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Previous research suggests that humans are not reliable judges of the amount of synchrony between individuals who are moving their bodies, but that the presence of more synchronicity enhanced how much the observers enjoyed watching the movements. This study aims to ascertain whether humans really are poor judges of synchrony, or successful quantification of synchrony is dependent on kinematic features of the movement, aesthetic judgements made by the perceiver, or perhaps even perceiver traits.

Using subjective behavioural and objective cortical (fNIRS) measures, we aim to explore the extent to which embodied experience, sociocultural background, and performance ability impact aesthetic perceptions of dance choreography.

Preregistration plan for a study investigating the role of benevolent sexism, within the mediated association between division of labour and sexual desire, through perceived fairness of division of labour.

This study will assess the impact of previous arts experience and sociocultural background on aesthetic perceptions of dance movements.

Access to the data, figures, and experiment builder used in the HeartBots experiment (Riddoch & Cross, 2020).

This study compares the influence of a synchronised audience on inhibition to that of a simple non-synchronised audience. We use the mirror game as a motor synchrony intervention and a Go/NoGo task to measure inhibition.

Describing interactions between objects often requires the speaker to choose a perspective, and mention one object first and the other second. Speakers have a tendency to make the most animate object in a scene the subject of a sentence, and to mention this object first (Prat-Sala & Branigan, 2000). For example, it is more common to say the man ran away from the dog rather than the dog chased the man, therefore taking the perspective of the man. This bias is considered to occur because of the way speakers perceive animacy: on a hierarchy, with humans at the top and concepts or inanimate objects near the bottom (Harris, 1978; Aissen, 2003). An animacy hierarchy is thought to be the product of conceptual accessibility; the more conceptually accessible something is, the easier it is to retrieve and the more likely it is to be uttered earlier on in a sentence. Animate entities are considered to be more conceptually accessible than less animate entities (McDonald, Bock & Kelly, 1993; Bock & Warren 1985), and this results in an animacy bias whereby speakers utter them earlier on in their sentences. Participants will view scenes of two figures interacting, who will differ in animacy (one human and one robot). Participants will describe these scenes using so-called symmetrical predicates (Gleitman, Gleitman, Miller, & Ostrin, 1996). These are verbs that imply a symmetry of action between the two figures (e.g. arguing, kissing, playing), but when used in a sentence they require the speaker to make a decision as to which figure should be mentioned first, which may be potentially conditioned on a factor such as animacy. Participants will form transitive sentences (e.g. “Kate is arguing with Botz”) to describe the events, which they will type into a text box, and we will measure if they mention the human or the robot first. The events depicted in this experiment constitute three “valences”; intimate (e.g. kissing), negative (e.g. arguing) and casual (e.g. playing). We will investigate if the animacy bias is stronger on trials depicting emotional (intimate/negative) events. In terms of animacy, robots constitute a special control category for human stimuli. Contemporary humanoid robots are capable of self-propelled motion, speech (in some cases), and can exhibit what appears to be intentional behaviour (for example through eye movements or gestures), hence, they provide some cues to animacy to human observers (Bartneck, Kulić, Croft & Zoghbi, 2009). However, as previous studies on the perception of mind have shown, humans would be hesitant to call any robot “alive” (Gray, Gray & Wegner, 2007). When rated on their capacity of mind, robots rank below human adults and infants, and receive somewhat comparable ratings to pets, with little means to sense or feel the world, but with some capacity of agency. A study that asked participants to rate robots’ ability to plan and act (their agency) and their ability to sense and feel (their experience of the world) confirmed that participants assigned high rankings to pictures of human faces for both agency and experience, whereas robots where ascribed some agency but no experience, and objects ranking lowest on both criteria (Henschel, Bargel, & Cross, 2020). We therefore propose that robots should rank lower than humans on the animacy hierarchy, while controlling for humanlike features (i.e. having a comparable body shape and similar set of facial features). Our previous research suggests that closeness to the speaker influences the conceptual accessibility of an entity. When describing an interaction between two people, speakers are more likely to take the perspective of the figure most similar to themselves in social group (e.g. the same race or gender as themselves). These studies are in progress, but the pre-registrations can be found here (https://osf.io/bfhrn) and here (https://osf.io/t928m). The results of these studies suggest that speakers often demonstrate a ‘Like Me’ bias to name the figure from their own social group first and the figure from the out-group second. Thus, in addition to our main analysis of the animacy bias, we will further consider the ‘Like Me’ bias in exploratory analyses by varying the social group of the human in these pairs (a black woman, a black man, a white woman and a white man) and conducting this experiment with participants from these four demographic groups. In doing so, we will measure if participants demonstrate an animacy bias by routinely taking the perspective of the human figure, and if this bias is stronger on trials where the human figure is in the participant’s ingroup than their outgroup. Prior to this main experiment, we conducted a norming study with a separate group of 80 participants (of the same four demographic groups as in this experiment) to validate our novel set of robot stimuli. We investigated how much mind is ascribed to the four robotic agents to be used in the main study, and found that two of our robots (‘Pepper’ and ‘NAO’) were rated as having higher (and similar levels of) perceived mind than the other two (‘Gresh’ and ‘Princess Firecracker’ – see Files for portraits of all four robots used in this experiment). According to this norming study, people perceive the humanoid robots (Pepper and NAO) as having greater agency and experience (Henschel et al., 2020) than the Lego robots (Gresh and Princess Firecracker). We expect the animacy bias to favour the human figure even more on trials where the human is paired with an object with low perceived mind. We will test this hypothesis by entering the continuous variables of perceived robot agency and perceived robot experience (ratings provided in the norming study) into our model as predictors of mentioning the human figure first. As gender and race are of importance in the current experiment, we also included rating items about the robots’ perceived race, sex and gender. In order to establish gender-neutral names for the robots that are not too distinct from the set of human names, participants further rated a set of six names on perceived gender and sex. The four robot names used in this study were those that were rated as being most gender-neutral on 9-point Likert scales (very female – very male, very feminine – very masculine). Descriptions of how we intend to use these items of perceived robot race and combined gender/sex from the norming study are explained in the Exploratory Analyses section.

A strong motivator for engaging in prosocial behaviour is empathy (Decety et al., 2016). Empathy is a multidimensional construct build from interrelated cognitive and affective components that lets individuals perceive and react to the emotional state of others (Davis, 1983, Preston & de Waal, 2002). In this study, we define empathy as an affective response caused by, and congruent with, the inferred emotional state of others (Eisenberg et al, 1991). An important characteristic of empathy is intergroup empathy bias. This bias describes the fact that humans feel more empathy for members of their ingroup as opposed to members of an outgroup (Cikara et al., 2011). On some occasions, people even experience pleasure when an outgroup member encounters misfortune, a response called schadenfreude (Vanman, 2016). Feelings of schadenfreude are known to be modulated by interpersonal factors, such as an individual’s responsibility for or the perceived deservingness of their misfortune (van Dijk et al., 2005), malicious envy (van der Ven et al., 2015) and ingroup-identification when the outgroup is a rival team (Hoogland et al., 2015). It is important to understand when and why these biases occur, as they are associated with motivating people to take part in collective violence (Cikara, 2015). Categorization of individuals in different groups or teams based on arbitrary criteria has already been shown to be successful in inducing intergroup empathy bias (Montalan et al., 2012), and intergroup schadenfreude bias (Cikara et al., 2014). However, participants in these studies would only rate their feelings of empathy and schadenfreude in relation to scenarios which were not directly relevant to the task or the source of the competition. In the current study, we will directly assess whether people display an intergroup empathy and schadenfreude bias when rating their feelings during a competitive game when they are arbitrarily assigned to competing teams.

Is there a relationship between anthropomorphism and Theory-of-Mind? So far, data provide mixed results. Partial support comes from neuroimaging studies looking at an association between situational (e.g., task-based) and dispositional measures of anthropomorphism and Theory-of-Mind network activation and anatomy (Chaminade et al., 2007; Cullen et al., 2014; Waytz, Morewedge, et al., 2010). Overall, activity patterns overlap when people engage in anthropomorphism and Theory-of-Mind (Hortensius & Cross, 2018). However, we found no consistent relationship between dispositional anthropomorphism and activity within and across the Theory-of-Mind network during the observation of a social animation film (Experiment 1). While a quadratic relationship described the data better than a linear relationship, the data were in favour of the null hypothesis. This is in line with a recent behavioural study that found no relationship between direct and indirect measures of situational and dispositional anthropomorphism and inter-individual differences in theory-of-mind in adults and children (Tahiroglu & Taylor, 2019). Research question: In this follow-up experiment, we explore the relationship between Theory-of-Mind and situational and dispositional anthropomorphism in more detail. Specifically, we ask if there is a linear or quadratic relationship between situational and dispositional anthropomorphism and behavioural measures of Theory-of-Mind.

This project is aimed at investigating the extent to which social robots’ cognitive architectures can elicit emotionally rich expressions and information from people to identify their needs and emotional states over time. By conducting an online experiment this research will examine people’s expressions to social robots, as well as how people disclose their needs and emotions to social robots via video chat over time. By measuring peoples’ disclosures and emotional states through different channels of communication and behaviour, emotional patterns of peoples’ experiences regarding different topics (i.e., coping with COVID-19 and general everyday experiences) will be further evaluated. Finally, the study results will provide evidence for peoples’ expressions, perceptions, and reactions to a social robot in reoccurring interactions over time, and will reduce the current knowledge gap regarding novel effects in human-robot interaction (HRI) research. The research findings will be employed for developing speech-based health and well-being interventions using social robots. Furthermore, these will contribute to our broader understanding of how individuals share information and disclose their emotions to social robots, and how social robots can be used as tools to support people's emotional health. This study investigates the extent to which social robots can elicit and support socially meaningful conversation with people over time. A between-subjects repeated measures online experiment is planned with participants completing 10 online video chats with a social robot across five weeks. By measuring the quantity and quality of peoples’ disclosures, expressions, and perceptions, this study aims to provide a nuanced overview of the impact of affective interactions with social robots over time.

Our aim is to understand whether dog behaviours important for the human-pet bond, translate well (are understood and perceived similarly) when demonstrated in a robotic platform.

Describing interactions between objects often requires the speaker to choose a perspective and mention one object first and the other second. Speakers have a tendency to make the most animate object in a scene the subject of a sentence, and to mention this object first (Prat-Sala & Branigan, 2000). For example, it is more common to say the man ran away from the dog rather than the dog chased the man, therefore taking the perspective of the man. This animacy bias is considered to occur because of the way speakers perceive animacy: on a hierarchy, with humans at the top and concepts or inanimate objects below (Harris, 1978; Aissen, 2003). The current experiment follows on from the work detailed in a previous pre-registration, Study 1, which can be found here: https://osf.io/a5fby. Speakers viewed scenes of humans and robot interacting, and described these interactions using transitive sentences (e.g. “Fred is dancing with Botz”). We found speakers would reliably mention the human first and the robot second, with statistical modelling showing a strong main effect of relative animacy. The following experiment attempts to explore the ways in which animacy is affected by perceived mind. In doing so, we question the extent to which mind attribution determines animacy and designates an object’s place on the “animacy hierarchy”. Researchers in psycholinguistics have offered a few explanations for the animacy bias phenomenon. According to the theory of conceptual accessibility, the more significant an object is in human thought, the more conceptually accessible it is considered to be, and therefore the greater the likelihood of a speaker mentioning that object earlier on in speech (McDonald, Bock & Kelly, 1993; Bock & Warren 1985). Animate entities are also considered to be more easily or rapidly lexically retrieved, meaning the speed with which we retrieve labels for animate objects from memory is faster than that for inanimate objects and concepts, thus increasing the likelihood of these labels appearing earlier in speech (Branigan, Pickering & Tanaka, 2008). Regarding the specific animacy differences in our own study (human vs robot), we might look to neuroimaging studies to understand how human and robot figures are processed differently neurologically. When Wang and Quadflieg (2015) compared participants’ responses to interactions between either two humans or between a human and a robot, they identified that different regions of a neural network involved in formulating impressions relating to beliefs, desires, motives, feelings or intentions were activated depending on the interaction type (human/human or human/robot). The results from this study suggest that social cognition relating to impression formation may differ when we are witnessing interactions between two figures of the same relative animacy, and two figures who fall on different positions on the animacy hierarchy. Crucially, humans and robots also differ in agency and experience. While “agency” designates the thematic role of an object doing something to another object in linguistics (Dowty (1979, 1991), in the study of mind perception, agency has been defined as an object’s ability to plan and act (Gray, Gray, & Wegner, 2007). Experience, a second important feature of perceived mind, refers to an object’s ability to sense and feel (Gray et al., 2007). Evidence for humans and robots differing in perceived mind comes from Henschel, Bargel and Cross (2020), who asked participants to rate humans, robots and objects for capacities of mind. They found that participants rated humans as high for both features and ascribed robots with some agency but no experience (while objects had the lowest ratings for both agency and experience). The current study therefore considers two definitions of “agency” - firstly who, linguistically, is designated the sentence agent, and secondly who appears to the speaker as being the most agentive figure. An important cognitive feature of perceiving social interactions is intention, which relates to both the representation of a figure’s covert mental state and that which arises from perceiving an action they are carrying out (Gobbini et al., 2007). In addition to Gray, Gray and Wegner’s definition of agency, Dowty (1991) also proposes features of intentionality as key facets of agenthood, such as causation (causing another object to experience an event or change in state) and volition (choosing to be involved the event or state change). In general, we understand that humans have the capacity to plan and act – an ability that is certainly more questionable in robots, and we are also aware that humans are able to execute actions and bring about change. Again, this is an ability many people are likely to be uncertain about for robots. Thus, we could argue that people might perceive intention differently between human and robot figures executing actions. Social cognition is dependent on a person’s ability or willingness to consider the mind of another person. Dehumanised perception results when someone fails to engage typical social cognition and automatically consider the mind of another person (Harris & Fiske, 2011). On the other side of the spectrum is empathy – the capacity to understand and share another’s feelings. Empathy is an important feature of mind attribution while processing social interactions, and is something we are capable of demonstrating for robots as well as humans, given the right circumstances (Küster & Swiderska, 2020, Rosenthal-von der Pütten et al., 2013). The current study explores the role that mind attribution has on perceptions of animacy. We are investigating if it is possible to use narrative to alter how people perceive the mind of a non-human object, such as a robot, to the extent that they reverse the normal rules of the animacy bias (human mentioned first, robot mentioned second, as seen in Study 1). We will also use this narrative to reduce the perceived mind of human figures. In doing so, we aim to encourage speakers to view events from the perspective of the usually less-animate robot, therefore making this figure the sentence agent over the depicted human and mentioning it first in their descriptions of events. We will use the same paradigm as in Study 1, but we will manipulate features of mind, including agency, experience, empathy and dehumanisation, by prefacing the critical trials with a short story. In a robot-oriented condition, this narrative will frame the robot figures as highly animate and the human figures as dehumanised clones grown by the robot figures. A second and control condition, human-oriented, will depict the human figures as animate agents and the robots as clones the humans have grown. We will also use this control condition to measure the extent to which linguistic accessibility influenced the human bias detected in Study 1. There, we used common Anglophone names for our human figures (e.g. Fred, Kate) and fictional names for our robot figures (e.g. Cybe, Mech). In the current study, both human and robot figures will have fictional names, which would allow us to confirm that the results of Study 1 could not be explained simply in terms of name familiarity. Participants will view scenes of two figures interacting, who will differ in animacy (one robot and one human) and will describe these scenes using so-called symmetrical predicates (Gleitman, Gleitman, Miller, & Ostrin, 1996). These are verbs that imply a symmetry of action between the two figures (e.g. arguing, kissing, playing), but when used in a sentence they require the speaker to make a decision as to which figure should be mentioned first. Participants will form transitive sentences (e.g. “botz is arguing with kote”) to describe the events, which they will type into a text box, and we will measure if they mention the robot or the human first.

The present study uses a story-stem method to gain qualitative insights into the narratives which people associate with human-robot encounters. Further, we propose to investigate whether and how these narratives differ between participants from the UK and Japan. We are particularly interested in how far participants ascribe agency and human-like characteristics (anthropomorphism) to a fictional robot, and how the human-robot relationship is portrayed in the narratives, including the proxemic behaviour of the characters. The study supplements an ongoing quantitative study (registered on OSF, embargoed until 28th April, 2022, Rooksby et al., 2020), which investigates how UK and Japanese residents compare on their proxemic preferences for an approaching robot.

An exploration into the attachment-promoting and engagement-sustaining features of children's companions, nonexclusive to any specific pet, animal, or toy, within the context of family units, culture, and communities. Furthermore, this study aims to identify commonalities in the preferences of children across sociodemographic groups.

Robots are becoming more prominent in daily life. Aside from being present in industrial environments, they are finding their ways in our homes and workplaces. One aim of roboticists is for these machines to not only serve functional roles, but also interact socially with humans. A more communicative robot is seen as more favourable than an efficient robot in some situations (Hamacher et al., 2016). Ideal human-robot interaction requires not only the robot’s ability to interact with humans but also the human’s willingness to interact socially with this robot. A strong motivator for engaging in such kind of prosocial behaviour is empathy (Decety et al., 2016). Empathy is a multidimensional construct build from interrelated cognitive and affective components that lets us perceive and react to the emotional state of others (Davis, 1983; Preston & de Waal, 2002). In this study, we further define empathy as an affective response caused by, and congruent with, the inferred emotional state of others (Eisenberg et al., 1991). An important characteristic of empathy is that we are more inclined to feel more empathy towards individuals with whom we share commonalities. This propensity is referred to as the intergroup empathy bias. People can feel more empathy for members of their ingroup as opposed to members of an outgroup, and on some occasions, people can even experience pleasure when an outgroup member encounters misfortune, a response called schadenfreude (Vanman, 2016). Feelings of schadenfreude are known to be modulated by interpersonal factors, such as an individual’s responsibility for, or the perceived deservingness of, their misfortune (Dijk et al., 2005), malicious envy (van de Ven et al., 2015), and ingroup-identification when the outgroup is a rival team (Hoogland et al., 2015). It is important to understand when and why these biases occur, as they are associated with motivating people to take part in collective violence (Cikara et al., 2011). Robots regarded and treated as social beings might also be subjected to this kind of social categorisation (Vanman & Kappas, 2019). However, it is not yet known whether and when people might classify robots as members of their ingroup, and thus feel more empathy toward them in certain situations, and also whether it might be the case that people can feel schadenfreude when outgroup robots experience misfortune. Previous studies had tried to make robots more human-like in order to facilitate participants’ perceptions that these robots belong to their ingroup (Eyssel & Kuchenbrandt, 2011). Another way to promote robots as being seen to belong to a person’s ingroup could be by assigning humans and robots to the same team (Fraune et al., 2017). Categorization of individuals into different groups or teams based on arbitrary criteria has already been shown to be successful in inducing intergroup empathy bias (Montalan et al., 2012), and intergroup schadenfreude bias (Cikara et al., 2011). So far, this has not yet been investigated in mixed human-robot teams. Across two studies, we will investigate whether humans show an intergroup empathy bias towards robots when humans and robots form a team (I). We will also examine whether we can generalise our findings across different robots (II), an often-unaddressed issue in the field of social robots (Cross et al., 2019; Hortensius et al., 2018). In the first study, we will use the Cozmo robot (Figure 1), a small entertainment robot with a mechanical design, while in the second we will use the NAO robot (Figure 2), a humanoid robot often used in HRI research.

Preregistration plan for a study investigating couples where men experience financial strain, examining the associations between endorsing benevolent sexism with men's personal well-being, as well as with women's relationship well-being

This project will investigate the potential for emotional exchange in chatbot-human-interaction (CHI), informing their appropriate role in mental health care. More specifically, we are interested in the extent to which the perception of a disembodied conversational agent’s origin will affect one’s emotional disclosure and their perceptions of the agent, the interaction, and their emotional well-being.

Intergroup dynamics shape the ways in which we interact with other people. We feel more empathy towards ingroup members compared to outgroup members, and can even feel pleasure when an outgroup member experiences misfortune, known as schadenfreude. Here, we test the extent to which these intergroup biases emerge during interactions with robots. We measured trial-by-trial fluctuations in emotional reactivity to the outcome of a competitive reaction time game to assess both empathy and schadenfreude in arbitrary human-human and human-robot teams. Across four experiments (total 'n' = 361), we observed a consistent empathy and schadenfreude bias driven by team membership. People felt more empathy towards ingroup members than outgroup members and more schadenfreude towards outgroup members. The existence of an intergroup bias did not depend on the nature of the agent: the same effects were observed for human-human and human–robot teams. People reported similar levels of empathy and schadenfreude towards a human and robot player. The human likeness of the robot did not consistently influence this intergroup bias. In other words, similar empathy and schadenfreude biases were observed for both humanoid and mechanoid robots. For all teams, this bias was influenced by the level of team identification; individuals who identified more with their team showed stronger intergroup empathy and schadenfreude bias. Together, we show that similar intergroup dynamics that shape our interactions with people can also shape interactions with robots. Our results highlight the importance of taking intergroup biases into account when examining social dynamics of human-robot interactions.

In everyday life, we often observe and learn from interactions between other individuals—so-called third-party encounters. As robots are poised to become an increasingly familiar presence in our daily lives, third-party encounters between other people and robots might offer a valuable approach to influence people’s behaviors and attitudes towards robots. Here, we conducted an online experiment where participants (n = 48) watched videos of human—robot dyads interacting in a cooperative or competitive manner. Following this observation, we measured participants’ behavior and attitudes towards the human and robotic agents. First, participants played a game with the agents to measure whether their behavior was affected by their observed encounters. Second, participants’ attitudes toward the agents were measured before and after the game. We found that the third-party encounters influenced behavior during the game but not attitudes towards the observed agents. Participants showed more effort towards robots than towards humans, especially when the human and robot agents were framed as competitive in the observation phase. Our study suggests that people’s behaviors towards robots can be shaped by the mere observation of third-party encounters between robots and other people.

Amidst the fourth industrial revolution, social robots are resolutely moving from fiction to reality. With sophisticated artificial agents becoming ever more ubiquitous in daily life, researchers across different fields are grappling with the questions concerning how humans perceive and interact with these agents and the extent to which the human brain incorporates intelligent machines into our social milieu. This theme issue surveys and discusses the latest findings, current challenges and future directions in neuroscience- and psychology-inspired human–robot interaction (HRI). Critical questions are explored from a transdisciplinary perspective centred around four core topics in HRI: technical solutions for HRI, development and learning for HRI, robots as a tool to study social cognition, and moral and ethical implications of HRI. Integrating findings from diverse but complementary research fields, including social and cognitive neurosciences, psychology, artificial intelligence and robotics, the contributions showcase ways in which research from disciplines spanning biological sciences, social sciences and technology deepen our understanding of the potential and limits of robotic agents in human social life. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

Understanding the mechanisms and consequences of attributing socialness to artificial agents has important implications for how we can use technology to lead more productive and fulfilling lives. Here, we integrate recent findings on the factors that shape behavioural and brain mechanisms that support social interactions between humans and artificial agents. We review how visual features of an agent, as well as knowledge factors within the human observer, shape attributions across dimensions of socialness. We explore how anthropomorphism and dehumanization further influence how we perceive and interact with artificial agents. Based on these findings, we argue that the cognitive reconstruction within the human observer is likely to be far more crucial in shaping our interactions with artificial agents than previously thought, while the artificial agent’s visual features are possibly of lesser importance. We integrate these findings to provide an integrative theoretical account based on the “like me” hypothesis, and discuss the key role played by the Theory-of-Mind network, especially the temporal parietal junction, in the shift from mechanistic to social attributions. We conclude by highlighting outstanding questions on the impact of long-term interactions with artificial agents on the behavioural and brain mechanisms of attributing socialness to these agents.

Learning new skills by watching others is important for social and motor development throughout the lifespan. Prior research has suggested that observational learning shares common substrates with physical practice at both cognitive and brain levels. In addition, neuroimaging studies have used multivariate analysis techniques to understand neural representations in a variety of domains, including vision, audition, memory, and action, but few studies have investigated neural plasticity in representational space. Therefore, although movement sequences can be learned by observing other people's actions, a largely unanswered question in neuroscience is how experience shapes the representational space of neural systems. Here, across a sample of male and female participants, we combined pretraining and posttraining fMRI sessions with 6 d of observational practice to determine whether the observation of action sequences elicits sequence-specific representations in human frontoparietal brain regions and the extent to which these representations become more distinct with observational practice. Our results showed that observed action sequences are modeled by distinct patterns of activity in frontoparietal cortex and that such representations largely generalize to very similar, but untrained, sequences. These findings advance our understanding of what is modeled during observational learning (sequence-specific information), as well as how it is modeled (reorganization of frontoparietal cortex is similar to that previously shown following physical practice). Therefore, on a more fine-grained neural level than demonstrated previously, our findings reveal how the representational structure of frontoparietal cortex maps visual information onto motor circuits in order to enhance motor performance.SIGNIFICANCE STATEMENTLearning by watching others is a cornerstone in the development of expertise and skilled behavior. However, it remains unclear how visual signals are mapped onto motor circuits for such learning to occur. Here, we show that observed action sequences are modeled by distinct patterns of activity in frontoparietal cortex and that such representations largely generalize to very similar, but untrained, sequences. These findings advance our understanding of what is modeled during observational learning (sequence-specific information), as well as how it is modeled (reorganization of frontoparietal cortex is similar to that previously shown following physical practice). More generally, these findings demonstrate how motor circuit involvement in the perception of action sequences shows high fidelity to prior work, which focused on physical performance of action sequences.