was surrounded by a five-foot-tall cardboard box, the interior painted flat white, to remove almost all of the allocentric cues used by the goldfish to navigate except those subtle cues related to imperfections in the aluminum partitions and the cardboard box, as made visible by the overhead light.

Three test mazes (Figures 3a, 3b, 3c) were used intermittently during the training period. The first test maze (Figure 3a) was the clear training maze, but surrounded on all four sides by the five-foot-tall cardboard box painted white on the inside. The purpose of the box was to remove allocentric cues and observe whether the goldfish relied on egocentric cues to navigate.

The second test maze (Figure 3b) was a double-chamber maze with two right cubicles and two left cubicles, designed to change the egocentric cues while the allocentric cues remained unchanged. The purpose was to observe whether the change in egocentric cues fooled or confused the goldfish into entering the cubicles closest to the start position, and whether the goldfish relied on allocentric cues to navigate.

The third test maze (Figure 3c) was an opposite-start shortcut maze, designed to change the egocentric cues while the allocentric cues remained the same. The purpose was to disorient the fish by placing them in a different start position in the maze and to observe whether the goldfish relied on allocentric cues to navigate. The shortcuts were openings on either side of the end cubicles that allowed the goldfish to enter them directly without having to turn. The purpose here was to observe whether the goldfish could recognize a second entrance to the cubicle they believed the food was in.

Experiment 1: Goldfish Long-Term Memory Span

• The trained fish from last year’s experiment were placed in the start cubicle of the clear training maze using a small net (Figure 1). Food pellets were placed in the left cubicle of the clear training maze. The experimenter quietly backed away from the maze to record the times and responses.
• The time in seconds from the moment the fish exited the net into the start cubicle to the moment the fish ate the food in the left cubicle was recorded. The goldfish were tested at two-, three-, four-, and six-month intervals to determine whether they recalled the location of the food after not performing the task for two, three, four, and six months.

Experiment 2: Goldfish Learning (Training) and Comparison

• Sample Size :Forty goldfish of different breeds were randomly assigned to either the training/testing group, called the clear maze group (n = 30), or the control group (n = 10).
• Clear Training Maze (n = 30): The goldfish were placed individually into the start cubicle of the clear maze once a day for 28 consecutive days (Figure 1). The time it took them to find the food in the left cubicle was recorded, as well as any other observations, including the route taken (left or right) and which cubicle (left or right) the fish initially entered.
• White Maze for the Control Group (n = 10): The goldfish were placed individually into the start cubicle of the white maze once a day for 28 consecutive days (Figure 2). The time it took them to find the food in the left cubicle was recorded, as well as the route taken and the initial cubicle entered. The goldfish in this group served as the control because they had to use response strategy (rote memory) for navigation, as there were few, if any, allocentric cues present beyond the texture of the painted aluminum partitions and the imperfections on the inside of the cardboard box surround.

Experiment 3: Testing During Training to Determine Place vs. Response Strategy in Learning

• Thirty goldfish were trained in a clear maze. In the clear training maze, a decal (a smiley face) marked the exact location of the food and was visible through the clear floor of the maze (Figure 1). The test mazes (Figures 3a, 3b, 3c) were then rotated 180° so that the cubicle that earlier in the day had held the food (Figure A) was now in the same location as the empty cubicle (Figures B, C). The goldfish were randomly assigned to one of the three test mazes. The mazes were rotated 180º to test whether the fish were using a response strategy. No food was placed in any of the test mazes to avoid giving olfactory clues to the fish to turn in a certain direction. The time the fish took to reach the goal was recorded, as well the route taken and which cubicle was initially entered.
• On Days 3, 7, 11, 15, 19, 23, and 27, the fish who had gotten food earlier in the day in the training maze were placed and observed in the test mazes, for seven trials per fish. These intervals were chosen based on last year’s experiment, which showed that goldfish learned to find the food in the maze by the third day, and made fewer mistakes and had faster times on the days that followed.  

Dependent Variables :

• Time in seconds to find the food in the training maze (Experiments 1, 2)
• Correct food cubicle (left cubicle in training maze) (Experiment 2)
• Whether fish turned to the right cubicle (place strategy) or the left cubicle (response strategy) in the three testing mazes (Experiment 3)

Independent Variables :

• Time in months absent from maze (Experiment 1)
• Days of training (Experiments 1, 2)
• Three different testing mazes with 180º rotation (Experiment 3)

The goldfish used in last year’s experiment were retested at two-, three-, four-, and six-month intervals in the original clear maze (Figure 1). Their average times were quick and improved over time: 36.35, 32.00, 29.85, and 12.82 seconds, respectively, at two, three, four, and six months. The average time at one month was the longest, at 189.58 seconds (Graph 1). All five average times were significantly improved compared to the first day of training (410.05 seconds), at p < 0.0005 using a one-sided lower-tail t-statistic.

For all of the fish tested—the goldfish trained for last year’s experiment, the goldfish trained in the clear maze, and the control group—the average time it took the goldfish to find the food in the maze was very similar, showing rapid improvement by the second day of training (Graph 2). By the ninth day, each group had an average time of less than 60 seconds. Overall, the control group was quickest at finding the food in the maze.

The initial cubicle entered by all the fish during training was recorded daily for 28 days (Table 1). In the training maze and the white maze, the food was consistently located in the left cubicle (Figures 1, 2). The fish in the control group were correct 94% of the time in the white maze, compared to the fish trained in the clear maze, who were correct 86% of the time in the white maze (Table 1). The control group made fewer mistakes after Day 7, compared to Day 20 for the fish trained in the clear maze. The difference was statistically significant at p < 0.001 (Mann-Whitney U-Test, p = 0.00019, U-value = 135, Z-score = 3.5572).

The initial cubicle entered by the fish in the rotated test mazes (Figures 3a, 3b, 3c) was recorded as place (the fish turned right, indicating a place strategy) or response (the fish turned left, indicating a response strategy) for each of the seven trials during the 28-day training period (Table 2). The number of response strategies declined (to 10 on Day 27, from 16 on Day 3) and the number of place strategies increased (to 13 on Day 27, from 6 on Day 3) during training (Graph 3). The change from response to place strategy was statistically significant at p < 0.05 (Mann-Whitney U-Test, p-value = 0.04746, U-value = 179, Z-score = 1.6689).

Although all the goldfish were from the same species, Carassius auratus , different breeds were used to help distinguish the fish during the experiment. Seven out of ten (70%) of the fancy goldfish used place strategy by the end of the 28-day training, in comparison to four out of seven (57% ) of the shubunkin goldfish, and 3 out of 6 (50%) of the comet goldfish (Table 2).

Experiment 1: Goldfish Long-Term Memory Span  

My hypothesis regarding goldfish having a long-term memory of at least six months was correct. The average time for the goldfish to find food in the maze after an absence of six months was 12.82 seconds, which was less than half of the time taken on the last day of training (30.19 seconds) 16 months earlier (Graph 1). These results suggest that the ability of goldfish to retain and recall an explicit memory is impressive considering its small brain size, short life span, and lowest place on the vertebrate hierarchy.

Of note, the one-month average time of 189.58 seconds was the highest of the five readings. There can be two explanations. First, the one-month time was an aberrancy. Although this is possible, I would use this explanation if, for example, the third-month time had been the longest, with the other four times being similar. The second explanation is that the fish began to lose their memory of finding the food in the maze because this was their first major length of time away from performing the task in the maze. As they no longer needed to find food in the maze because they were being fed in the fish tank, the relevance or importance of remembering the task was diminished. Simply, this was a memory that they did not need.

But ultimately, something happened in the maze for the goldfish to remember the location of the food. From my observations, when the fish returned to the maze after an absence of one month, they stayed near the start position, eventually moving slowly along the maze wall to the middle of the maze, when some cue spurred their memory, causing them to suddenly swim quickly to the left cubicle containing the food (Figure 1). As the maze at the middle point is symmetrical and the food was not visible, I wonder whether the fish noticed an allocentric (extra-maze) cue that triggered their memory of food being in the maze. This would be consistent with the findings discussed below, that fish rely on spatial memory to solve a task. In addition, as the number of months since performing the task increased, goldfish performance improved, suggesting that spatial memory becomes ingrained in the brain, perhaps with further protein synthesis that makes recall easier.

In summary, this data suggests that goldfish have a spatial memory of at least six months, and reinforcement of the task at the one-month period appears to be critical to establishing long-term memory.

All three groups of goldfish—the trained fish from last year, the clear maze group, and the control group—were able to learn to find the food in the maze in comparable times (Graph 2). The fish were initially cautious when placed into the maze, especially on the first day, quietly observing the environment by looking up, swimming slowly along the right wall out of the start cubicle to explore the rest of the maze, and, finally, finding the food by trial and error, taking on average 12.35 minutes. By the second day, the fish cut their time by more than 60% for the clear maze group and more than 80% for the control group (Graph 2). By the end of one week, the average time for all of the goldfish to find the food in the maze dropped to 1.2 minutes, and by the end of four weeks, the average time was 17.21 seconds. These findings demonstrate that goldfish learn quickly and can improve their ability to complete a task when it is repetitive and reinforced positively with food.

In general, the goldfish frequently followed the right wall of the maze out of the start cubicle (the opening of the start cubicle faced the right wall) to explore their surroundings in the training maze (Figure 1). Once accustomed to the maze, most goldfish swam directly to the goal, taking the left or right route. Sixty-four percent of the clear maze fish preferred to take the right route, in comparison to 71% of the control group fish; this difference was not statistically significant. I observed that some fish, if they did not initially swim to the food cubicle because they were distracted by something, such as their reflections in the clear maze walls, or if they were swimming back and forth along the maze walls, would eventually circle back to the start cubicle, which was a familiar spot, and appear to “reset” their brains as to why they were in the maze. Then the fish suddenly headed directly to the food cubicle from the start position.

The control group had the fastest times for finding the food and the fewest mistakes in entering the correct food cubicle (left cubicle) during training, the latter dramatically noted by the absence of yellow highlight after Day 7 of training (Table 1). This finding is reflected in my observations that the control group was able to establish an association between food and the left cubicle seven to ten days before the clear maze fish established an association. Certainly, the control group faced none of the distractions encountered by the clear maze fish, such as fighting their reflections in the clear walls of the maze, or spending time observing the objects in the room (the allocentric cues). Although the control group had the fastest times and fewest mistakes, these results do not suggest that the control group fish were smarter than the clear maze fish, just more focused, without the distractions.

My hypothesis that the goldfish would initially use place strategy in learning to find the food in the maze, then switch to response strategy as the task became routine, was wrong. The results showed the opposite (Graph 3). Seventy-three percent of the training/testing goldfish (the clear maze group) used response strategy initially, but by the end of four weeks of training, only 43% used response strategy. In contrast, only 27% of the training/testing goldfish used place strategy initially, but by the end of four weeks of training, 57% used place strategy. This shift from using response strategy (rote memory) to place strategy (spatial cognition) was statistically significant.

To explain these results, I believe that the goldfish initially used response strategy (turning left into both the training and the test mazes) because it was the easiest to remember (i.e., turn left in training, so turn left again in the rotated test maze later on). As the goldfish became more familiar with the location of objects in the room, including the brown and white cardboard box, a salient allocentric (extra-maze) cue, they began to use place strategy (turning left in the training maze and right in the testing maze) on Day 11 (Graph 3). In fact, I noticed that several goldfish would stop at the end of the test maze, look at the cardboard box, and then turn right. This switch from response strategy to place strategy makes sense because it would take several days, as the goldfish were in that part of the room for only a few minutes once a day, to observe and commit to long-term memory the placement of objects in the room. Moreover, the fish that used place strategy were able to pinpoint the location of where they thought the food would be (in the right cubicle), an area separated from the left cubicle by only ten inches. As a result, I believe that the ability of goldfish to learn quickly, observe and memorize their environment, and recall that information, is very impressive. Whether the goldfish would continue to use place strategy to solve the task after four weeks remains unknown; however, the trend is that they would.

In addition, my findings confirm research on the necessity of allocentric cues for goldfish spatial cognition and navigation. The five-foot-tall cardboard box used to surround the clear maze (Figure 3a) had a profound effect on the fish. In the beginning they seemed confused by the sudden lack of allocentric cues and studied their new environment intently, looking up at the ceiling, slowly swimming around the maze while peering through the clear walls, and spinning their upper bodies around while their tails remained stationary, all in an attempt to understand the altered setting. The goldfish tested in Figure 3a initially used a response strategy, indicating that, without allocentric cues to guide them, the fish relied on rote memory. By the end of four weeks of training, however, the goldfish used place strategy (spatial cognition) to navigate, making a strong case for a cognitive map in order for them to be able to navigate to the goal while, in a sense, blindfolded by the lack of allocentric cues.

In regard to the other two test mazes (Figures 3b, 3c), which changed egocentric cues, the effect of changing the non-salient cues was negligible. The double-chamber maze (Figure 3b) did not fool many fish into initially entering the cubicles closest to the start position. The fish knew that the relevant cubicles were at the far back of the testing maze. The opposite-start maze (Figure 3c) did not affect the ability of the fish to navigate to the back of the maze and did not appear to affect their decision to turn left or right. Many fish did use the shortcut into the response cubicle (left cubicle), but this may have been an artifact of the maze design (the opening of the start cubicle faced the left wall), as the fish followed the entire left wall out of the start position without stopping, swimming into the left cubicle without much thought.

Food was purposefully omitted from the test mazes (Figures 3a, 3b, 3c) to allow the goldfish to turn freely into the cubicle they thought contained the food. Two of the test mazes (Figures 3b, 3c) never contained any food pellets, and consequently the fish could not have been influenced by olfactory cues. The Figure 3a maze, the clear maze with the cardboard box surround, was the same maze used in training earlier in the day and did contain food pellets. Any uneaten food pellets were removed after each trial and before each testing session. I do not think that any lingering smell from the food remnants influenced the fish, because the number of goldfish turning into the right cubicle (which never had any food in it) increased and the number of fish turning into the left cubicle (which consistently contained food) declined over time during the 28-day training period.

The range of goldfish learning ability seemed to mirror what is seen with human learning. Most of the goldfish quickly learned to find the food in the maze. Some fish learned faster than others; some were easily distracted. One was very clever, namely Goldfish D: on Day 27, the last day of testing, Goldfish D swam to the end of the test maze (Figure 3a), but instead of entering the right cubicle, it only looked in, taking several minutes to determine whether any food pellets were present, and, when none were visible, it turned to face the left cubicle, looking in and again taking several minutes, but not entering. When nothing was visible, Goldfish D returned to the middle of the maze and sat there. This goldfish had previously been cautious, not entering any cubicle at the beginning of the seven testing trials, but did eventually enter the left cubicle on two subsequent days and then the right cubicle on another day, before deciding not to enter either cubicle on Day 27. This goldfish was the only one that exhibited this unique behavior, suggesting a high degree of sophistication in understanding that food was present in the open training maze but not necessarily in the closed test maze and, to avoid falling into a trap, acted cautiously while being tempted with the hope of finding food. On the other end of the spectrum, seven goldfish were not included in the statistical analysis because they were untrainable, did not seem to understand the task, and required more than two minutes to find the food by the end of training (the average time for the rest of the fish was less than 20 seconds). These fish either entered the cubicle without food or seldom entered the food cubicle, remaining near the start position or in another area of the maze.

During training, certain goldfish remained response-strategy users, while others predominantly used place strategy, suggesting a possible genetic component to strategy preferences. Most of the fish used both strategies during the seven testing trials, consistent with previous research indicating that goldfish have parallel navigation systems. However, individual fish appeared to have a preference over time as to which strategy to use (Table 2). Further analysis showed there were differences among the same species of goldfish: namely, 70% of fancy goldfish used place strategy by the end of four weeks of training, in comparison to 57% of shubunkin goldfish and 50% of comet goldfish (Table 2). I suspect that the data from the fancy goldfish had some impact on the conclusion, because the fancy goldfish accounted for 42% of the training/testing group and 70% of them used place strategy. Consequently, the results might have been stronger had I used only fancy goldfish in the experiment, but it does raise an interesting question about differences in learning ability and strategies among the same species of fish. This finding of differences within the same species of fish, aside from one study that noted differences within stickleback species (Odling-Smee & Braithwaite, 2003), has not been reported in goldfish. As a result, future experiments should be conducted with one breed of goldfish or with equal numbers of different breeds.

In summary, my hypothesis on goldfish learning was incorrect because I thought that goldfish would use rote memory to find the food in the maze over time as the task was repetitive. Instead, the goldfish used allocentric cues to navigate to the goal over time, giving themselves an opportunity to observe, memorize, and commit to long-term memory the location of objects in the room. I underestimated the ability of goldfish to keenly observe their surroundings, as they spend their time swimming in a glass tank, but goldfish are more complex animals than I thought. Even though these results were not expected, I am encouraged more than ever to study the learning capabilities of fish because, as the findings suggest, goldfish do rely on allocentric cues to navigate and appear to map their environment in their brains, making a strong case for fish having a cognitive map.

My research indicates that goldfish learning a spatial task switch from predominantly using a response strategy (rote memory) to predominantly using place strategy (spatial cognition). This switch starts at approximately ten days, after the goldfish have had time to observe and memorize the allocentric cues in the environment integral to developing a spatial map in their brains, known as a cognitive map. For the goldfish to turn in the opposite direction from where they had turned earlier in the day, during training, implies that goldfish are able to recall and use this cognitive map to swim to the goal. The ability of goldfish to recall their surroundings in this way indicates that goldfish have a higher or more complex component to their brains, perhaps something akin to place or grid cells in mammals. Moreover, goldfish are able to retain a spatial memory for at least six months, solving the task with increasing speed despite not performing the task for long periods of time. These findings are very exciting and compelling for further research on the spatial cognition of goldfish, which are more complex animals than previously believed.

Further Research

Spurred by my encouraging results—that goldfish develop a spatial memory while learning to solve a task and have long-term recall of that task—I would like to continue to test the spatial cognition of goldfish by constructing a larger and more geometrically complicated maze while altering the allocentric and egocentric cues. I would also continue testing the duration of long-term memories in goldfish. This research could be important because significant differences may occur in the goldfish brain between when it has a memory of a specific task and when it loses that memory. 

Further research on fish would include looking for the equivalent of the place and grid cells found in mammals that are responsible for learning and spatial memory. Grid cells exist in the medial entorhinal cortex of mammals, which is the area of the human brain frequently affected in early stages of Alzheimer’s disease, which deals with the loss of spatial memory (Moser et al., 2014). Finding the analogous region in the fish brain is important because it would provide an evolutionary link to the development of the mammalian cortex and serve as a somewhat simpler model in understanding the loss of spatial memory, with the hope one day of unlocking the mystery of Alzheimer’s disease.

Bibliography

Benhamou, Simon. “Place Navigation in Mammals: A Configuration-Based Model.” Animal Cognition 1 (1998): 55-63.

Broglio, Christina, et al. “Selective Involvement of the Goldfish Lateral Pallium in Spatial Memory.” Behavioral Brain Research 210 (2010): 191-201.

Brown, Culum. “Fish Intelligence, Sentience, and Ethics.” Animal Cognition 18.1 (1 Jan. 2015): 1-17.

Bshary, Redouan, Wolfgang Wickler, and Hans Fricke. “Fish Cognition: A Primate’s Eye View.” Animal Cognition 5 (2002): 1-13.

Burgess, Neil. “Spatial Memory: How Egocentric and Allocentric Combine.” Trends in Cognitive Sciences (2006). doi:10.1016/j.tics.2006.10.005.

Fish Net . Web. http://www.fishnet.org.

Hurtado-Parrado, Camilo. “Neuronal Mechanisms of Learning in Teleost Fish.” Universitas Psychologica 9.3 (2010): 663-678.

Ingle, David, and Dianne Shagian. “Solution of a Spatial Constancy Problem by Goldfish.” Physiological Psychology 1.2 (1973): 83-84.

Kandel, Eric R. In Search of Memory: The Emergence of a New Science of Mind . New York: W.W. Norton & Company, Inc., 2006.

Knierin, James J., and Derek A. Hamilton. “Framing Spatial Cognition: Neural Representations of Proximal and Distal Frames of Reference and Their Roles in Navigation.” Physiological Review 91 (2011): 1245-79.

López, Juan Carlos, et al. “Multiple Spatial Learning Strategies in Goldfish ( Carassius auratus ).” Animal Cognition 2.2 (1999): 109-120.

López, Juan Carlos, et al. “Reversal Learning Deficit in a Spatial Task But Not in a Cued One After Telencephalic Ablation in Goldfish.” Behavioral Brain Research 109 (2000): 91-98.

López, Juan Carlos, et al. “Dissociation of Place and Cue Learning by Telencephalic Ablation in Goldfish.” Behavioral Neuroscience 114.4 (2000): 687-699.

Moser, Edvard I., et al. “Grid Cells and Cortical Representation.” Nature 15 (2014): 466-481.

Nadel, Lynn. “The Hippocampus and Space Revisited.” Hippocampus 1.2 (1991): 221-239.

Odling-Smee, Lucy, and Victoria A. Braithwaite. “The Role of Learning in Fish Orientation.” Fish and Fisheries 4 (2003): 235-246.

Osborne, Darren. “Three-Second Fish Memory ‘Rubbish.’” News in Science, Australian Broadcasting Corporation. 14 Jan. 2010. Web. http://www.abc.net.au/science/articles/2010/01/14/2792407.htm.

Portavella, Manuel, and Juan P. Vargas. “Emotional and Spatial Learning in Goldfish is Dependent on Different Telencephalic Pallial Systems.” European Journal of Neuroscience 21 (2005): 2800-06.

Rodríguez, Fernando, et al. “Cognitive and Emotional Functions of the Teleost Fish Cerebellum.” Brain Research Bulletin 66.4-6 (2005): 365-370.

Rodríguez, Fernando, et al.. “Conservation of Spatial Memory Function in the Pallial Forebrain of Reptiles and Ray-Finned Fishes.” The Journal of Neuroscience 22.7 (2002): 2894-03.

Rodríguez, Fernando, et al. “Performance of Goldfish Trained in Allocentric and Egocentric Maze Procedures Suggests the Presence of a Cognitive Mapping System in Fishes.” Animal Learning and Behavior 4 (1994): 409-420.

Saito, Kotaro, and Shigeru Watanabe. “Experimental Analysis of Spatial Learning in Goldfish.” The Psychological Record 55 (2005): 647-662.

Salas, Cosme, et al. “Telencephalic Ablation in Goldfish Impairs Performance in a ‘Spatial Constancy’ Problem But Not in a Cued One.” Behavioral Brain Research 79 (1996): 193-200.

Salas, Cosme, et al. “Neuropsychology of Learning and Memory in Teleost Fish.” Zebrafish 3.2 (2006): 157-171.

Salas, Cosme, et al. “Spatial Learning and Memory Deficits After Telencephalic Ablation in Goldfish Trained in Place and Turn Maze Procedures.” Behavioral Neuroscience 110.5 (1996): 965-980.

Shubin, Neil. Your Inner Fish: A Journey into the 3.5-Billion-Year History of the Human Body . New York: Vintage Books, 2009.

Tolman, Edward C. “Cognitive Maps in Rats and Men.” Psychological Review 55.4 (1948): 189-208.

Tolman, E.C., B.F. Ritchie, and D. Kalish. “Studies in Spatial Learning: II. Place Learning Versus Response Learning.” Journal of Experimental Psychology 36 (1946): 221-229.

Tolman, E.C., B.F. Ritchie, and D. Kalish. “Studies in Spatial Learning: I. Orientation and the Shortcut.” Journal of Exploratory Psychology 1 (1946): 13-24.

Vargas, Juan Pedro, et al. “Encoding of Geometric and Featural Spatial Information by Goldfish ( Carassius auratus ).” Journal of Comparative Psychology 118.2 (2004): 206-216.

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
• News Feature
• Published: 26 May 2015

Animal behaviour: Inside the cunning, caring and greedy minds of fish

• Alison Abbott  

Nature volume  521 ,  pages 412–414 ( 2015 ) Cite this article

2683 Accesses

6 Citations

478 Altmetric

Metrics details

• Animal behaviour
• Neuroscience

By revealing that fish cooperate, cheat and punish, Redouan Bshary has challenged ideas about brain evolution.

Redouan Bshary well remembers the moment he realized that fish were smarter than they are given credit for. It was 1998, and Bshary was a young behavioural ecologist with a dream project: snorkelling in Egypt's Red Sea to observe the behaviour of coral-reef fish. That day, he was watching a grumpy-looking grouper fish as it approached a giant moray eel.

As two of the region's top predators, groupers and morays might be expected to compete for their food and even avoid each other — but Bshary saw them team up to hunt. First, the grouper signalled to the eel with its head, and then the two swam side by side, with the eel dipping into crevices, flushing out fish beyond the grouper's reach and getting a chance to feed alongside. Bshary was astonished by the unexpected cooperation; if he hadn't had a snorkel in his mouth, he would have gasped.

This underwater observation was the first in a series of surprising discoveries that Bshary has gone on to make about the social behaviour of fish. Not only can they signal to each other and cooperate across species, but they can also cheat, deceive, console or punish one another — even show concern about their personal reputations. “I have always had a lot of respect for fish,” says Bshary. “But one after the other, these behaviours took me by surprise.”

His investigations have led him to take a crash course in scuba diving, go beach camping in Egypt and build fake coral reefs in Australia. The work has also destroyed the stereotypical idea that fish are dumb creatures, capable of only the simplest behaviours — and it has presented a challenge to behavioural ecologists in a different field. Scientists who study primates have claimed that human-like behaviours such as cooperation are the sole privilege of animals such as monkeys and apes, and that they helped to drive the evolution of primates' large brains. Bshary — quiet, but afraid of neither adventure nor of contesting others' ideas — has given those scientists reason to think again.

“Redouan has thrown down the gauntlet to us primatologists,” says Carel van Schaik, an expert in orang-utan culture at the University of Zurich in Switzerland. “He has made us realize that some of the explanations of primate intelligence that we have cherished don't hold water anymore.”

Stream fishing

Bshary says that he was “pre-imprinted to like fish”. As a child in Starnberg, Germany, he played constantly in the stream at the edge of the family garden, building dams and pools and trapping fish. Passionate about animal behaviour, he studied evolutionary ecology at the University of Munich, and then did a PhD at the Max Planck Institute for Behavioural Physiology in Starnberg. But for his field work, he journeyed to the Côte d'Ivoire, where he followed tree-living monkeys and discovered that different species collaborate to reduce predator risk.

His PhD supervisor, Ronald Noë, thought it would be “near impossible” to stalk monkeys that leap from tree-top to tree-top, but Bshary seemed to have a flair for it. On occasion, he even camouflaged himself under a leopard skin to imitate one of their predators. And he became fascinated by one question: what makes animals cooperate when standard natural selection would predict selfish behaviour to be the norm?

Noë, a primate behavioural ecologist now at the Hubert Curien Multidisciplinary Institute in Strasbourg, France, had come up with a biological market-based theory of cooperation. It proposed that animals cooperate to trade a specific commodity — such as food — for a service that would promote their survival, such as protection from a predator 1 . “An attractive theory — but, there were no strong data to support it,” says Bshary.

He looked around for a system where market forces might be operating. And he found one when Hans Fricke, a fish ecologist working at the Max Planck institute, told him the strange tale of barrier-reef fish that operate a remarkable system of cooperation. 'Cleaner' fish, such as the brightly striped wrasse, will nibble parasites off the skin of 'client' fish in small coral territories known as cleaning stations. Bshary realized that this provided a perfect situation in which to test the market theory because client fish seemed to be trading food — in the form of parasites — for a skin-cleaning service. He decided to follow his hunch and study the coral-reef fish.

There was one small problem: Bshary had never been scuba diving. He took his first lessons during a snowy winter in Lake Starnberg, then set off for the Red Sea, setting up camp in Ras Mohammed National Park in Egypt. Together with a few students, Bshary spent two full months a year camped on a scorching beach, sleeping under the stars, eating a diet of fruit and vegetables and doing four exhausting, 75-minute dives a day. “In the mornings he would wake up and immediately put on his wetsuit and jump straight into the sea,” recalls former student Erica van de Waal, now a research fellow at the University of Zurich. Armed with a plastic underwater writing slate, a pencil and a stopwatch, he shadowed client fish, observing their interactions with the wrasse cleaner fish — and soon collected evidence of a well-functioning market. “For me this system was a gold mine,” Bshary says, and he mined a lot of gold.

He discovered, for example, that fish did not just trade parasites for skin cleaning; the cleaner fish also cheated on the deal. Rather than eating parasites, they actually preferred the nutritious protective mucus that covers fish skin, and were constantly tempted to take a quick, illicit bite of it. Bshary could count how often this happened — and therefore whether the clients were getting a good or a bad cleaning service — because the clients gave a jolt when they were bitten.

The market theory predicted that if there were lots of clients around, the cleaners would enjoy a seller's market and would risk taking more bites of mucus. This is just like a mechanic getting away with shoddy car services when there are no competing businesses in town. Bshary found this to be true, and he also found that the buyers could protest. Because some client fish roam large territories, they could choose to boycott any cleaning stations that deliver a bad service — just as someone who received a poor car service might travel farther to find a better garage 2 .

While racking up evidence for the market theory, Bshary also observed a range of other social behaviours that had never been seen before in fish. He saw that unsatisfied clients sometimes punish cheating cleaners by chasing them around, and that this punishment makes these fish less likely to cheat 3 . He saw cleaners ingratiating themselves with certain clients: they gave preference to visiting fish such as groupers, rather than the smaller, local fish that did not have the option of going elsewhere. He found that the cleaners cheated less when they were being watched by other potential clients — a sign that they were buffing their reputations 4 . And he saw reconciliation: if cleaners behaved badly, they then massaged the backs of offended clients with their pelvic fins 5 .

It was all adding up to a catalogue of behaviours worthy of Niccolò Machiavelli's The Prince — but it was based on observation alone. Bshary needed to move to an experimental set-up where he could test how the fish behaved. And so in 2003, he began experiments at Lizard Island Research Station on Australia's Great Barrier Reef. He was employed, however, on the other side of the world: first at the University of Liverpool, UK, and now at the University of Neuchâtel, Switzerland. “It was not difficult to sign up to a lifetime of fieldwork at warm coral beach locations,” he admits.

Over the next few years Bshary would capture fish in the wild reefs, house them in tanks for the duration of his experiments, then release them. He simulated the choice that cleaners make between parasites and mucus by building moveable plastic plates smeared with prawn, which the fish love, and fish flakes, which they enjoy less. In this set-up, the plates may be snatched away if the cleaners go for the prawns — just like a client fish may swim away if its mucus gets bitten too often. So the cleaners learned to cooperate and eat fish flakes instead.

Such experiments take patience: some fish take a month just to adjust to the tanks. But in this way, Bshary proved that all the behaviours he had observed in the wild could be repeated under experimental conditions. And he discovered even more bizarre facts about the social lives of fish. In one experiment, he showed that when cleaners work in male and female pairs, as frequently happens in the wild, they are much less likely to cheat than when they work alone 6 ; and that this is mostly because the female gets punished by being chased around by the male if she slacks off 7 .

Perhaps the laboratory's most imaginative experiment involved the construction of an entire fake coral reef, complete with dummy eels. The job fell to doctoral student Alex Vail, who glued together bits of coral rubble and put them in a hot-tub-sized tank. Vail then made models of moray eels by printing, gluing together and laminating two life-sized photographs, and attaching nylon strings that allowed him to pull the fake eels out of the fake coral, like a puppet. (Vail subsequently went on to a successful career in underwater filming.) Using this set-up, the team explored the behaviour that so shocked Bshary when he observed it in 1998: a grouper and eel teaming up to flush out fish to eat. They showed that the grouper quickly learnt to signal — by turning and shaking its head — only to those moray eels that responded by moving towards, rather than away from, the fake reef.

Bshary amassed ample evidence that fish engage in a range of social behaviours, and he assumed that all of them resulted from simple evolution at work. Natural selection favoured fish that could learn, by simple association, which choices allowed them to efficiently rid themselves of parasites or access food.

By 2010, Bshary's thoughts were turning back to the world of primatology, in which he had been immersed during his PhD. He knew that he had observed in fish many of the behaviours that primatologists had shown in monkeys and apes. But primatologists had made grander claims for their observations. The 'social brain' theory argues that primates evolved brains that are large for their body size to manage their unusually complex social systems. Only primate brains, the theory says, have the depth of cognitive analysis necessary to cooperate, deceive and solve other problems in a social world.

Bshary disagreed. Maybe, he thought, these particular social behaviours in primates were also learnt by simple association and did not require the extra computing power of their big brains. And his findings meshed with those emerging from studies on the social behaviours of other animals, ranging from elephants to birds. “I think primatologists tend to make big claims because they look up the evolutionary chain and compare the primates' behaviours to humans, instead of looking down the evolutionary chain to see if the phenomena also existed in lower species,” he says.

At the time, primatologists were certainly not looking at fish. But that changed when Bshary teamed up with primatologist Sarah Brosnan at Georgia State University in Atlanta to directly pit the skills of cleaner fish against capuchin monkeys, chimpanzees and orang-utans in a foraging test. Each animal was presented with food on two differently coloured plates, one of which was a permanent fixture in their tanks or pens, whereas the other was temporary. The challenge was to learn to eat from the temporary plate first, before it disappeared — and the scientists counted how many trials it took for the animal to figure this out.

The cleaner fish solved the problem first 8 ; they have evolved in their ecological niche to preferentially feast from visiting clients before they disappear. For fun, Bshary set up an equivalent 'foraging' test for his four-year-old daughter, complete with temporary and permanent plates, each bearing one chocolate M&M. In a series of 100 different trials, she never learnt to eat from the temporary plate.

The fish, meanwhile, were already aceing a more advanced test. When Bshary and Brosnan switched the coloured plates so that the permanent one suddenly became temporary and vice versa, the fish again understood the switch faster than the apes did (and equally as fast as the capuchins) 8 . This is known as reversal learning — and when the primatologists read that result, they took note. “Reversal learning has often been touted as the gold standard of general cognitive abilities,” says van Schaik — a sophisticated skill that correlates with brain size. “Since small-brained fish do it quite well, maybe we'll have to abandon this idea.”

He would wake up and immediately put on his wetsuit and jump straight into the sea.

“The ball is in our court,” says evolutionary psychologist Robin Dunbar of the University of Oxford, UK, who developed the social brain theory. Dunbar now accepts that the evolution of large brains was not driven by the need to carry out single 'smart' behaviours such as cooperation or deception. But that doesn't mean the social brain theory has to be abandoned, he says — just refined. He and other primatologists now propose that primates evolved bigger brains because they needed an all-round high level of general intelligence to survive the pressures of living in tight social groups — for example, to recognize large numbers of individuals and remember their complicated genetic and hierarchical relationships.

Fish, which tend to have one-on-one interactions and live in loose schools, do not need to multi-task in quite the same way, Dunbar says. “It may boil down to the speed of cognitive processing and accuracy of judgement,” he suggests.

Intelligence tests

Michael Tomasello, an evolutionary psychologist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, bounces the ball right back to Bshary, challenging him to show how smart fish really are. “Perhaps the most pressing question is how flexible and general fish cognition is,” he says — something Bshary is already testing by designing further fish intelligence tests.

The mysteries of the fish brain deepened in 2009, when Bshary's team chanced across a habitat in the reefs around Lizard Island that had relatively few fish and therefore less competition and social complexity. To Bshary's surprise, the cleaner fish there turned out to be much less socially smart than cleaner fish just 20 metres away 9 . But their skill level may be optimal for their environment — another hypothesis that he now plans to explore.

Whatever the next instalment brings, colleagues say that Bshary has already shifted a view of animal cognition in which humans and their primate cousins tower over everything else. “Primate chauvinism may now be poised to decline, thanks in large part to Bshary's fish work,” says primatologist and ethologist Frans de Waal of Emory University in Atlanta, Georgia. “They now really do have to take on board that most species are going to have a type of smart intelligence.”

Noë, R. &amp; Hammerstein, P. Trends Ecol. Evol. 10 , 336–339 (1995).

Article   Google Scholar  

Bshary, R. &amp; D'Souza, A. in Animal Communication Networks (ed. McGregor, P. K.) 521–539 (Cambridge Univ. Press, 2005).

Book   Google Scholar  

Bshary, R. &amp; Grutter, A. S. Biol. Lett. 1 , 396–399 (2005).

Bshary, R. &amp; Grutter, A. S. Nature 441 , 975–978 (2006).

Article   ADS   CAS   Google Scholar  

Bshary, R. &amp; Würth, M. Proc. R. Soc. Lond. B 268 , 1495–1501 (2001).

Article   CAS   Google Scholar  

Bshary, R., Grutter, A. S., Willener, A. S. T. &amp; Leimar, O. Nature 455 , 964–966 (2008).

Raihani, N. J., Pinto, A. I., Grutter, A. S., Wismer, S. &amp; Bshary, R. Proc. R. Soc. Lond. B 279 , 365–370 (2012).

Google Scholar  

Salwiczek, L. H. et al. PLoS ONE 7 , e49068 (2012).

Wismer, S., Pinto, A. I., Vail, A. L., Grutter, A. S. &amp; Bshary, R. Ethology 120 , 519–531 (2014).

Download references

You can also search for this author in PubMed   Google Scholar

Electronic supplementary material

Clever fish

Related links

Related links in nature research.

Neuroscience: The rat pack 2010-May-19

Science at the movies: The fabulous fish guy 2004-Feb-19

Related external links

Lizard Island Research Station

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Abbott, A. Animal behaviour: Inside the cunning, caring and greedy minds of fish. Nature 521 , 412–414 (2015). https://doi.org/10.1038/521412a

Download citation

Published : 26 May 2015

Issue Date : 28 May 2015

DOI : https://doi.org/10.1038/521412a

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

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.

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
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Understanding fish cognition: a review and appraisal of current practices

Affiliations.

• 1 Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada. [email protected].
• 2 Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada.
• 3 Department of Biology, McMaster University, Hamilton, Ontario, Canada.
• 4 Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario, Canada.
• 5 Department of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
• 6 Department of Biological Sciences, Macquarie University, Sydney, Australia.
• PMID: 33595750
• DOI: 10.1007/s10071-021-01488-2

With over 30,000 recognized species, fishes exhibit an extraordinary variety of morphological, behavioural, and life-history traits. The field of fish cognition has grown markedly with numerous studies on fish spatial navigation, numeracy, learning, decision-making, and even theory of mind. However, most cognitive research on fishes takes place in a highly controlled laboratory environment and it can therefore be difficult to determine whether findings generalize to the ecology of wild fishes. Here, we summarize four prominent research areas in fish cognition, highlighting some of the recent advances and key findings. Next, we survey the literature, targeting these four areas, and quantify the nearly ubiquitous use of captive-bred individuals and a heavy reliance on lab-based research. We then discuss common practices that occur prior to experimentation and within experiments that could hinder our ability to make more general conclusions about fish cognition, and suggest possible solutions. By complementing ecologically relevant laboratory-based studies with in situ cognitive tests, we will gain further inroads toward unraveling how fishes learn and make decisions about food, mates, and territories.

Keywords: Behavior; Decision-making; Intelligence; Learning; Memory; Teleosts.

PubMed Disclaimer

Similar articles

• Changing tides: ecological and historical perspectives on fish cognition. Patton BW, Braithwaite VA. Patton BW, et al. Wiley Interdiscip Rev Cogn Sci. 2015 Mar-Apr;6(2):159-176. doi: 10.1002/wcs.1337. Epub 2015 Jan 29. Wiley Interdiscip Rev Cogn Sci. 2015. PMID: 26263070 Review.
• Fish cognition: a primate's eye view. Bshary R, Wickler W, Fricke H. Bshary R, et al. Anim Cogn. 2002 Mar;5(1):1-13. doi: 10.1007/s10071-001-0116-5. Anim Cogn. 2002. PMID: 11957395 Review.
• Understanding the origin of number sense: a review of fish studies. Agrillo C, Bisazza A. Agrillo C, et al. Philos Trans R Soc Lond B Biol Sci. 2017 Feb 19;373(1740):20160511. doi: 10.1098/rstb.2016.0511. Philos Trans R Soc Lond B Biol Sci. 2017. PMID: 29292358 Free PMC article. Review.
• How ecology shapes prey fish cognition. Beri S, Patton BW, Braithwaite VA. Beri S, et al. Behav Processes. 2014 Nov;109 Pt B:190-4. doi: 10.1016/j.beproc.2014.09.020. Epub 2014 Sep 20. Behav Processes. 2014. PMID: 25245304
• Social cognition in fishes. Bshary R, Gingins S, Vail AL. Bshary R, et al. Trends Cogn Sci. 2014 Sep;18(9):465-71. doi: 10.1016/j.tics.2014.04.005. Epub 2014 May 9. Trends Cogn Sci. 2014. PMID: 24815200 Review.
• Trait sensitivity to stress and cognitive bias processes in fish: A brief overview. Buenhombre J, Daza-Cardona EA, Mota-Rojas D, Domínguez-Oliva A, Rivera A, Medrano-Galarza C, de Tarso P, Cajiao-Pachón MN, Vargas F, Pedraza-Toscano A, Sousa P. Buenhombre J, et al. Personal Neurosci. 2024 Jan 31;7:e3. doi: 10.1017/pen.2023.14. eCollection 2024. Personal Neurosci. 2024. PMID: 38384666 Free PMC article. Review.
• Teleosts as behaviour test models for social stress. Lai NHY, Mohd Zahir IA, Liew AKY, Ogawa S, Parhar I, Soga T. Lai NHY, et al. Front Behav Neurosci. 2023 Sep 7;17:1205175. doi: 10.3389/fnbeh.2023.1205175. eCollection 2023. Front Behav Neurosci. 2023. PMID: 37744951 Free PMC article. Review.
• Environmental enrichment improves cognitive flexibility in rainbow trout in a visual discrimination task: first insights. Brunet V, Lafond T, Kleiber A, Lansade L, Calandreau L, Colson V. Brunet V, et al. Front Vet Sci. 2023 Jun 15;10:1184296. doi: 10.3389/fvets.2023.1184296. eCollection 2023. Front Vet Sci. 2023. PMID: 37396987 Free PMC article.
• Maze design: size and number of choices impact fish performance in cognitive assays. Jones NAR, Cortese D, Munson A, Spence-Jones HC, Storm Z, Killen SS, Bethel R, Deacon AE, Webster MM, Závorka L. Jones NAR, et al. J Fish Biol. 2023 Nov;103(5):974-984. doi: 10.1111/jfb.15493. Epub 2023 Jul 14. J Fish Biol. 2023. PMID: 37386747 Free PMC article.
• Experimental expansion of relative telencephalon size improves the main executive function abilities in guppy. Triki Z, Fong S, Amcoff M, Vàsquez-Nilsson S, Kolm N. Triki Z, et al. PNAS Nexus. 2023 Jun 20;2(6):pgad129. doi: 10.1093/pnasnexus/pgad129. eCollection 2023 Jun. PNAS Nexus. 2023. PMID: 37346268 Free PMC article.
• Agrillo C, Dadda M, Serena G, Bisazza A (2008) Do fish count? Spontaneous discrimination of quantity in female mosquitofish. Anim Cogn 11:495–503. https://doi.org/10.1007/s10071-008-0140-9 - DOI - PubMed
• Agrillo C, Dadda M, Serena G, Bisazza A (2009) Use of number by fish. PLoS ONE. https://doi.org/10.1371/journal.pone.0004786 - DOI - PubMed - PMC
• Agrillo C, Piffer L, Bisazza A (2011) Number versus continuous quantity in numerosity judgments by fish. Cognition 119:281–287. https://doi.org/10.1016/j.cognition.2010.10.022 - DOI - PubMed
• Agrillo C, Petrazzini MEM, Bisazza A (2017) Numerical abilities in fish: a methodological review. Behav Process 141:161–171. https://doi.org/10.1016/j.beproc.2017.02.001 - DOI
• Aoki R, Tsuboi T, Okamoto H (2015) Y-maze avoidance: An automated and rapid associative learning paradigm in zebrafish. J Neurosci Res 91:69–72. https://doi.org/10.1016/j.neures.2014.10.012 - DOI

Publication types

• Search in MeSH

LinkOut - more resources

Full text sources, other literature sources.

• scite Smart Citations

Miscellaneous

• NCI CPTAC Assay Portal
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Share this on:

The 'michigan fish test' and the middle east.

• Sheena Iyengar says a picture of sea life can tell much about our cultural expectations
• Japanese who viewed picture saw the whole scene, American viewers focused on big fish
• She says Americans apply individualistic narrative to view of Mid-East, N. Africa conflicts
• Iyengar: As events unfold there, we must be aware our context for comprehension is limited

Editor's note: TED is a nonprofit dedicated to "Ideas worth spreading," which it makes available through talks posted on its website. Sheena Iyengar is a social psychologist and professor at Columbia Business School. She is Research Director of the Chazen Institute and author of " The Art of Choosing ."

New York (CNN) - When I read about the popular uprisings in the Middle East and North Africa, I think about life under the sea.

Wonder what I mean?

Study the image below for five seconds, then look away and quickly describe it to yourself.

What did you see, and what did you say? Did three large fish, the prominent individuals of the scene, hold your gaze?

Or was your eye drawn more to the environment, taking in the rocks, the bubbles, the kelp?

Just what does this visual exercise have to do with Tunisia, Egypt, or Libya?

It turns out that how you go about even this simple and straightforward task of describing what's in this image depends on your worldview, which is greatly shaped by your culture.

Essentially, people from different cultures will perceive and remember different aspects of the same picture. A perfect example of this phenomenon is how we perceive the current events in the Middle East.

We Americans observe the social shifts sweeping the Arab world--the protests, the struggles, the upheaval. By and large, we hope that we're witnessing the beginning of a new societal story there, one that seems more similar to our own, with its progress towards greater individual control and agency.

Watch an interview with Sheena Iyengar

However, our own culture shapes our expectations at a more subconscious level. In fact, the study associated with the fish image reveals that even basic perceptions are seeded with cultural narrative. So as we regard the tumult in Egypt, Tunisia, and elsewhere, let's consider just how our own perceptions accent the situation.

TED.com: Wael Ghonim's inside look at the Egyptian Revolution

The image here, known in psychology as the Michigan Fish Test, was presented to American and Japanese participants in a study conducted by Richard Nisbett and Takahiko Masuda.

In their five-second viewing, Americans paid more attention to the large fish, the "main characters" of the scene, while Japanese described the scene more holistically. For Americans, the large fish were the powerful agents, influencing everything around them. For Japanese, the environment dominated, interacting with and influencing all the characters.

After the initial test, the researchers offered participants different versions of the fish picture, with some elements changed and some not. With the altered pictures, the Japanese were more likely to notice changes in the scenery or context. The Americans, on the other hand, proved adept at recognizing the large fish wherever they appeared, while the Japanese had more trouble recognizing the fish in new contexts, outside the original environment.

So members of two different cultures--the more individualist Americans and the more collectivist Japanese--"saw" the pictures with differing emphasis on individuals, the environment, and how these elements interacted. The divergent accounts point to differing narratives of what controls what in the world, and how individual people fit into it.

How then might people in the Arab world describe the image? That research hasn't been done. However, since Middle Eastern cultures are generally less individualistic and more collectivist, we can make an educated guess that they would see the picture differently than Americans, probably with more emphasis on environment and context.

Let's return, though, to what we Americans described, and what that might tell us. In the Nisbett and Masuda's study, we distinguished the big fish as the main characters, largely independent of environment. As Americans, our narrative has been one of individuals changing the world through their own actions. This common thread runs from Benjamin Franklin's maxim "God helps those who help themselves" straight to Barack Obama's "Yes we can."

At TED conference, 15 reasons for wonder

Many other cultures take a more holistic approach to personal agency. In perhaps the most famous passage of the Hindu scripture Bhagavad Gita, the god Khrishna tells the hero Arjuna, "You have control only over your actions, never over the fruit of your actions. You should never act for the sake of reward, nor should you succumb to inaction."

Popular expressions from other cultures echo this approach. "Shikata ga nai"--it can't be helped--goes a Japanese saying to mitigate unpleasant circumstances, while the Arabic phrase, "in sha'Allah"-- God willing --signals a similar limit to control for Muslims. The individual isn't powerless in these conceptions, but he or she is just one player in a larger drama of life, not its center.

TED.com: A historic moment in the Arab world

In the last decade, America has tried applying our individualistic narrative to the Middle East. Now, as the people in multiple countries there struggle to take greater control for themselves, we want to see our story play out in their efforts, and we worry that it won't.

Yet even as we set the scene for ourselves, we subtly set our expectations. This recent New York Times article on Libya begins with a lengthy description of main actors--the rebels and their leaders. In contrast, similar stories from non-Western, large-circulation dailies--for example, The Hindu in India , and Sabah in Turkey --begin by laying out broader contexts. The different stories set different conditions for where meaningful action will come from.

Our own culture enters the picture--whether it's one of undersea life or social uprising--at the very moment we understand it. With this in mind, let's be rigorous in trying to comprehend the events in the Middle East, but humble in trying to predict just where they may lead.

The opinions expressed in this commentary are solely those of Sheena Iyengar .

Most Popular

Fine art from an iphone the best instagram photos from 2014, after ivf shock, mom gives birth to two sets of identical twins, inside north korea: water park, sacred birth site and some minders, 10 top destinations to visit in 2015, what really scares terrorists.

Edward Thorndike: The Law of Effect

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

• The law of effect states that connections leading to satisfying outcomes are strengthened while those leading to unsatisfying outcomes are weakened.
• Positive emotional responses, like rewards or praise, strengthen stimulus-response connections. Unpleasant responses weaken them.
• This establishes reinforcement as central to efficient and enduring learning. Reward is more impactful than punishment.
• Connections grow most robust when appropriate associations lead to fulfilling outcomes. The “effect” generated shapes future behavioral and cognitive patterns.

Thorndike Theory

The law of effect states that behaviors followed by pleasant or rewarding consequences are more likely to be repeated, while behaviors followed by unpleasant or punishing consequences are less likely to be repeated.

The principle was introduced in the early 20th century through experiments led by Edward Thorndike, who found that positive reinforcement strengthens associations and increases the frequency of specific behaviors.

The law of effect principle developed by Edward Thorndike suggested that:

“Responses that produce a satisfying effect in a particular situation become more likely to occur again in that situation, and responses that produce a discomforting effect become less likely to occur again in that situation (Gray, 2011, p. 108–109).”

Edward Thorndike (1898) is famous in psychology for his work on learning theory that leads to the development of operant conditioning within behaviorism .

Whereas classical conditioning depends on developing associations between events, operant conditioning involves learning from the consequences of our behavior.

Skinner wasn’t the first psychologist to study learning by consequences.  Indeed, Skinner’s theory of operant conditioning is built on the ideas of Edward Thorndike.

Experimental Evidence

Thorndike studied learning in animals (usually cats).  He devised a classic experiment using a puzzle box to empirically test the laws of learning.

• Thorndike put hungry cats in cages with automatic doors that could be opened by pressing a button inside the cage. Thorndike would time how long it took the cat to escape.
• At first, when placed in the cages, the cats displayed unsystematic trial-and-error behaviors, trying to escape. They scratched, bit, and wandered around the cages without identifiable patterns.
• Thorndike would then put food outside the cages to act as a stimulus and reward.  The cats experimented with different ways to escape the puzzle box and reach the fish.
• Eventually, they would stumble upon the lever which opened the cage.  When it had escaped, the cat was put in again, and once more, the time it took to escape was noted.  In successive trials, the cats would learn that pressing the lever would have favorable consequences , and they would adopt this behavior, becoming increasingly quick at pressing the lever.
• After many repetitions of being placed in the cages (around 10-12 times), the cats learned to press the button inside their cages, which opened the doors, allowing them to escape the cage and reach the food.

Edward Thorndike put forward a  Law of Effect, which stated that any behavior that is followed by pleasant consequences is likely to be repeated, and any behavior followed by unpleasant consequences is likely to be stopped.

Critical Evaluation

Thorndike (1905) introduced the concept of reinforcement and was the first to apply psychological principles to the area of learning.

His research led to many theories and laws of learning, such as operant conditioning. Skinner (1938), like Thorndike, put animals in boxes and observed them to see what they were able to learn.

Thorndike’s theory has implications for teaching such as preparing students mentally, using drills and repetition, providing feedback and rewards, and structuring material from simple to complex.

B.F. Skinner built upon Thorndike’s principles to develop his theory of operant conditioning. Skinner’s work involved the systematic study of how the consequences of a behavior influence its frequency in the future. He introduced the concepts of reinforcement (both positive and negative) and punishment to describe how consequences can modify behavior.

The learning theories of Thorndike and Pavlov were later synthesized by Hull (1935). Thorndike’s research drove comparative psychology for fifty years, and influenced countless psychologists over that period of time, and even still today.

Criticisms 

Critiques of the theory include that it views humans too mechanistically like animals, overlooks higher reasoning, focuses too narrowly on associations, and positions the learner too passively.

Here is a summary of some of the main critiques and limitations of Thorndike’s learning theory:

• Using animals like cats and dogs in experiments is controversial when making inferences about human learning, since animal and human cognition differ.
• The theory depicts humans as mechanistic, like animals, driven by automatic trial-and-error processes. However, human learning is more complex and not entirely explained through stimulus-response connections.
• By overemphasizing associations, the theory overlooks deeper reasoning, understanding, and meaning construction involved in learning.
• Definitions and conceptual knowledge are ignored in favor of strengthening mechanistic stimulus-response bonds.
• Learners are passive receptors rather than active or creative; educators provide rigid structured curricula rather than let learners construct knowledge.
• Learners require constant external motivation and reinforcement rather than having internal drivers.
• Failures are punished, and discipline is stressed more than conceptual grasp or successful processes.
• The focus is on isolated skills, facts, and hierarchical sequencing rather than integrated understanding.
• Evaluation only measures passive responses and test performance rather than deeper learning processes or contexts.

Application of Thorndike’s Learning Theory to Students’ Learning

Thorndike’s theory, when applied to student learning, emphasizes several key factors – the role of the environment, breaking tasks into detail parts, the importance of student responses, building stimulus-response connections, utilizing prior knowledge, repetition through drills and exercises, and giving rewards/praise.

Learning is results-focused, with the measurement of observable outcomes. Errors are immediately corrected. Repetition aims to ingrain behaviors until they become habit. Rewards strengthen desired behaviors, punishment weakens undesired behaviors.

Some pitfalls in the application include teachers becoming too authoritative, one-way communication, students remaining passive, and over-reliance on rote memorization. However, his theory effectively promotes preparation, readiness, practice, feedback, praise for progress, and sequential mastery from simple to complex.

Teachers arrange hierarchical lesson materials starting from simple concepts, break down learning into parts marked by specific skill mastery, provide examples, emphasize drill/repetition activities, offer regular assessments and corrections, deliver clear brief instructions, and utilize rewards to motivate. This style is most applicable for skill acquisition requiring significant practice.

For students, the theory instills habits of repetition, progress tracking, and associate positive outcomes to effort.

It can, however, be limited if students remain passive receivers of instruction rather than active or collaborative learners. Proper application encourages student discipline while avoiding strict, punishing environments.

Additional Laws of Learning In Thorndike’s Theory 

Thorndike’s theory explains that learning is the formation of connections between stimuli and responses. The laws of learning he proposed are the law of readiness, the law of exercise, and the law of effect.

Law of Readiness

• The law of readiness states that learners must be physically and mentally prepared for learning to occur.  This includes not being hungry, sick, or having other physical distractions or discomfort.
• Mentally, learners should be inclined and motivated to acquire the new knowledge or skill. If they are uninterested or opposed to learning it, the law states they will not learn effectively.
• Learners also require certain baseline knowledge and competencies before being ready to learn advanced concepts. If those prerequisites are lacking, acquisition of new info will be difficult.
• Overall, the law emphasizes learners’ reception and orientation as key prerequisites to successful learning. The right mindset and adequate foundation enables efficient uptake of new material.

Law of Exercise

• The law of exercise states that connections are strengthened through repetition and practice. 
• Frequent trials allow errors to be corrected and neural pathways related to the knowledge/skill to become more engrained.
• As associations are reinforced through drill and rehearsal, retrieval from long term memory also becomes more efficient.
• In sum, repeated exercise of learned material cements retention and fluency over time. Forgetting happens when such connections are not actively preserved through practice.

Gray, P. (2011). Psychology (6th ed.) New York: Worth Publishers.

Hull, C. L. (1935). The conflicting psychologies of learning—a way out . Psychological Review, 42(6) , 491.

Skinner, B. F. (1938). The behavior of organisms: An experimental analysis . New York: Appleton-Century.

Thorndike, E. L. (1898). Animal intelligence: An experimental study of the associative processes in animals. Psychological Monographs: General and Applied, 2(4), i-109.

Thorndike, E. L. (1905). The elements of psychology . New York: A. G. Seiler.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Sweepstakes
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

Ideas for Psychology Experiments

Inspiration for psychology experiments is all around if you know where to look

Psychology experiments can run the gamut from simple to complex. Students are often expected to design—and sometimes perform—their own experiments, but finding great experiment ideas can be a little challenging. Fortunately, inspiration is all around if you know where to look—from your textbooks to the questions that you have about your own life.

Always discuss your idea with your instructor before beginning your experiment—particularly if your research involves human participants. (Note: You'll probably need to submit a proposal and get approval from your school's institutional review board.)

At a Glance

If you are looking for an idea for psychology experiments, start your search early and make sure you have the time you need. Doing background research, choosing an experimental design, and actually performing your experiment can be quite the process. Keep reading to find some great psychology experiment ideas that can serve as inspiration. You can then find ways to adapt these ideas for your own assignments.

15 Ideas for Psychology Experiments

Most of these experiments can be performed easily at home or at school. That said, you will need to find out if you have to get approval from your teacher or from an institutional review board before getting started.

The following are some questions you could attempt to answer as part of a psychological experiment:

• Are people really able to "feel like someone is watching" them ? Have some participants sit alone in a room and have them note when they feel as if they are being watched. Then, see how those results line up to your own record of when participants were actually being observed.
• Can certain colors improve learning ? You may have heard teachers or students claim that printing text on green paper helps students read better, or that yellow paper helps students perform better on math exams. Design an experiment to see whether using a specific color of paper helps improve students' scores on math exams.
• Can color cause physiological reactions ? Perform an experiment to determine whether certain colors cause a participant's blood pressure to rise or fall.
• Can different types of music lead to different physiological responses ? Measure the heart rates of participants in response to various types of music to see if there is a difference.
• Can smelling one thing while tasting another impact a person's ability to detect what the food really is ? Have participants engage in a blind taste test where the smell and the food they eat are mismatched. Ask the participants to identify the food they are trying and note how accurate their guesses are.
• Could a person's taste in music offer hints about their personality ? Previous research has suggested that people who prefer certain styles of music tend to exhibit similar  personality traits. Administer a personality assessment and survey participants about their musical preferences and examine your results.
• Do action films cause people to eat more popcorn and candy during a movie ? Have one group of participants watch an action movie, and another group watch a slow-paced drama. Compare how much popcorn is consumed by each group.
• Do colors really impact moods ? Investigate to see if the  color blue makes people feel calm, or if the color red leaves them feeling agitated.
• Do creative people see  optical illusions  differently than more analytical people ? Have participants complete an assessment to measure their level of creative thinking. Then ask participants to look at optical illusions and note what they perceive.
• Do people rate individuals with perfectly symmetrical faces as more beautiful than those with asymmetrical faces ? Create sample cards with both symmetrical and asymmetrical faces and ask participants to rate the attractiveness of each picture.
• Do people who use social media exhibit signs of addiction ? Have participants complete an assessment of their social media habits, then have them complete an addiction questionnaire.
• Does eating breakfast help students do better in school ? According to some, eating breakfast can have a beneficial influence on school performance. For your experiment, you could compare the test scores of students who ate breakfast to those who did not.
• Does sex influence short-term memory ? You could arrange an experiment that tests whether men or women are better at remembering specific types of information.
• How likely are people to conform in groups ? Try this experiment to see what percentage of people are likely to conform . Enlist confederates to give the wrong response to a math problem and then see if the participants defy or conform to the rest of the group.
• How much information can people store in short-term memory ? Have participants study a word list and then test their memory. Try different versions of the experiment to see which memorization strategies, like chunking or mnemonics, are most effective.

Once you have an idea, the next step is to learn more about  how to conduct a psychology experiment .

Psychology Experiments on Your Interests

If none of the ideas in the list above grabbed your attention, there are other ways to find inspiration for your psychology experiments.

How do you come up with good psychology experiments? One of the most effective approaches is to look at the various problems, situations, and questions that you are facing in your own life.

You can also think about the things that interest you. Start by considering the topics you've studied in class thus far that have really piqued your interest. Then, whittle the list down to two or three major areas within psychology that seem to interest you the most.

From there, make a list of questions you have related to the topic. Any of these questions could potentially serve as an experiment idea.

Use Textbooks for Inspiration for Psychology Experiments

Your psychology textbooks are another excellent source you can turn to for experiment ideas. Choose the chapters or sections that you find particularly interesting—perhaps it's a chapter on  social psychology  or a section on child development.

Start by browsing the experiments discussed in your book. Then think of how you could devise an experiment related to some of the questions your text asks. The reference section at the back of your textbook can also serve as a great source for additional reference material.

Discuss Psychology Experiments with Other Students

It can be helpful to brainstorm with your classmates to gather outside ideas and perspectives. Get together with a group of students and make a list of interesting ideas, subjects, or questions you have.

The information from your brainstorming session can serve as a basis for your experiment topic. It's also a great way to get feedback on your own ideas and to determine if they are worth exploring in greater depth.

Study Classic Psychology Experiments

Taking a closer look at a classic psychology experiment can be an excellent way to trigger some unique and thoughtful ideas of your own. To start, you could try conducting your own version of a famous experiment or even updating a classic experiment to assess a slightly different question.

Famous Psychology Experiments

Examples of famous psychology experiments that might be a source of further questions you'd like to explore include:

• Marshmallow test experiments
• Little Albert experiment
• Hawthorne effect experiments
• Bystander effect experiments
• Robbers Cave experiments
• Halo effect experiments
• Piano stairs experiment
• Cognitive dissonance experiments
• False memory experiments

You might not be able to replicate an experiment exactly (lots of classic psychology experiments have ethical issues that would preclude conducting them today), but you can use well-known studies as a basis for inspiration.

Review the Literature on Psychology Experiments

If you have a general idea about what topic you'd like to experiment, you might want to spend a little time doing a brief literature review before you start designing. In other words, do your homework before you invest too much time on an idea.

Visit your university library and find some of the best books and articles that cover the particular topic you are interested in. What research has already been done in this area? Are there any major questions that still need to be answered? What were the findings of previous psychology experiments?

Tackling this step early will make the later process of writing the introduction  to your  lab report  or research paper much easier.

Ask Your Instructor About Ideas for Psychology Experiments

If you have made a good effort to come up with an idea on your own but you're still feeling stumped, it might help to talk to your instructor. Ask for pointers on finding a good experiment topic for the specific assignment. You can also ask them to suggest some other ways you could generate ideas or inspiration.

While it can feel intimidating to ask for help, your instructor should be more than happy to provide some guidance. Plus, they might offer insights that you wouldn't have gathered on your own. Your instructor probably has lots of ideas for psychology experiments that would be worth exploring.

If you need to design or conduct psychology experiments, there are plenty of great ideas (both old and new) for you to explore. Consider an idea from the list above or turn some of your own questions about the human mind and behavior into an experiment.

Before you dive in, make sure that you are observing the guidelines provided by your instructor and always obtain the appropriate permission before conducting any research with human or animal subjects.

Frequently Asked Questions

Finding a topic for a research paper is much like finding an idea for an experiment. Start by considering your own interests, or browse though your textbooks for inspiration. You might also consider looking at online news stories or journal articles as a source of inspiration.

Three of the most classic social psychology experiments are:

• The Asch Conformity Experiment : This experiment involved seeing if people would conform to group pressure when rating the length of a line.
• The Milgram Obedience Experiment : This experiment involved ordering participants to deliver what they thought was a painful shock to another person.
• The Stanford Prison Experiment : This experiment involved students replicating a prison environment to see how it would affect participant behavior. 

Jakovljević T, Janković MM, Savić AM, et al. The effect of colour on reading performance in children, measured by a sensor hub: From the perspective of gender .  PLoS One . 2021;16(6):e0252622. doi:10.1371/journal.pone.0252622

Greenberg DM, et al. Musical preferences are linked to cognitive styles . PLoS One. 2015;10(7). doi:10.1371/journal.pone.0131151

Kurt S, Osueke KK. The effects of color on the moods of college students . Sage. 2014;4(1). doi:10.1177/2158244014525423

Hartline-Grafton H, Levin M. Breakfast and School-Related Outcomes in Children and Adolescents in the US: A Literature Review and its Implications for School Nutrition Policy .  Curr Nutr Rep . 2022;11(4):653-664. doi:10.1007/s13668-022-00434-z

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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
• Front Psychol

The Big-Fish-Little-Pond Effect on Academic Self-Concept: A Meta-Analysis

The Big-fish-little-Pond effect is well acknowledged as the negative effect of class/school average achievement on student academic self-concept, which profoundly impacts student academic performance and mental development. Although a few studies have been done with regard to this effect, inconsistence exists in the effect size with little success in finding moderators. Here, we present a meta-analysis to synthesize related literatures to reach a summary conclusion on the BFLPE. Furthermore, student age, comparison target, academic self-concept domain, student location, sample size, and publication year were examined as potential moderators. Thirty-three studies with fifty-six effect sizes (total N = 1,276,838) were finally included. The random effects model led to a mean of the BFLPE at β = −0.28 ( p < 0.001). Moreover, moderator analyses revealed that the Big-Fish-Little-Pond effect is an age-based process and an intercultural phenomenon, which is stronger among high school students, in Asia and when verbal self-concept is considered. This meta-analysis is the first quantitative systematic overview of BFLPE, whose results are valuable to the understanding of BFLPE and reveal the necessity for educators from all countries to learn about operative means to help students avoid the potential negative effect. Future research expectations are offered subsequently.

Introduction

In educational psychology, Academic Self-Concept (ASC) refers to students' self-perception in specific disciplines (e.g., math self-concept, science self-concept) or more general academic areas (i.e., global/general ASC) (Marsh et al., 2008a ). As a prominent construct in educational psychology, student ASC showed substantial positive relations with many desirable educational outcomes, such as academic effort (Traütwein et al., 2006 ), academic interest and long-term educational attainment (Marsh et al., 2005 , 2007 ; Pinxten et al., 2010 ). Earlier empirical researches and a meta-analysis manifested that academic achievement and ASC are reciprocally related (Guay et al., 2003 ; Valentine and Dubois, 2005 ; Marsh and Craven, 2006 ). Positive ASC is an important means of facilitating student academic accomplishments and has been regarded as one of the key objectives of education (Seaton et al., 2009 ), therefore delving into the ASC forming process and revealing the forming mechanism make an impact both academically and practically.

The Big Fish Little Pond Effect (BFLPE) is one of the most influential theories about student ASC forming process, which was proposed by Marsh ( 1984 ) to describe the phenomenon that students in selective schools always have lower ASC compared to those with comparable ability but attend regular schools, which means that being a big fish in a small pond does good to one's ASC. Considerable evidence substantiated that the BFLPE is thought to be the outcome of individuals comparing their ability with the average ability of their group (Marsh, 1987 ; Plieninger and Dickhäuser, 2015 ).

It has been demonstrated that student's ASC is shaped not only by his or her performance but also by social comparisons (Marsh, 1988 ; Marsh et al., 1995 ; Möller et al., 2009 ; Parker et al., 2013 ; Niepel et al., 2014 ). Students compare their own achievement with that of their class- or schoolmates, which leads them to feel more negative about their own competencies in high-achieving atmosphere than in low-achieving atmosphere. Marsh ( 1987 ) argued that this social comparison mechanism lies at the heart of the BFLPE.

Evidence accumulated for several decades supported the BFLPE (Marsh and Hau, 2003 ; Huguet et al., 2009 ; Chiu, 2012 ; Becker and Neumann, 2016 ; Areepattamannil et al., 2017 ). The BFLPE was proved to be intercultural and stable: Marsh and Hau ( 2003 ) found that the effect of school-average achievement on student ASC is negative in 26 countries ( β ¯ = −0.20, SD = 0.08), and it exhibits across all student ability levels. Besides, the BFLPE was also observed for students who were at the end of high school or even graduated 2 years or 4 years later (Marsh et al., 2007 ), students with special education needs (Marsh and Craven, 2006 ), and students who were identified as gifted (Preckel et al., 2008 ).

While the BFLPE generally occurs, there are exceptions. Researches by Sung et al. ( 2014 ) and Liou ( 2014 ) provided evidence for no BFLPE. And results on the size of the BFLPE have been largely mixed. The size of this negative effect ranges from extremely weak (Thijs et al., 2010 ; Liou, 2014 ; Becker and Neumann, 2016 ), to weak (Nagengast and Marsh, 2012 ; Marsh, 2016 ) and to moderate (Huguet et al., 2009 ; Chiu, 2012 ). These inconsistencies in the reported findings make it difficult to draw a general conclusion concerning the BFLPE and provide useful suggestions for educational practice. As it usually makes more sense to summary existing researches than doing further research (Card, 2012 ), it is of great importance to carry out a systematic review of the BFLPE. While Marsh et al. ( 2008b ) have summarized the theoretical model underlying the BFLPE, there still lacks quantitative summary in this field.

Discrepancies in reported results provide sufficient incentive for a meta-analysis, and also suggest that there might exist moderating factors accounting for different links. Identifying constructs that may moderate the BFLPE can help further BFLPE theory (Seaton et al., 2009 ), while little progress has been made in finding factors that strengthen or weaken this effect. Hence, the principal focus of the present investigation is to examine potential moderating variables.

Related results indicated that there may exist one or more variables moderating the BFLPE, such as student age, comparison target, and ASC domain. The first is student age. Marsh ( 1987 ) proposed that the BFLPE is more likely to occur when young children begin to form ASC, and Becker and Neumann ( 2016 ) supposed that older students are capable enough to deal with conflicting information obtained from contexts, so that they may not suffer the BFLPE. Subjects from a wide range of age groups have been included in BFLPE researches completed to date. Some researchers focused on 15-year-olds from the Programme for International Student Assessment (PISA) (e.g., Nagengast and Marsh, 2012 ; Marsh, 2016 ), some took sample of students at grade 4 and grade 8 from the Trends in International Mathematics and Science Study (TIMSS) (e.g., Chiu, 2012 ; Liou, 2014 ), and others assessed independent samples at different ages. They usually came out with different results. In Marsh's 2016 study, 276,165 students from PISA 2003 led to the BFLPE at −0.30, while in Preckel's study carried out in 2010, which took a sample of 722 primary school students got a weaker effect (−0.19). Liou ( 2014 ) found that the BFLPE was stronger in 8th grade students than 4th grade students, but he didn't do further moderating analysis. The second is the comparison target. In BFLPE researches, students' comparison target was assumed to be a generalized other (Marsh et al., 2008b ), which was operationalized by either class-average achievement (e.g., Huguet et al., 2009 ; Marsh et al., 2009 ; Preckel and Brull, 2010 ; Thijs et al., 2010 ) or school-average achievement (e.g., Seaton et al., 2009 ; Chiu, 2012 ; Marsh, 2016 ; Areepattamannil et al., 2017 ), and the results varied accordingly. Areepattamannil et al. ( 2017 ) assessed the school effect and got the BFLPE at −0.43, while Preckel and Brull ( 2010 ) took the class-average achievement as comparison target and got a weaker effect (−0.19). The third is ASC domain. Among the numerous researches about ASC in the BFLPE, some focused on general ASC (e.g., Marsh et al., 2008b ; Albert and Dahling, 2016 ), while others were interested in domain-specific ASC (e.g., Huguet et al., 2009 ; Jansen et al., 2014 ), and the size of the effect varies correspondingly. For example, Marsh et al. ( 2008b ) measured general ASC and math ASC in two independent samples simultaneously, while the former got the effect of −0.20, and the latter was −0.44.

In addition to above-mentioned three potential moderators, other study characteristic variables, such as sample size, publication year and student location that have been examined in many published meta-analysis articles were also included in the moderation analyses. Summing up, six potential moderators would be examined in this meta-analysis: student age, comparison target, ASC domain, sample size, publication year, and student location.

We present the first Meta-analysis of the BFLPE synthesizing previous researches on the BFLPE to: (1) provide an integrated effect size of the BFLPE; (2) investigate whether the size of BFLPE will change accordingly when student age changes; (3) find out whether taking class-average achievement as comparison target will lead to different effect size compared with taking school-average achievement as reference; (4) explore the influence of ASC domain on the size of BFLPE; (5) other potential moderating variables, such as sample size, publication year and student location were also examined.

Literature search

Search strategies.

We systematically searched the quantitative studies evaluating the effect of class- or school-average achievement on student ASC. To find all articles that met our criteria, we conducted a literature search using the Educational Database, Research Library, Psychology Database, PsycARTICLES, PsycINFO, and ERIC. Each database was searched using the following key terms: Big fish little pond or academic self-concept in the abstract and average in the full text. We searched for all full-text and peer-review articles written in English and published from January 1st 1984 to January 1st 2018. Because the BFLPE was first put forward by Marsh and Parker ( 1984 ). The initial search revealed 386 articles in total.

Inclusion and exclusion criteria

Articles were included based on the following criteria: (1) quantitative researches whose topic was the BFLPE on student ASC; (2) used the classic BFLPE model that test the class/school effect after controlling for student effect; (3) explicitly reported the regression coefficients of class/school average achievement on student ASC; (4) provided detailed information about class/school that was taken as the comparison target; (5) results derived from subjects with intellectual disability or learning disability were not considered here.

This preliminary selection procedure resulted in 39 studies. After excluding the studies using the same data resource, we got 33 studies in total with 56 effect sizes ( N = 1,276,838) in the end. The whole process was based on PRISMA and detailed information about the process through literature search, study selection, and study inclusion for the meta-analysis was illustrated in Figure ​ Figure1 1 .

Flow diagram showing the process through the literature search, study selection, and study inclusion for the meta-analysis.

Coding procedures

Outcome variable.

We focus on the effect of class- or school-average achievement on student ASC, so the multilevel regression coefficients β and sample size of each study were recorded.

Regression coefficients were coded based on an independent sample, and separately coded if a study had several independent samples. Besides, if a study included repeated measurement experiments at different time, the result retrieved from the last measurement would be chosen.

Potential moderating variables

Six potential moderators would be examined in this meta-analysis: student age, comparison target, ASC domain, sample size, publication year and student location.

These 33 studies were carefully coded for the following variables.

• Student Age. Student age was coded as “primary school,” “middle school,” “high school,” or “college.”
• Comparison target. The comparison target was recorded as “school” or “class.”
• ASC domain. The domain that student ASC was measured was recorded as “general,” “verbal,” or “STEM” (Science, Technology, Engineering, Mathematics). For example, studies using measuring scales that contain statements like “I am good at English/French/Verbal” would be codes as “verbal.”
• Sample size.
• Pub-year. The publication year was recorded.
• Student location. The student location refers to the area where participants come from, it was coded as “Asia,” “Europe,” “North America,” “Oceania,” or “Mix.”

We didn't consider student gender because the BFLPE was tested to be robust over gender (Marsh and Hau, 2003 ). And the type of measuring tool was not considered because this variable can't be categorized that many researchers just reported the achievement measure as quote from some International Education Survey Project or offered vague information about item type, so we didn't examine its moderating effect here. The coding was conducted by two researchers twice with an interval of 2 months.

Statistical analysis

Effect size.

Comprehensive Meta-Analysis software program version 3.0 was used to conduct the meta-analysis. Each regression coefficient was transformed into a Fisher's Z score as an effect size (ES), and all weighted mean ESs and corresponding confidence intervals were converted back at last for a better understanding.

Heterogeneity

Cochran's Q -Test and the I 2 statistic were used for the homogeneity test. Moderator analyses were conducted after the homogeneity test. I 2 values of 0–25% were interpreted as no heterogeneity, 25–50% as low heterogeneity, 50–75% as moderate heterogeneity, and 75–100% as high heterogeneity among studies.

Publication bias

The funnel plot and Egger regression test were used to test whether the results were biased due to different publication sources.

Characteristic of the studies included

Study name (presented as “first author's last name & publication year”), regression coefficient, N (sample size), ES (effect size) and student age of each study included are reported in Table ​ Table1. 1 . Comparison target, ASC domain, and student location are reported in Table ​ Table2 2 .

Summary of studies included in the meta-analysis (1).

We use first author and publication year to represent each study, and if one study provided more than one effect size, we use ①,②,③,④ to indicate .

Regression coefficient refers to original coefficient in each study .

The formula to get the effect size: Z β = 0.5 × l n ( 1 + β 1 - β ) .

Summary of studies included in the meta-analysis (2).

STEM, refers to Science, Technology, Engineering, Mathematics .

A total of N = 1,276,838 were involved in the included 33 studies, and 56 ESs were coded out of the studies.

Thirty-nine of the ESs were based on Large-scale assessments (7 for PISA, 6 for TIMSS, and 26 for other assessments like TOSCA), other 17 were retrieved from studies collecting data independently.

Seven of the ESs were based on students from Asia (4 for Singapore, 1 for United Arab Emirates, and 2 for Taiwan, China), 29 were based on Europe students (19 for Germany, 3 for Belgium, 2 for France, 1 for Netherlands, 1 for Norway, 2 for Poland, and 1 for UK), 10 were based on North America students, 1 was based on Oceanian students and 9 were Mix (e.g., from 27 countries).

Fourteen of the ESs were based on general ASC, 30 were based on STEM ASC (22 for mathematics ASC, 8 for science ASC), and 12 were based on verbal ASC (4 for French ASC, 6 for English ASC, 2 for general verbal ASC).

As we can see from Figure ​ Figure2, 2 , the Funnel plot showed that all the 56 ESs are evenly distributed on both sides and gather at the top of the plot, and the Egger regression revealed no significant bias with t = 0.32 ( df = 54, p > 0.05). Together, we can conclude that the results were not biased due to the publication sources.

Funnel plot.

Mean effect size

The homogeneity test results were Q = 25,478.88 ( df = 55, p < 0.001), I 2 = 99.78%, so the random effects model was chosen. The integrated results showed a significant negative effect of class/school average achievement on student ASC: β = −0.28 ( Z = −13.84, p < 0.001, 95% CI = [−0.32, −0.24]), which means that students in class/school with an average ability level one standard deviation above the mean have ASC that is 0.28 of a standard deviation below the average ASC level. These effect sizes were suitable for subsequent moderator analyses.

Moderator analyses

Student age.

The mixed effects model was chosen here. As showed in Table ​ Table3, 3 , the main effect of student age was significant: Z = −17.56, p < 0.001, and the heterogeneity test was significant with Q = 7.86 ( df = 3, p < 0.05), which meant that student age significantly moderates the BFLPE. From Table ​ Table3, 3 , we can also see that students in high school indicate the strongest effect (β highschool = −0.32), while middle school and college students show a moderate effect (β middleschool = −0.28, β college = −0.23), and primary school students show the weakest effect (β primaryschool = −0.21). These results indicated that the BFLPE is the strongest when students in high school, weaker in middle school and college, and shows the weakest in primary school.

Student age as moderator of the BFLPE.

CI, Confidence Interval; * p < 0.05, ** p < 0.01,

Comparison target

There was no significant influence of comparison target: Q = 0.01 ( df = 1, p > 0.05), which meant that whether the study takes class-average achievement or school-average achievement as comparison target has little influence on the size of BFLPE.

Academic self-concept domain

As showed in Table ​ Table4, 4 , the main effect of ASC domain was significant: Z = −15.62, p < 0.001, and the heterogeneity test was significant with Q = 7.23 ( df = 2, p < 0.05), which meant that ASC domain significantly moderates the BFLPE. From Table ​ Table4, 4 , we can also see that verbal ASC indicates the strongest effect (β verbalASC = −0.31), while STEM ASC shows moderate effect (β STEMASC = −0.30), and general ASC shows the weakest effect (β generalASC = −0.22). These results indicated that the BFLPE varies with the domain of ASC and indicates strongest when verbal ASC is considered.

ASC domain as moderator of the BFLPE.

Sample size

Meta-regression showed that there was no significant influence of sample size with Q = 0.00 ( df = 1, p > 0.05).

Publication year

Meta-regression showed that there was no significant influence of publication year with Q = 0.35 ( df = 1, p > 0.05).

Student location

As showed in Table ​ Table5, 5 , the main effect of student location was significant: Z = −14.56, p < 0.001, and the heterogeneity test was significant with Q = 11.07 ( df = 4, p < 0.05), which meant that student location significantly moderates the BFLPE. From Table ​ Table5, 5 , we can also see that Asian students indicate the strongest effect (β Asia = −0.35), while North American students show the weakest effect (β NorthAmerica = −0.20), and students in Europe, Oceania and mixed countries show the moderate effect (β Europe = −0.30, β Oceania = −0.27, β Mix = −0.26). These results indicated that the BFLPE varies with student location of participants and indicates strongest in Asia.

Student location as moderator of the BFLPE.

As the first meta-analysis of the BFLPE, this paper presents a new perspective into this theory and provides a reliable synthesized result of the effect size of the BFLPE based on empirical researches. More importantly, six potential moderators were examined and student age was found to significantly moderate the BFLPE.

The combined results show a significant negative effect of class/school average achievement on student ASC: β = −0.28 ( Z = −13.84, p < 0.001, 95% CI = [−0.32, −0.24]), which means that students in class/school with an average ability level one standard deviation above the mean have ASC that is 0.28 of a standard deviation below the average ASC level. The result confirms that the BFLPE is prevailing and robust in educational psychology, as supported by many other cross-culturable empirical studies (Marsh et al., 2014 , 2015 ; Marsh, 2016 ).

The results of the meta-analysis contribute to the BFLPE realm both theoretically and practically. First of all, confirmation of the persistence of the BFLPE demonstrates the point that students' perception of oneself can be understood in consideration of social comparison theory, which argues that unpleasant social comparison experienced in higher ability educating environment may induce lower ASC (Marsh et al., 1995 ; Huguet et al., 2009 ). Since there lacks less able students to make favorable comparison with and overflows with more able students in a highly capable group, it is possible for students to experience uncertainty about one's own ability and ambiguity in verifying their own competence, which may induce lower ASC. Second, the BFLPE could give explanations for educational phenomena. For example, average students in general classes or schools always have more positive ASC than those abler ones attending advanced placement, which can be interpreted by the BFLPE that the former usually rank favorably in their local environment, while the latter frequently rank unfavorably with much more high-quality peers in their surroundings. Last but not least, negative consequences of being in a more competitive educational setting should not be ignored. From the perspective of parents who consider sending their children to high-achieving schools or transferring children to advanced classes, they should be informed of the potential negative consequence on ASC; as for educators, understanding how ASC might be influenced by the BFLPE can facilitate application of appropriate teaching practices, so that they can help students develop proper ASC, which is necessary for fine academic development. It has been demonstrated that differentiated instruction strategies can be used to attenuate the BFLPE (Roy et al., 2015 ); besides, it reveals the necessity of special education classes or schools: when disadvantaged students are put in regular schools/classes, they are very likely to suffer from low ASC for being small fishes in the big pond.

Moderating role of student age

The BFLPE was found significant in all age groups in this study, from primary school to college, which coincides with the point that the BFLPE is more likely to occur in elementary (primary) school, during when children are in the formatting self-concepts (Marsh, 1987 ).

Moreover, this meta-analysis found that student age significantly moderates the BFLPE, that is, the BFLPE is the strongest when students in high school, weaker in middle school and college, and shows the weakest in primary school. It coincides with past assumptions that inferring a person's ability is a process underlying ASC, and only those who have developed the most differentiated conceptions of ability are able to infer other's ability based on their achievement and efforts (Marsh, 1984 ). Besides, social comparison that plays an important role in the BFLPE largely correlates with cognitive development.

Early adolescents, as primary school students in this study, begin to master social comparison, but still lack the ability to integrate different information about themselves (Harter, 2003 ), so they show a significant BFLPE but very small in size. As their cognitive skills and academic pressure grow, the effect size increases a bit in middle school. Students in college are old and experienced enough to get rid of relying too much on others, which means that they are capable to assess their own academic skills independent of the performance of their classmates (Marsh, 1987 ; Becker and Neumann, 2016 ), so the decline happens in the BFLPE. As for high school students' strongest effect, we can explain it in two ways. First, the tracking effect. Academic tracking system has been the most-implemented curriculum delivery model in almost all schools, which mostly happens during high school (Lüdtke et al., 2006 ; Falkenstein, 2007 ; Liu and Wang, 2008 ; Wouters and Fraine, 2010 ; Houtte and Stevens, 2015 ; Salchegger, 2016 ; Dumont et al., 2017 ). The academic tracking system divides students into class/school levels for low, medium, and high achievers in each grade based on past performance, which may increase the chances of experiencing unpleasant comparison for students in intermediate-track or high-track schools; second, high school students are experiencing a period of life characterized by increased self-consciousness, and they always face more academic pressure. So synthetically considering, students in this age group would be much more influenced by the class/school-average ability.

These results suggest that the BFLPE is an age-based process, which occurs at primary school age and reaches peak value during high school. Considered that ASC in high school has been found to be more salient than actual academic achievement in predicting learning effort, educational and occupational aspirations, and subsequent university course selection (Guay et al., 2004 ; Marsh et al., 2008a ), special caution from teachers and parents should be paid for high school students, who are at risk of suffering the strongest BFLPE.

Moderating role of academic self-concept domain

The BFLPE was found significant in all three domains of ASC and the size of the BFLPE was found to vary by different ASC domains: general ASC resulted the lowest effect, verbal ASC showed the strongest effect, and STEM ASC indicated medium effect.

In 1976, Shavelson, Hubner, and Stanton presented the Shavelson model (cf. Byrne and Worth Gavin, 1996 ), which posited ASC to be hierarchically organized, with general ASC at the apex of the hierarchy. Empirical researches strongly support the hypotheses of the hierarchical organization (Marsh et al., 1988 ; Marsh, 1990 ; Martin et al., 2010 ). General ASC is regarded as relatively stable competence beliefs that is independent of the situation (e.g., Scherbaum et al., 2006 ). Besides, general ASC is found to directly influences domain-general and subject-specific measures of ASC. Hence, general ASC directly accounts for a substantial amount of variance in all measures of ASC (Martin et al., 2010 ). Summing up the above, general ASC has the ability to maintain relative stability, so it may suffer less from the negative effect of class/school average achievement.

There exists clear distinction between verbal ASC and STEM ASC (Marsh, 1986 ). Compared with STEM ASC, verbal ASC exposes more to external comparison. Generally speaking, various language activities will be held in class or school, which will bring rich success-failure experience, so that students more frequently compare their own verbal abilities with the perceived abilities of other students in their frame of reference and use this external impression as one basis of their self-perceptions of verbal ASC. Besides, external observers usually form the evaluation of one's verbal ability based on their speaking skills, which in turns lead to change in verbal ASC. Thus, verbal ASC may be more easily influenced by the average ability of classmates or schoolmates, which will show the strongest BFLPE.

Moderating role of student location

The BFLPE was found significant in all student locations here, which verifies the BFLPE is intercultural and stable (Marsh and Hau, 2003 ). The result also reveals that learning to avoid the negative effect of the BFLPE is necessary for educators from all countries.

Besides, the size of BFLPE was found to be strongest for Asian students and weakest in North America. Asian participants here were most from Taiwan, China and Singapore, which are highly industrialized and always perform outstandingly in international large-scale assessments (Liou, 2014 ). The possible reason for the strongest relation between class/school-average achievement and ASC may be the cultural difference. Seaton et al. ( 2009 ) put forward that the size of BFLPE varies across countries and the different population may lead to different patterns between student ASC and achievement (Liou, 2014 ). Most Asian students are raised up in surroundings highly value academic achievement while students from other student locations face less academic stress than Asian ones, and Asian schools always emphasize the competition with their peers, so they may compare with classmates and schoolmates more frequently, besides, Asian students are found to have a high level of test anxiety and self-doubt compared with their counterparts (Stankov, 2010 ), which result in the strongest BFLPE in Asian students.

The non-significant moderating effect of sample size, and publication year reveal that the size of the BFLPE doesn't vary as sample size or publication year changes, which confirm the BFLPE's universality and robustness (Marsh et al., 2014 , 2015 ; Marsh, 2016 ).

Limitations

There exists an apparent gap between the number of different comparison targets (39 for school-average achievement and 17 for class-average achievement). This may result in the insignificant result in the moderation analyses, so future research can broaden the scope of literature search to obtain enough studies. Furthermore, the dependence of ESs caused by deriving more than one ES from a study or from studies conducted by the same research team was not examined here, which can be further discussed using a multilevel model.

Future research

Regarding the direction of future research, the possible moderating role of student ability can be taken into consideration. Although the BFLPE was found in students across different level of ability (Marsh and Hau, 2003 ), some researches (Marsh and Rowe, 1996 ; Trautwein et al., 2009 ) found that the ASC of relatively high-achieving students appear to be less affected by BFLPE than those of relatively low-achieving students. Roy et al. ( 2015 ) also found that significant BFLPE was only for students with low individual achievement and for whom teachers reported less frequent use of differentiated instruction strategies. So, it is worth exploring whether the BFLPE is moderated by students' ability level.

This research made these main contributions: (1) presents a new perspective of the BFLPE by conducting a meta-analysis, which goes beyond prior work by providing a reliable quantitative conclusion of the BFLPE; (2) examines six potential moderating variables and identifies three moderators of the BFLPE: student age, student location and ASC domain. The findings help further the understanding of the BFLPE and make it clear that BFLPE is an age-based process, which occurs at primary school age and reaches peak value during high school. Besides, the BFLPE varies with student location and ASC domain, indicating strongest when verbal ASC is considered and for Asian students. Furthermore, these findings have utility for educators. A better understanding of these processes may enable teachers to better motivate students and provides credible reinforcement to seek measures to reduce the negative BFLPE.

Author contributions

JF, XH, and MZ came up with the experiment ideas. JF and FH did literature research. JF, XH, and ZL analyzed experimental results. JF and QY wrote the manuscript.

Conflict of interest statement

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.

Funding. This work was supported by the South China Normal University (The growth model of students in Guangdong Province, grant number 538/339124).

• Albert M. A., Dahling J. J. (2016). Learning goal orientation and locus of control interact to predict academic self-concept and academic performance in college students . Pers. Indiv. Diff. 97 , 245–248. 10.1016/j.paid.2016.03.074 [ CrossRef ] [ Google Scholar ]
• Areepattamannil S., Khine M. S., Al Nuaimi S. (2017). The big-fish-little-pond effect on mathematics self-concept: evidence from the United Arab Emirates . J. Adolesc. 59 , 148–154. 10.1016/j.adolescence.2017.06.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Arens A. K., Watermann R. (2015). How an early transition to high-ability secondary schools affects students' academic self-concept: contrast effects, assimilation effects, and differential stability . Learn. Indiv. Diff. 37 , 64–71. 10.1016/j.lindif.2014.11.007 [ CrossRef ] [ Google Scholar ]
• Becker M., Neumann M. (2016). Context-related changes in academic self-concept development: on the long-term persistence of big-fish-little-pond effects . Learn. Instruct. 45 , 31–39. 10.1016/j.learninstruc.2016.06.003 [ CrossRef ] [ Google Scholar ]
• Byrne B. M., Worth Gavin D. A. (1996). The Shavelson model revisited: testing for the structure of academic self-concept across pre-, early, and late adolescents . J. Educ. Psychol. 88 , 215–228. 10.1037/0022-0663.88.2.215 [ CrossRef ] [ Google Scholar ]
• Card N. A. (2012). Applied Meta-Analysis for Social Science Research . New York, NY: The Guilford Press. [ Google Scholar ]
• Chiu M. S. (2012). The internal/external frame of reference model, big-fish-little-pond effect, and combined model for mathematics and science . J. Educ. Psychol. 104 , 87–107. 10.1037/a0025734 [ CrossRef ] [ Google Scholar ]
• Dumont H., Protsch P., Jansen M., Becker M. (2017). Fish swimming into the ocean: how tracking relates to students' self-beliefs and school disengagement at the end of schooling . J. Educ. Psychol. 109 , 855–870. 10.1037/edu0000175 [ CrossRef ] [ Google Scholar ]
• Falkenstein R. (2007). Student Experiences of Participation in Tracked Classes Throughout High School: The Ethic of Justice, School Leadership, and Curriculum Design . Electronic Thesis or Dissertation. Available online at: https://etd.ohiolink.edu/ [ Google Scholar ]
• Guay F., Larose S., Boivin M. (2004). Academic self-concept and educational attainment level: a ten-year longitudinal study . Self Identity , 3 , 53–68. 10.1080/13576500342000040 [ CrossRef ] [ Google Scholar ]
• Guay F., Marsh H. W., Boivin M. (2003). Academic self-concept and academic achievement: a developmental perspective on their causal ordering . J. Educ. Psychol. 95 , 124–136. 10.1037/0022-0663.95.1.124 [ CrossRef ] [ Google Scholar ]
• Harter S. (2003). The development of self-representations during childhood and adolescence , in Handbook of Self and Identity , eds Leary M. R., Tangney, Price J. (New York, NY: Guilford Press; ), 610–642. [ Google Scholar ]
• Houtte M. V., Stevens P. (2015). Tracking and sense of futility: the impact of between-school tracking versus within-school tracking in secondary education in flanders (belgium) . Br. Educ. Res. J. 41 , 782–800. 10.1002/berj.3172 [ CrossRef ] [ Google Scholar ]
• Huguet P., Dumas F., Marsh H., Wheeler L., Seaton M., Nezlek J., et al.. (2009). Clarifying the role of social comparison in the big-fish-little-pond effect (BFLPE): an integrative study . J. Pers. Soc. Psychol. 97 , 156. 10.1037/a0015558 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jansen M., Schroeders U., Lüdtke O. (2014). Academic self-concept in science: multidimensionality, relations to achievement measures, and gender differences . Learn. Indiv. Diff. 30 , 11–21. 10.1016/j.lindif.2013.12.003 [ CrossRef ] [ Google Scholar ]
• Jansen M., Schroeders U., Lüdtke O., Marsh H. W. (2015). Contrast and assimilation effects of dimensional comparisons in five subjects: an extension of the I/E model . J. Educ. Psychol. 107 , 1086–1101. 10.1037/edu0000021 [ CrossRef ] [ Google Scholar ]
• Liem G. A. D., Yeung A. S. (2013). The big-fish-little-pond effect and a national policy of within-school ability streaming alternative frames of reference . Am. Educ. Res. J. 50 , 326–370. 10.3102/0002831212464511 [ CrossRef ] [ Google Scholar ]
• Liou P. (2014). Investigation of the big-fish-little-pond effect on students' self-concept of learning mathematics and science in Taiwan: Results from TIMSS 2011 . Asia Pacific Educ. Res. 23 , 769–778. 10.1007/s40299-013-0152-3 [ CrossRef ] [ Google Scholar ]
• Liu W. C., Wang C. K. J. (2008). Home environment and classroom climate: an investigation of their relation to students' academic self-concept in a streamed setting . Curr. Psychol. 27 , 242–256. 10.1007/s12144-008-9037-7 [ CrossRef ] [ Google Scholar ]
• Lohbeck A., Moller J. (2017). Social and dimensional comparison effects on math and reading self-concepts of elementary school children . Learn Individ. Diff. 54 , 73–81. 10.1016/j.lindif.2017.01.013 [ CrossRef ] [ Google Scholar ]
• Lüdtke O., Marsh H. W., Köller O., Baumert J. (2006). Tracking, grading, and student motivation: using group composition and status to predict self-concept and interest in ninth-grade mathematics . J. Educ. Psychol. 98 , 788–806. 10.1037/0022-0663.98.4.788 [ CrossRef ] [ Google Scholar ]
• Marsh H. W. (1984). Self-concept: the application of a frame of reference model to explain paradoxical results . Aust. J. Educ. 28 , 165–181. 10.1177/000494418402800207 [ CrossRef ] [ Google Scholar ]
• Marsh H. W. (1986). Verbal and math self-concepts: an internal/external frame of reference model . Am. Educ. Res. J. 23 , 129–149. [ Google Scholar ]
• Marsh H. W. (1987). The big-fish-little-pond effect on academic self-concept . J. Educ. Psychol. 79 , 280–295. 10.1037/0022-0663.79.3.280 [ CrossRef ] [ Google Scholar ]
• Marsh H. W. (1988). Influences of internal and external frames of reference on the formation of math and English self-concepts . J. Educ. Psychol. 82 , 107–116. 10.1037/0022-0663.82.1.107 [ CrossRef ] [ Google Scholar ]
• Marsh H. W. (1990). The structure of academic self-concept: the Marsh/Shavelson model . J. Educ. Psychol. 82 , 623–636. 10.1037//0022-0663.82.4.623 [ CrossRef ] [ Google Scholar ]
• Marsh H. W. (1994). Using the national longitudinal study of 1988 to evaluate theoretical models of self-concept: the self-description questionnaire . J. Educ. Psychol. 86 , 439–456. 10.1037/0022-0663.86.3.439 [ CrossRef ] [ Google Scholar ]
• Marsh H. W. (2016). Cross-cultural generalizability of year in school effects: negative effects of acceleration and positive effects of retention on academic self-concept . J. Educ. Psychol. 108 , 256–273. 10.1037/edu0000059 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Abduljabbar A. S., Morin A. J. S., Parker P., Abdelfattah F., Nagengast B., et al. (2015). The big-fish-little-pond effect: generalizability of social comparison processes over two age cohorts from Western, Asian, and middle Eastern Islamic countries . J. Educ. Psychol. 107 , 258–271. 10.1037/a0037485 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Byrne B. M., Shavelson R. J. (1988). A multifaceted academic self-concept: its hierarchical structure and its relation to academic achievement . J. Educ. Psychol. 80 , 366–380. 10.1037/0022-0663.80.3.366 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Chessor D., Craven R., Roche L. (1995). The effects of gifted and talented programs on academic self-concept: the big fish strikes again . Am. Educ. Res. J. 32 , 285–319. 10.3102/00028312032002285 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Craven R. G. (2006). Reciprocal effects of self-concept and performance from a multidimensional perspective: beyond seductive pleasure and unidimensional perspectives . Perspect. Psychol. Sci. 1 , 133–163. 10.1111/j.1745-6916.2006.00010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Hau K. T. (2003). Big-fish-little-pond effect on academic self-concept. a cross-cultural (26-country) test of the negative effects of academically selective schools . Am. Psychol. 58 , 364–376. 10.1037/0003-066X.58.5.364 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Koller O., Baumert J. (2001). Reunification of East and West German school systems: longitudinal multilevel modelling study of the big-fish-little-pond effect on academic self-concept . Am. Educ. Res. J. 38 , 321–350. 10.3102/00028312038002321 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Kong C. K., Hau K. T. (2000). Longitudinal multilevel models of the big-fish-little-pond effect on academic self-concept: counterbalancing contrast and reflected-glory effects in Hong Kong schools . J. Pers. Soc. Psychol. 78 , 337–349. [ PubMed ] [ Google Scholar ]
• Marsh H. W., Kuyper H., Morin A. J. S., Parker P. D., Seaton M. (2014). Big-fish-little-pond social comparison and local dominance effects: integrating new statistical models, methodology, design, theory and substantive implications . Learn. Instruct. 33 , 50–66. 10.1016/j.learninstruc.2014.04.002 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Lüdtke O., Robitzsch A., Traütwein U., Asparouhov T., Muthén B. O., et al.. (2009). Doubly-latent models of school contextual effects: Integrating multilevel and structural equation approaches to control measurement and sampling error . Multivariate Behav. Res. 44 , 764–802. 10.1080/00273170903333665 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Marsh H. W., O'Mara A. J. (2010). Long-term total negative effects of school-average ability on diverse educational outcomes: direct and indirect effects of the big-fish-little-pond effect . German J. Educ. Psychol. 24 , 51–72. 10.1024/1010-0652.a000004 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Parker J. W. (1984). Determinants of student self-concept: is it better to be a relatively large fish in a small pond even if you don't learn to swim as well? J. Pers. Soc. Psychol. 47 , 213–231. 10.1037/0022-3514.47.1.213 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Rowe K. J. (1996). The negative effects of school-average ability on academic self-concept: an application of multilevel modelling the negative effects of school-average ability on academic self-concept: an application of multilevel modelling . Aust. J. Educ. 40 , 65–87. 10.1177/000494419604000105 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Seaton M., Trautwein U., Lüdtke O., Hau K. T., O'Mara A. J., et al. (2008a). The big-fish-little-pond-effect stands up to critical scrutiny: implications for theory, methodology, and future research . Educ. Psychol. Rev. , 20 , 319–350. 10.1007/s10648-008-9075-6 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Trautwein U., Lüdtke O., Baumert J., Köller O. (2007). The big-fish-little-pond effect: persistent negative effects of selective high schools on self-concept after graduation . Am. Educ. Res. J. 44 , 631–669. 10.3102/0002831207306728 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Trautwein U., Lüdtke O., Köller O. (2008b). Social comparison and big-fish-little-pond effects on self-concept and other self-belief constructs: role of generalized and specific others . J. Educ. Psychol. 100 , 510–524. 10.1037/0022-0663.100.3.510 [ CrossRef ] [ Google Scholar ]
• Marsh H. W., Trautwein U., Lüdtke O., Koller O., Baumert J. (2005). Academic self-concept, interest, grades, and standardized test scores: Reciprocal effects models of causal ordering . Child Dev. 76 , 397–416. 10.1111/j.1467-8624.2005.00853.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Martin B., Ulrich K., Christophe D., Monique R., Sonja U., Antoine F., et al. (2010). The structure of academic self-concepts revisited: the nested Marsh/Shavelson model . J. Educ. Psychol. 102 , 964–981. 10.1037/a0019644 [ CrossRef ] [ Google Scholar ]
• Möller J., Pohlmann B., Köller O., Marsh H. W. (2009). A meta-analytic path analysis of the internal/external frame of reference model of academic achievement and academic self-concept . Rev. Educ. Res. 79 , 1129–1167. 10.3102/0034654309337522 [ CrossRef ] [ Google Scholar ]
• Nagengast B., Marsh H. W. (2011). The negative effect of school-average ability on science self-concept in the UK, the UK countries and the world: the Big-Fish-Little-Pond-Effect for PISA 2006 . Educ. Psychol. 31 , 629–656. 10.1080/01443410.2012.696353 [ CrossRef ] [ Google Scholar ]
• Nagengast B., Marsh H. W. (2012). Big fish in little ponds aspire more: mediation and cross-cultural generalizability of school-average ability effects on self-concept and career aspirations in science . J. Educ. Psychol. 104 , 1033–1053. 10.1037/a0027697 [ CrossRef ] [ Google Scholar ]
• Niepel C., Brunner M., Preckel F. (2014). The longitudinal interplay of students' academic self-concepts and achievements within and across domains: replicating and extending the reciprocal internal/external frame of reference model . J. Educ. Psychol. 106 , 1170–1191. 10.1037/a0036307 [ CrossRef ] [ Google Scholar ]
• Parker P. D., Marsh H. W., Lüdtke O., Trautwein U. (2013). Differential school contextual effects for math and english: integrating the big-fish-little-pond effect and the internal/external frame of reference . Learn. Instruct. 23 , 78–89. 10.1016/j.learninstruc.2012.07.001 [ CrossRef ] [ Google Scholar ]
• Pinxten M., De Fraine B., Van Damme J., D'Haenens E. (2010). Causal ordering of academic self-concept and achievement: effects of type of achievement measure . Brit. J. Educ. Psychol. 80 , 689–709. 10.1348/000709910X493071 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Plieninger H., Dickhäuser O. (2015). The female fish is more responsive: gender moderates the bflpe in the domain of science . Educ. Psychol. 35 , 213–227. 10.1080/01443410.2013.814197 [ CrossRef ] [ Google Scholar ]
• Preckel F., Brull M. (2010). The benefit of being a big fish in a big pond: contrast and assimilation effects on academic self-concept . Learn. Individ. Differ. 20 , 522–531. 10.1016/j.lindif.2009.12.007 [ CrossRef ] [ Google Scholar ]
• Preckel F., Goetz T., Pekrun R., Kleine M. (2008). Gender differences in gifted and average-ability students . Gifted Child Quart. 52 , 146–159. 10.1177/0016986208315834 [ CrossRef ] [ Google Scholar ]
• Roy A., Guay F., Valois P. (2015). The big-fish–little-pond effect on academic self-concept: the moderating role of differentiated instruction and individual achievement . Learn. Individ. Differ. 42 , 110–116. 10.1016/j.lindif.2015.07.009 [ CrossRef ] [ Google Scholar ]
• Salchegger S. (2016). Selective school systems and academic self-concept: how explicit and implicit school-level tracking relate to the big-fish—little-pond effect across cultures . J. Educ. Psychol. 108 , 405–423. 10.1037/edu0000063 [ CrossRef ] [ Google Scholar ]
• Scherbaum C. A., Cohen-Charash Y., Kern M. J. (2006). Measuring general self-efficacy: a comparison of three measures using item response theory . Educ. Psychol. Meas. 66 , 1047–1063. 10.1177/0013164406288171 [ CrossRef ] [ Google Scholar ]
• Scherer R., Siddiq F. (2015). The big-fish–little-pond-effect revisited: do different types of assessments matter? Comput. Educ. 80 , 198–210. 10.1016/j.compedu.2014.09.003 [ CrossRef ] [ Google Scholar ]
• Seaton M., Marsh H. W., Craven R. G. (2009). Earning its place as a pan-human theory: universality of the big-fish-little-pond effect across 41 culturally and economically diverse countries . J. Educ. Psychol. 101 , 403–419. 10.1037/a0013838 [ CrossRef ] [ Google Scholar ]
• Stäbler F., Dumont H., Becker M., Baumert J. (2017). What happens to the fish's achievement in a little pond? A simultaneous analysis of class-average achievement effects on achievement and academic self-concept . J. Educ. Psychol. 109 , 191–207. 10.1037/edu0000135 [ CrossRef ] [ Google Scholar ]
• Stankov L. (2010). Unforgiving Confucian culture: a breeding ground for high academic achievement, test anxiety and self-doubt? Learn. Individ. Differ. 20 , 555–563. 10.1016/j.lindif.2010.05.003 [ CrossRef ] [ Google Scholar ]
• Sung Y. T., Huang L. Y., Tseng F. L., Chang K. E. (2014). The aspects and ability groups in which little fish perform worse than big fish: examining the big-fish-little-pond effect in the context of school tracking . Contemp. Educ. Psychol. 39 , 220–232. 10.1016/j.cedpsych.2014.05.002 [ CrossRef ] [ Google Scholar ]
• Szumski G., Karwowski M. (2015). Emotional and social integration and the big-fish-little-pond effect among students with and without disabilities . Learn. Individ. Differ. 43 , 63–74. 10.1016/j.lindif.2015.08.037 [ CrossRef ] [ Google Scholar ]
• Thijs J., Verkuyten M., Helmond P. (2010). A further examination of the big-fish-little-pond effect: perceived position in class, class size, and gender comparisons . Sociol. Educ. 83 , 333–345. 10.1177/0038040710383521 [ CrossRef ] [ Google Scholar ]
• Traütwein U., Ludtke O., Koller O., Baumert J. (2006). Self-esteem, academic self-concept, and achievement: how the learning environment moderates the dynamics of self-concept . J. Personality Soc. Psychol. 90 , 334–349. 10.1037/0022-3514.90.2.334 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trautwein U., Lüdtke O., Marsh H. W., Nagy G. (2009). Within-school social comparison: how students perceive the standing of their class predicts academic self-concept . J. Educ. Psychol. 101 , 853–866. 10.1037/a0016306 [ CrossRef ] [ Google Scholar ]
• Valentine J. C., Dubois D. L. (2005). Effects of self-beliefs on academic achievement and vice versa. Separating the chicken from the egg , in International Advances in self Research: New Frontiers for Self Research . Vol.2 , eds Marsh H. W., Craven R. G., McInerney D. M. (Greenwich, CT: Information Age; ), 53–78. [ Google Scholar ]
• Wouters S., Fraine B. D. (2010). The effect of track changes on the development of academic self-concept in high school: a dynamic test of the big-fish-little-pond effect . J. Educ. Psychol. 104 , 793–805. 10.1037/a0027732 [ CrossRef ] [ Google Scholar ]
• Wouters S., Germeijs V., Colpin H., Verschueren K. (2011). Academic self-concept in high school: predictors and effects on adjustment in higher education . Scand. J. Psychol. 52 , 586–594. 10.1111/j.1467-9450.2011.00905.x [ PubMed ] [ CrossRef ] [ Google Scholar ]

We have emailed you a PDF version of the article you requested.

Can't find the email?

Please check your spam or junk folder

You can also add [email protected] to your safe senders list to ensure you never miss a message from us.

An Artist Placed Goldfish In Blenders And Asked Visitors To Turn Them On – They Did

Complete the form below and we will email you a PDF version

Cancel and go back

IFLScience needs the contact information you provide to us to contact you about our products and services. You may unsubscribe from these communications at any time.

For information on how to unsubscribe, as well as our privacy practices and commitment to protecting your privacy, check out our Privacy Policy

Complete the form below to listen to the audio version of this article

Advertisement

Subscribe today for our  Weekly Newsletter  in your inbox!

Jack Dunhill

Social Media Coordinator and Staff Writer

Jack is a Social Media Coordinator and Staff Writer for IFLScience, with a degree in Medical Genetics specializing in Immunology.

Book View full profile

Book Read IFLScience Editorial Policy

DOWNLOAD PDF VERSION

Across human history, there have been examples of people doing utterly terrible things for no other reason than the fact that they can. The Stanford Prison experiment showed that given a little power, a person would easily begin to mistreat peers under their own free will; a public art piece by Marina Abramovich showed that simply giving a gun and instructions to “do as they wish” to random bystanders was enough to make them almost commit murder. 

These are just a couple of examples of how easy it is to get regular humans to do awful things, and one museum "experiment" in 2000 took things just as far. 

Debuting at the Trapholt Museum, Denmark, an "art" piece called “Helena & El Pascador” by the somewhat-infamous Marco Evaristti presented museum-goers with 10 blenders, filled with water and a single goldfish in each swimming around the blades. The visitors were given a simple choice: press the large “ON” button and kill the fish (for absolutely no reason), or don’t touch the button and let the fish live. 

The piece was intended to force people to "do battle with their conscience", according to a BBC  article.  

"It was a protest against what is going on in the world, against this cynicism, this brutality that impregnates the world in which we live," Evaristti continued.  

Perhaps if the blenders were unplugged, the piece would have made for an evocative display of morals – however, the blenders were entirely real, and the ON button actually worked. 

While most people did not press the button, at least one visitor did and killed two goldfish in a blitz of horrific cruelty.  

Quickly, complaints began pouring in over the fact that the blenders were plugged in, and the police demanded museum owner Peter Meyer to immediately unplug the blenders. Meyer refused, and the police issued a 2,000 kroner ($205 at today’s rate) fine. Protesting the fine in the name of “artistic freedom”, Mayer did not pay the fine and was dragged into court for animal cruelty. 

Shockingly, the court acquitted Meyer for animal cruelty, after veterinary testimonies explained that the fish would have died almost instantly, thus were not exposed to prolonged suffering. Meyer escaped any punishment, including the fine.  

Years on, the piece remains a disturbing glimpse into the sadism of some humans, and the fact that cruelty does not necessarily need a reason or justification. Evaristti returned years later with another deeply troubling “art” piece in which he hosted a dinner party featuring meatballs made of his own fat .  

ARTICLE POSTED IN

science and society

More Nature Stories

link to article

IFLScience The Big Questions: How Did Dinosaurs Have Sex?

Why Are People In Iceland Throwing Baby Puffins Off Cliffs?

Watch A Swimming Lion Escape From An Angry Hippo

When Was The First Fire On Earth?

Killer Whale Pirates, Hunting Aliens, And Flying Spaghetti Monsters