The lateral occipitotemporal cortex (LOTC) is a part of the brain network thatprocesses human body recognition. It has been implicated in variousneurodevelopmental conditions, including autism spectrum disorder (ASD). Intypically developing (TD) individuals, functional magnetic resonance imaging(fMRI) studies have shown three distinct response patterns to three categoriesof body parts in the LOTC, namely, action effector body parts, non-effector bodyparts, and facial parts. It is currently unclear whether the similar topologicalorganization of the LOTC is observed in individuals with ASD, and if socialinteraction difficulties in this group may partially result from differences inbody part recognition in this area. In this fMRI study, adults with ASD and TDadults viewed photographs of hands, feet, arms, legs, chests, waists,upper/lower faces, whole bodies, and chairs. Mass univariate analysis showed nodifferences in the LOTC response to whole-body images (relative to images ofchairs) in the bilateral LOTC between adults with ASD and TD adults. Inaddition, there were no group differences in the responses to body parts.Furthermore, multivariate (representational similarity) analyses revealed asignificant similar body part representation organized into three clusters(limbs, torsos, and faces) in the bilateral LOTC between TD adults and thosewith ASD. These results indicate that TD adults and those with ASD havecomparable neural representations within the LOTC for whole bodies and bodyparts.

So-called ‘mismatch accounts’ propose that, rather than arising from a socio-cognitive deficit present in autistic people, mentalising difficulties are the product of a mismatch in neurotype between interaction partners. Although this idea has grown in popularity over recent years, there is currently only limited empirical evidence to support mismatch theories. Moreover, the social model of disability such theories are grounded in demands a culturally situated view of social interaction, yet research on mentalising and/or autism is largely biased towards Western countries, with little knowledge on how successful mentalising is defined differently, and how tools to assess socio-cognitive ability compare, across cultures. Using a widely employed mentalising task—the animations task—, the current study investigated and compared the bi-directional mentalising performance of British and Japanese autistic and non-autistic adults and assessed observer-agent kinematic similarity as a potential dimension along which mismatches may occur between neurotypes. Participants were asked to depict various mental state- and action-based interactions by moving two triangles across a touch-screen device before viewing and interpreting stimuli generated by other participants. In the UK sample, our results replicate a seminal prior study in showing poorer mentalising abilities in non-autistic adults for animations generated by the autistic group. Crucially, the same pattern did not emerge in the Japanese sample, where there were no mentalising differences between the two groups. Limitations of the current study include that efforts to match all samples within and across cultures in terms of IQ, gender, and age were not successful in all comparisons, but control analyses suggest this did not affect our results. Furthermore, any performance differences were found for both the mental state- and action-based conditions, mirroring prior work and raising questions about the domain-specificity of the employed task. Our results add support for a paradigm shift in the autism literature, moving beyond deficit-based models and towards acknowledging the inherently relational nature of social interaction. We further discuss how our findings suggest limited cultural transferability of common socio-cognitive measures rather than superior mentalising abilities in Japanese autistic adults, underscoring the need for more cross-cultural research and the development of culturally sensitive scientific and diagnostic tools.

Recent findings suggest that stigma and camouflaging contribute to mental health difficulties for autistic individuals, however, this evidence is largely based on UK samples. While studies have shown cross-cultural differences in levels of autism-related stigma, it is unclear whether camouflaging and mental health difficulties vary across cultures. Hence, the current study had two aims: (1) to determine whether significant relationships between autism acceptance, camouflaging, and mental health difficulties replicate in a cross-cultural sample of autistic adults, and (2) to compare these variables across cultures. To fulfil these aims, 306 autistic adults from eight countries (Australia, Belgium, Canada, Japan, New Zealand, South Africa, the United Kingdom, and the United States) completed a series of online questionnaires. We found that external acceptance and personal acceptance were associated with lower levels of depression but not camouflaging or stress. Higher camouflaging was associated with elevated levels of depression, anxiety, and stress. Significant differences were found across countries in external acceptance, personal acceptance, depression, anxiety, and stress, even after controlling for relevant covariates. Levels of camouflaging also differed across countries however this effect became non-significant after controlling for the covariates. These findings have significant implications, identifying priority regions for anti-stigma interventions, and highlighting countries where greater support for mental health difficulties is needed.

Difficulties in reasoning about others’ mental states (i.e., mentalising/Theory of Mind) are highly prevalent among disorders featuring dopamine dysfunctions (e.g., Parkinson’s disease) and significantly affect individuals’ quality of life. However, due to multiple confounding factors inherent to existing patient studies, currently little is known about whether these sociocognitive symptoms originate from aberrant dopamine signalling or from psychosocial changes unrelated to dopamine. The present study, therefore, investigated the role of dopamine in modulating mentalising in a sample of healthy volunteers. We used a double-blind, placebo-controlled procedure to test the effect of the D2/D3 antagonist haloperidol on mental state attribution, using an adaptation of the Heider and Simmel (1944) animations task. On 2 separate days, once after receiving 2.5 mg haloperidol and once after receiving placebo, 33 healthy adult participants viewed and labelled short videos of 2 triangles depicting mental state (involving mentalistic interaction wherein 1 triangle intends to cause or act upon a particular mental state in the other, e.g., surprising) and non-mental state (involving reciprocal interaction without the intention to cause/act upon the other triangle’s mental state, e.g., following) interactions. Using Bayesian mixed effects models, we observed that haloperidol decreased accuracy in labelling both mental and non-mental state animations. Our secondary analyses suggest that dopamine modulates inference from mental and non-mental state animations via independent mechanisms, pointing towards 2 putative pathways underlying the dopaminergic modulation of mental state attribution: action representation and a shared mechanism supporting mentalising and emotion recognition. We conclude that dopaminergic pathways impact Theory of Mind, at least indirectly. Our results have implications for the neurochemical basis of sociocognitive difficulties in patients with dopamine dysfunctions and generate new hypotheses about the specific dopamine-mediated mechanisms underlying social cognition.

A body of research implicates dopamine in the average speed of simple movements. However, naturalistic movements span a range of different shaped trajectories and rarely proceed at a single constant speed. Instead, speed is reduced when drawing "corners" compared to "straights" (i.e., speed modulation), and the extent of this slowing down is dependent upon the global shape of the movement trajectory (i.e., speed meta-modulation) - for example whether the shape is an ellipse or a rounded square. At present, it is not known how (or whether) dopaminergic function controls continuous changes in speed during movement execution. The current paper reports effects on these kinematic features of movement following two forms of dopamine manipulation: Study One highlights movement differences in individuals with PD both ON and OFF their dopaminergic medication (N = 32); Study Two highlights movement differences in individuals from the general population on haloperidol (a dopamine receptor blocker, or "antagonist") and placebo (N = 43). Evidence is presented implicating dopamine in speed, speed modulation and speed meta-modulation, whereby low dopamine conditions are associated with reductions in these variables. These findings move beyond vigour models implicating dopamine in average movement speed, and towards a conceptualisation that involves the modulation of speed as a function of contextual information.

Difficulties in reasoning about others’ mental states (i.e., mentalising / Theory of Mind) are highly prevalent among disorders featuring dopamine dysfunctions (e.g., Parkinson’s disease) and significantly affect individuals’ quality of life. However, due to multiple confounding factors inherent to existing patient studies, currently little is known about whether these socio-cognitive symptoms originate from aberrant dopamine signalling or from psychosocial changes unrelated to dopamine. The present study therefore investigated the role of dopamine in modulating mentalising in a sample of healthy volunteers. We used a double-blind, placebo-controlled procedure to test the effect of the D2 antagonist haloperidol on mental state attribution, using an adaptation of the Heider & Simmel (1944) animations task. On two separate days, once after receiving 2.5mg haloperidol and once after receiving placebo, 33 healthy adult participants viewed and labelled short videos of two triangles depicting mental state (e.g., surprising) and non-mental state (e.g., following) interactions. Using Bayesian mixed effects models we observed that haloperidol decreased accuracy in labelling both mental- and non-mental state animations. Our secondary analyses suggest that dopamine modulates inference from mental- and non-mental state animations via independent mechanisms, pointing towards two putative pathways underlying the dopaminergic modulation of mental state attribution: Action representation and a shared mechanism supporting mentalising and emotion recognition. We conclude that dopamine is causally implicated in Theory of Mind. Our results have implications for the neurochemical basis of socio-cognitive difficulties in patients with dopamine dysfunctions and generate new hypotheses about the specific dopamine-mediated mechanisms underlying social cognition.

Some theories of human cultural evolution posit that humans have social-specific learning mechanisms that are adaptive specialisations moulded by natural selection to cope with the pressures of group living. However, the existence of neurochemical pathways that are specialised for learning from social information and individual experience is widely debated. Cognitive neuroscientific studies present mixed evidence for social-specific learning mechanisms: some studies find dissociable neural correlates for social and individual learning, whereas others find the same brain areas and, dopamine-mediated, computations involved in both. Here, we demonstrate that, like individual learning, social learning is modulated by the dopamine D2 receptor antagonist haloperidol when social information is the primary learning source, but not when it comprises a secondary, additional element. Two groups (total N = 43) completed a decision-making task which required primary learning, from own experience, and secondary learning from an additional source. For one group, the primary source was social, and secondary was individual; for the other group this was reversed. Haloperidol affected primary learning irrespective of social/individual nature, with no effect on learning from the secondary source. Thus, we illustrate that dopaminergic mechanisms underpinning learning can be dissociated along a primary-secondary but not a social-individual axis. These results resolve conflict in the literature and support an expanding field showing that, rather than being specialised for particular inputs, neurochemical pathways in the human brain can process both social and non-social cues and arbitrate between the two depending upon which cue is primarily relevant for the task at hand.

Emotion recognition abilities are fundamental to our everyday social interaction. A large number of clinical populations show impairments in this domain, with emotion recognition atypicalities being particularly prevalent among disorders exhibiting a dopamine system disruption (e.g., Parkinson's disease). Although this suggests a role for dopamine in emotion recognition, studies employing dopamine manipulation in healthy volunteers have exhibited mixed neural findings and no behavioral modulation. Interestingly, while a dependence of dopaminergic drug effects on individual baseline dopamine function has been well established in other cognitive domains, the emotion recognition literature so far has failed to account for these possible interindividual differences. The present within-subjects study therefore tested the effects of the dopamine D2 antagonist haloperidol on emotion recognition from dynamic, whole-body stimuli while accounting for interindividual differences in baseline dopamine. A total of 33 healthy male and female adults rated emotional point-light walkers (PLWs) once after ingestion of 2.5 mg haloperidol and once after placebo. To evaluate potential mechanistic pathways of the dopaminergic modulation of emotion recognition, participants also performed motoric and counting-based indices of temporal processing. Confirming our hypotheses, effects of haloperidol on emotion recognition depended on baseline dopamine function, where individuals with low baseline dopamine showed enhanced, and those with high baseline dopamine decreased emotion recognition. Drug effects on emotion recognition were related to drug effects on movement-based and explicit timing mechanisms, indicating possible mediating effects of temporal processing. Results highlight the need for future studies to account for baseline dopamine and suggest putative mechanisms underlying the dopaminergic modulation of emotion recognition. SIGNIFICANCE STATEMENT A high prevalence of emotion recognition difficulties among clinical conditions where the dopamine system is affected suggests an involvement of dopamine in emotion recognition processes. However, previous psychopharmacological studies seeking to confirm this role in healthy volunteers thus far have failed to establish whether dopamine affects emotion recognition and lack mechanistic insights. The present study uncovered effects of dopamine on emotion recognition in healthy individuals by controlling for interindividual differences in baseline dopamine function and investigated potential mechanistic pathways via which dopamine may modulate emotion recognition. Our findings suggest that dopamine may influence emotion recognition via its effects on temporal processing, providing new directions for future research on typical and atypical emotion recognition.

The kinematics of peoples' body movements provide useful cues about emotional states: for example, angry movements are typically fast and sad movements slow. Unlike the body movement literature, studies of facial expressions have focused on spatial, rather than kinematic, cues. This series of experiments demonstrates that speed comprises an important facial emotion expression cue. In Experiments 1a-1c we developed (N = 47) and validated (N = 27) an emotion-induction procedure, and recorded (N = 42) posed and spontaneous facial expressions of happy, angry, and sad emotional states. Our novel analysis pipeline quantified the speed of changes in distance between key facial landmarks. We observed that happy expressions were fastest, sad were slowest, and angry expressions were intermediate. In Experiment 2 (N = 67) we replicated our results for posed expressions and introduced a novel paradigm to index communicative emotional expressions. Across Experiments 1 and 2, we demonstrate differences between posed, spontaneous, and communicative expression contexts. Whereas mouth and eyebrow movements reliably distinguished emotions for posed and communicative expressions, only eyebrow movements were reliable for spontaneous expressions. In Experiments 3 and 4 we manipulated facial expression speed and demonstrated a quantifiable change in emotion recognition accuracy. That is, in a discovery (N = 29) and replication sample (N = 41), we showed that speeding up facial expressions promotes anger and happiness judgments, and slowing down expressions encourages sad judgments. This influence of kinematics on emotion recognition is dissociable from the influence of spatial cues. These studies demonstrate that the kinematics of facial movements provide added value, and an independent contribution to emotion recognition. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Atypical motor learning has been suggested to underpin the development of motoric challenges (e.g., handwriting difficulties) in autism. Bayesian accounts of autistic cognition propose a mechanistic explanation for differences in the learning process in autism. Specifically, that autistic individuals overweight incoming, at the expense of prior, information and are thus less likely to (a) build stable expectations of upcoming events and (b) react to statistically surprising events. Although Bayesian accounts have been suggested to explain differences in learning across a range of domains, to date, such accounts have not been extended to motor learning. 28 autistic and 35 non-autistic controls (IQ > 70) completed a computerised task in which they learned sequences of actions. On occasional "surprising" trials, an expected action had to be replaced with an unexpected action. Sequence learning was indexed as the reaction time difference between blocks which featured a predictable sequence and those that did not. Surprise-related slowing was indexed as the reaction time difference between surprising and unsurprising trials. No differences in sequence-learning or surprise-related slowing were observed between the groups. Bayesian statistics provided anecdotal to moderate evidence to support the conclusion that sequence learning and surprise-related slowing were comparable between the two groups. We conclude that individuals with autism do not show atypicalities in response to surprising events in the context of motor sequence-learning. These data demand careful consideration of the way in which Bayesian accounts of autism can (and cannot) be extended to the domain of motor learning.

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The ability to ascribe mental states, such as beliefs or desires to oneself and other individuals forms an integral part of everyday social interaction. Animations tasks, in which observers watch videos of interacting triangles, have been extensively used to test mental state attribution in a variety of clinical populations. Compared to control participants, individuals with clinical conditions such as autism typically offer less appropriate mental state descriptions of such videos. Recent research suggests that stimulus kinematics and movement similarity (between the video and the observer) may contribute to mental state attribution difficulties. Here we present a novel adaptation of the animations task, suitable to track and compare animation generator and -observer kinematics. Using this task and a population-derived stimulus database, we confirmed the hypotheses that an animation’s jerk and jerk similarity between observer and animator significantly contribute to the correct identification of an animation. By employing random forest analysis to explore other stimulus characteristics, we reveal that other indices of movement similarity, including acceleration- and rotation-based similarity, also predict performance. Our results highlight the importance of movement similarity between observer and animator and raise new questions about reasons why some clinical populations exhibit difficulties with this task.

The ability to ascribe mental states, such as beliefs or desires to oneself and other individuals forms an integral part of everyday social interaction. One task that has been extensively used to test mental state attribution in a variety of clinical populations is the animations task, where participants are asked to infer mental states from short videos of interacting triangles. In this task, individuals with clinical conditions such as autism spectrum disorders typically offer fewer and less appropriate mental state descriptions than controls, however little is currently known about why they show these difficulties. Previous studies have hinted at the similarity between an observer’s and the triangles’ movements as a key factor for the successful interpretation of these animations. In this study we present a novel adaptation of the animations task, suitable to track and compare animation generator and -observer kinematics. Using this task and a population-derived stimulus database, we demonstrate that an animation’s kinematics and kinematic similarity between observer and generator are integral for the correct identification of that animation. Our results shed light on why some clinical populations show difficulties in this task and highlight the role of participants’ own movement and specific perceptual properties of the stimuli.

Event Abstract Back to Event The role of movement kinematics in facial emotion expression Sophie L. Sowden1, Bianca A. Schuster1 and Jennifer L. Cook2* 1 University of Birmingham, United Kingdom 2 University of Birmingham, Institute of Cognitive Neuroscience, United Kingdom Background Facial emotion expression and recognition play an important role in successful social interaction (Ekman and Friesen, 1975), providing cues about others’ affective states to guide behavior. The last decade has seen the use of facial emotion tracking software to dynamically adapt and personalize online learning platforms for education, in response to users’ spontaneous expressions (Saneiro et al., 2014). Such platforms use algorithms to detect the presence and intensity of key facial action units (actions of groups of muscles) typical for each emotion (Littlewort et al., 2011). However, this relies on the detection of spatial rather than temporal features. A recent body of evidence suggests that temporal features, such as whole-body movement kinematics, provide key information about emotional states: faster (high velocity) body movements are associated with anger and happiness, whilst low velocity movements are indicative of sadness (Ada et al., 2003; Edey et al., 2017; Michalak et al., 2009). Although studies have investigated the effect of manipulating the speed of video or expression-morphing playback on facial emotion recognition (Fayolle and Droit-Volet, 2014; Kamachi et al., 2001, Pollick et al., 2003), studies have not quantified whether, like whole-body movements, the velocity of face movements is indicative of emotional state. Although emotion tracking software typically aims to detect spontaneous expressions, much of our knowledge of movement kinematics comes from the posed expressions of professional actors. Preliminary evidence suggests that spontaneous and posed expressions differ with regards to timing and amplitude of facial movements (Schmidt et al., 2006; Valstar et al., 2006). Moreover, there are data to suggest that different facial regions may be more or less informative for recognizing different emotions (Bassili, 1979); movement kinematics may play a role in this. Consequently, the current study aims to investigate a) whether the velocity of movement of different parts of the face (i.e. face “actions”) differs as a function of emotion (happy, angry and sad), and b) whether this relationship differs for spontaneous and posed expressions. Method Forty-two healthy student volunteers (39 female) from the University of Birmingham gave written informed consent to take part and were reimbursed with a course credit or monetary incentive for their time. 7 further participants were excluded from analyses either due to poor registration with the facial tracking system (N = 4) or because of missing data (N = 3). Participants were seated with their head at the center of a polystyrene frame, positioned 80 cm from the computer monitor (21.5-inch iiyama display) and 1 m from a tripod-docked video camcorder (Sony Handycam HDR-CX240E). Participants’ facial movements were recorded during two conditions: 1) Spontaneous - wherein participants watched videos (selected from a pilot study) that prompted spontaneous expression of the target emotions happy, sad, and angry; 2) Posed - wherein participants posed the 3 target emotional expressions following the instruction to move from neutral, to peak expression, and back to neutral upon detecting a fixation cross along with a beep (9 seconds duration). The order of emotions was counterbalanced for posed and spontaneous conditions. Following each video, for the spontaneous condition, participants rated each target emotion plus surprise and disgust on a scale from 1-10. Recordings for the spontaneous condition were cropped to a 10-second scene rated, across all participants, as the most emotionally intense scene for each target emotion. Data analysis followed a novel pipeline (Figure 1), whereby recordings for each emotion, for posed and spontaneous (6 videos per participant), were fed into the open-source software OpenFace (Baltrušaitis et al., 2018) which identifies locations in pixels of 68 2D facial landmarks, sampled at a rate of 25 Hz. 9 facial ‘distances’ were calculated (following the procedure outlined in Zane et al. (2018)) by identifying key points on the face and calculating the distance between key points (i.e. the square root of the sum of the squared differentials of the x and y coordinates of each key point). These distances were then summed to create 5 face “actions”. For example, the distance between points 21 and 22 (Figure 1A), comprises the eyebrow widen action. Velocity was calculated as the differential of the action vector and represented as absolute values of each face distance collapsed across all movements within a given time window. Thus, this is not the onset or speed taken to reach peak expressions, which may be confounded with the difference in time taken to feel the emotion. Velocity vectors were low pass filtered at 10 Hz and absolute velocity was averaged, for each action, across each video. Results Emotion induction was successful: emotion rating discreteness (target emotion rating minus average rating of all non-target emotions) scores for each video were significantly greater than zero (ps < .001). A repeated-measures ANOVA (RM-ANOVA) with within-subjects factors of condition (posed, spontaneous), emotion (happy, angry, sad) and action (eyebrow widen, nose lengthen, lip raise, mouth open, mouth widen) revealed a significant main effect of emotion [F(2,82) = 6.16, p = .003, ηP2 = .69]. Happy expressions had the highest velocities (mean [pixels/frame] = 0.37, standard error of the mean [SEM] = 0.01), sad expressions were the slowest (mean = 0.34, SEM = 0.01) and angry expressions were intermediate (mean = 0.36, SEM = 0.01). A main effect of action was also observed [F(4,164) = 92.07, p < .001, ηP2 = .69]: lip raise actions were fastest (mean = 0.47, SEM = 0.02), mouth widen (mean = 0.27, SEM = 0.01) and eyebrow widen (mean = 0.26, SEM = 0.01) were slowest, and mouth open (mean = 0.39, SEM = 0.02) and nose lengthen (mean = 0.39, SEM = 0.02) were intermediate. There was no main effect of condition (p = .97). An action x emotion interaction indicated that the velocity of eyebrow widening [F(2,82) = 21.11, p < .001, ηP2 = .34], mouth widening [F(2,82) = 37.36, p < .001, ηP2 = .48], and mouth opening [F(2,82) = 18.67, p < .001, ηP2 = .31], but not lip raising (p = .84) or nose lengthening (p = .07), differed as a function of emotion. Velocity of eyebrow widening was highest for angry (mean = 0.29, SEM = 0.01), lowest for sad (mean = 0.25, SEM = 0.01) and happy (mean = 0.26, SEM = 0.01), whilst velocity for mouth movements were highest for happy (mouth widening: mean = 0.31, SEM = 0.01; mouth opening: mean = 0.45, SEM = 0.02), followed by angry (mouth widening: mean = 0.26, SEM = 0.01; mouth opening: mean = 0.40, SEM = 0.02) and lowest for sad (mouth widening: mean = 0.23, SEM = 0.01; mouth opening: mean = 0.34, SEM = 0.02). However, a significant interaction between condition, emotion and action was also observed [F(8,328) = 11.85, p < .001, ηP2 = .23]. Separate RM-ANOVAs, for each action, revealed condition x emotion interactions for mouth opening [F(2,82) = 15.01, p < .001, ηP2 = .27] and mouth widening [F(2,82) = 28.58, p < .001, ηP2 = .41]. Post-hoc t-tests revealed velocity of mouth movements during posed expression production to be highest for happy expressions when compared to both angry (mouth widening [t(41) = 11.54, p < .001]; mouth opening [t(41) = 4.51, p < .001]) and sad (mouth widening [t(41) = 11.62, p < .001]; mouth opening [t(41) = 9.63, p < .001]), whilst no such differences were observed for spontaneous expression production (ps > .05). See Figure 2 for a visual representation of the results. Discussion These data demonstrate that face movement kinematics provide important cues about emotional states. In line with the whole-body literature, anger and happiness were associated with high velocity face movements, from the eyebrows and mouth areas respectively, whereas sadness was characterized by low velocity movements. This has important implications for expression-centered adaptive learning platforms which employ facial emotion tracking, such algorithms currently overlook temporal features of facial expressions which carry useful information regarding emotional states. Emotion tracking algorithms may benefit from incorporating information about the kinematics of face movements, with a particular focus on the actions that were found to be significant differentiators of emotional state (eyebrow widening and mouth widening and opening). However, importantly, in the current study, mouth movements only differentiated posed, not spontaneous, expressions. Thus, any algorithm aiming to detect spontaneous emotions should not rely on posed expression datasets for training purposes. Figure 1 Figure 2 Acknowledgements The work in this paper was funded by a European Research Council Starting Grant held by J.C. We thank Alexandru-Andrei Moise, Maya Burns and Lucy D’Orsaneo for their help with data collection. References Ada, M. S., Suda, K., and Ishii, M. (2003). Expression of emotions in dance: Relation between arm movement characteristics and emotion. Percept. Motor Skills. 97, 697-708. Baltrušaitis, T., Zadeh, A., Lim, Y. C., and Morency, L. (2018). OpenFace 2.0: Facial behavior analysis toolkit, In IEEE International Conference on Automatic Face and Gesture Recognition. 59-66. https://github.com/TadasBaltrusaitis/OpenFace/wiki. Bassili, J. N. (1979). Emotion recognition: The role of facial movement and the relative importance of upper and lower areas of the face. J. Pers. Soc. Psychol. 37, 2049-2058. Edey, R., Yon, D., Cook, J., Dumontheil, I., and Press, C. (2017). Our own action kinematics predict the perceived affective states of others. J. Exp. Psychol. Human. 43, 1263-1268. Fayolle, S. L., and Droit-Volet, S. (2014). Time perception and dynamics of facial expressions of emotions. PLoS One. 9, e97944. Kamachi, M., Bruce, V., Mukaida, S., Gyoba, J., Yoshikawa, S., and Akamatsu, S. (2013). Dynamic Properties Influence the Perception of Facial Expressions. Perception. 42, 1266-1278 Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., and Bartlett, M. (2011) The Computer Expression Recognition Toolbox (CERT). In Proc. IEEE International Conference on Automatic Face and Gesture Recognition. 298-305. Michalak, J., Troje, N. F., Fischer, J., Vollmar, P., Heidenreich, T., and Schulte, D. (2009). Embodiment of sadness and depression – gait patterns associated with dysphoric mood. Psychos. Med. 71, 580–587. Pollick, F. E., Hill, H., Calder, A., and Paterson, H. (2003). Recognising facial expression from spatially and temporally modified movements. Perception. 32, 813-826. Saneiro, M., Santos, O. C., Salmeron-Majadas, S., and Boticario, J. G. (2014). Towards emotion detection in educational scenarios from facial expressions and body movements through multimodal approaches. Sci. World J. 484873. Schmidt, K. L., Ambadar, Z., Cohn, J. F., and Reed, L. I. (2006). Movement differences between deliberate and spontaneous facial expressions: Zygomaticus major action in smiling. J. Nonverbal Behav. 30, 37-52. Valstar, M. F., Pantic, M., Ambadar, Z., and Cohn, J. F. (2006). Spontaneous vs. posed facial behavior: automatic analysis of brow actions. In Proceedings of the 8th international conference on multimodal interfaces. 162-170. Zane, E., Yang, Z., Pozzan, L., Guha, T., Narayanan, S., and Grossman, R. B. (2018). Motion-capture patterns of voluntarily mimicked dynamic facial expressions in children and adolescents with and without ASD. J. Autism Dev. Disord. 1-18. Keywords: emotion, kinematics, facial emotion expression, Biological motion, happy, SAD, angry Conference: 4th International Conference on Educational Neuroscience, Abu Dhabi, United Arab Emirates, 10 Mar - 11 Mar, 2019. Presentation Type: Oral Presentation (invited speakers only) Topic: Educational Neuroscience Citation: Sowden SL, Schuster BA and Cook JL (2019). The role of movement kinematics in facial emotion expression. Conference Abstract: 4th International Conference on Educational Neuroscience. doi: 10.3389/conf.fnhum.2019.229.00018 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 10 Feb 2019; Published Online: 27 Sep 2019. * Correspondence: Dr. Jennifer L Cook, University of Birmingham, Institute of Cognitive Neuroscience, Birmingham, WC1N 3AR, United Kingdom, j.l.cook@bham.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Sophie L Sowden Bianca A Schuster Jennifer L Cook Google Sophie L Sowden Bianca A Schuster Jennifer L Cook Google Scholar Sophie L Sowden Bianca A Schuster Jennifer L Cook PubMed Sophie L Sowden Bianca A Schuster Jennifer L Cook Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Event Abstract Back to Event Acting is not the same as feeling: Emotion expression in gait is different for posed and induced emotions Bianca A. Schuster1, Sophie L. Sowden1, Diar Abdlkarim1, Alan M. Wing1 and Jennifer L. Cook1* 1 University of Birmingham, School of Psychology, United Kingdom INTRODUCTION The past decade has witnessed an unprecedented growth in human–computer interaction. With this progress comes growing demand for computers to sense and recognize users’ affective states (Cowie et al., 2001; Pantic & Rothkrantz, 2003; Hudlicka, 2003). Indeed, the development of emotion-sensitive computer systems may have important implications for variety of areas from automatic customer services (Fragopanagos & Taylor, 2005) to early recognition and diagnosis of clinical conditions (Michalak, et al., 2009). Automated emotion detection has largely focused on facial expressions (e.g., Kenji, 1991), however, whole-body movement carries numerous emotion-related cues, which humans can rapidly detect (e.g., Clarke et al., 2005; De Meijer, 1989; Dittrich et al., 1996; Montepare et al., 1987, 1999; Walk and Homan, 1984; Wallbott and Scherer, 1986). Velocity, for instance, comprises an important cue as to a person’s underlying emotional state: faster (high velocity) body movements tend to indicate anger and happiness, whilst low velocity movements are indicative of sadness (Chouchourelou et al., 2006; Edey et al., 2017; Gross et al., 2012; Halovic & Kroos, 2018; Michalak et al., 2009; Roether et al., 2009). Consequently, whole-body cues are increasingly being incorporated into computerized emotion recognition technologies (Janssen et al., 2008; Pantic & Rothkrantz, 2003). However, at present this remains a field which lags behind the advances made in the detection of emotion from face cues. One issue which has received interest in the context of emotion recognition from faces, but which has been overlooked with respect to whole-body emotion recognition, is the question of differences between posed and induced/spontaneous expressions. Although emotion tracking software typically aims to detect naturally occurring, spontaneous expressions, much of our knowledge of movement kinematics comes from the posed expressions of professional actors (e.g., Jannsen et al., 2008; Roether et al., 2009, Venture et al., 2014). Even when posing is aided by induction methods such as autobiographical recall, the actor remains aware of the effects they are expected to produce, and likely exaggerates particular movement patterns. Consequently, kinematic measures derived from studies using posed expressions alone may not correspond to naturally occurring emotional expressions. Indeed, with respect to facial expressions, preliminary evidence suggests that induced and posed expressions differ with regards to timing and amplitude (Schmidt et al., 2006; Valstar et al., 2006). The current study recorded happy, angry and sad walks, as executed by student volunteers. We compared the velocity of these walks when the emotion was ‘posed’ and when the emotion was naturally ‘induced’ by watching emotional film clips. METHODS Kinematic data was obtained from 31 healthy participants (24 females) with self-reported unimpaired motor function. All participants gave informed consent to participate and received course credit or a monetary incentive as reimbursement. The study was approved by the University of Birmingham Ethics Committee. Walking data was recorded using the Zeno™ Walkway (ProtoKinetics LLC, Havertown, USA) gait mat. All participants first carried out a ‘baseline’ walk for a duration of 120 seconds. Following this, participants watched 3 film clips (average length: 2.5 minutes) which had been selected for their propensity to induce happy, angry and sad emotional states, as assessed in a pilot study. Film-clip order was pseudo-randomized between participants. Between films participants viewed a 1-minute-long filler clip, to reset their mood to neutral. Immediately after each clip, participants walked continuously across the gait mat, stepping off the end to turn around each time. Walks were recorded for 30 seconds, resulting in 7 passes, across the full length of the gait mat, on average. Subsequently, participants rated their current mood (positive – negative), arousal (calm – excited), intensity for the target emotion and 4 other basic emotions (anger, happiness, sadness, disgust, surprise) and the extent to which they felt emotionally neutral on a 10-point scale. After watching all the film clips, participants executed posed walks, simulating happy, angry and sad emotional states according to the instruction … “Imagine you were angry (happy/sad). Walk across the mat how you think you would walk if you were angry (happy/sad)”. PKMAS software (ProtoKinetics LLC, Havertown, USA) was employed to process each walk and calculate the average velocity (distance travelled/ambulation time, centimeters/second (cm/s)) across the walk period (120 seconds for baseline walks, 30 seconds for all other walks). RESULTS Emotion induction was successful: emotion rating discreteness (target emotion rating minus average rating of all non-target emotions) scores for each video were significantly greater than zero (ps < .001). A repeated-measures ANOVA with within-subjects factors of condition (posed, induced), and emotion (happy, angry, sad) revealed a significant main effect for emotion (Figure 1; F(2,60) = 60.09, p < .001, partial eta squared = .67). There was no main effect for condition (F(1,30) = .041, p = .841, partial eta squared= .00). Collapsing across posed and induced revealed that angry and happy walks were the fastest, and sad walks were the slowest (angry: mean [M] = 118.90, standard error of the mean [SEM] = 3.29; happy: M = 114.96, SEM = 2.34; sad: M = 101.34, SEM = 2.95). Bonferroni-corrected post-hoc t-tests reveal that while there was no difference in velocity for happy and angry walks (t(30) = 2.49, p = .019) , sad and angry (t(30) = 9.56, p < .001) and happy and sad (t(30) = 8.46, p < .001) were significantly different. However, the ANOVA also revealed a significant condition x emotion interaction (F(2,60) = 38.16, p < .001, partial eta squared= .56). Separate ANOVAs for each condition revealed that, whereas velocity differed as a function of emotion for posed walks (F(2,60) = 57.39 p < .001, partial eta squared = .66), this was not the case for the induced condition (F(2,60) = 2.34, p =.105, partial eta squared = .07). Post-hoc tests further showed that, for the posed condition alone, there was no difference in velocity for happy and angry walks (t(30) = 1.98, p = .058). However, velocities for posed sad walks were significantly lower than those for posed angry walks (sad: M = 92.35, SEM = 3.82; angry: M = 124.50, SEM = 4.33; t(30) = 9.77, p < .001) and posed happy walks (happy: M = 117.85 , SEM = 2.40; t(30) = 9.04 p < .001). The equivalent tests, for the induced condition, showed no difference in velocity for sad and angry walks (sad: M = 110.32, SEM = 2.60; angry: M = 113.31, SEM = 2.74 t(30) = 2.40, p = .024), sad and happy (happy: M = 112.10, SEM = 2.63; t(30) = 1.10, p = .281) or happy and angry walks (t(30) = .96, p = .343). DISCUSSION The current study investigated whether the velocity of happy, angry and sad walks differed for walks that comprised posed simulations of emotion, compared to those that followed emotion induction and thus comprised natural expressions of emotion. Velocity differed as a function of emotion for posed simulations: in line with previous literature we observed faster velocities for angry walks and slower velocities for sad walks. Velocities for posed happiness were also, as expected, faster than sad but slower than angry, albeit the difference between happy and angry was not statistically significant. This pattern of data was not observed for walks that followed emotion induction. Although our emotion induction methods were successful, as evidenced by higher post-film-clip intensity ratings for the target emotion compared to non-target emotions (i.e. if a participant watched a happy video they gave high ratings on the happy scale and low ratings for sad, angry, disgusted and surprised) we saw no velocity differences between happy, angry and sad walks for the induced condition. These data highlight important differences between posed and naturally-occurring whole-body expressions of emotion, demonstrating in particular that, for induced emotions, gait velocity should not be relied upon to discriminate emotional state. Further exploration is required to identify the gait characteristics (e.g. cadence, step width, force, stride length) that are the best predictors of emotional state for induced, naturally-occurring, emotions. 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In 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) (p. 149). https://doi.org/10.1109/CVPRW.2006.85 Venture, G., Kadone, H., Zhang, T., Grèzes, J., Berthoz, A., & Hicheur, H. (2014). Recognizing Emotions Conveyed by Human Gait. International Journal of Social Robotics, 6(4), 621–632. https://doi.org/10.1007/s12369-014-0243-1 Keywords: emotion, Biological motion, kinematics, velocity, Gait, happy, angry, SAD Conference: 4th International Conference on Educational Neuroscience, Abu Dhabi, United Arab Emirates, 10 Mar - 11 Mar, 2019. Presentation Type: Oral Presentation (invited speakers only) Topic: Educational Neuroscience Citation: Schuster BA, Sowden SL, Abdlkarim D, Wing AM and Cook JL (2019). Acting is not the same as feeling: Emotion expression in gait is different for posed and induced emotions. Conference Abstract: 4th International Conference on Educational Neuroscience. doi: 10.3389/conf.fnhum.2019.229.00010 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 10 Feb 2019; Published Online: 27 Sep 2019. * Correspondence: Dr. Jennifer L Cook, University of Birmingham, School of Psychology, Birmingham, B15 2TT, United Kingdom, j.l.cook@bham.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Bianca A Schuster Sophie L Sowden Diar Abdlkarim Alan M Wing Jennifer L Cook Google Bianca A Schuster Sophie L Sowden Diar Abdlkarim Alan M Wing Jennifer L Cook Google Scholar Bianca A Schuster Sophie L Sowden Diar Abdlkarim Alan M Wing Jennifer L Cook PubMed Bianca A Schuster Sophie L Sowden Diar Abdlkarim Alan M Wing Jennifer L Cook Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.