Movement differences in autism have attracted growing attention in recent years. Anecdotally, autistic movement has been likened to that of Parkinson’s Disease (PD). Given that PD assessments are primarily movement-based, it is important to ensure that autistic individuals are not scoring highly on PD diagnostic criteria due to autism-related movement differences. Quantifying overlap in movement profiles and identifying distinguishing features is essential, particularly given increased PD diagnosis rates in the autistic population. We conducted the first direct comparison study of autistic and parkinsonian movement. Autistic individuals (N = 31), individuals with PD (N = 32) and control participants (N = 31) completed a Shapes Tracing Task and a Reaction Time Task. Kinematic features were compared between groups and classification algorithms were run to distinguish between groups. Groups were distinguishable based on kinematic features. The autistic group differed from both PD and control groups in speed modulation and sub-movements, and from the PD group in reaction time. Classification algorithms for clinical (autism and PD) versus non-clinical groups, and for autism versus PD, were most accurate when combining kinematic and questionnaire data. There were no kinematic similarities between autism and PD that were also distinct from controls. Whilst kinematic features did not appear similar between autism and PD, they were informative for group classification. This proof-of-concept study highlights that movement-based metrics may aid in identifying whether someone belongs to a clinical group, and which one – suggesting potential for refining diagnostic approaches for both autism and PD.

A growing field documents differences in autism in movement-based tasks such as handwriting, throwing a ball and social gestures. Usefully, complex movements such as social gestures and cursive handwriting can be decomposed to signature regularities that match those of “pure frequency” shapes including spirals, ellipses and rounded triangles. By studying the way that autistic and non-autistic people draw pure frequency shapes we can therefore predict movement patterns for a range of complex functional actions and gestures, and we can gain insights that may help us understand potential mechanisms underlying any differences. Correspondingly, we recorded the x and y position of a stylus tip as 21 autistic and 19 non-autistic adults (matched for age, IQ, and sex) traced a range of pure frequency shapes on a tablet. The relationship between speed and curvature across the pure frequency shapes typically follows a set of mathematical equations which can be thought of as a “spectrum of power laws”. We compared the speed-curvature relationship between groups and, additionally used fast Fourier transform (FFT) to investigate potential mechanisms. Autistic and non-autistic adults differed in the relationship between speed and curvature. FFT revealed that non-autistic participants narrowly modulated speed oscillations around target frequencies, while autistic participants showed broader speed modulation profiles, potentially indicative of differences in the bodily filtering of outgoing movement signals coming from the brain. Our results enable predictions about how autistic individuals might execute a range of functional movements, and may help in the development of support structures for complex tasks like writing.

Bodily movements exhibit kinematic invariances, with the “one-third power law” relating velocity to curvature amongst the most established. Despite being heralded amongst the “kinematic laws of nature” (Flash 2021, p. 4), there is no consensus on its origin, common reporting practice, or vetted analytical protocol. Many legacy elements of analytical protocols in the literature are suboptimal, such as noise amplification from repeated differentiation, biases arising from filtering, log transformation distortion, and injudicious linear regression, all of which undermine power law calculations. This article reviews prior power law calculation protocols, identifies suboptimal practices, before proposing solutions grounded in the kinematics literature and related fields of enquiry. Ultimately, we synthesise these solutions into a vetted, modular protocol which we make freely available to the scientific community. The protocol’s modularity accommodates future analytical advances and permits re-use of modules useful in broader kinematic science applications. We propose that adoption of this protocol will eliminate spurious confirmation of the law and enable more sensitive quantification of recently noted power law divergences. These divergences have been linked to neurochemical disturbances arising from ingestion of dopaminergic drugs, and in neurological conditions such as Parkinson’s and autism.

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.

Extant work reliably demonstrates that autistic individuals move with increased jerk (where jerk concerns change in acceleration). Although it follows that autistic movement may therefore diverge from fundamental power laws that govern movement, this hypothesis has not been directly tested to date. This lack of insight holds back progress in understanding the mechanisms underpinning differences in autism in motor control particularly with respect to movement jerkiness. Here we investigated whether movements executed by autistic adults diverged from the typical power law relationship that links movement speed and curvature. x and y position of the stylus tip was recorded at 133 Hz while 21 autistic and 19 non-autistic age-, intelligence quotient- and sex-matched adults traced, on a tablet device, a range of shapes that varied in angular frequency from 2/33 (spiral-like shapes) to 4 (square-like shapes). The gradient of the relationship between speed and curvature for each angular frequency-defined shape is reliably predicted by a set of mathematical equations often referred to as fundamental power laws thus, to assess deviations from power laws, we compared autistic and non-autistic participants in terms of speed-curvature gradients. To gain insight into potential mechanisms underpinning any differences we also used fast Fourier transform to explore amplitude spectral density across all angular frequencies. Compared to non-autistic adults, autistic adults exhibited significantly steeper speed-curvature gradients. Fast Fourier transform further revealed that non-autistic participants exhibited highly precise modulation of speed oscillations around the target frequency. For example, when drawing an ellipse their speed profile was dominated by speed changes in a band centred around the angular frequency 2 with minimal changes in other bands. Autistic adults, in contrast, exhibited less precise modulation of speed oscillations around the target frequency, a result that is reminiscent of a literature reporting broader auditory filters in autistic individuals. These results evidence, in autistic adults, a deviation from the power laws that typically govern movement and suggest differences in motor cortical control policies and/or biomechanical constraints.

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).

A burgeoning literature suggests that alexithymia, and not autism, is responsible for the difficulties with static emotion recognition that are documented in the autistic population. Here we investigate whether alexithymia can also account for difficulties with dynamic facial expressions. Autistic and control 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 that varied in speed and spatial exaggeration. The ASD group exhibited significantly lower recognition accuracy for angry, but not happy or sad, expressions with normal speed and spatial exaggeration. The level of autistic, and not alexithymic, traits was a significant predictor of accuracy for angry expressions with normal speed and spatial exaggeration.

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.

Accurate timing of movement in the hundreds of milliseconds range is a hallmark of human activities such as music and dance. Its study requires accurate measurement of the times of events (often called responses) based on the movement or acoustic record. This chapter provides a comprehensive over - view of methods developed to capture, process, analyse, and model individual and group timing [...] This chapter is structured in five main sections, as follows. We start with a review of data capture methods, working, in turn, through a low cost system to research simple tapping, complex movements, use of video, inertial measurement units, and dedicated sensorimotor synchronisation software. This is followed by a section on music performance, which includes topics on the selection of music materials, sound recording, and system latency. The identification of events in the data stream can be challenging and this topic is treated in the next section, first for movement then for music. Finally, we cover methods of analysis, including alignment of the channels, computation of between channel asynchrony errors and modelling of the data set.

Background The differential contributions of the cerebellum and parietal lobe to coordination between hand transport and hand shaping to an object have not been clearly identified. Objective To contrast impairments in reach-to-grasp coordination, in response to object location perturbation, in patients with right parietal and cerebellar lesions, in order to further elucidate the role of each area in reach-to-grasp coordination. Method A two-factor design with one between subject factor (right parietal stroke; cerebellar stroke; controls) and one within subject factor (presence or absence of object location perturbation) examined correction processes used to maintain coordination between transport-to-grasp in the presence of perturbation. Sixteen chronic stroke participants (eight with right parietal lesions and eight with cerebellar lesions) were matched in age (mean = 61 years; standard deviation = 12) and hand dominance with 16 healthy controls. Hand and arm movements were recorded during unperturbed baseline trials (10) and unpredictable trials (60) in which the target was displaced to the left (10) or right (10) or remained fixed (40). Results Cerebellar patients had a slowed response to perturbation with anticipatory hand opening, an increased number of aperture peaks and disruption to temporal coordination, and greater variability. Parietal participants also exhibited slowed movements, with increased number of aperture peaks, but in addition, increased the number of velocity peaks and had a longer wrist path trajectory due to difficulties planning the new transport goal and thus relying more on feedback control. Conclusion Patients with parietal or cerebellar lesions showed some similar and some contrasting deficits. The cerebellum was more dominant in controlling temporal coupling between transport and grasp components, and the parietal area was more concerned with using sensation to relate arm and hand state to target position.

Biologically motivated techniques are used to record onset data structures [L. S. Smith and D. S. Fraser, IEEE TNNS 15, 1125–1134 (2004)]. These structures record sets of events detailing both the spectral and intensity structure of sound onsets. They are inspired by the output spikes from onset cells in the cochlear nucleus. We suggest that the spectro-temporal characteristic of these structures provides useful information for classifying initial segments of sounds. The original onset data structure is of high dimensionality, and is difficult to interpret directly. A self-organizing feature map (SOFM or Kohonen network) is therefore used to provide a lower dimensional coding. The SOFM requires fixed-length data vectors, preferably not of too high a dimensionality. A number of different reformatting techniques are used to provide this. For the SOFM, identified activated map areas are associated with particular sounds. Using the TIMIT database these areas can be labelled, providing a testable classification scheme. This is compared with other work classsifying TIMIT phonemes. We believe that the SOFM can be extended to classify the onsets of other (non-speech) sounds. [Work supported by UK EPSRC.]

The aim of this work is separation of foreground speech from background sound sources using selective remixing of bandpass filtered channels. Clearly, the remixing parameters must be dynamic since the speech and noise spectra are highly non-stationary. Remixing parameters are recomputed at onsets, detected using biologically motivated techniques [L. S. Smith and D. S. Fraser, IEEE TNNS 15, 1125–1134 (2004)]. However, onsets may originate from the foreground or the background. To select appropriate onsets from the foreground source (whose direction is known) a two microphone system is used, selecting onsets for which the estimated direction in that channel corresponds to the foreground direction. Two different techniques for direction estimation are used: a channel by channel short-term autocorrelation technique, and a channel by channel spike based phase synchronous system (SBPSS), computing ITDs [L. S. Smith, in Artificial Neural Networks, Proc ICANN 2001, LNCS 2130, pp. 1103–1108 (Springer, 2001)] and IIDs [L. S. Smith, in From Animals to Animats, Vol. 7, pp. 60–61 (MIT Press, 2002)]. Results comparing the performance of autocorrelation and SBPSS on single source and source plus noise signals in an office environment are presented. [Work supported by UK EPSRC.]

We justify the usage of onsets in sound processing by appealing to an ecological view of auditory processing. The biological basis for onset processing is briefly discussed, and we describe our biologically motivated approach for a spike-based system for onset detection. This is based on a auditory-nerve like representation (with multiple spike trains per filter-bank band) followed by a leaky integrateand-fire neuron with depressing synapses. Onsets are detected with essentially zero latency relative to the filter-bank. We show how this can be used to find the starts of certain phonemes in the TIMIT database, and how, by a small variation in the parameters, it can be used to detect amplitude modulation.

A biologically inspired technique for detecting onsets in sound is presented. Outputs from a cochlea-like filter are spike coded, in a way similar to the auditory nerve (AN). These AN-like spikes are presented to a leaky integrate-and-fire neuron through a depressing synapse. Onsets are detected with essentially zero latency relative to these AN spikes. Onset detection results for a tone burst, musical sounds and the DARPA/NIST TIMIT speech corpus are presented.