Encyclopedia of Evolutionary Psychological Science

Living Edition
| Editors: Todd K. Shackelford, Viviana A. Weekes-Shackelford

Imitation and Mimicry

  • Marco IacoboniEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-16999-6_3336-1



Using someone as a model for one’s own actions


Scientific interest in imitation and mimicry in the animal kingdom goes back at least to the times of Darwin. The debate on the cognitive aspects of imitation, however, is more recent. Influenced by serial sensory-motor, stimulus-response models of perception-action coupling, the cognitive mechanisms that make imitation possible were initially framed as the “correspondence problem.” That is, how does retino-centric, visual information about the actions of other people get translated in body-centered information that the imitator uses to copy the observed action? The correspondence problem, and the model of perception-action coupling that inspires it, obviously assumes no shared representations (or codes) between perceptual inputs and motor outputs. A longtime alternative model to the sensory-motor model of perception-action coupling is the ideomotor model (Hommel et al. 2001). This model assumes at least some level of shared representations between perception and action, thus making the correspondence problem fundamentally irrelevant. The ideomotor model of perception-action coupling, however, is typically framed in terms of intentional actions: whenever I plan to turn on my computer, I can anticipate the sensory consequences of my action plan. While imitation can obviously be intentional (for instance, observational learning is a form of intentional imitation), humans do display very often a type of imitative behavior that is unintentional and pre-reflective, what is typically called “automatic imitation” (Iacoboni 2009).

Top Down and Bottom Up

Observational learning and automatic imitation are perhaps the two imitative behaviors that are situated at the opposite ends of a continuum: the continuum that represents the ever-varying balance between top-down and bottom-up processing of the actions of other people. Observational learning is a top-down intentional imitative behavior that focuses on specific actions and behaviors with very specific goals (how to hit a tennis forehand or how to behave, speak, and even deliberate in specific contexts and situations, for instance). Automatic imitation, in contrast, is clearly a bottom-up process in which the visual input of the actions of others “sweeps us up” so that we cannot help but reproduce that behavior, generally in terms of the postures, facial expressions, and mannerisms of the people we are interacting with. Automatic imitation, however, is not as automatic as its name may suggest. We don’t parrot each other in a zombie-like fashion. Pre-reflective, unintentional imitative behavior is clearly influenced by fairly high-level factors, and in turn, it does influence those factors. For instance, we tend to imitate unintentionally more people that belong to our social group, compared to out-group members. However, even the influence of social group membership on automatic imitation can be modulated by other contextual factors. Indeed, a competitive situation reduces automatic imitation for in-group members, whereas a cooperative situation increases it for out-group members (Miles et al. 2010). Furthermore, automatic imitation is associated with empathic predisposition. More empathic individuals tend to display more automatic imitation. In experimental settings, being imitated has also been associated with increased liking (Chartrand and Bargh 1999).

Neural Correlates

All these behavioral data suggest that low-level bottom-up processes continuously interact with high-level top-down ones during imitative behavior and that these interactions are modulated by contextual variables and intentional predispositions. Imitation is in itself a complex behavior, and these complex, nuanced contextual modulations make it even more so. What are the neural mechanisms of imitation? Given its complexity, especially in real-world scenarios, for many years, the neuroscience community had largely avoided the study of the neural underpinnings of imitation. A breakthrough discovery in monkey neurophysiology, however, has inspired a recent wave of neuroscience studies on human imitation.

A subset of motor neurons in the monkey brain have surprising visual properties. Even when the monkey does not move at all and is simply watching, these motor neurons fire, as long as the monkey is observing someone else making the same action triggered by the firing of these motor neurons when the monkey moves (or a similar action that achieves the same goal) (Gallese et al. 1996). In other words, the visual properties of these neurons mirror their motor properties, and for this reason, they have been called mirror neurons.

Mirror neurons clearly support more the ideomotor model of perception-action coupling, which assumes shared coding or representations for perception and action, than the sensory-motor model which assumes independent coding for perception and action. They also seem to have ideal properties to support imitative behavior, especially of the automatic type, since these cells seem to fire in a stimulus-dependent fashion (when the action is seen).

Depth-electrode recordings in both monkeys and humans have provided evidence for mirror cells in a number of neural systems coding for different kinds of actions performed with different body parts (Iacoboni 2011). These findings support the variety of imitative behavior that humans display. However, an outstanding question with regard to the neural underpinning of imitative behavior is also how neural mirroring gets controlled so that the nuanced modulation of imitative behavior due to contextual and dispositional factors can also be accounted for. Early studies pointed to the dorsomedial prefrontal cortex (dmPFC) and the temporoparietal junction (TPJ) as two areas controlling imitation. These areas have been associated with a number of behaviors and cognitive functions, including theorizing about the mental states of other people (theory of mind or ToM). Insofar as imitation control requires a differentiation between the action of the self and the actions of other people and theory of mind requires a differentiation between one’s mental state and the mental states of others, the involvement of dmPFC and TPJ in both imitation control and ToM makes sense.

Recent studies have provided more insights on the complex and continuous interplay between bottom-up and top-down neural processes associated with imitative behavior and its control. For instance, a neurophysiological phenomenon analogous to automatic imitation is “motor resonance.” When observing someone else making an action, the excitability of our own motor system increases. This increase is “muscle specific,” that is, it is restricted to the same muscles used in the action we observe. Motor resonance has been considered for many years a typical automatic, stimulus-dependent process. However, a recent study shows that when planning not to imitate, motor resonance is abolished (Cross and Iacoboni 2014). Note that the plan “not to imitate” is a very general, high-level plan in this study, because subjects still don’t know which action they are supposed not to imitate, which means that fairly high-level intentions (“I am not going to imitate whatever I see”) are able to modulate in a top-down fashion a typical bottom-up process like motor resonance.

A brain imaging study on automatic imitation recently attempted a more detailed description of the frontal lobe circuitry for imitation control. Here, dynamic causal modeling (DCM) revealed a circuitry composed of dmPFC and anterior cingulate cortex (ACC) that together trigger top-down inhibitory control – via the anterior insula – of the posterior inferior frontal cortex (pIFC), an area strongly associated with imitation and mirror neurons (Cross et al. 2013). Furthermore, another brain imaging study on controlled imitation, in which subjects were instructed whether to imitate or not, suggested that connectivity changes between brain areas activated by the task was a key regulatory factor for inhibition of imitation (Cross and Iacoboni 2013). Taken together, these findings may explain why the dmPFC is so often implicated in the top-down regulation of imitation. Indeed, dmPFC has a rich connectivity pattern with other neural systems, and this anatomical feature may make it ideally situated for the control of imitative behavior, also in light of the presence of mirror neurons in a number of neural systems. Recent studies using noninvasive neuromodulation have shown that by transiently downregulating dmPFC, it is possible to modulate fairly high-level forms of social conformity (Holbrook et al. 2015), thus suggesting that imitation grounds social conformity processes through bodily synchronization.


The picture that emerges from these recent studies is rich and intricate, yet exciting, due to the fairly rapid progress in knowledge obtained in the last two decades. Progress on our understanding of imitation and mimicry is likely due to the interdisciplinary work of social, cognitive, and neuroscientists.



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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Semel Institute for Neuroscience and Human BehaviorDavid Geffen School of Medicine at UCLALos AngelesUSA

Section editors and affiliations

  • Christopher Watkins
    • 1
  1. 1.Abertay UniversityDundeeScotland