experiments on observational learning

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• v.27(1); 2011

Understanding Observational Learning: An Interbehavioral Approach

Mitch j fryling.

The Chicago School of Professional Psychology

Cristin Johnston

University of Nevada, Reno

Linda J Hayes

Observational learning is an important area in the field of psychology and behavior science more generally. Given this, it is essential that behavior analysts articulate a sound theory of how behavior change occurs through observation. This paper begins with an overview of seminal research in the area of observational learning, followed by a consideration of common behavior analytic conceptualizations of these findings. The interbehavioral perspective is then outlined, shedding light on some difficulties with the existing behavior analytic approaches. The implications of embracing the interbehavioral perspective for understanding the most complex sorts of behavior, including those involved in observational learning are considered.

Research in observational learning represents a critical development in the history of psychology. Indeed, the research and scholarly work conducted by Bandura and colleagues set the occasion for the social cognitive perspective of learning ( Bandura, 1986 ), which seemed to challenge the possibility that all behavior could be accounted for by respondent and operant processes alone. Toward this, the social cognitive perspective focused more explicitly on both modeling and cognition, and their role in understanding behavior. Meanwhile, behavior analysts have continued to contend that observational learning can be explained through processes of generalized imitation, conditioned reinforcement, and rule-governed behavior (e.g., Catania, 2007 ; Pear, 2001 ; Pierce & Cheney, 2008 ). However, these contentions become increasingly difficult when we take a closer look at the psychological event of interest in observational learning. Further, while behavior analysts have continued to conduct research in the area of observational learning, relatively little progress has been made toward developing a theoretical understanding of this work. The primary aim of the current paper is to consider the general findings of the observational learning research within a thoroughly naturalistic, behavioral perspective. Of course, verbal processes play an important role in understanding observational learning, and thus, they are given both general and specific treatment throughout. In pursuing this work, J. R. Kantor's philosophy of interbehaviorism and scientific system of interbehavioral psychology are reviewed. The potential benefits of embracing the interbehavioral perspective with respect to understanding observational learning and complex behavior more generally are considered.

OBSERVATIONAL LEARNING

In the 1960s and 70s Albert Bandura and his colleagues became well known for their social psychology research in the area of observational learning. Indeed, several of the early experiments in this area are very well known, and considered hallmarks in the field of psychology and behavior science (e.g., Bandura & McDonald, 1963 ; Bandura, Ross, & Ross, 1963 ). These studies were pursued for a variety of reasons; partially to undermine the value of common psychoanalytic ( Bandura & Huston, 1961 ; Bandura, Ross, et al., 1963 ) and developmental theories ( Bandura & McDonald, 1963 ), and also to evaluate the role of observation as a primary determinant of behavior change. Early studies examined the role of modeling 1 on the acquisition of aggression ( Bandura, Ross, & Ross, 1963 ) and moral judgment ( Bandura & McDonald, 1963 ), for example, and provided a foundation upon which the social cognitive theory was built. Importantly, this theory is often considered to extend beyond behavioral theories, questioning the possibility that behaviorism alone could provide a comprehensive understanding of learning. Given the importance of this research, we will now provide a brief overview of some of the general findings of studies on observational learning. It is important to note that our review is admittedly less than comprehensive, and that our primary aim is to describe some common themes within this literature.

The Role of Modeling

An early and longstanding aim of the observational learning literature is to understand the role of modeling in behavior change (e.g., Bandura & Huston, 1961 ; Bandura & McDonald, 1963 ; Bandura, Ross, & Ross, 1961 ). For example, an early study examined how the incidental behaviors of an experimenter might be acquired in the context of learning another task (Bandura & Huston). The important conclusion of these studies is that behavior change can and does occur through observation, even when such observation is incidental, occurring in the context of other activities. While this finding seems rather simple, it has significant implications for how we conceptualize learning. As we will discuss in the coming paragraphs, this general finding may present specific conceptual challenges for behavioral theories of learning.

The role of consequences

Specific emphasis was also placed on the role of consequences in the observational learning literature (e.g., Bandura, 1965 ; Bandura, Grusec, & Menlove, 1966 ; Bandura & McDonald, 1963 , Bandura, Ross, & Ross, 1963 ). Experiments that added to our understanding of the role of consequences generally compared behavior change between children who either observed a model who was rewarded, a model who was punished, or a control condition (e.g., observing non-aggressive play or observing no consequences). Generally, less behavior change is observed when a child observes a model being punished (e.g., Bandura, Ross, & Ross, 1963 ). 2

Interestingly, there is often no difference between conditions involving rewards and conditions involving no consequences at all. For example, Bandura and McDonald (1963) compared the effects of three different variables on the acquisition of moral judgment responses. In this study, the three variables involved three different groups of adult/child dyads: group one involved both the model and child's target judgments be reinforced, group two involved the model's behavior being reinforced but not the child's, and group three involved no model and only child reinforcement. Importantly, in the model/child groups trials alternated between the model and the child. Groups one and two demonstrated more behavior change than group three at a 1–3 week post-treatment assessment. Thus, the researchers concluded that modeling was the significant factor involved in the acquisition of the moral judgment repertoire. 3 Other experiments also found no difference between the reward and no consequence groups, while the model punished group continued to yield different results (e.g., Bandura, 1965 ).

Along similar lines, other studies seemed to raise questions about the potentially detrimental effects of incentives on the acquisition of behavior. For example, at the beginning of one experiment ( Bandura, Grusec, & Menlove, 1966 ) half of the participants were placed into an incentive condition where they were told that they would be given candy treats for correctly demonstrating what they learned after watching a movie. More specifically, after watching a film, children in both conditions were asked to demonstrate what they observed on the movie. Generally, the researchers found that children in the incentive condition did slightly worse than those in the no incentive condition, raising questions about the benefits of incentives on learning (see Bandura, et al., p. 505). 4

At this point we must note that the terms reward , reinforcement , and operant conditioning are used rather loosely within this literature. From a behavior analytic perspective, a stimulus change can only be classified as a reinforcer if it increases the future frequency of the class of behavior it was made contingent upon (e.g., Cooper, Heron, & Heward, 2007 ). Given this, the majority of stimulus changes called “rewards” or “reinforcers” in the observational learning literature do not technically meet the criteria to be classified as reinforcers, or as being involved in the process of reinforcement or operant conditioning in general. Nevertheless, we can say that consequences seem to play some role in observational learning. Again, there are studies suggesting that there are no differences between observation with reinforcement and observation with no consequence at all, leaving us more confident that if consequences have a role, aversive consequences seem to play a large part. Given these important concerns, however, these findings need to be interpreted with caution.

The Role of Verbal Behavior

As this line of researched progressed, increasing attention was paid to the role of cognitive factors, often described with the terms coding and rehearsal . Generally, coding can be thought of as describing what is observed in some way, whereas rehearsal can be thought of as practicing what was observed. For example, Bandura, Grusec, & Menlove. (1966) examined the effects of describing the activity of the model (“coding”) on the acquisition of observed behavior. Of specific interest, this study was fueled by motivation to discredit behavior analysts who failed to account for “delayed reproduction of modeling behavior” (p. 499), which was assumed to necessarily involve some sort of cognitive activity. In this study three groups of children all viewed a video; one group was asked to “verbalize every action of the model as it is being performed” (p. 501), the second group to “count 1 and a 2, and a 3, and a 4, and a 5” (p. 501) repeatedly while watching the video, and a third group observed without any instruction. The researchers found that those individuals who verbally described every action of the model were the most successful when tested for behavior change at a later time. Importantly, this study highlights the early recognition of “cognitive” factors in observational learning.

In an effort to elaborate upon this sort of research, Bandura and Jeffrey (1973) examined the role of “coding and rehearsal” on the acquisition of observed behavior. The researchers found that participants who “symbolically coded” (i.e., developed number or letter coding systems) the model's actions, and also immediately rehearsed (i.e., practiced) those codes had the best outcomes. Neither coding without symbolic rehearsal or symbolic rehearsal without coding was found to be sufficient. Put differently, developing a coded description of the models actions and practicing that description were both found to be important factors in the acquisition of observed behavior. Interestingly, physically practicing (“motor rehearsal”) the observed behavior was found to be less important. This seemed to support a growing distinction between different aspects of an individual's repertoire and the various processes that contribute to their existence (see below).

Learning and performance

Related to the role of verbal behavior, Bandura and colleagues began to notice a difference between the observers imitative performance at a later time compared to their ability to describe what was observed when asked. The ability to describe what was observed was viewed as a measure of learning, while engaging in the observed behavior at a later time was viewed as performance. For example, Bandura, Ross, & Ross (1963) found that children in both the aggressive-reward (participants observed a model be rewarded for engaging in a sequence of responses) and aggressive-punished (participants observed a model be punished for engaging in a sequence of responses) groups were able to describe the observed sequences of behavior, despite differences in imitative behavior change. Similarly, Bandura (1965) found that differences between group measures on imitation of observed behavior were removed on an “acquisition index,” where children were told they would get a reward for telling the experimenter what the model did. These findings further highlighted the role of verbal behavior in the process of learning from observation, including the various ways in which such learning from observation might be measured. That is, one way of measuring learning from observation is through imitation of the observed response at a later time, while another is through descriptions of the observed behavior. As these repertoires seemed to be influenced by different factors, Bandura and colleagues began to distinguish between them more and more.

Theoretical Developments

Throughout the above studies Bandura and colleagues began to articulate a theoretical model of observational learning. Fueled by findings that individuals might be able to describe observed behavior at a later time, even if they did not actually engage in the behavior themselves during a testing condition (e.g., Bandura, 1965 ; Bandura, Ross, & Ross, 1963 ), Bandura and colleagues began to distinguish between learning and performance (also see Greer, Singer-Dudek, & Gautreaux, 2006 ). Specifically, Bandura and colleagues noted that verbal processes were more likely to influence learning, 5 whereas consequences were more likely to influence the extent to which the individual's behavior changed through observation (i.e., that they actually engaged in the observed behavior). Indeed, theoretical accounts of observational learning highlight this distinction (e.g., Bandura & Jeffrey, 1973 ; Greer, Singer-Dudek, & Gautreaux, 2006 ).

Bandura and colleagues assumed that learning from observation occurred via an input-output, cognitive model. Specifically, Bandura and Jeffrey (1973) described four processes that account for learning from observation: attentional, retention, motor reproduction, and motivational. Bandura and Jeffery (1973) say, “Within this framework acquisition of modeled patterns is primarily controlled by attention and retention processes. Whereas performance of observationally learned responses is regulated by motor reproduction and incentive processes” (p. 122).

Attentional processes were described as cognitive abilities that “regulate sensory registration of modeled actions” and retention processes were those that took “transitory influences and converted to enduring internal guides for memory representation” ( Bandura & Jeffery, 1973 , p. 122). Motor reproduction processes are those that move component actions stored in memory into overt action resembling that of the modeled behaviors. Finally, motivational processes determine whether or not those behaviors emerge as overt action.

According to the authors, this model not only explains how a modeled response can be imitated immediately after it is observed, but can also explain how this behavior can be reproduced later under many different circumstances. Bandura and Jeffrey (1973) conclude, “After modeled activities have been transformed into images and readily utilizable verbal symbols, these memory codes can function as guides for subsequent reproduction” (p. 123). The authors also concluded that participants who engage in transforming modeled actions into either descriptive words or visual images achieve higher levels of observational learning than those who did not.

As a result of these and other experiments, Bandura theorized that observational learning was an integral part of human development, which accounted for the development of the personality ( Bandura & Walters, 1963 ), as well as social and antisocial behaviors in children ( Bandura, 1973 ). Importantly, this research shows that humans can learn without directly experiencing the consequences of their own actions. Thus, if behavior analysts aim to develop a comprehensive account of learning it must include an adequate description of these instances. In particular, behavior analysts must account for the acquisition of novel behavior in the absence of contingent reinforcement for the individual engaging in those responses, and also articulate the role of verbal behavior in observational learning.

In summary, the studies conducted by Bandura and colleagues seemed to question the role of rewards on the behavior of the observer. Importantly, Bandura believed that reinforcement history alone was not sufficient, and that the observation of a model was the most critical factor. Moreover, learning from observation was viewed to be a result of other processes, of which “verbal coding” was one. These general findings seemed to devalue the comprehensiveness of the behavioral position, and set the stage for the social cognitive perspective. However, it is crucial that we reiterate the fact that Bandura and colleagues often misused the terms reinforcer and reinforcement , and thus, it is difficult to draw valid conclusions about the role of consequences from this line of research. What can be said is that observational learning is an important area for behavior science to consider.

Bandura found limitations with the operant interpretation of behavior, albeit a less than thoroughly informed understanding of it. Observational learning seems to defy traditional discriminative stimulus—response—reinforcer analyses, even when more contemporary concepts (e.g., the motivating operation) are considered. Specifically, novel responses occur in observational learning models, responses that have obviously never been reinforced. Added to this, delayed responding is common, and such responding presents conceptual challenges to traditional behavioral concepts (e.g., Bandura, Grusec, & Menlove, 1966 , p. 499). As mentioned earlier, it is perhaps not surprising that Bandura's work may be considered by some to be an extension or move beyond the behavioral position. The limitations of Bandura's work not withstanding, Bandura and colleagues raised several important issues regarding the role of observation and verbal behavior in behavior change processes.

Still, Bandura's model relies upon the existence of hypothetical entities that do not exist in the spatiotemporal event matrix comprising the natural world. In other words, Bandura's theoretical constructs are not derived from events, and as such cannot be found and thereby can never actually be studied (see Kantor, 1957 ; Smith, 2007 ). Rather, they are inferences derived from a thoroughly mentalistic, dualistic worldview. Behavior analysts have long held that embracing such constructs can only distract workers from a scientific analysis (e.g., Skinner, 1953 ). It isn't surprising, then, that behavior analysts have proposed an alternative conceptualization of observational learning. In the following section we provide an overview of the behavior analytic position on observational learning.

THE BEHAVIOR ANALYTIC POSITION

The behavior analytic account of observational learning rests squarely upon the process of generalized imitation ( Baer, Peterson, & Sherman, 1967 ; Baer & Sherman, 1964 ; Pierce & Cheney, 2008 ). This is a familiar process, where the organism is asked to imitate several responses of the model (e.g., “do this” while the model is touching their nose), and after multiple exemplars have been successfully trained, the organism is asked to engage in a response which has never been modeled before. Generalized imitation is said to occur when the organism engages in a response that has never been modeled or reinforced in the past; that is, when imitation has “generalized” to new behaviors. Furthermore, it is assumed that the social community shapes up delays in imitative responses, and thus, it is said that “all instances of modeling and imitation involve the absence of the Sd” ( Pierce & Cheney, 2008 , p. 252). For example, a child might watch their favorite TV show, and at a much later time repeat a phrase from the show, perhaps while sitting in the car, and their parent might say “yes, that's what you heard on TV!”. In other words, the organism is said to learn to imitate observed behavior in the absence of any particular stimulus, and perhaps at a much later point in time. In this sense, the organism may be said to “emit” behaviors, which typically fall under the purview of generalized imitation.

Importantly, conditioned reinforcement hypotheses are also central to the behavior analytic conceptualization of observational learning and imitation in general. In this sense, behaviors that closely resemble the observed behavior of models are presumed to have a history of reinforcement, and thus, behaving in a manner which is similar to the model may become conditioned reinforcer itself. This sort of conceptualization seems to be particularly helpful toward the behavior analytic understanding of delayed imitation (see Gladstone & Cooley, 1975 ; Rosales-Ruiz & Baer, 1997 ).

Behavior analysts have also provided an account of the verbal coding that is said to participate in observational learning. For example, behavior analysts propose that individuals derive self-rules when they observe their environment (e.g., Hayes, Barnes-Holmes, & Roche, 2001 ; Hayes, Zettle, & Rosenfarb, 1989 ; Poppen, 1989 ). It is assumed that society teaches the organism to tact ( Skinner, 1957 ) relationships in their environment, and that these descriptions exert tremendous control over behavior. Indeed, it is suggested that a large amount of rule-following behavior is reinforced throughout the organisms lifetime, and when combined with a history of tact repertoires being reinforced, individuals both derive self-rules (i.e., tact if-then relations in their environment) and subsequently engage in a great deal of rule-following with respect to those rules.

For example, a child might observe a teacher praising another child for accurately matching a Spanish flashcard to the corresponding English flashcard (“Good job matching perro with dog!”). Two days later, the child who observed the incident may be asked to “match same” when given that same Spanish flashcard, and correctly place it on the corresponding English flashcard. From the behavior analytic perspective it may be assumed that the child already has a generalized imitative repertoire, so they are imitating the child they observed at a later point in time (see conditioned reinforcement hypotheses above). Furthermore, the child may or may not have tacted the observed relationship when it occurred (rule-stating), and engaged in rule-following behavior when she interacted with the card at a later time. Both of these possibilities are consistent with the behavior analytic position. Importantly, the behavior analytic position does not require the individual to engage in rule-stating and following for observational learning to occur. Related to the latter, a recent series of studies conducted by Greer and colleagues seems to support the notion that observational learning may occur without rule-following. For example, individuals have acquired the ability to learn new words through experiences that do not involve observing consequences of another, and stimuli have been conditioned as reinforcers through the observation of others interacting with them, both of which do not require analyses of rule-governed behavior (see Greer & Ross, 2008 , Greer & Speckman, 2009 ).

It must be noted that many of these issues are at the center of current controversy, debate, and development in the field of behavior analysis. For example, the perspectives of joint control (e.g., Lowenkron, 1998 ) naming ( Horne & Lowe, 1996 ), relational frame theory ( Hayes, Barnes-Holmes, & Roche, 2001 ), and verbal behavior development (e.g., Greer & Ross, 2008 ; Greer & Speckman, 2009 ) all seem to account for the type of phenomena we have commented on herein. Given the importance of these issues, this is a good sign. We primarily mention this to acknowledge the current fact that there is not a behavior analytic position on many of these issues. Nevertheless, missteps may occur while we are on our journey to account for such phenomena, missteps that could have more or less dangerous implications for behavior analysis as an enterprise. It is our perspective that the interbehavioral position may be a rather useful foundation for workers as we continue on this journey (see Morris, Higgins, & Bickel, 1982 ).

Generally speaking, the behavior analytic conceptualization of observational learning relies on generalized imitation, conditioned reinforcement, and a range of verbal processes, depending on ones theoretical preference. These processes seem to account for the fact that imitative responses which have never been reinforced occur at a later time, and also for the role of verbal behavior in observational learning. The fact that there are a number of different perspectives on many of these issues may be considered a sign of progress and growth within behavior analysis, but at the same time highlights the need for further system building in this area. In the following sections we take a closer look at the behavior analytic position through the lens of interbehavioral psychology. Before doing so, we briefly introduce the reader to the interbehavioral position, as it is relatively less familiar to most behavior analysts.

THE INTERBEHAVIORIAL POSITION

From the perspective of interbehavioral psychology the event of interest is always a thoroughly naturalistic, psychological event. Specifically, this event is always the stimulus function ( sf ) ←→response function ( rf ) interaction ( Kantor, 1958 ). Moreover, this interaction always participates in a multifactored, inter related field. This field is conceptualized by the following formula: PE  =  C ( k , sf , rf , hi , st , md ); where PE is the psychological event, C is the interrelationship of all of the participating factors, k is the unique organization of all factors, sf is the stimulus function, rf is the response function, hi is the interbehavioral history, st is setting factors, and md is the medium of contact. Importantly, this is one event, one interbehavioral field. When one factor is changed the entire field is altered. This is to say none of the above factors are viewed as independent, dependent, or having causal status. Rather, all of the factors are equal participants in the one, integrated whole (see Smith, 2006 ).

Of particular relevance to our discussion of observational learning and complex behavior in general is the explicit distinction between stimulus objects and stimulus functions made within Kantor's system (e.g., Kantor, 1924 , pp. 47–48; Parrott, 1983a , 1983b , 1986 ). In other words, the stimulating action of stimulus objects is differentiated from the formal properties of those objects in Kantor's system. Kantor has suggested that the borrowing of the terms stimulus and response from biology, where stimulus and response functions are at least relatively more determined by their structural properties, has perhaps contributed to the failure to distinguish between object and functional properties in the domain of psychology ( Kantor, 1958 , p. 68). For example, in Kantor's system a picture as a stimulus object would be explicitly distinguished from its psychological functions, such that accounting for seeing something in the absence of the thing seen (as when looking at a picture “reminds you” of the time or place it was taken) is not difficult (see Parrott, 1983a , 1983b , 1986 ; Skinner, 1974 ). The process by which this happens is central to understanding complex behavior, including those that typically fall within the purview of observational learning, and we will now describe this process in more detail.

Kantor suggested that association conditions are fundamental psychological processes (1921, 1924). The term association is used here to refer to spatiotemporal relationships; that is, to relationships among various factors that occur in the environment together in space and time. To be clear, these factors are associated in the environment , and not within the organism. Further, it is not the organism who is associating; rather, the environment is where all associating takes place. Association conditions may involve stimuli and responses, stimuli and stimuli, settings and stimuli, settings and reactions, settings and settings, and reactions and reactions (including implicit and nonimplicit variations thereof; Kantor, 1924 , pp. 321–322).

Stimulus Substitution

Stimulus substitution is the outcome of a history of an organism interacting with various association conditions ( Kantor, 1924 , 1958 ; Parrott, 1983a , 1983b , 1986 ). That is, given an organisms history of interacting with spatiotemporal relationships ( A -coffee shop←→ B -Peter), stimulus objects may have the stimulational properties of other objects, even when those other objects are no longer physically present. This is how you might see Peter when you enter a coffee shop you frequented with him, even when he isn't physically there. In this example, stimulus A (coffee shop) and B (Peter) occurred together in space and time, and an organism interacted with that relationship, such that B becomes A ( B [ A ]) and A becomes B ( A [ B ]), psychologically speaking (see Hayes, 1992a ). This process is of particular importance to understanding complex behavior of various sorts. Furthermore, this is how interbehaviorists are able to conceptualize the past and present as one, avoiding both mentalistic and reductionistic practices which place the past within the organism in one way or another (see Hayes, 1992b ).

Added to this, through processes of generalization, stimuli that share physical features of those that participated in spatiotemporal association conditions may also develop substitute stimulus functions. For example, a coffee shop that is physically similar to the coffee shop you went to with your friend Peter might also substitute for Peter. Specifically, you might see Peter in the presence of a coffee shop that is physically similar to the shop you frequented with him. That is to say, substitute stimulus functions also generalize to stimuli which have never actually participated in spatiotemporal association conditions, but which are physically similar to stimuli which have, and thereby involve similar stimulus functions. This type of process may become particularly subtle, and is likely to be involved in a range of complex behaviors, including imagining and dreaming.

At this point it is important to address one potential misunderstanding with the interbehavioral perspective, specifically with respect to association conditions and the development of substitute stimulus functions. 6 We are suggesting that all stimuli which occur together in space and time, and which the organism interacts with, may develop substitute stimulus functions of one another. That is, it is possible for all stimuli to develop substitute stimulus functions of any other stimulus, given the appropriate interbehavioral history. Indeed, as an individual's interbehavioral history becomes more and more elaborate, one might imagine how all stimuli could develop substitute stimulus functions of all other stimuli, such that everything might become one, psychologically speaking. However, recall that the stimulus function←→response function interaction is always a participant in an exceptionally unique, complex, multifactored field. Indeed, Kantor stated “Each interaction is always absolutely specific. What the reacting organism and the stimulus object do in each interaction constitutes a distinctly unique relational happening” (1977, p. 38). Thus, while a specific stimulus object may indeed substitute for a wide range of things given an appropriate interbehavioral history, specific substitute stimulus functions are always actualized (or not) in a unique interbehavioral field. For example, a glass of sangria might substitute for a particular friend in a specific multifactored field (you might see your friend and remember drinking sangria together), whereas that same glass of sangria might substitute for the music of a live band in a different multifactored field (you might hear the music that was playing at a restaurant where you drank sangria in the past). As this example demonstrates, while there may be a wide range of potential substitute stimulus functions for every stimulus object, in each and every specific psychological event, particular substitute stimulus functions are actualized.

Thus far we have briefly introduced some important features of interbehavioral psychology, which we find to be particularly relevant to our understanding of observational learning. From the interbehavioral perspective, individuals observe (i.e., interact with) spatiotemporal association conditions in the environment (e.g., a child putting scrap paper in the recycling bin and this being followed by praise), such that at a later time the stimulus objects involved might substitute for the prior observation (e.g., the scrap paper might have the stimulus functions of praise in the previous observation). In other words, the scrap paper develops the stimulational properties of the observed relations; it substitutes for them. Psychologically speaking, the scrap paper is those relations (see Hayes, 1992a , 1992b ).

The role of verbal behavior must also be considered in the context of our analysis thus far. Generally speaking, one outcome of interacting with an observed relationship is being able to describe it. In other words, describing an observed relationship requires the organism to interact with it, and thus, descriptions are a particularly strong indication that the relations assumed to be observed have indeed actually been contacted. However, from our perspective verbal behavior, including rules more generally, does not explain observational learning. This is to say, whether or not the organism describes the observed relationship does not explain behavior change at a later time; however, not surprisingly, it is likely to be correlated with it, as it assures the organism has interacted with the observed relation. Moreover, to the extent that rule-statements substitute for a history of reinforcement, they may further enhance any learning by observation. Importantly, in this sense verbal behavior does not “mediate” responding. Its participation in the process of observational learning, however, seems to be worth considering. In doing so, it is important that verbal behavior not be given any causal or special sort of status. Observational learning certainly can, and does occur in the absence of verbal behavior, as is the case in animal research within this area (e.g., Biederman, Robertson, & Vanayan, 1986 ; Meyers, 1970 ; Reiss, 1972 ).

Our contention that verbal behavior not be given any causal status within the conceptualization of observational learning may seem to be at odds with a number of popular perspectives in behavior analysis. For example, a growing body of research on naming (e.g., Miguel, Petursdottir, Carr, & Michael, 2008 ), joint control (e.g., Lowenkron, 1998 ), and generalized imitation (e.g., Horne & Erjavec, 2007 ) seems to support the idea that verbal behavior is mediational. Again, as stated above, we do not deny that verbal behavior is likely to be helpful in a number of circumstances, but caution against giving it any sort of special status. That is, verbal behavior may, but importantly also may not, participate in learning from observation. In this sense, verbal behavior need not be considered “meditational.” Our perspective on this matter seems to be both parsimonious and comprehensive. That is, it does not employ any unnecessary assumptions or constructs, and accounts for observational learning that occurs with and without verbal behavior. 7

We hope we have made it clear that observational learning isn't puzzling from an interbehavioral perspective. Stimulus substitution offers a straight forward, naturalistic, and parsimonious way to conceptualize complex processes, including those involved in observational learning. Importantly, the interbehavioral perspective also avoids some shortcomings found with the behavior analytic interpretation of observational learning. In the following section we outline and address these issues specifically.

Review of the Behavior Analytic Perspective

As described earlier, the behavior analytic conceptualization of observational learning rests on the processes of generalized imitation, conditioned reinforcement, rule-governed behavior, and verbal processes more generally. From our perspective these analyses fail to fully articulate the nature of stimulation in the psychological event. Again, from the interbehavioral perspective the psychological event is always the stimulus function←→response function interaction. The generalized imitation analysis leaves us questioning the nature of the stimulus interacted with. In other words, it is not clear what the stimulus is. This problem is further underscored by the suggestion that generalized imitation involves responding in the absence of a discriminative stimulus ( Pierce & Cheney, 2008 , p. 252). Given our assumption that psychological events always involve sf ←→ rf interactions, as participants in multifactored fields, this account is problematic. The process of deriving and following self-rules leaves us in a similar situation. Again, we are left questioning the nature of the stimulus interacted with. That is, it unclear what the organism is interacting with when he/she derives a self-rule, and similarly, when he/she follows such a rule. Again, given our assumptions about the psychological event, both of these analyses require further consideration of the stimulus involved.

Added to the concerns described above, behavior analytic conceptualizations also fail to explicitly articulate the location of the stimulus. In other words, it is unclear where the stimulus interacted with is located. Failing to fully describe the nature and location of the stimulus leaves the door open for common mentalistic explanations to thrive. In the case of generalized imitation we find ourselves saying that the response is “in the repertoire” of the organism, because the stimulus is private, covert, or biological in nature (also see Hayes & Fryling, 2009 ). Alternatively, the organism may be said to “derive” or “relate” with respect to participating verbal processes. In other words, we either avoid attempting to specify the stimulus, place it within the organism, or, alternatively, suggest that it is available only to those involved in other scientific disciplines, namely biology. 8 In each of these cases, we fail to provide a thoroughly psychological account of the event we are interested in, leaving our job unfinished. As has been the case throughout history, where our work is left unfinished, both dualistic and reductionistic workers are quick to complete the job. While it may be argued that much of the contemporary work in the area of complex behavior does in fact avoid many of the concerns we have described, a failure to be explicit about these important issues can only result in long-term confusion, and a possible resurfacing of mentalistic thinking.

The behavior analytic community continues to be interested in the important processes involved in observational learning (e.g., Alvero & Austin, 2004 ; Bruzek & Thompson, 2007 ; Greer & Singer-Dudek, 2008 ; Greer, Singer-Dudek, Longano, & Zrino, 2008 ; Moore & Fisher, 2007 ; Ramirez & Rehfeldt, 2009 ; Rehfeldt, Latimore, & Stromer, 2003 ). Added to this, there are some interesting reasons to believe that this process has important clinical value when compared to other procedures (see Hayes, Kohlenberg, & Melancohn, 1989 ). What is needed is a thoroughly naturalistic conceptualization of observational learning, one that avoids all mentalism (i.e., no intermediate steps within the organism). As we have described, the interbehavioral perspective offers us just that, a clear, consistent, and thoroughly naturalistic conceptualization of observational learning. Moreover, it is one that does not require any additional constructs to explain complex processes, remaining comprehensive all the while.

It is our perspective that the position described in this paper may be integrated with contemporary research and scholarship in behavior analysis. This is especially so when we make clear distinctions between investigative constructs and events, as is advocated by interbehaviorists (see Fryling & Hayes, 2009 ; Kantor, 1957 ; Smith, 2007 ). Kantor (1958) has suggested that investigative constructs are acceptable within the context of the investigative subsystem of science, but that these constructs should not be confused with the constructions of the subject matter and philosophy more generally. That is, the constructs we employ to understand various interrelations among factors participating in psychological events should never be confused to be representations of the subject matter as a whole, as being explanatory of one another, or as having more or less causal status. For example, both operant and respondent processes can be conceptualized within the more global processes of association and subsequent outcomes of stimulus substitution. Contemporary research in behavior analysis requires us to emphasize specific aspects to the interbehavioral position, particularly with respect to the role of the context (unique multifactored fields), and the actualization of specific substitute stimulus functions. In this regard, the research on relational responding is particularly stimulating. In this line of research a multitude of historical association conditions are manipulated in unique ways, under various contextual conditions, and the development or “emergence” of a wide range of events is then tested. When these interesting outcomes are conceptualized as unique sorts of substitute stimulation, operating in historical, multifactored fields, their explanations remain wholly consistent and naturalistic. We think most contemporary research and scholarship in behavior analysis can and should be integrated with the interbehavioral perspective. Importantly, such integration might serve to coordinate the efforts of various workers in the field, and ultimately maximize on our productivity as a scientific enterprise.

The limitations of Bandura's work not withstanding, the process of learning from observation is interesting and relevant to a comprehensive analysis of behavior. Indeed, if one values such comprehensiveness, our most basic concepts and principles must be relevant to, and provide an account of observational learning. Moreover, this comprehensiveness is only valuable when it is achieved within the context of validity (internal consistency) and significance (external consistency within the greater field of the sciences; see Clayton, Hayes, & Swain, 2005 ; Kantor, 1958 ). The interbehavioral perspective is particularly valuable in this regard. Kantor's conceptualization of the psychological event, with all of its fullness, provides an avenue by which the most complex sorts of behavior, including those involved in observational learning, might be fully integrated into a natural science approach to the analysis of behavior.

Cristin Johnston is affiliated with Spectrum Center, Oakland, CA.

1 The term modeling is used synonymously with observation and demonstration in this context. In other words, when something has been modeled the individual has observed a demonstration of the response and factors surrounding it.

2 See Greer et al., 2004 for a description of related studies on peer tutoring, where it was the observation of corrections, and not simply of reinforcement, that resulted in observational learning.

3 Of note, the researchers acknowledged the possibility that their positive statements may not have been the most optimal reinforcers, and thus, it is possible that the modeling plus reinforcement condition would have been superior had more powerful reinforcers been used ( Bandura & McDonald, 1963 , p. 281).

4 The idea that rewards distract individuals from learning seems to be related to the concerns raised by Alfie Kohn (1999) .

5 In this literature the term learning is used to describe the individual's ability to describe observed behavior at a later time.

6 For example, some have criticized interbehaviorism for its “loose form of associationism” (e.g., Hayes, Barnes-Holmes, & Roche, 2001 , p. 8).

7 A number of socially significant behaviors involve language, and we are not questioning the interest in it for the purposes of understanding how to promote such behaviors (e.g., categorization). However, we are arguing that language not be given special status in the conceptualization of observational learning.

8 Here, it is important to note that even when biological factors are observed (and indeed, they increasingly are) they are never observed to be engaging in the psychological event of interest. That is to say, we can never observe the brain or any biological component of the organism engaging in the behavior we are most interested in (see Kantor, 1947 ). Confusions between what is measured and what ones says they measuring are common in science (see Kantor, 1957 ; Smith, 2007 ), and are especially likely when there is a failure to fully articulate the boundary conditions between individual scientific disciplines.

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Module 8: Observational Learning

Module Overview

In Module 8, and the only one of Part IV, we will tackle the final learning model – observational learning. Outside of describing it and determining factors on making imitation stronger, we will also see how it links to operant conditioning and can be used in behavior modification.

Module Outline

8.1. What is Observational Learning?

8.2. bandura’s classic experiment, 8.3. do we imitate everything we see, 8.4. observational learning and behavior modification.

Module Learning Outcomes

• Clarify how we learn by observing others.
• Describe Bandura’s Bobo doll experiment.
• Clarify why we do not model everything we see.
• Describe how observational learning could be applied to behavior modification.

Section Learning Objectives

• Differentiate observational and enactive learning.
• Describe Bandura’s social learning theory.
• Define vicarious reinforcement and punishment.
• Define imitation.

8.1.1. Defining Observational Learning

There are times when we learn by simply watching others. This is called observational learning, and is contrasted with enactive learning , which is learning by doing. There is no firsthand experience by the learner in observational learning, unlike enactive.

As you can learn desirable behaviors such as watching how your father bags groceries at the grocery store (I did this and still bag the same way today), you can learn undesirable ones too. If your parents resort to alcohol consumption to deal with the stressors life presents, then you too might do the same. What is critical is what happens to the model in all of these cases. If my father seems genuinely happy and pleased with himself after bagging groceries his way, then I will be more likely to adopt this behavior. If my mother or father consumes alcohol to feel better when things are tough, and it works, then I might do the same. On the other hand, if we see a sibling constantly getting in trouble with the law then we may not model this behavior due to the negative consequences.

8.1.2. Social Learning Theory

Observational learning can, in fact, be referred to as social learning, and Bandura (1986) proposed a social learning theory, which is composed of observational learning and operant conditioning. How so? Consider that you may learn not to rob the local convenience store because you saw your brother get arrested, prosecuted, and is now spending 10 years in prison. You observed his actions and the consequences of those actions. Remember, there is no firsthand experience. This is called vicarious punishment . If we see a coworker praised by our supervisor for a job well done (and likely going way above and beyond), we will want to behave this way in the future so that we can receive the praise, plaque, extra time off, or monetary award. According to Bandura, this is called vicarious reinforcement and notice that there are both positive and negative reinforcers in the list.

The gist of social learning theory is this: we learn by observing how other people behave and seeing the consequences of their behavior. Later we visualize the consequences (we remember what we saw before) of a particular behavior we would like to make, and decide whether or not to behave in that way. Most likely, if the consequence of the similar behavior was positive then we will make the behavior, but if negative then we will not, in keeping with the principles of operant conditioning.

8.1.3. Imitation

When we use the word imitation, we are implying that we behave in a way that resembles or duplicates the behavior of another person, and that our behavior is novel or not the way we usually act. For instance, in the Avenger’s: Infinity War (2018), Star-Lord imitates Thor’s Asgardian accent and mannerisms out of jealousy. He is jealous for how well-received Thor is when he is first retrieved from outer space, and how much manlier Thor is. In fact, Rocket even asks Star-Lord, “Are you making your voice deeper?” to which he replies, “No” (in Thor-voice). Mantis then points out that he “just did it again.” Star-Lord replies, “This is my voice.” The interaction is comical, but an excellent example of true imitation. Star-Lord does not actually talk like an Asgardian, and so the behavior is novel. In fact, Drax even says that he is “imitating the godman.”

Sometimes we imitate not only a behavior that is reinforced, but any behavior. This tendency is called generalized imitation and is based on the work of Baer and Sherman (1964). In their study, a puppet was used to provide reinforcement in the form of approving comments when children imitated three behaviors that it made — mouthing, head nodding, and speaking nonsense. The puppet did not reinforce level-pressing, a fourth behavior. The more the children imitated the behaviors that were reinforced, the more they imitated the behavior that was not reinforced too. Eventually, the researchers discontinued reinforcing the three behaviors, and the children stopped making them. But the children also stopped pressing the lever. Once reinforcement was re-established for mouthing, head nodding, and speaking nonsense, their frequency increased in the children, as well as the frequency of pressing the lever. The authors concluded that the children had developed a generalized tendency to imitate the model.

• Describe Bandura’s classic experiment.

Albert Bandura (1965) conducted the pivotal research on observational learning, and you likely already know all about it. In Bandura’s experiment, 66 children (33 boys, 33 girls) aged 42 to 71 months, were randomly assigned to one of three conditions, each with 11 boys and 11 girls. The experiment started with an exposure procedure. Children were brought individually into a room and watched a film of about 5 minutes. It began with a model (an adult male) walking up to an adult-sized plastic Bobo doll and ordered it to clear the way. After glaring for a bit at the non-compliant doll, the model made one of four novel aggressive responses followed by a distinct verbalization.

First, the model laid the Bobo doll on its side, sat on it, and punched it in the nose. While doing this he said, “Pow, right in the nose, boom, boom.” The model then stood it up and pommeled it on the head with a mallet. This response was accompanied by, “Sockeroo…stay down.” The model then kicked the doll around the room and said, “Fly away.” Finally, rubber balls were thrown at the Bobo doll and “Bang” was uttered with each hit. The sequence included both physical and verbal aggression and was repeated twice.

Remember that children were assigned to one of three conditions. In the model-rewarded condition, a second adult male appeared with candies and soft drinks. He told the model that he was a “strong champion” and that his aggressive behavior deserved a generous treat. He poured the model a glass of 7-Up and gave him chocolate bars, Cracker Jack popcorn, and candy. The model ate the treats while being showered with additional positive social reinforcement.

In the model-punished condition, the second adult male appeared on the scene “shaking his finger menacingly and commenting reprovingly, “Hey there, you big bully. You quit picking on that clown. I won’t tolerate it.” The model moved back, tripped and fell, and the other adult sat on him and spanked him with a rolled-up magazine while reminding him that his aggressive behavior was wrong. The model ran off and the other man yelled, “If I catch you doing that again, you big bully, I’ll give you a hard spanking. You quit acting that way” (pg. 591).

Finally, children in the no-consequences condition just viewed the film the children in the other two groups did. There was no reinforcement ending though.

Once the exposure session ended, children were taken to an experimental room which had in it a “Bobo doll, three balls, a mallet and pegboard, dart guns, cars, plastic farm animals, and a dollhouse equipped with furniture and a doll family” (pg. 591). The variety of objects allowed the children to make imitative or nonimitative behaviors. The experimenter told the children they could play with the toys freely and left the room to obtain additional toys. Children were left in the room for 10 minutes, though the experimenter returned about halfway through to tell the children she was still looking for the toys. Two observers recorded the children’s behavior every 5 seconds using predetermined imitative response categories.

The experimenter than returned to the room with an assortment of fruit juices in a colorful juice-dispensing fountain. She also had booklets of sticker-pictures. After a brief juice treat, the children were told that for each imitative response they reproduced, they would receive a pretty sticker-picture to place on a pastoral scene on the wall and the experimenter wanted to see how many stickers they might be able to get. They were also offered additional juice treats. The children were asked to demonstrate what Rocky did in the TV program and to say what he said. Rewards were delivered immediately for correct, matching responses.

Results showed that children who witnessed a model behave aggressively to the Bobo doll tended to do so themselves. The consequences of that action were important too. When the children saw the model get punished for his aggressive behavior, they were less likely to make the same response. If the model was reinforced, they were more likely to engage in aggressive behavior of their own. When children were offered incentives to act in an aggressive manner, they did so.

The authors note though, “Exposing a person to a complex sequence of stimulation is no guarantee that he will attend to the entire range of cues, that he will necessarily select from a total stimulus complex only the most relevant stimuli, or that he will even perceive accurately the cues to which is attention is directed. Motivational variables, prior training in discriminative observation, and the anticipation of positive or negative reinforcements contingent on the emission of matching responses may be highly influential in channeling, augmenting, or reducing observing responses, which is a necessary precondition for imitative learning” (pg. 593; see also Bandura & Walters, 1963 and Bandura, 1962). Let’s further examine factors affecting observational learning.

• Outline factors on observational learning.
• Define stimulus enhancement.
• Define and describe amnesia.

So, do we model everything we see? The answer is no. Why is that? First, we cannot pay attention to everything going on around us. We are more likely to model behaviors by someone who commands our attention. Consider the phenomena of stimulus enhancement which says that we will focus our attention on a stimulus if others are paying attention to it. I was walking through Walmart one day in May 2019 and suddenly I noticed that there was a large group of people standing around the jewelry counter and other people were looking that way and walking over to it. I stopped and started to move in that direction, like my fellow shoppers, to see what was going on. My behavior was changed because of the behavior of others. I soon realized Walmart was just giving out “free” jewelry and so I returned to my business of going to the register to check out. Likewise, have you ever noticed a large group of people looking up at the sky? If you altered your behavior to look up to (as what happens in many disaster movies), then you encountered stimulus enhancement.

In terms of the model him or herself, any guess as to whether an attractive or unattractive model will catch our attention? If you said attractive, you are correct (Baker & Churchill, 1977).

We also pay attention to models when trying to gain a new skill. If we want to learn the skill and determine what we need to do to obtain positive results, we can use a skilled model. The advantage of using an unskilled model, sometimes called a learning model , is that he/she will make mistakes maybe as often as they have success, and we can learn from both mistakes and success. If we are learning how to hit a baseball, we could watch a Major League Baseball player take batting practice. Of course, very few balls will be missed and the sound of the crack of the bat will be reinforcing for the model and observer. Alternatively, we might have our father take us out to practice hitting at the batting cage. Unless he is an MLB player, he will likely hit some and miss many more. We can observe which stance works best for him, how high he holds the bat, the timing of when he swings, etc. He will hit the ball sometimes and not on other occasions. In a way, we learn more from success than failure, and you might say that his pattern of success will be more like ours than compared to the MLB player.

Second, we must remember what a model does in order to imitate it. If a behavior is not memorable, it will not be imitated. But what if we have a medical condition that makes remembering information difficult, such as anterograde amnesia? According to the Mayo Clinic’s website, when we experience difficulty learning new information since the onset of amnesia , or the loss of memories, such as facts, information, and experiences; we are experiencing a specific form called anterograde amnesia (not to be confused with retrograde amnesia or when we cannot remember past events and previous familiar information).

We must try to convert what we see into action. Consider the phenomena of deferred imitation , or when we observe a model but do not show such learning until a later time. A child observes her mother set the dinner table one night but does not go into the kitchen to try and set the table until days later. When she does go in to try, the parent thanks the child for trying to set the table but tells her that she does not know how. The child says, ‘Yes I do,’ and proceeds to do just that — set the table. The parent is impressed, and the child has shown that she learned by observing the parent either a few nights before or possibly over a few days.

Hopefully, the parent praises the child so that she will be motivated to set the table again in the future. If we are not motivated to perform an observed behavior, we probably will not show what we have learned and may not acquire the behavior at all or will not remember to do it later.

• Clarify how observational learning can be used in behavior modification.

Bandura said if all behaviors are learned by observing others and we model our behavior on their behavior, then undesirable behaviors can be altered or relearned in the same way. Modeling techniques are used to change behavior by having subjects observe a model in a situation that usually causes them some anxiety.  By seeing the model interact nicely with the fear evoking stimulus, their fear should subside. This form of behavior therapy is widely used in clinical, business, and classroom situations. In the classroom, we might use modeling to demonstrate how to do a math problem for a student. Then through a prompt delay, we encourage the student to try the problem for him/herself. If the student can solve the problem, no further action is needed, but if the student struggles we can use any of the four types of prompts — verbal, gestural, modeling, or physical to help them solve it. In fact, in many college classrooms this is exactly what the instructor does.

In the business setting, a model or trainer demonstrates how to use a computer program or run a register for a new employee. Like in the example above, prompt delays and prompts can be used to test the level of learning the employee has gained. Through social support, reinforcers can be delivered.

See Module 6 and 7 for a discussion of behavior modification as it relates to operant conditioning. Keep in mind what you learned about observational learning and how it intersects with operant conditioning through social learning theory. Examples in the clinical setting are given in Module 6 as they relate to learning and unlearning fears.

Module Recap

In Module 8 we discussed the last of the three major learning models called observational learning. We discussed what observational learning was and how it differed from enactive learning, outlined how it intersects with operant conditioning through social learning theory, described imitation, outlined Bandura’s classic experiment, explored factors on how likely we are to model/imitate another person, and briefly discussed the application of observational learning to behavior modification.

Part IV consisted of this one module and now with it complete, we can Take a Pause and explore how the three models of how we learn are complementary with one another, and not in competition.

2nd edition

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ORIGINAL RESEARCH article

Observational learning: tell beginners what they are about to watch and they will learn better.

• Département de kinésiologie, Université de Montréal, Montréal, QC, Canada

Observation aids motor skill learning. When multiple models or different levels of performance are observed, does learning improve when the observer is informed of the performance quality prior to each observation trial or after each trial? We used a knock-down barrier task and asked participants to learn a new relative timing pattern that differed from that naturally emerging from the task constraints ( Blandin et al., 1999 ). Following a physical execution pre-test, the participants observed two models demonstrating different levels of performance and were either informed of this performance prior to or after each observation trial. The results of the physical execution retention tests of the two experiments reported in the present study indicated that informing the observers of the demonstration quality they were about to see aided learning more than when this information was provided after each observation trial. Our results suggest that providing advanced information concerning the quality of the observation may help participants detect errors in the model's performance, which is something that novice participants have difficulty doing, and then learn from these observations.

Introduction

You are an avid golfer and you want to learn a new shot. How would you proceed? There is a fair chance that you will observe someone (live, on video, on Youtube, etc.) who knows how to perform this shot, and you will try to understand what to do and how to do it. Research clearly indicates that this learning strategy is successful because observation has been shown to promote the learning of a wide variety of motor skills (see McCullagh et al., 1989 ; Hodges et al., 2007 ; Vogt and Thomaschke, 2007 ; Ste-Marie et al., 2012 ; Lago-Rodríguez et al., 2014 , for reviews on observational learning). This is because observation has much commonality with physical practice, which is the first determinant of motor skill learning. Specifically, it has been demonstrated that variables, such as the amount of practice ( Carroll and Bandura, 1990 ; Blandin, 1994 ), the frequency of knowledge of results ([KR], Badets and Blandin, 2004 , 2005 ; Badets et al., 2006 ), and the practice schedule ( Blandin et al., 1994 ; Wright et al., 1997 ), affect learning via observational practice and physical practice in similar ways. These data led to the proposition that observation and physical practice use very similar processes. This proposition is supported by the results of neuroimaging studies that showed that an ensemble of neural structures (including the premotor cortex, the inferior parietal lobule, the superior temporal sulcus, the supplementary motor area, the cingulate gyrus, and the cerebellum), also called the “action observation network” (AON) ( Kilner et al., 2009 ; Oosterhof et al., 2010 ), is activated both when individuals perform a given motor task and when they observe others performing that same motor task ( Grafton et al., 1997 ; Buccino et al., 2001 ; Gallese et al., 2002 ; Cisek and Kalaska, 2004 ; Frey and Gerry, 2006 ; Cross et al., 2009 ; Dushanova and Donoghue, 2010 ; Rizzolatti and Fogassi, 2014 ; Rizzolatti et al., 2014 ).

Observation favors motor skill learning, but who should you observe to learn that new golf shot? An expert who masters the shot presumably will help you develop a reference of what to do and how to do it, but should you observe someone like you who is learning that shot and who presumably gives you a better chance of detecting and learning from errors or changes in strategy? Research has shown that observing both a skilled model ( Martens et al., 1976 ; McCullagh et al., 1989 ; Lee et al., 1994 ; Al-Abood et al., 2001 ; Heyes and Foster, 2002 ; Hodges et al., 2003 ; Bird and Heyes, 2005 ) and a novice model leads to significant learning ( Lee and White, 1990 ; McCullagh and Caird, 1990 ; Pollock and Lee, 1992 ; McCullagh and Meyer, 1997 ; Black and Wright, 2000 ; Buchanan et al., 2008 ; Buchanan and Dean, 2010 ; Hayes et al., 2010 ). However, recent results from our laboratory showed that observational learning of a new motor skill is improved following observation of both novice and expert models rather than either a novice or an expert model alone ( Rohbanfard and Proteau, 2011 ; Andrieux and Proteau, 2013 , 2014 ). We believe that this “variable” observation format leads to not only the development of a good movement representation (expert observation) but also the development of efficient processes for error detection and correction (novice observation).

In the present study, the question of interest is a simple but important one. When using a variable schedule of observation, will learning be better when the observers are informed beforehand of the “quality” of the performance they are about to see or will it be better when the observers are left to evaluate the performances before receiving feedback. Informing the observers of what they are about to see may enable them to select whether they will observe to imitate or rather observe to detect error, or weaknesses in the model's performance, which might facilitate the development of these processes. Alternatively, having the participants evaluate the performance quality they observed may activate more elaborate cognitive processes than when this information is fed forward (e.g., error detection and recognition, or evaluation of alternative strategy), thus resulting in better learning of the task.

The task that we chose required the participants to change the relative timing pattern that naturally emerged from the task constraints ( Collier and Wright, 1995 ; Blandin et al., 1999 ) to a new, imposed pattern of relative timing. This is similar to changing one's tempo when executing a serve in tennis or a drive in golf ( Rohbanfard and Proteau, 2011 ). The participants observed two models demonstrating a wide variety of performances. In one group, observers were informed before each trial of the quality level (expert, advanced, intermediate, novice, or beginner performance) of what they were about to see, whereas a second group of observers was provided the same information only after each observation trial was completed.

Experiment 1

Participants.

Ninety right-handed students (45 males and 45 females; mean age = 20.5 years; SD = 0.9 years) from the Département de kinésiologie at the Université de Montréal participated in this experiment. The participants were naive to the purpose of the study and had no prior experience with the task, and all participants were self-declared as being right-handed. None of the participants reported neurological disorders, and all had normal or corrected-to-normal vision. The participants completed and signed individual consent forms before participation. The Health Sciences Research Ethics Committee of the Université de Montréal approved this experiment.

Apparatus and Task

The apparatus was similar to that used by Rohbanfard and Proteau (2011) . As illustrated in Figure 1 , it consisted of a wooden base (45 × 54 cm), three wooden barriers (11 × 8 cm), and a starting button embedded in a target (11 × 8 cm). The distance between the starting button and the first barrier was 15 cm. The distances of the remaining three segments of the task were 32, 18, and 29 cm, respectively. The barriers were placed perpendicular to the wooden base at the beginning of each trial, yielding a closed microswitch circuit. All of the microswitches were connected to a computer via the I/O port of an A–D converter (National Instruments, Austin, Texas, USA), and a millisecond timer was used to record both the total movement time (TMT) and the time required to complete each segment of the task (intermediate times, ITs).

Figure 1. Sketch of the apparatus . Participants had to leave the starting button and hit the first, second, and third barriers in a clockwise motion before finally reaching the target.

For the physical practice trials (see below), the participants sat close to the starting position in front of the apparatus. Then, from the starting button, the participants were asked to successively knock down the first, second, and third barriers (thus releasing the microswitches) and finally hit the target in a clockwise motion as illustrated in Figure 1 . Each segment of the task had to be completed in an IT of 300 ms, for a TMT of 1200 ms. The movement pattern, ITs, and TMT were illustrated on a poster located directly in front of the apparatus during all of the experimental phases.

Experimental Phases and Procedure

The participants were randomly assigned to one of the three groups, each consisting of 30 participants (15 females per group): control (C), feedforward KR and observation (FW), and observation and feedback KR (FB). All groups performed four experimental phases, spread over 2 successive days.

All participants received verbal instructions regarding TMT and ITs before the first experimental phase. The first experimental phase was a pre-test, in which all participants performed 20 physical practice trials without knowledge of the results (KR) on the TMT and the ITs.

The second phase was an acquisition phase and consisted of 60 observation trials for the participants in the two observation groups (FW and FB). These participants individually watched a video presentation of two models physically performing the experimental task. For each observation trial, KR concerning the model's performance (both TMT and ITs) was presented in ms (see Figure 1 ) either before the demonstration for the FW group or after the demonstration for the FB group. The model was changed every five trials (i.e., model 1: trials 1–5 and model 2: trials 6–10, and so on), for a total of 30 trials performed by one model and 30 trials performed by the other model. For both the FW and FB groups, the two models, who participated in previous work from our laboratory, were chosen because for both models, we had six video clips that illustrated performances in each one of five subcategories. Thus, the participants in the FW and FB groups could not associate one particular model with either a better or a poorer performance. An expert performance corresponded to a root mean square error (RMSE; see data analysis section for computation details) ranging between 0 and 15 ms; advanced, intermediate, novice, and beginner performances corresponded to RMSEs of 30–45 ms, 60–75 ms, 90–105 ms and 120+ ms, respectively. The participants in the FW and FB groups were informed of the model's performance in ms; they were also informed of the level of performance to which it referred. The resulting 30 trials of each model (five levels of performance × six repetitions) were randomized so that the five levels of performance were presented once into each set of five trials. To avoid physical imitation of the sequence, which could interfere with the observational processes, we asked the participants in the FW and FB groups to keep their hands on their thighs during the acquisition phase and to not reproduce the movements while watching the model(s). It was the Experimenter's main task to ensure that the participants complied with these instructions. The participants' overt behavior suggests that they did. Finally, participants of the control group did not physically practice or observe anything during this phase. Instead, they read a provided newspaper or magazine for the same duration as the observation for the other groups (approximately 10 min).

The third and fourth experimental phases were 10-min and 24-h retention phases. In each phase, all participants physically performed 20 trials with no KR. The participants were asked to complete each segment of the task in 300 ms, for a TMT of 1200 ms.

Data Analysis

The data from the pre-test and the two retention phases were regrouped into blocks of five trials. For each successive block of five trials (i.e., trials 1–5, 6–10, etc.), we computed the absolute value of each participant's constant error (|CE|, the constant error indicates whether a participant undershot [negative value] or overshot [positive value] the total movement time) and variable error of the total movement time (VE or within-participant variability) to determine the accuracy and consistency of TMT, respectively. For intermediate times, we computed a RMSE, which indicates how much each participant deviated from the prescribed relative timing pattern in a single score. For each trial,

where ITi represents the intermediate time for segment “i,” and target represents the goal movement time for each segment of the task (i.e., 300 ms).

Because the data were not normally distributed (RMSE and time data are positively skewed), each dependent variable underwent a logarithmic transformation (ln). The transformed data for each dependent variable were independently submitted to an ANOVA contrasting three groups (C, FW, and FB) × three phases (pre-test, 10-min retention, 24-h retention) × four blocks of trials (1–5, 6–10, 11–15, and 16–20), with repeated measures on the last two factors. All of the significant main effects and simple main effects involving more than two means were broken down using Bonferroni's adjustment. For all comparisons, an effect was deemed significant if p < 0.05. Partial eta square ( η p 2 ) is the effect size reported for all significant effects ( Cohen, 1988 ).

Total Movement Time

The ANOVA computed on |CE| (Figure 2 , upper panel) revealed significant main effects for the variable group, F (2, 87) = 5.04, p = 0.08, η p 2 = 0.10, and phase, F (2, 174) = 5.16, p = 0.007, η p 2 = 0.06, as well as a significant phase × group interaction, F (4, 174) = 4.93, p = 0.001, η p 2 = 0.10. The breakdown of this interaction did not reveal any significant group differences in the pre-test ( F < 1). In the 10-min retention test, F (2, 87) = 10.12, p < 0.001, η p 2 = 0.19, the post-hoc comparisons revealed that the control group had a significantly larger | CE | than both the FW and the FB groups ( p < 0.05 in both cases), which did not differ significantly from one another ( p = 0.19). In the 24-h retention test, F (2, 87) = 4.34, p = 0.016, η p 2 = 0.09, the FW group had a significantly smaller |CE| than the control group ( p = 0.012) 1 .

Figure 2. Absolute constant error of TMT and root mean square error of relative timing as a function of the experimental phases and experimental groups (Experiment 1) . * p < 0.05. Error bars indicate standard error of the mean.

The ANOVA computed on VE (not shown) revealed significant main effects for the variable phase, F (2, 174) = 13.12, p < 0.001, η p 2 = 0.13, and block, F (3, 261) = 48.79, p < 0.001, η p 2 = 0.36. Post-hoc comparisons of the phase effect revealed a larger VE of total time in the pre-test than in both the 10-min and the 24-h retention tests ( p < 0.002 in both cases), which did not differ significantly from one another ( p = 0.68). The block main effect resulted from a significantly larger VE of total time for the first than for the three remaining blocks of trials ( p < 0.001 in all cases), which did not differ significantly from one another ( p >0.05 in all cases).

Relative Timing

The ANOVA computed on the RMSE of relative timing revealed significant main effects for the variable group, F (2, 87) = 21.49, p < 0.001, η p 2 = 0.33, phase, F (2, 174) = 39.98, p < 0.001, η p 2 = 0.31 and block, F (3, 261) = 14.77, p < 0.001, η p 2 = 0.14, as well as a significant phase × group interaction, F (4, 174) = 12.81, p < 0.001, η p 2 = 0.23. The block main effect resulted from a significantly larger RMSE of relative timing for the first than for the three remaining blocks of trials ( p < 0.001 in all cases), which did not differ significantly from one another ( p >0.3 in all cases). More interestingly, the breakdown of the phase × group interaction (Figure 2 , lower panel) did not reveal any significant group differences in the pre-test ( F < 1). In the 10-min, F (2, 87) = 14.85, p < 0.001, η p 2 = 0.34, and the 24-h retention tests, F (2, 87) = 23.23, p < 0.001, η p 2 = 0.35, although the FB group significantly outperformed the control group ( p = 0.001 in both cases), the FB group was, in turn, significantly outperformed by the FW group ( p = 0.001 and p = 0.02, respectively) 2 .

The present experiment was designed to extend our knowledge of the observation conditions that optimize learning of a new relative timing pattern. In this learning situation, two observation groups, which observed a variety of demonstrations, were provided KR either before or after each trial during the acquisition phase. Specifically, we wanted to assess whether learning would be enhanced when the learners know the “quality” or characteristics of a demonstration before they observe the demonstration. The results are straightforward.

First, as illustrated in Figure 2 , both the FW and the FB groups outperformed the control group on the retention tests. This was true for the learning of both the TMT and the relative timing. This expected result confirms previous findings that indicated that observation enables one to learn a new motor skill (see McCullagh et al., 1989 ; Hodges et al., 2007 ; Vogt and Thomaschke, 2007 ; Ste-Marie et al., 2012 ; Lago-Rodríguez et al., 2014 , for reviews on observational learning) and, notably, a new relative timing pattern ( Rohbanfard and Proteau, 2011 ; Andrieux and Proteau, 2013 , 2014 ).

The most important finding of the present study is that the FB group was outperformed by the FW group in the retention tests. Although the two groups observed the same demonstrations, the results revealed that learning is optimized when one is given advance knowledge of the quality or characteristics of the witnessed demonstration. This finding fits well with previous reports from our laboratory ( Rohbanfard and Proteau, 2011 ; Andrieux and Proteau, 2013 ) showing that a mixed observation regimen, in which the observers know who is the expert model and who is the novice model, favors learning of a new relative timing pattern better than either expert or novice observation alone.

Having advance knowledge that a less than perfect demonstration will be shown may be critical, considering that it has been reported that novice participants, such as in the present study, are not good at evaluating the quality of a demonstration. For example, Aglioti et al. (2008) had novice and expert basketball players observe video clips showing free-throw shots, and the video clips were stopped at different times before or immediately after the ball release. Expert basketball players and coaches/specialized journalists were better and quicker at predicting the fate of the shot (successful or not) than were novices (for similar results see also Wright et al., 2010 ; Abreu et al., 2012 ; Tomeo et al., 2013 ; Balser et al., 2014 ; Candidi et al., 2014 ; Renden et al., 2014 ).

The advantage of the FW over the FB protocol is important and, as far as we know, a similar finding has not been reported thus far. Therefore, a replication of this finding appeared important. In addition, we wondered whether the advantage noted for the FW protocol occurred only after a limited amount of observation. Finally, we were curious to see whether alternating the FW and the FB protocol would result in additive effects. We conducted Experiment 2 to address these questions.

Experiment 2

The 60 participants who volunteered for this experiment were drawn from the same population as that of Experiment 1 (36 males and 24 females; mean age = 22.7 years; SD = 4.9 years). The participants were naive concerning the purpose of this study and had no prior experience with the task. They completed and signed individual consent forms before participation. The Health Sciences Research Ethics Committee of the Université de Montréal approved this experiment.

Apparatus, Task, Experimental Phases, Procedure, and Data Analysis

We used the same task, apparatus, and procedures as in Experiment 1. The major difference between the present experiment and Experiment 1 is that participants performed two acquisition sessions, which led to a total of five experimental phases: pre-test, acquisition 1, immediate retention test, acquisition 2, and 24-h retention test.

The participants were randomly assigned to one of the three groups, each consisting of 20 participants (8 females per group): feedforward KR and observation during both acquisition 1 and 2 (FW1-2); feedforward observation and KR during acquisition 1 but observation and feedback KR during acquisition 2 (FW/FB); and observation and KR feedback during both acquisition 1 and 2 (FB1-2). We used the same video and models as in Experiment 1; however, the order of video presentation was different in acquisition 2 from that in acquisition 1. All participants were also informed that they would perform the same task after each acquisition phase, but with no KR concerning their own performance.

We used the same dependent variables and data transformation as in Experiment 1. For each dependent variable, we conducted a two-way ANOVA contrasting the three groups (FW1-2, FW/FB and FB1-2) × three experimental phases (pre-test, immediate retention, and 24-h retention). All of the significant main effects and simple main effects involving more than two means were broken down using Bonferroni's adjustment. For all comparisons, an effect was deemed significant if p < 0.05. Partial eta square ( η p 2 ) is the effect size reported for all significant effects ( Cohen, 1988 ).

The ANOVA computed for the |CE| of movement time (Figure 3 ) revealed significant main effects for the variable group, F (2, 57) = 8.13, p = 0.001, η p 2 = 0.22, and phase, F (2, 114) = 21.13, p < 0.001, η p 2 = 0.27, as well as a significant group × phase interaction, F (4, 114) = 2.57, p = 0.042, η p 2 = 0.08. The breakdown of this interaction did not reveal any significant group differences in the pre-test ( F < 1). In the immediate retention test, F (2, 57) = 10.27, p < 0.002, η p 2 = 0.27, the FB1-2 group had a significantly larger | CE | than both the FW1-2 and the FW/FB groups ( p < 0.001 in both cases), which did not differ significantly from one another ( p >0.20). In the 24-h retention test, F (2, 57) = 3.19, p = 0.049, η p 2 = 0.10, the FW1-2 group had a slightly smaller |CE| than the FB1-2 group ( p = 0.079) 3 .

Figure 3. Absolute constant error of TMT as a function of the experimental phases and experimental groups (Experiment 2) . * p < 0.05. Error bars indicate standard error of the mean.

The ANOVA computed on VE (not shown) revealed significant main effects for the variable group, F (2, 57) = 7.82, p = 0.001, η p 2 = 0.21, and phase, F (2, 114) = 21.10, p < 0.001, η p 2 = 0.27, as well as a significant group × phase interaction, F (4, 114) = 4.38, p = 0.002, η p 2 = 0.13. The breakdown of this interaction did not reveal any significant group differences in the pre-test ( F < 1) and in the 24-h retention test, F (2, 57) = 1.26, p >0.20. In the immediate retention test, F (2, 57) = 10.26, p < 0.002, η p 2 = 0.27, the FB1-2 group (62.7 ms) had a significantly larger VE than both the FW1-2 (51.1 ms) and the FW/FB (53.4 ms) groups ( p < 0.001 in both cases), which did not differ significantly from one another ( p >0.20) 4 .

The ANOVA computed for the RMSE of relative timing revealed significant main effects for the variable group, F (2, 57) = 4.86, p = 0.01, η p 2 = 0.15, and phase, F (2, 114) = 78.21, p < 0.001, η p 2 = 0.58. There was a significantly larger RMSE of relative timing in the pre-test than in both the immediate retention test and the 24-h retention test ( p < 0.001 in both cases; see Figure 4 , right panel), which did not differ significantly from one another ( p >0.20). Finally, the FW1-2 and the FW/FB groups outperformed the FB1-2 group ( p = 0.01 and p = 0.07; see Figure 4 , left panel) but did not significantly differ from one another ( p >0.20).

Figure 4. Root mean square error of relative timing (Experiment 2) as a function of the experimental groups (left panel) and experimental phases (right panel) . * p < 0.05. Error bars indicate standard error of the mean.

As expected, the decrease in error noted when going from pre-test to the retention tests supports previous findings indicating that observation aids learning of a new relative timing pattern ( Blandin et al., 1999 ; Rohbanfard and Proteau, 2011 ; Andrieux and Proteau, 2013 , 2014 ). More importantly, the results of Experiment 2 replicated those of Experiment 1, in that the FW1-2 group outperformed the FB1-2 group. Therefore, it can be safely concluded that learning to change the relative timing pattern that naturally emerges from the task's constraints to a new, imposed relative timing through observation is favored when one is informed of the model's performance prior to rather than after observation. Finally, the results also showed that what has been learned in a FB protocol does not add up to what can be learned in a FW protocol.

General Discussion

The main goal of the present study was to determine when in an observation protocol should KR concerning the model performance be provided, i.e., before or after each demonstration. The results of the two experiments of the present study clearly indicated that being informed of the model's performance before each demonstration favored learning of a new relative timing pattern better than when the observer was informed of the model's performance after each demonstration. Moreover, the results of Experiment 2 suggest that the advantage of the FW over the FB protocol remained significant even when the number of observation trials was doubled. Concerning this last point, we do not argue that a FW protocol should be favored in all cases and with all levels of expertise of the observers. Rather, we underline that the effect is reliable when novice observers are considered.

Our results may indicate that a feedforward observation protocol prepares the observer to engage specifically in either imitation processes when an expert or advanced performance is shown or in error detection processes when a beginner or novice performance is presented. This idea fits well with previous work from Decety et al. (1997) , which stated that the patterns of brain activation during action observation depend on both the nature of the required executive processing and the extrinsic properties of the action presented. Specifically, these authors demonstrated that different areas of the brain become more active when one observes to recognize, which could be the case when observing a novice model or a poor or intermediate performance, and when one observes to imitate, which is likely to be the case when observing an expert model.

An alternative explanation of our findings could be that a FW protocol results in a “deactivation” of the AON when the participants were explicitly informed that a poor demonstration would follow. For instance, in an object-lifting task, it has been shown that the modulation of motor evoked potential (MEP) by transcranial magnetic stimulation (TMS) during observation of the lifting action is scaled to the force required to perform the grasping and lifting action ( Alaerts et al., 2010a ). It was also shown that when visual cues suggested that the object was heavier than in really was, the MEP modulation depended primarily on the observed kinematic profile rather than on the apparent weight of the object ( Alaerts et al., 2010b ; Senot et al., 2011 ). However, in a study by Senot et al. (2011) , false explicit information concerning the weight of the object was provided in one experimental condition. This resulted in a conflict between the expected kinematic profile given the announced weight and the actual kinematic profile of the grasping and lifting action, leading to a “general inhibition of the corticospinal system.” Stated differently, at least a portion of the AON had been turned off. Therefore, it could be that the participants in our study turned off the AON when poor performance of the model was expected, leaving the AON active only for good trials.

This proposition is difficult to reconcile, however, with recent reports from our laboratory showing that observing both an expert and a novice model resulted in better learning of a new relative timing pattern than observing either a novice model or an expert model alone. If one could turn off the AON when informed that a poor demonstration will be shown (i.e., a novice model), then learning of the mixed observation group would have matched and not surpassed that of the expert observation group. Rather, going back to our first proposition, we suggest that a FW protocol helps novice performers detect and quantify errors in the model's performance, something they usually do poorly ( Aglioti et al., 2008 ; Wright et al., 2010 ; Abreu et al., 2012 ; Tomeo et al., 2013 ; Balser et al., 2014 ; Candidi et al., 2014 ; Renden et al., 2014 ). In turn, the better detection and quantification of the model's performance may favor the development of inverse ( Jordan, 1996 ) and forward models ( Wolpert and Miall, 1996 ) of motor control.

In conclusion, observation is a powerful learning tool that is available to anyone and requires only minimal equipment to be used. It is now well-demonstrated that the benefits of observation for modifying the relative timing (i.e., tempo) of motor skill are enhanced when one has access to a variety of performances ranging from novices to experts either through variable or mixed observation schedules. The results of the present study suggest that those benefits are optimized if the observer knows beforehand the quality of the performance that she or he is about to observe during the first observation session. This could be very important in a classroom context in which a teacher/trainer would use a video observation protocol. For example, if the intention of the observer is to learn a specific aspect of a golf swing, it is likely that the result of the swing (i.e., the ball flight) will not be shown on the video. Therefore, the observer would not be able to “guess” the expertise of the model from the result of the swing and, as we have shown in the present study, learn better if he or she was informed in advance of the quality of what he or she is about to observe.

Author Contributions

All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.

This work was supported by a Discovery Grant (LP) provided by the Natural Sciences and Engineering Research Council of Canada (grant no. 111280-2013).

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.

1. ^ To ascertain that the differences noted in the two retention tests between the control group and the FW and FB groups resulted from a significant decrease in the |CE| of total movement time, in a supplementary analysis we decomposed the group × phase interaction reported in the main text by computing a separate ANOVA for each group. The results revealed that for the control and the FB groups, the |CE| of total movement time did not significantly differ across the phases, [ F (2, 86) = 1.58, p = 0.21, η p 2 = 0.04, and F (2, 86) < 1, p = 0.37, η p 2 = 0 . 02 ], respectively. On the contrary, for the FW groups, there was a significant main effect of the phases, F (2, 86) = 11.60, p < 0.01, η p 2 = 0 . 1 , that revealed a significant decrease in the |CE| of total movement time going from the pre-test to the two retention tests ( p < 0.01), which did not differ significantly from one another ( p >0.10).

2. ^ As we did for the |CE| of total movement time, in a supplementary analysis we decomposed the group × phase interaction reported in the main text for the RMSE of relative timing by computing a separate ANOVA for each group. The results revealed that for the control group, the RMSE of relative timing did not significantly differ across the phases, F (2, 86) = 0.32, p = 0.72, η p 2 = 0 . 01 . On the contrary, for both the FB and the FW groups, there was a significant main effect of the phases [FB: F (2, 86) = 11.82, p < 0.001, η p 2 = 0 . 22 ; FW: F (2, 86) = 35.62, p < 0.001, η p 2 = 0 . 45 ] that revealed a significant decrease in the RMSE of relative timing going from the pre-test to the two retention tests ( p < 0.01), which did not differ significantly from one another ( p > 0.10).

3. ^ As we did in Experiment 1, in a supplementary analysis we decomposed the group × phase interaction reported in the main text by computing a separate ANOVA for each group. The results revealed that for the FB1-2 group, the |CE| of total movement time did not significantly differ across the phases, F (2, 56) < 1, p = 0.45, η p 2 = 0 . 03 . On the contrary, for both the FW1-2 and FW-FB groups, there was a significant main effect of the phases, [FW1-2: F (2, 56) = 8.20, p = 0.001, η p 2 = 0 . 23 ; FW-FB: F (2, 56) = 13.76, p < 0.001, η p 2 = 0 . 33 ] that, for both groups, revealed a significant decrease in the |CE| of total movement time going from the pre-test to the two retention tests ( p < 0.01), which did not differ significantly from one another ( p >0.10).

4. ^ For the VE of total movement time, the breakdown of the group × phase interaction revealed a significant main effect of phases for all three groups [FW1-2: F (2, 56) = 10.27, p < 0.001, η p 2 = 0 . 27 ; FW-FB: F (2, 56) = 4.71, p = 0.013, η p 2 = 0 . 14 ; FB1-2: F (2, 56) = 7.38, p = 0.001, η p 2 = 0 . 21 ]. For the FW1-2 and the FB1-2 groups, pos-hoc comparisons revealed a significantly larger VE in pre-test than in both retention tests ( p < 0.01), which did not differ significantly from one another ( p >0.30). For the FW-FB group, the VE of total movement time was significantly larger in the pre-test than in the 24-h retention test ( p < 0.01).

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Keywords: action observation network, motor learning, knowledge of results, feedback, feedforward, relative timing

Citation: Andrieux M and Proteau L (2016) Observational Learning: Tell Beginners What They Are about to Watch and They Will Learn Better. Front. Psychol . 7:51. doi: 10.3389/fpsyg.2016.00051

Received: 06 July 2015; Accepted: 11 January 2016; Published: 29 January 2016.

Reviewed by:

Copyright © 2016 Andrieux and Proteau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Luc Proteau, [email protected]

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

Albert Bandura’s Bobo Doll Experiment (Explained)

The Bobo Doll Experiment was a study by Albert Bandura to investigate if social behaviors can be learned by observing others in the action. According to behaviorists, learning occurs only when a behavior results in rewards or punishment. However, Bandura didn't believe the framework of rewards and punishments adequately explained many aspects of everyday human behavior.

According to the Social Learning Theory, people learn most new skills through modeling, imitation, and observation. Bandura believed that people could learn by observing how someone else is rewarded or penalized instead of engaging in the action themselves.

In the hit television show Big Little Lies, tensions run high as an unknown child is accused of choking another student. The child is revealed as Max throughout the series (spoiler alert!). Max has an abusive father, and once Max’s mother realizes that her child is learning behaviors from her husband, she decides to take action. 

This cycle of abuse is sad but extremely common. Many abusers were abused themselves or grew up in an abusive household. These ideas seem obvious, but in the mid-20th century, evidence that supports these ideas was becoming known. 

What is the Bobo Doll Experiment?

In 1961, Albert Bandura conducted the Bobo doll experiment at Stanford University. He placed children in a room with an adult, toys, and a five-foot Bobo Doll. (Bobo Dolls are large inflatable clowns shaped like a bowling ball, so they  roll upward if punched or knocked down.)

Who Conducted the Bobo Doll Experiment?

This experiment made Albert Bandura one of the most renowned psychologists in the history of the world. He is now listed in the ranks of Freud and B.F. Skinner, the psychologist who developed the theory of operant conditioning . 

How Was The Bobo Doll Experiment Conducted?

Let’s start by discussing Bandura’s first Bobo doll experiment from 1961. Bandura conducted the experiment in three parts: modeling, aggression arousal, and a test for delayed imitation. 

Stage 1: Modeling 

The study was separated into three groups, including a control group. An aggressive adult behavior model was shown to one group, a non-aggressive adult behavior model to another, and no behavior models were shown to the third group. In the group with the aggressive adult, some models chose to hit the Bobo doll over the head with a mallet. 

The group with a nonaggressive adult simply observed the model playing with blocks, coloring, or doing non-aggressive activities. 

Stage 2: Aggression Arousal 

After 10 minutes of being in the room with the model, the child was taken into another room. This room had attractive toys; the researchers briefly allowed the children to play with the toys of their choice. Once the child was engaged in play, the researchers removed the toys from the child and took them into yet another room. It’s easy to guess that the children were frustrated, but the researchers wanted to see how they would release that frustration. 

Stage 3: Test For Delayed Imitation 

The third room contained a set of “aggressive” and “non-aggressive toys.” The room also had a Bobo doll. Researchers watched and recorded each child’s behavior through a one-way mirror. 

So what happened?

As you can probably guess, the children who observed the adults hitting the Bobo doll were more likely to take their frustration out on the Bobo doll. They kicked, yelled at, or even used the mallet to hit the doll. The children who observed the non-aggressive adults tended to avoid the Bobo doll and take their frustration out without aggression or violence. 

The Second Bobo Doll Experiment

Albert Bandura did not stop with the 1961 Bobo doll experiment. Two years later, he conducted another experiment with a Bobo doll. This one combined the ideas of modeling with the idea of conditioning. Were people genuinely motivated by consequences, or was there something more to their behavior and attitudes? 

In this experiment, Bandura showed children a video of a model acting aggressively toward the Bobo doll. Three groups of children individually observed a different final scene in the video. The children in the control group did not see any scene other than the model hitting the Bobo doll. In another group, the children observed the model getting rewarded for their actions. The last group saw the model getting punished and warned not to act aggressively toward the Bobo doll. 

All three groups of children were then individually moved to a room with toys and a Bobo doll. Bandura observed that the children who saw the model receiving a punishment were less likely to be aggressive toward the doll. 

A second observation was especially interesting. When researchers asked the children to act aggressively toward the Bobo doll, as they did in the movie, the children did.

This doesn’t sound significant, but it does make an interesting point about learned behaviors. The children learn the behavior by watching the model and observing their actions. Learning (aka remembering) the learning of the model’s actions occurred simply because the children were there to observe them.

Consequences simply influenced whether or not the children decided to perform the learned behaviors. The memory of the aggression was still present, whether or not the child saw that the aggression was rewarded or punished. 

Is The Bobo Doll Experiment An Example of Operant Conditioning or Classical Conditioning?

Neither! Since operant and classical conditioning rely on explicit rewards or penalties to affect behavior repetition, they fall short of capturing the full scope of human learning. Conversely, observational learning is not dependent on these rewards. Albert Bandura's well-known "Bobo Doll" experiment is a striking example.

This experiment proved that without firsthand experience or outside rewards and penalties, people might learn only by watching others. The behaviorist ideas of the time, which were primarily dependent on reinforcement, faced a severe challenge from Bandura's research.

Criticism of the Bobo Doll Experiment

A Reddit user on the TodayILearned subreddit made a good point on how the Bobo Doll Experiment was conducted: 

"A significant criticism of this study is that the Bobo doll is MEANT to be knocked around. It’s an inflatable toy with a weight at the bottom, it rocks back and forth and stands back up after it is hit.

How do we know that the kids didn’t watch the adults knock over the toy and say, 'That looks fun!' and then mimic them? These types of toys are still often sold as punching bag toys for kids. This study would have much more validity if they had used a different type of toy."

Bobo Doll Impact

There’s one more piece of the 1963 study that is worth mentioning. While some children in the experiment watched a movie, others watched a live model. Did this make a huge difference in whether or not the child learned and displayed aggressive behaviors?

Not really.

The Bobo Doll experiment has frequently been cited in discussions among psychologists and researchers, especially when debating the impact of violent media on children. A wealth of research has sought to determine whether children engage with violent video games and consume violent media, does it increase their likelihood to act out violently? Or, as suggested by the Bobo Doll experiment, do children merely internalize these behaviors and still maintain discretion over whether to act on them or not?

Multiple studies have aimed to tackle this question. For instance, research from the American Psychological Association has pointed to a link between violent video games and increased aggression, though not necessarily criminal violence. However, other sources, such as the Oxford Internet Institute , have found limited evidence to support a direct link between game violence and real-world violent actions. Despite the varying findings, the influence of Albert Bandura's introduction of observational learning and social learning theory cannot be understated. His Bobo Doll experiments remain pivotal in psychology's rich history.

Related posts:

• Albert Bandura (Biography + Experiments)
• 3 Theories of Aggression (Psychology Explained)

Observational Learning

• 40+ Famous Psychologists (Images + Biographies)
• Learning (Psychology)

Reference this article:

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Operant Conditioning

Classical Conditioning

Latent Learning

Experiential Learning

The Little Albert Study

Bobo Doll Experiment

Spacing Effect

Von Restorff Effect

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What the Bobo Doll Experiment Reveals About Kids and Aggression

• The Experiment

The question of how children learn to engage in violent behavior has been of great interest to parents and researchers alike. In the 1960s, psychologist Albert Bandura and his colleagues conducted what is now known as the Bobo doll experiment, and they demonstrated that children may learn aggression through observation.

Aggression lies at the root of many social ills ranging from interpersonal violence to war. It is little wonder, then, that the subject is one of the most studied topics within psychology.

This article covers what the Bobo doll experiment is, its findings on childhood aggression, as well as its impact on psychology.

The Bobo Doll Experiment

The participants for the experiment were 36 boys and 36 girls enrolled at the Stanford University Nursery School. The children ranged in age between 3 and almost 6 years.

The experiment involved exposing one group of 24 children to an adult modeling aggressive behavior, and another group of 24 children to an adult modeling non-aggressive behavior. The final group of 24 children acted as the control group that was not exposed to adult models.

These groups were divided again into groups of boys and girls. Each of these subgroups was then divided so that half of the participants would be exposed to a same-sex adult model and the other half would be exposed to an opposite-sex adult model.

Each child was tested individually to ensure that their behavior would not be influenced by other children. The child was first brought into a playroom where there were a number of different activities to explore. The experimenter then invited the adult model into the playroom.

In the non-aggressive condition, the adult model simply played with the toys and ignored the Bobo doll for the entire period. In the aggressive model condition, however, the adult models would violently attack the Bobo doll.

The aggressive models would punch Bobo, strike Bobo with a mallet, toss the doll in the air, and kick it around the room. They would also use " verbally aggressive phrases" such as "Kick him" and "Pow." The models also added two non-aggressive phrases: "He sure is a tough fella" and "He keeps coming back for more."

After the ten-minute exposure to the adult model, each child was then taken to another room that contained a number of appealing toys including a doll set, fire engine, and toy airplane.

The children were permitted to play for a brief two minutes, then told they were no longer allowed to play with any of these tempting toys. The purpose of this was to build up frustration levels among the young participants.

Finally, each child was taken to the last experimental room. This room contained a number of "aggressive" toys including a mallet, a tether ball with a face painted on it, dart guns, and, of course, a Bobo doll. The room also included several "non-aggressive" toys including crayons, paper, dolls, plastic animals, and trucks.

Each child was then allowed to play in this room for a period of 20 minutes. During this time, researchers observed the child's behavior from behind a one-way mirror and judged each child's levels of aggression.

Predictions

Bandura made several key predictions about what would occur during the Bobo doll experiment.

• Boys would behave more aggressively than girls.
• Children who observed an adult acting aggressively would be likely to act aggressively, even when the adult model was not present.
• Children would be more likely to imitate models of the same sex rather than models of the opposite sex.
• The children who observed the non-aggressive adult model would be less aggressive than the children who observed the aggressive model; the non-aggressive exposure group would also be less aggressive than the control group.

The results of the experiment supported some of the original predictions, but also included some unexpected findings:

• Bandura and his colleagues had predicted that children in the non-aggressive group would behave less aggressively than those in the control group. The results indicated that while children of both genders in the non-aggressive group did tend to exhibit less aggression than the control group, boys who had observed a non-aggressive, opposite-sex model were more likely than those in the control group to engage in violence.
• Children exposed to the violent model tended to imitate the exact behavior they had observed when the adult model was no longer present.
• Researchers were correct in their prediction that boys would behave more aggressively than girls. Boys engaged in more than twice as many acts of physical aggression than the girls.
• There were important gender differences when it came to whether a same-sex or opposite-sex model was observed. Boys who observed adult males behaving violently were more influenced than those who had observed female models behaving aggressively.
• Interestingly, the experimenters found in same-sex aggressive groups, boys were more likely to imitate physical acts of violence while girls were more likely to imitate verbal aggression.

Impact of the Bobo Doll Experiment

Results of the experiment supported Bandura's social learning theory.

According to Bandura's social learning theory, learning occurs through observations and interactions with other people. Essentially, people learn by watching others and then imitating these actions.

Bandura and his colleagues believed that the Bobo doll experiment demonstrates how specific behaviors can be learned through observation and imitation.

According to Bandura, the violent behavior of the adult models toward the dolls led children to believe that such actions were acceptable.

Bandura also suggested that as a result, children may be more inclined to respond to frustration with aggression in the future.

In a follow-up study conducted in 1965, Bandura found that while children were more likely to imitate aggressive behavior if the adult model was rewarded for his or her actions, they were far less likely to imitate if they saw the adult model being punished or reprimanded for their hostile behavior.

The conclusions drawn from the Bobo doll experiment may help explain human behavior in many areas of life. For instance, the idea that children will imitate the abusive behavior that they witness may provide insight into domestic violence .

Adolescents who grow up witnessing abuse in their homes may be more likely to display violent behavior themselves, and view aggression as an appropriate response to solve interpersonal problems.

Research has found that the Bobo doll experiment and its follow-up study shed light on bullying . For instance, when leadership doesn't give negative consequences for workplace bullying, the bullying is more likely to persist.

Therefore, it's important that aggressive or violent behavior is not tolerated by those with power—whether it's at the workplace, in schools, or at home—or else the aggression is likely to continue and may influence young people who witness it.

Criticism of the Bobo Doll Experiment

Critics point out that acting violently toward a doll is a lot different than displaying aggression or violence against another human being in a real-world setting.

In other words, a child acting violently toward a doll doesn't necessarily indicate they'll act violently toward a person.

Because the experiment took place in a lab setting, some critics suggest that results observed in this type of location may not be indicative of what takes place in the real world.

It has also been suggested that children were not actually motivated to display aggression when they hit the Bobo doll; instead, they may have simply been trying to please the adults. It's worth noting that the children didn't actually hurt the Bobo doll, nor did they think they were hurting it.

In addition, by intentionally frustrating the children, some argue that the experimenters were essentially teaching the children to be aggressive.

It's also not known whether the children were actually aggressive or simply imitating the behavior without aggressive intent (most children will imitate behavior right after they see it, but they don't necessarily continue it in the long term).

Since data was collected immediately, it is also difficult to know what the long-term impact might have been.

Additional criticisms note the biases of the researchers. Since they knew that the children were already frustrated, they may have been more likely to interpret the children's actions as aggressive.

The study may also suffer from selection bias. All participants were drawn from a narrow pool of students who share the same racial and socioeconomic background. This makes it difficult to generalize the results to a larger, more diverse population.

A Word From Verywell

Bandura's experiment remains one of the most well-known studies in psychology. Today, social psychologists continue to study the impact of observed violence on children's behavior. In the decades since the Bobo doll experiment, there have been hundreds of studies on how observing violence impacts children's behavior.

Today, researchers continue to ponder the question of whether the violence children witness on television, in the movies, or through video games translates to aggressive or violent behavior in the real world.

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

Albert Bandura’s Social Learning Theory

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.

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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.

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What is Social Learning Theory?

SLT is often described as the ‘bridge’ between traditional learning theory ( behaviorism)  and the cognitive approach. This is because it focuses on how mental (cognitive) factors are involved in learning.

Unlike Skinner, Bandura (1977) believes humans are active information processors and think about the relationship between their behavior and its consequences.

Albert Bandura’s social learning theory suggests that people learn new behaviors by observing and imitating others.

The theory emphasizes the importance of observational learning, where individuals acquire knowledge, skills, attitudes, and beliefs by watching the actions of others and the consequences that follow, leading to the modeling and adoption of observed behaviors.

Assumptions

Social learning theory, proposed by Albert Bandura, emphasizes the importance of observing, modeling, and imitating the behaviors, attitudes, and emotional reactions of others.

Social learning theory considers how both environmental and cognitive factors interact to influence human learning and behavior.

In social learning theory, Albert Bandura (1977) agrees with the behaviorist learning theories of classical conditioning and operant conditioning . However, he adds two important ideas:

• Mediating processes occur between stimuli & responses.
• Behavior is learned from the environment through the process of observational learning.

Mediational Processes

Observational learning could not occur unless cognitive processes were at work. These mental factors mediate (i.e., intervene) in the learning process to determine whether a new response is acquired.

Therefore, individuals do not automatically observe the behavior of a model and imitate it. There is some thought prior to imitation, and this consideration is called the mediational process.

This occurs between observing the behavior (stimulus) and imitating it or not (response).

There are four mediational processes proposed by Bandura (1969, 1971, 1977). Each of these components is crucial in determining whether or not imitation occurs upon exposure to a model:

1. Attention

Attentional processes are crucial because mere exposure to a model doesn’t ensure that observers will pay attention (Bandura, 1972).

The model must capture the observer’s interest, and the observer must deem the model’s behavior worth imitating. This decides if the behavior will be modeled.

The individual needs to pay attention to the behavior and its consequences and form a mental representation of the behavior.

For a behavior to be imitated, it has to grab our attention. We observe many behaviors on a daily basis, and many of these are not noteworthy. Attention is, therefore, extremely important in whether a behavior influences others to imitate it.

2. Retention

Bandura highlighted the retention process in imitation, where individuals symbolically store a model’s behavior in their minds.

For successful imitation, observers must save these behaviors in symbolic forms, actively organizing them into easily recalled templates (Bandura, 1972).

How well the behavior is remembered. The behavior may be noticed, but it is not always remembered, which obviously prevents imitation.

It is important, therefore, that a memory of the behavior is formed to be performed later by the observer.

Much of social learning is not immediate, so this process is especially vital in those cases. Even if the behavior is reproduced shortly after seeing it, there needs to be a memory to refer to.

3. Motor Reproduction

This is the ability to perform the behavior that the model has just demonstrated. We see much behavior daily that we would like to be able to imitate, but this is not always possible.

Our physical ability limits us, so even if we wish to reproduce the behavior, we sometimes cannot.

This influences our decisions whether to try and imitate it or not. Imagine the scenario of a 90-year-old lady who struggles to walk while watching Dancing on Ice.

She may appreciate that the skill is desirable, but she will not attempt to imitate it because she physically cannot do it.

Motor reproduction processes use internal symbolic images of observed behaviors to guide actions (Bandura, 1972). An observer internally replicates a behavior using these symbols as a reference, even if it’s not externally shown (Manz & Sims, 1981).

4. Motivation

Lastly, motivational and reinforcement processes refer to the perceived favorable or unfavorable consequences of mimicking the model’s actions that are likely to increase or decrease the likelihood of imitation.

The will to perform the behavior. The observer will consider the rewards and punishments that follow a behavior.

If the perceived rewards outweigh the perceived costs (if any), the observer will more likely imitate the behavior.

If the vicarious reinforcement is unimportant to the observer, they will not imitate the behavior.

What is Observational Learning?

Observational learning is a key aspect of social learning theory, where individuals learn and adopt behaviors by observing others.

This process often involves modeling after those who are similar, high-status, knowledgeable, rewarded, or nurturing figures in our lives.

Children observe the people around them behaving in various ways. This is illustrated during the famous Bobo doll experiment (Bandura, 1961).

What is a model?

Individuals that are observed are called models. In society, children are surrounded by many influential models, such as parents within the family, characters on children’s TV, friends within their peer group, and teachers at school.

These models provide examples of behavior to observe and imitate, e.g., masculine and feminine, pro and anti-social, etc.

Children pay attention to some of these people (models) and encode their behavior.  At a later time, they may imitate (i.e., copy) the behavior they have observed.

They may do this regardless of whether the behavior is ‘gender appropriate’ or not, but there are several processes that make it more likely that a child will reproduce the behavior that society deems appropriate for its gender.

Albert Bandura, through his work on social learning theory, identified three primary models of observational learning:

Live Model : Observing an actual individual perform a behavior.

Verbal Instructional Model : Listening to detailed descriptions of behavior and then acting based on that description.

Symbolic Model : Learning through media, such as books, movies, television, or online media, where behaviors are demonstrated.

Through these models, individuals can vicariously learn by watching others without necessarily undergoing direct firsthand experiences.

Influences on Observational Learning

Based on Bandura’s research, several factors enhance the likelihood of a behavior being imitated. We are more prone to imitate behaviors when the following conditions apply:

Attentional Processes

1. similarity of the model.

We are more likely to model our behaviors after individuals who are similar to us. This is because we are more likely to identify with these individuals, making their behaviors seem more relevant and attainable.

This can include similarity in terms of age, gender, ethnicity, or even shared interests and values (e.g., Lockwood & Kunda, 1997; Marx & Ko, 2012).

2. Identification with the Model

Identification occurs with another person (the model) and involves taking on (or adopting) observed behaviors, values, beliefs, and attitudes of the person you identify with.

The motivation to identify with a particular model is that they have a quality that the individual would like to possess.

The more an individual identifies with the model (for instance, because they are similar or aspire to be like the model), the more likely they are to imitate their behavior.

This relates to an attachment to specific models that possess qualities seen as rewarding. Children will have several models with whom they identify. These may be people in their immediate world, such as parents or older siblings, or they could be fantasy characters or people in the media.

Identification differs from imitation as it may involve adopting several behaviors, whereas imitation usually involves copying a single behavior.

Motivational Processes

3. rewarded behaviors.

Individuals who see that a model is rewarded for their behaviors are likelier to imitate them, while behavior resulting in negative outcomes is less likely to be copied.

This is known as vicarious reinforcement. For instance, if a student sees that another student gets praised by the teacher for asking questions, they are likelier to ask questions themselves.

The way role models achieve success impacts their effectiveness. People benefit more from role models whose success is due to factors they can control, like effort, rather than uncontrollable factors like innate talent (Weiner, 1979, 1985).

Studies showed girls performed better in math when their role model’s success was linked to effort. In contrast, if the success was attributed to natural talent, their performance declined compared to boys (Bàges, Verniers, & Martinot, 2016).

4. Status of the Model

We are likelier to imitate individuals who hold high-status positions, such as leaders, celebrities, or successful people in our field of interest.

High-status individuals are often admired and seen as role models, so their behaviors are likelier to be seen as desirable and worth imitating.

People are also more likely to imitate experts or knowledgeable individuals in a certain area. These individuals’ behaviors are seen as effective and efficient ways of achieving goals in that area.

5. Reinforcement and punishment

The people around the child will respond to the behavior it imitates with either reinforcement or punishment.  If a child imitates a model’s behavior and the consequences are rewarding, the child will likely continue performing the behavior.

If a parent sees a little girl consoling her teddy bear and says, “what a kind girl you are,” this is rewarding for the child and makes it more likely that she will repeat the behavior.  Her behavior has been positively reinforced (i.e., strengthened).

Reinforcement can be external or internal and can be positive or negative.  If a child wants approval from parents or peers, verbal approval is an external reinforcement, but feeling happy about being approved of is an internal reinforcement.  A child will behave in a way that it believes will earn approval because it desires approval.

Positive (or negative) reinforcement will have little impact if the external reinforcement does not match an individual’s needs.  Reinforcement can be positive or negative , but the important factor is that it will usually change a person’s behavior.

Sense of Belonging : Exposure to positive role models in education enhances a sense of belonging, especially for groups subjected to negative stereotypes like women and racial minorities in STEM (Dasgupta, 2011; Rosenthal et al., 2013).

For instance, women who read about successful female physicians in male-dominated careers felt a stronger connection to their own paths (Rosenthal et al., 2013).

Self-Efficacy : Self-efficacy, the belief in one’s abilities, greatly influences whether a person will imitate an observed behavior.

Women in calculus classes reported higher self-efficacy and participation when taught by female professors compared to male professors (Stout et al., 2011).

The women’s identification with their female professors significantly predicted this increased belief in their own abilities. 

Increased Achievement : Students who read about the challenges overcome by famous scientists performed better than those who read only about their achievements (Lin-Siegler et al., 2016). Observing perseverance fosters personal performance.

For example, college freshmen were more motivated by successful seniors than fourth-year students were, likely because the freshmen felt they had more time to achieve similar success (Lockwood & Kunda, 1997).

For example, women were more interested in computer science when interacting with relatable models, like a casually dressed and socially skilled computer scientist, than with stereotypical ones (Cheryan et al., 2011).

 Media Violence

• Children observe violent behavior in media and tend to mimic or imitate it. This imitation occurs through social learning processes and is likely mediated by “mirror neurons” that activate when actions are observed or performed (Huesmann, 2005).
• Extensive observation of violence can bias children’s world schemas toward attributing hostility or negative intentions to others’ actions. These hostile attributions increase the likelihood of behaving aggressively (Huesmann & Kirwil, 2007).
• Children acquire social scripts for behaviors they observe around them, including in the media. Once learned, these scripts can automatically control social behavior. Exposure to media violence provides aggressive scripts.
• Normative beliefs about acceptable social behaviors crystallize as children mature. These beliefs act as filters limiting inappropriate behaviors. Observing violence in media can influence which behaviors children see as normative or acceptable.
• Repeated exposure to media violence can lead to desensitization – the diminishing of emotional responses to violence. This makes it easier for children to think about and plan aggressive acts without negative affect.
• Playing violent video games allows for enactive learning of aggression, as players actively participate and are rewarded for violent actions in the game. This should strengthen the learning of aggression beyond passive media observation.

Social Learning Theory Evaluation

The social learning approach takes thought processes into account and acknowledges the role that they play in deciding if a behavior is to be imitated or not.

As such, SLT provides a more comprehensive explanation of human learning by recognizing the role of mediational processes.

For example, Social Learning Theory can explain many more complex social behaviors (such as gender roles and moral behavior) than models of learning based on simple reinforcement .

Lack of Clarity about Cognitive Processes

Some critics argue that social learning theory does not fully explain the cognitive processes involved in learning or how they interact with environmental and individual factors.

However, although it can explain some quite complex behavior, it cannot adequately account for how we develop a range of behavior, including thoughts and feelings.

We have a lot of cognitive control over our behavior, and just because we have had experiences of violence does not mean we have to reproduce such behavior.

For this reason, Bandura modified his theory and, in 1986, renamed his Social Learning Theory, Social Cognitive Theory (SCT), as a better description of how we learn from our social experiences.

Overemphasis on Observation

Critics suggest that the theory might overstate the role of observational learning while undervaluing other forms of learning, such as operant conditioning or individual exploration and discovery.

Some criticisms of social learning theory arise from their commitment to the environment as the chief influence on behavior.

Describing behavior solely in terms of either nature or nurture is limiting, and attempts to do this underestimate the complexity of human behavior.

It is more likely that behavior is due to an interaction between nature (biology) and nurture (environment).

Finally, observational learning does not happen in isolation. Each individual brings their unique personal characteristics, prior experiences, and current circumstances to the learning process.

These factors can all influence what is learned, how it is interpreted, and whether and when it is acted upon.

Difficulty in Predicting Behavior

Social learning theory provides a valuable framework for understanding how learning occurs. However, predicting behavior in real-world contexts can be challenging, given the many potential models and reinforcements in a person’s environment.

The complexity of predicting behavior based on the social learning theory stems from the number of potential influencing factors in a person’s environment.

In real-world contexts, an individual is exposed to countless potential role models across various settings, including family, friends, teachers, and media figures.

Moreover, these models’ behaviors are often rewarded or punished inconsistently, further complicating the learning process.

Neglect of Biological Factors

Social learning theory has been critiqued for not adequately addressing biological factors, such as genetic predispositions, which can also impact behavior.

Social learning theory is not a full explanation for all behavior. This is particularly the case when there is no apparent role model in the person’s life to imitate for a given behavior.

The discovery of mirror neurons has lent biological support to the social learning theory. Although research is in its infancy, the recent discovery of “mirror neurons” in primates may constitute a neurological basis for imitation.

These are neurons that fire if the animal does something itself and if it observes the action being done by another.

Freud vs. Bandura

Freud’s psychoanalytic theory and Bandura’s social learning theory both acknowledge the importance of identification, but their perspectives differ significantly.

While both theories acknowledge the importance of identification, they conceptualize it differently and have distinct views on human behavior, learning, and the potential for change.

Focus : Freud’s theory focuses heavily on the unconscious mind , instinctual drives, and early childhood experiences.

On the other hand, Bandura’s social learning theory emphasizes learning through observation and modeling, taking into account cognitive and environmental factors.

Identification : Freud’s concept of identification in the Oedipus complex involves a child identifying with the same-sex parent and internalizing their characteristics.

This process is driven by psychosexual development and often results in the development of gender roles. In contrast, social learning theory sees identification as a more flexible process.

Regardless of age, individuals can identify with and learn from anyone around them, not necessarily limited to parents or same-sex individuals.

Determinism vs. Agency : Freud’s theory leans toward psychic determinism, suggesting that unconscious desires largely shape our behaviors and feelings.

Social learning theory, while acknowledging the influence of environment, also stresses personal agency – our capacity to influence our own behavior and the environment in a purposeful, goal-directed way.

Change : In Freudian theory, personality is largely formed by age 5, making change difficult. Social learning theory suggests that because learning is a lifelong process, individuals can change their behaviors and attitudes throughout life.

Future Research

The motor reproduction process, where observers externally mimic modeled behaviors based on their internalized symbols, is also significant but less explored.

Most research showcases role model successes instead of the actionable steps taken to achieve them (Bandura, 1972).

Detailed behavioral scripts, outlining step-by-step actions, are crucial for observational learning but are often overlooked.

Current role model studies in education don’t emphasize the observer’s cognitive and motivational processes as much as Bandura did, indicating a research gap that needs bridging.

What are the 4 stages of social learning theory?

• Attention : In this stage, individuals must first pay attention to the behavior they are observing. This requires focus and concentration on the model’s behavior.
• Retention : In this stage, individuals must remember the behavior they observed. This involves cognitive processing and memory storage.
• Reproduction : In this stage, individuals attempt to reproduce the behavior they observe. This may involve practicing and refining the behavior until it can be performed accurately.
• Motivation : In this stage, individuals must have a reason or motivation to perform the behavior. This may involve reinforcement, punishment, social approval, disapproval, or other incentives.

What is the main idea of social learning theory?

Social Learning Theory, proposed by Albert Bandura, posits that people learn through observing, imitating, and modeling others’ behavior. This theory posits that we can acquire new behaviors and knowledge by watching others, a process known as vicarious learning.

Bandura emphasized the importance of cognitive processes in learning, which set his theory apart from traditional behaviorism.

He proposed that individuals have beliefs and expectations that influence their actions and can think about the links between their behavior and its consequences.

Why is social learning theory important?

Social learning theory helps us understand how our environment and the people around us shape our behavior. It helps explain how individuals develop new skills and behaviors by paying attention to the behavior of others and then trying to reproduce that behavior themselves.

It is an important theory for psychologists, educators, and anyone interested in human behavior and development.

Who is Albert Bandura?

Albert Bandura was a prominent Canadian-American psychologist known for his work in social learning theory and the concept of self-efficacy.

His groundbreaking research on observational learning, through experiments such as the Bobo Doll experiment, shifted the focus of psychological theory from behaviorism to cognitive processes.

Bandura’s work significantly influenced the understanding of how individuals learn within social contexts.

Albert Bandura is best known for his contributions to the field of psychology, particularly in the areas of social learning theory, self-efficacy, and aggression. He is considered one of the most influential psychologists of the 20th century.

Bandura’s work has significantly impacted our understanding of human behavior and has informed fields such as education, psychology, and social work.

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Albert Bandura Biography, Theories, and Impact

Categories History

Albert Bandura was an influential Canadian-American psychologist known for his social learning theory, the Bobo doll experiment, observational learning, and self-efficacy. Throughout his long career, he left an indelible mark on the field of psychology and influenced other areas such as education and psychotherapy.

In this article, learn more about Albert Bandura, including his early life, research, and impact on psychology.

Table of Contents

Albert Bandura Biography

Albert Bandura was born in Mundare, Canada, a small town in Alberta, on December 4, 1925. He was the youngest of six siblings born to his parents, who immigrated to Canada as teens, his father was from Poland, and his mother was from Ukraine. Two of his older siblings died in childhood—one due to the flu and the other in a hunting accident.

While his parents were not formally educated, they instilled in him a love for learning. He attended a tiny school with only two teachers and few educational materials. As a result, he found that he had to direct much of his own educational pursuits through his own efforts and curiosity.

It was when he started school at the University of British Columbia that he became fascinated with psychology. He had started taking electives to fill extra time, which was how he started with his first psychology course.

After completing his degree in 1949, he went to the University of Iowa for graduate school. He completed his master’s degree in 1951 and his doctorate in clinical psychology in 1952. In 1953, he began teaching at Stanford University, where he would continue to teach for the rest of his career.

Albert Bandura’s Theories

No Albert Bandura biography would be complete without taking a closer look at his influential theories. He developed a social learning theory that emphasized the importance of social learning theory as part of the learning process. During much of the first half of the 20th century, behaviorism dominated the field of psychology.

Bandura believed that conditioning processes, including association and reinforcement , were important, but they couldn’t account for all learning on their own, as behaviorists such as B. F. Skinner suggested.

Among Bandura’s most influential theories, ideas, and research include:

Bandura’s Bobo Doll Experiments

These experiments involved children observing adults behaving aggressively toward a toy Bobo doll. When the children later played with the same doll, they imitated the violent actions the adults previously modeled.

Observational Learning

Observational learning describes the process of observing and imitating others as a way of learning. As Bandura’s experiments demonstrated, this can involve direct and indirect demonstrations.

Social Learning Theory

Social learning theory describes how people learn by observing and imitating others. Bandura later renamed his approach social cognitive theory to emphasize the cognitive factors, including attention and memory, that play a role in social learning.

Self-Efficacy

Self-efficacy is a person’s belief in their own ability to succeed. Bandura was the first to demonstrate that a person’s self-belief influenced what people are close to doing, how they feel about what they do, and how much effort they put in.

His work on self-efficacy had notable parallels to his own life.

“Self-directedness has really served me very well throughout my whole career,” he suggested in a 2012 episode of Inside the Psychologist’s Studio .

“In a way, my psychological theory is founded on human agency, which means that people have a hand in determining the course their lives take, and in many respects, my theory is really a reflection of my life path.”

Albert Bandura’s Impact 

Bandura is widely regarded as one of the most influential psychologists of the 20th century. In a 2002 survey published in the General Review of Psychology , Bandura was named the fourth most influential psychologist of the 20th century.

The other psychologists who ranked ahead of him were Sigmund Freud, Jean Piaget, and B.F. Skinner. 

Throughout his almost 60-year career, Bandura wrote hundreds of scientific papers, and several books, and influenced thousands of students.

His many awards and honors included:

• The Outstanding Lifetime Contribution to Psychology Award from the American Psychological Association
• The James McKeen Cattell Award from the American Psychological Society 
• The Gold Medal Award for Distinguished Lifetime Contribution to Psychological Science from the American Psychological Foundation

He was also made an Officer of the Order of Canada in 2014. In 2016, President Barack Obama presented Bandura with the National Medal of Science.

Bandura died on July 26, 2021, at the age of 95. 

Haggbloom SJ. The 100 Most Eminent Psychologists of the Twentieth Century . PsycEXTRA Dataset. 2001. doi:10.1037/e413802005-787

Maccormick HA. Stanford psychology professor Albert Bandura has died . Published July 30, 2021.

16 Observational Learning Examples

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Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Definition of Observational Learning

There are 4 key factors involved in observational learning according to Albert Bandura (1977), the father of social learning theory (later merging into the social cognitive theory ).

• Attention: The first key element is attention . Observers cannot learn if they are not actually paying attention.
• Retention: Retention means that the observation must be placed in memory.
• Reproduction: Reproduction refers to the observer’s ability to reproduce the behavior observed.
• Motivation: And finally, without motivation to engage in the behavior, it will not be reproduced.

In addition, the person being observed is also a key factor. Models that are in high-status positions, considered experts, are rewarded for their actions, or provide nurturance to the observer, are more likely to have their actions imitated.

Situated Learning Theory also highly values observational learning. This theory argues that people learn best when ‘situated’ within a group of people who are actively completing the tasks that need to be learned. For example, being an apprenticeship getting on-the-job training is seen to be far more valuable than learning about theory in a classroom.

Examples of Observational Learning

1. the bobo dolls experiment.

Dr. Albert Bandura conducted one of the most influential studies in psychology in the 1960s at Stanford University. 

His intention was to demonstrate that cognitive processes play a fundamental role in learning. At the time, Behaviorism was the predominant theoretical perspective, which completely rejected all inferences to constructs not directly observable.

So, Bandura made two versions of a video. In version #1, an adult behaved aggressively with a Bobo doll by throwing it around the room and striking it with a wooden mallet. In version #2, the adult played gently with the doll by carrying it around to different parts of the room and pushing it gently.

After showing children one of the two versions, they were taken individually to a room that had a Bobo doll.

Their behavior was observed and the results indicated that children that watched version #1 of the video were far more aggressive than those that watched version #2. Not only did Bandura’s Bobo doll study form the basis of his social learning theory, it also helped start the long-lasting debate about the harmful effects of television on children.

Note that this study had features of both experiment and observational research – for the difference, see: experiment vs observation .

2. Apprenticeships

Apprenticeships are a perfect example of observational learning. Through an apprenticeship, you can actually watch what the professional is doing rather than simply learning about it in a classroom.

Apprenticeships are therefore very common in practical and hands-on professions like plumbing, carpentry, and cooking.

A similar concept, the internship, occurs in white-collar professions. Internships involve a blend of theoretical learning and apprenticeship scenarios to allow people to learn about the nexus between theory and practice. For example, doctors will often complete their medical degree then do an internship for a few years under more experienced doctors.

3. Learning How to Walk

One of the earliest manifestations of observational learning comes as toddlers learn how to walk. They undoubtedly watch their caregivers stroll across the room countless times a day.

By watching how the legs move forward and backward, accompanied by the slight movements of the upper limbs, they begin to see the basic sequence required to walk.

Of course, standing up is first, followed by maintaining balance and not falling. Those sound easier than they really are. So, children will start by holding on to a table or the edge of the sofa and scoot along.

After some time, a toddler might manage to put together 3 or 4 steps in a row; to the great joy of mom and dad.  

4. Gender Norms

In the last 20 years, there has been increasing recognition that our traditional notions of masculinity and femininity are learned rather than innate.

In other words, boys learn to “act like men” through observing male role models in their lives. Similarly, girls learn to “be girly” by observing other females.

One way that we know that gender stereotypes are learned rather than natural is that different societies have different expressions of gender. In some societies, for example, women are the decision-makers in the household, while in others, men are seen as the head of the household. This has led sociologists and cultural theorists to claim the gender norms are ‘ social constructs ’ which create hierarchies of privilege in society (aka social stratification ).

5. Parallel Play

Parallel play is a stage of play in child development where children observe one another playing. Generally, this stage occurs between ages 2 and 4.

During parallel play, children tend not to play with other children. Instead, they will watch from a distance. You might observe, for example, a child playing with a toy while their sibling or peer keeps an eye on them. Later, that sibling will come up to the toy and play with it in a similar way to the first child. Here, we can see that the second child observed then attempted to mimic the first child.

Observational learning can also occur during cooperative play , a later play stage in Parten’s theory of play-based learning. During cooperative play, children will play together , including each other into their play narratives.

6. Chimpanzee Tool Use

Chimps have been observed in a naturalistic environment using a variety of tools. In nearly all of these observed instances of chimp tool-use, a young chimp can be seen nearby observing. This is how the skill is passed down to younger generations.

For example, some chimps use a twig to collect termites out of a termite hill. The twig is used like a fishing pole to probe the termite hill and then retrieve it covered in termites. Other chimps have been observed using rocks to crack open nuts.

Furthermore, surprisingly, the mother chimp provides very little assistance. “Non-human primates are often thought to learn tool skills by watching others and practicing on their own, with little direct help from mothers or other expert tool users,” says Stephanie Musgrave, first author of the study found here , which includes some amazing videos.

7. Wolf Pack Hunting

Like Chimpanzees, wolves also learn from observation. Wolves are extremely competent hunters. They hunt in packs and take cues from the wolf pack leader.

Young wolves are given roles that are less important in the pack. Their job is to learn, watch, and develop their skills. As they get older and stronger, they move up the hierarchy and take more active roles in the hunt.

In these instances, we can see how hunting in a wolf pack is an example of observational learning. In fact, it’s the perfect example of situated learning : learning by being part of the group. You start out in the periphery, and as you get more competent, you’re given bigger and more important roles.

8. Table Manners and Cutlery

Table manners and learning how to use cutlery are other examples of observational learning.

Young children start by imitating how the spoon is held, how it scoops up food, and then moved to the mouth for consumption. Of course, there are a lot of mistakes along the way and more times than not, more food ends up on the floor than in the mouth.

Over the next few years, the child’s caregivers will demonstrate various table etiquette, such as chewing with one’s mouth closed, keeping the head upright, and not using the plate as a place to mix food and juice.

Obviously, table manners are culturally defined so what a child observes as appropriate behavior in one country might be the exact opposite of “manners” in another.

9. Culturally Defined Gestures

One lesson learned in the age of the internet and cross-cultural communication is that a simple gesture can have a multitude of meanings, completely depending on the culture it is displayed in.

For example, in many Western cultures, a thumbs-up gesture means “okay”. When you show someone that sign it means that you approve and is considered to be encouraging. However, in some Middle Eastern countries, it can be a very insulting taboo . In fact, it can be the equivalent of the middle finger in the West.

This is just one example of an observed behavior that is culturally defined. When travelling abroad, it’s best to do some research beforehand.  

10. Observing Bad Habits on T.V.

People learn a lot valuable skills and habits by watching others. Unfortunately, the same can be said of bad habits. For example, watching movie stars smoke can lead to a lot of people taking up smoking. It looks so cool on the big screen.

Drinking in excess is also a bad habit that can be observed on television. One interesting note here is that you will never see someone actually drinking on a TV commercial in the United States. Although there is no federal law prohibiting it, the industry has imposed this regulation on themselves.

So, observational learning can teach us both constructive and destructive habits.

11. YouTube Tutorial Videos

No matter what it is you want to learn how to do, there is probably a YouTube video tutorial for it.

If you want to learn how to use Photoshop or a specific video-editing program, just type in the appropriate search terms and there you go. The results will show at least a couple dozen options to choose from.

In the video, you can watch someone take you through all the necessary steps. If they go too fast, then just click pause. If what they said seemed a little unclear, then just scroll the video back a little and listen again. It’s super easy and super convenient, and a super example of observational learning.

12. Language Acquisition

Learning to speak a language is a long process. Even if the language is in your native tongue, it still takes years. If you are a second language teacher, then helping your students can take even longer.

One trick of the trade for language teachers is to instruct students to watch the teacher’s lips and mouth as they speak. Correct pronunciation has a lot to do with getting the lips to form a particular shape. Just listening to the instructor can help some, but unless the students form the right shape with their lips, their pronunciation will always be off.

We don’t usually think of learning how to speak a language as an example of observational learning, but it most definitely is.

13. Language on the Playground

The playground is a learning environment all its own. Children learn how to deal with conflicts, develop coordination, and unfortunately, sometimes foul language.

Children have a tendency to imitate others, and sometimes that doesn’t always mean imitating behaviors that are constructive.

Probably most children have heard something on the playground and then went home and repeated it do mom and dad. That can be a big mistake. Hopefully, the parents will understand and not freak out.

That leads to the next example of observational learning: when the parents show their child what is the proper way to handle this kind of situation. Remember, children observe their parents as well. So, if the parents model a certain way to handle this kind of situation, the children will likely imitate later when they have children too.

14. Wearing Seatbelts

The power of observational learning is a double-edged sword. It can lead to people picking up bad habits, or adopting good ones. Wearing a seatbelt is a classic example of how watching a public service announcement condoning a healthy habit has helped save lives.

When seatbelts were first introduced in the early days of the automobile back in the 1880s, they were not greeted warmly by the public, with tragic results.

Eventually, in 1959 Volvo offered the first three-point seatbelt in its cars and shared the patent with other manufacturers. Still, adoption by the public was reluctant.

The tide began to change, however, with the prevalence of dramatic public service announcements that showed what would happen in a car crash if you don’t wear a seatbelt. We call this vicarious punishment .

As a result, in most industrialized countries, seatbelt use is a widely accept social norm .

This is an example of how observational learning has helped save lives around the world.  

15. Cooking Shows

Learning the art of great cooking is a big part trial-and-error. It’s also something that can be learned by watching others. Fortunately, there are tons of cooking shows out there to choose from.

The chefs do a great job of demonstrating how they put together a dish. They will show viewers how to mix certain ingredients, how to slice and dice various items, and what the meal will look like when finished. Sometimes they will even go to the local farmer’s market and show viewers what to look for when selecting the ingredients.

These are all things that can be learned by reading a recipe book, but seeing it first-hand is much more informative.

16. Latent Learning

Latent learning is a form of delayed observational learning. The observed behaviors are only exhibited by the person who learned them at a much later date.

A good example is of a child who might learn new words (often swear words!), but then they do not use them until a week later. The parents turn to the child, shocked, and say “when did you learn that language!?”

But unlike most versions of observational learning (like vicarious learning,  operant conditioning , and  classical conditioning ), there doesn’t seem to be much use of rewards or punishments  in latent learning that is usually assumed to be required for learning to occur.

See More: 17 Examples of Behaviorism

Learning by observation can explain how human beings learn to do so many things. It is probably one of the most fundamental ways that people learn.

Unfortunately, that has both positive and negative manifestations. For example, toddlers learn to walk by observing their parents. Students learn proper pronunciation habits by looking closely at their teacher’s lip formations.

Yet, observing others can also get us in trouble. It can teach us bad habits such as smoking and excessive drinking. It can also be the source of innocent children going home and shocking their parents.

Like most things in life, the good must come with the bad.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790. https://doi.org/10.1037/0022-3514.78.4.772

Bandura, A. (1977).  Social Learning Theory . Prentice Hall.

Berkowitz, L. (1990). On the formation and regulation of anger and aggression: A cognitive-neoassociationistic analysis. American Psychologist , 45 , 494–503.

Musgrave, S., Lonsdorf, E., Morgan, D., Prestipino, M., Bernstein-Kurtycz, L., Mundry, R., & Sanz, C. (2019). Teaching varies with task complexity in wild chimpanzees. Proceedings of the National Academy of Sciences, 117 , 201907476. https://doi.org/10.1073/pnas.1907476116

Stover, C. (2005). Domestic violence research: What have we learned and where do we go from here? Journal of Interpersonal Violence, 20 , 448-454. https://doi.org/10.1177/0886260504267755

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 23 Achieved Status Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Defense Mechanisms Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Theory of Planned Behavior Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 18 Adaptive Behavior Examples

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 23 Achieved Status Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Ableism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Defense Mechanisms Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Theory of Planned Behavior Examples

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Bobo doll experiment

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• Academia - Bobo Doll Experiment
• Frontiers - Albert Bandura's experiments on aggression modeling in children: A psychoanalytic critique
• Simply Psychology - Bobo Doll Experiment
• University of Central Florida Pressbooks - Psych in Real Life: The Bobo Doll Experiment
• Verywell Mind - What the Bobo Doll Experiment Reveals About Kids and Aggression

Bobo doll experiment , groundbreaking study on aggression led by psychologist Albert Bandura that demonstrated that children are able to learn through the observation of adult behaviour. The experiment was executed via a team of researchers who physically and verbally abused an inflatable doll in front of preschool-age children, which led the children to later mimic the behaviour of the adults by attacking the doll in the same fashion.

Bandura’s study on aggression—the experiment for which he is perhaps best known—was carried out in 1961 at Stanford University , where Bandura was a professor. For this study he used 3- and 5-foot (1- and 1.5-metre) inflatable plastic toys called Bobo dolls, which were painted to look like cartoon clowns and were bottom-weighted so that they would return to an upright position when knocked down. The subjects were preschoolers at Stanford’s nursery school and were divided into three groups: one group observed aggressive adult behaviour models; another group observed nonaggressive behaviour models; and the third group was not exposed to any behaviour models.

The three groups were then divided by gender into six subgroups in which half of the subgroups would observe a same-sex behaviour model and half would observe an opposite-sex behaviour model. In the first stage of the experiment, the children were individually seated at a table in one corner of an experimental room and presented with diverting activities that had previously been shown to be of high interest to the children (e.g., stickers, pictures, prints) in order to discourage active participation and encourage mere observation. The behaviour model was then taken to the opposite corner—which contained another table and chair, a mallet, a Tinkertoy set, and a 5-foot Bobo doll—and was told he or she could play with these materials. In the aggressive behaviour model groups, the model abused the Bobo doll both physically (e.g., kicked, punched, threw, and assaulted with various objects) and verbally (e.g., made aggressive statements such as “Sock him in the nose” and “Pow” or nonaggressive statements such as “He sure is a tough fella” and “He keeps coming back for more”). In the nonaggressive behaviour model groups, the model ignored the Bobo doll and instead quietly assembled the Tinkertoys. After 10 minutes had elapsed, the behaviour models in both groups left the room.

In the second phase of the experiment, the children were taken individually into a different experimental room, where they were presented with a new group of appealing toys (e.g., train, fire engine, cable car, jet airplane, spinning top , doll with wardrobe, baby crib, and doll carriage). To test the hypothesis that the observation of aggression in others would increase the likelihood of aggression in the observer, the children were subjected to aggression arousal in the form of being told after two minutes that they could no longer play with the toys. The children were then told that they could, however, play with the toys in another room, where they were presented with various toys that were considered both aggressive (e.g., 3-foot Bobo doll, mallet, and dart guns) and nonaggressive (e.g., crayons, paper, farm animals, tea set, ball, and dolls).

In the final stage of the experiment, the children’s behaviour was observed over the course of 20 minutes and rated according to the degree of physically and verbally aggressive behaviour they modeled, the results of which yielded significantly higher scores for children in the aggressive behaviour model groups compared with those in both the nonaggressive behaviour model and control groups. Subsequent experiments in which children were exposed to such violence on videotape yielded similar results, with nearly 90 percent of the children in the aggressive behaviour groups later modeling the adults’ behaviour by attacking the doll in the same fashion and 40 percent of the those children exhibiting the same behaviour after eight months.

Although the study yielded similar results for both genders, it nonetheless suggested at least some difference depending on the degree to which a behaviour is sex-typed—that is, viewed as more common of or appropriate for a specific gender. For example, the data suggest that males are somewhat more prone to imitate physical aggression—a highly masculine-typed behaviour—than are females, with male subjects reproducing more physical aggression than female subjects; there were, however, no differences in the imitation of verbal aggression, which is less sex-typed. Additionally, both male and female subjects were more imitative of the male behaviour models than of the female models in terms of physical aggression but were more imitative of the same-sex models in terms of verbal aggression.

• Our Mission

Using Classroom Observations for Support as a New Teacher

Constructive feedback from colleagues helps new teachers identify areas of strength and growth and develop strategies to achieve their goals.

Stepping into the classroom for the first time as a new teacher can feel like navigating uncharted territory. The mix of excitement and nerves is palpable, and the fear of making mistakes looms large. Amid the whirlwind of lesson planning and classroom management, one often-overlooked opportunity for growth is classroom observations. Approach observations with an open mind and a willingness to learn. Embrace vulnerability as a sign of strength, and use feedback as a springboard for growth . 

The Power of Observation

Allowing colleagues to observe your teaching, whether for an entire class period or just a short segment, can be transformative. It’s natural to focus on the negatives when things don’t go as planned. We tend to dwell on what went wrong, labeling the entire lesson as a disaster. However, inviting someone to observe can provide a fresh perspective. It allows us to see the positives that occurred, no matter how small, and offers insight into why things may have veered off course. 

As a practicum adviser on a faculty of education, I find that part of my role is to observe teacher candidates teaching and then offer my feedback. Through this process, I’ve come to realize the powerful impact observation can have on growth, confidence, and self-efficacy. Observation is a cornerstone of the educational journey.

When we observe, we immerse ourselves in the nuances of teaching and learning, capturing moments that might otherwise be overlooked. These moments often hold the key to understanding and improving our teaching practices. For teacher candidates, being observed provides an opportunity to receive constructive feedback that is crucial for their professional development.

The act of observing is a dynamic engagement that allows both the observer and the observed to reflect and grow. For teacher candidates, knowing that someone is attentively watching and valuing their efforts can significantly boost their confidence. It reassures them that their work is important and worthy of attention, which in turn enhances their self-efficacy. While my role focuses on teacher candidates, the insights gained from these experiences are equally valuable for new teachers.

One of the greatest benefits of classroom observations is the opportunity to gain constructive feedback . Observers can offer insights into teaching practices and classroom dynamics that we may not have considered. They can identify strengths we didn’t realize we had and highlight areas for improvement. Rather than viewing feedback as criticism, see it as a gift—a chance to learn and grow as an educator.

When I was a new teacher, whenever I felt things were going badly, I’d close my classroom door so my colleagues wouldn’t know how bad things were. I worried they would judge me and conclude I was a bad teacher. I didn’t realize until much later in my career that not allowing others to offer support and solutions meant that I didn’t learn diverse strategies to resolve the problems I was encountering.

Making the Most of Observations

It’s tempting for new teachers to invite a colleague to observe a lesson plan or class when they feel confident. However, it’s more beneficial to invite observations for specific challenges, such as classroom management issues or difficulties with facilitating whole class discussions. Ask a seasoned educator in your department to observe you. They don’t have to stay for the entire class period—observing a short segment of a class can be incredibly valuable.

Communicate the feedback you’re hoping to receive. When they arrive for the observation, set up a place for them to sit, and teach your class as you normally would. After the observation, schedule a time to discuss their feedback so that they can share areas of strength and growth.

When they identify areas for growth, ask for specific suggestions or strategies you could try. Implement their suggestions and update them on your progress. This ongoing dialogue will allow them to see that their time and insight are valued. When you feel you’ve improved, invite them back to observe again and witness your growth.

New teachers should also take the opportunity to observe other teachers in action. Seeing how experienced teachers manage the complexities of a classroom can be extremely helpful. This can provide practical insights and strategies that can be adapted to one’s own teaching practice.

Building a Supportive Professional Learning Community

Even brief classroom observations can yield valuable feedback. Set specific goals, such as improving classroom management techniques or refining instructional strategies. Seek feedback on targeted areas, and be open to suggestions for improvement.

Once I became comfortable with being observed, I sought specific strategies from my colleagues. In one of my classes, discussions were dominated by three students—discouraging others from participating. I asked a colleague to observe the last 15 minutes of my class to see how I facilitated discussions. They offered me strategies for making discussions more inclusive for all students.

New teachers often worry about being a burden to their peers. A good frequency for observations is about once a month. This frequency allows you enough time to implement feedback and make improvements on your own before the next observation. It also ensures that you are regularly reflecting on and refining your teaching practices without overwhelming yourself or your colleagues.

As an experienced teacher and department head, I’ve always encouraged new teachers to observe my classes, and I actively seek opportunities to observe theirs. This practice has fostered a culture where teachers in my department frequently drop by each other’s classrooms to see what’s happening.

This open-door policy helps build a supportive community where sharing ideas and strategies becomes second nature and enhances the teaching and learning experience within our department. This environment of mutual respect and ongoing professional development shows our students that education is a lifelong journey and that we are committed to working together to improve.

As you embark on your teaching journey, remember that you’re not alone. Lean on your colleagues for guidance and encouragement. Share your successes and struggles and celebrate each other’s growth. Your colleagues are standing by you—there to support you every step of the way as you navigate your path. Together, you’ll grow and thrive as educators, making a positive impact on the lives of your students.

Approach observations with confidence and an open mind, knowing that each one brings you closer to becoming the best educator you can be.

Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning

• Liu, Yunhui
• Zhang, Huaisong
• Zhao, Jianhua

Contrastive learning is a significant paradigm in graph self-supervised learning. However, it requires negative samples to prevent model collapse and learn discriminative representations. These negative samples inevitably lead to heavy computation, memory overhead and class collision, compromising the representation learning. Recent studies present that methods obviating negative samples can attain competitive performance and scalability enhancements, exemplified by bootstrapped graph latents (BGRL). However, BGRL neglects the inherent graph homophily, which provides valuable insights into underlying positive pairs. Our motivation arises from the observation that subtly introducing a few ground-truth positive pairs significantly improves BGRL. Although we can't obtain ground-truth positive pairs without labels under the self-supervised setting, edges in the graph can reflect noisy positive pairs, i.e., neighboring nodes often share the same label. Therefore, we propose to expand the positive pair set with node-neighbor pairs. Subsequently, we introduce a cross-attention module to predict the supportiveness score of a neighbor with respect to the anchor node. This score quantifies the positive support from each neighboring node, and is encoded into the training objective. Consequently, our method mitigates class collision from negative and noisy positive samples, concurrently enhancing intra-class compactness. Extensive experiments are conducted on five benchmark datasets and three downstream task node classification, node clustering, and node similarity search. The results demonstrate that our method generates node representations with enhanced intra-class compactness and achieves state-of-the-art performance.

• Computer Science - Machine Learning