To date, studies have not established whether autistic and non-autistic individuals differ in emotion recognition from facial motion cues when matched in terms of alexithymia. Here, autistic and non-autistic adults (N = 60) matched on age, gender, non-verbal reasoning ability and alexithymia, completed an emotion recognition task, which employed dynamic point light displays of emotional facial expressions manipulated in terms of speed and spatial exaggeration. Autistic participants exhibited significantly lower accuracy for angry, but not happy or sad, facial motion with unmanipulated speed and spatial exaggeration. Autistic, and not alexithymic, traits were predictive of accuracy for angry facial motion with unmanipulated speed and spatial exaggeration. Alexithymic traits, in contrast, were predictive of the magnitude of both correct and incorrect emotion ratings.

/pdf/differences-between-autistic-and-non-autistic-adults-in-the-3bjcpkuzw6.pdf

Differences Between Autistic and Non-Autistic Adults in the Recognition of Anger from Facial Motion Remain after Controlling for Alexithymia.

The role of movement kinematics in facial emotion expression

Alexithymia explains atypical spatiotemporal dynamics of eye gaze in autism

Dealing with non-verbal communications will be a key breakthrough for future technologies as much of the effort of the 21st century technologies has been in dealing with numbers and verbal communications. The automatic recognition of facial expressions is of theoretical and commercial interests and to this end there must exist video databases that incorporate the idiosyncrasies of human existence – ethnicity, gender and age. We compare the performance of three major emotion recognition software systems on real life videos of politicians from across the world. Our sample of 45 videos (total length of 2 h 26 min, with 219150 frames) is composed of male and female politicians ranging in age from 40 to 78 with well-defined differences related to gender and nationality/ethnicity. Our sample of images are partially posed and partially spontaneous – the demeanour of politicians when they engage in speech making. Our target systems, Micorosoft Azure Cognitive Services Face API, Affectiva AFFDEX and Emotient FACET, have been trained on posed expressions usually, with limited testing on spontaneous images, so in effect we are operating at the edge of the performance of these systems. There are similarities in the performance of these systems on some emotions, especially joy, but there are differences in emotion recognition, such as anger. There are also gender differences as well as differences based on age and race. This is an important issue as more and more video data is becoming available and video analytics that can deal with aspects of cognition, like emotion, accurately and across cultural/gender/ethnic divides will be a major component of future technologies.

Comparing the Performance of Facial Emotion Recognition Systems on Real-Life Videos: Gender, Ethnicity and Age

Emotion recognition across visual and auditory modalities in autism spectrum disorder: A systematic review and meta-analysis

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).

/pdf/the-role-of-movement-kinematics-in-facial-emotion-expression-4c8b8mmohl.pdf

The role of movement kinematics in facial emotion expression production and recognition.

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.

Dopaminergic Modulation of Dynamic Emotion Perception


 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.

/pdf/attributing-minds-to-triangles-kinematics-and-observer-1accqxsl64.pdf

Attributing Minds to Triangles: Kinematics and Observer-Animator Kinematic Similarity predict Mental State Attribution in the Animations Task

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.

/pdf/intact-predictive-motor-sequence-learning-in-autism-spectrum-4y4rhuyfp4.pdf

Bianca A. Schuster

Papers

The role of movement kinematics in facial emotion expression production and recognition.

The role of movement kinematics in facial emotion expression

Dopaminergic Modulation of Dynamic Emotion Perception

Attributing Minds to Triangles: Kinematics and Observer-Animator Kinematic Similarity predict Mental State Attribution in the Animations Task

Intact predictive motor sequence learning in autism spectrum disorder.