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1.
Dev Sci ; 25(3): e13195, 2022 05.
Article in English | MEDLINE | ID: mdl-34800316

ABSTRACT

Movement is prospective. It structures self-generated engagement with objects and social partners and is fundamental to children's learning and development. In autistic children, previous reports of differences in movement kinematics compared to neurotypical peers suggest that its prospective organisation might be disrupted. Here, we employed a smart tablet serious game paradigm to assess differences in the feedforward and feedback mechanisms of prospective action organisation, between autistic and neurotypical preschool children. We analysed 3926 goal-directed finger movements made during smart-tablet ecological gameplay, from 28 children with Childhood Autism (ICD-10; ASD) and 43 neurotypical children (TD), aged 3-6 years old. Using linear and generalised linear mixed-effect models, we found the ASD group executed movements with longer movement time (MT) and time to peak velocity (TTPV), lower peak velocity (PV), with PV less likely to occur in the first movement unit (MU) and with a greater number of movement units after peak velocity (MU-APV). Interestingly, compared to the TD group, the ASD group showed smaller increases in PV, TTPV and MT with an increase in age (ASD × age interaction), together with a smaller reduction in MU-APV and an increase in MU-APV at shorter target distances (ASD × Dist interaction). Our results are the first to highlight different developmental trends in anticipatory feedforward and compensatory feedback mechanisms of control, contributing to differences in movement kinematics observed between autistic and neurotypical children. These findings point to differences in integration of prospective perceptuomotor information, with implications for embodied cognition and learning from self-generated action in autism.


Subject(s)
Autism Spectrum Disorder , Autistic Disorder , Child , Child, Preschool , Goals , Humans , Prospective Studies , Tablets
2.
Conscious Cogn ; 75: 102798, 2019 10.
Article in English | MEDLINE | ID: mdl-31398574

ABSTRACT

Studies of perceptual awareness require sensitive measures reflecting subjective judgments of visibility. Two scales have been proposed for this purpose: the Continuous Scale (CS) and the Perceptual Awareness Scale (PAS). Here we compare the scales in the context of the Gabor patch orientation discrimination task and propose a Continuous Perceptual Awareness Scale (C-PAS) that aims to combine their advantages. The results of the study shown no differences in sensitivity between the scales. However, we observed differences between the scales in awareness ratings frequencies and accuracy associated with the lowest ratings. We concluded that visibility ratings are often biased, and thus, the scale sensitivity may not be optimal. Furthermore, based on the additional analyses, we argued that there is an advantage of using C-PAS over CS. The scale allows to use an additional variability of judgment within PAS categories and thus it may enable more fine-grained measurement of visibility at near-threshold conditions.


Subject(s)
Awareness/physiology , Discrimination, Psychological/physiology , Neuropsychological Tests/standards , Pattern Recognition, Visual/physiology , Space Perception/physiology , Adult , Female , Humans , Male
3.
BMJ Open ; 9(7): e026226, 2019 07 16.
Article in English | MEDLINE | ID: mdl-31315858

ABSTRACT

INTRODUCTION: Recent evidence suggests an underlying movement disruption may be a core component of autism spectrum disorder (ASD) and a new, accessible early biomarker. Mobile smart technologies such as iPads contain inertial movement and touch screen sensors capable of recording subsecond movement patterns during gameplay. A previous pilot study employed machine learning analysis of motor patterns recorded from children 3-5 years old. It identified those with ASD from age-matched and gender-matched controls with 93% accuracy, presenting an attractive assessment method suitable for use in the home, clinic or classroom. METHODS AND ANALYSIS: This is a phase III prospective, diagnostic classification study designed according to the Standards for Reporting Diagnostic Accuracy Studies guidelines. Three cohorts are investigated: children typically developing (TD); children with a clinical diagnosis of ASD and children with a diagnosis of another neurodevelopmental disorder (OND) that is not ASD. The study will be completed in Glasgow, UK and Gothenburg, Sweden. The recruitment target is 760 children (280 TD, 280 ASD and 200 OND). Children play two games on the iPad then a third party data acquisition and analysis algorithm (Play.Care, Harimata) will classify the data as positively or negatively associated with ASD. The results are blind until data collection is complete, when the algorithm's classification will be compared against medical diagnosis. Furthermore, parents of participants in the ASD and OND groups will complete three questionnaires: Strengths and Difficulties Questionnaire; Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations Questionnaire and the Adaptive Behavioural Assessment System-3 or Vineland Adaptive Behavior Scales-II. The primary outcome measure is sensitivity and specificity of Play.Care to differentiate ASD children from TD children. Secondary outcomes measures include the accuracy of Play.Care to differentiate ASD children from OND children. ETHICS AND DISSEMINATION: This study was approved by the West of Scotland Research Ethics Service Committee 3 and the University of Strathclyde Ethics Committee. Results will be disseminated in peer-reviewed publications and at international scientific conferences. TRIAL REGISTRATION NUMBER: NCT03438994; Pre-results.


Subject(s)
Autistic Disorder/diagnosis , Autistic Disorder/physiopathology , Video Games , Autistic Disorder/epidemiology , Child, Preschool , Clinical Trials, Phase III as Topic , Female , Humans , Male , Prospective Studies , Sweden/epidemiology , United Kingdom/epidemiology
4.
Atten Percept Psychophys ; 81(7): 2171-2176, 2019 Oct.
Article in English | MEDLINE | ID: mdl-31168700

ABSTRACT

This article discusses how the analysis of interactions between action and awareness allows us to better understand the mechanisms of visual awareness. We argue that action is one of several factors that influence visual awareness and we provide a number of examples. We also discuss the possible mechanisms that underlie these influences on both the cognitive and the neural levels. We propose that action affects visual awareness for the following reasons: (1) it serves as additional information in the process of evidence accumulation; (2) it restricts the number of alternatives in the decisional process; (3) it enables error detection and performance monitoring; and (4) it triggers attentional mechanisms that modify stimulus perception. We also discuss the possible neuronal mechanisms of the aforementioned effects, including feedback-dependent prefrontal cortex modulation of the activity of visual areas, error-based modulation, interhemispheric inhibition of motor cortices, and attentional modulation of visual cortex activity triggered by motor processing.


Subject(s)
Attention/physiology , Awareness/physiology , Prefrontal Cortex/physiology , Visual Cortex/physiology , Visual Perception/physiology , Humans
5.
Cogn Sci ; 2018 Jan 19.
Article in English | MEDLINE | ID: mdl-29349802

ABSTRACT

Fazekas and Overgaard () present a novel, multidimensional model that explains different ways in which conscious representations can be degraded. Moreover, the authors discuss possible mechanisms that underlie different kinds of degradation, primarily those related to attentional processing. In this letter, we argue that the proposed mechanisms are not sufficient. We propose that (1) attentional mechanisms work differently at various processing stages; and (2) factors that are independent of attentional ones, such as expectation, previous experience, and context, should be accounted for if we are aiming to construct a comprehensive model of conscious visual perception.

6.
Sci Rep ; 7(1): 13863, 2017 10 24.
Article in English | MEDLINE | ID: mdl-29066747

ABSTRACT

The article presents a research study on recognizing therapy progress among children with autism spectrum disorder. The progress is recognized on the basis of behavioural data gathered via five specially designed tablet games. Over 180 distinct parameters are calculated on the basis of raw data delivered via the game flow and tablet sensors - i.e. touch screen, accelerometer and gyroscope. The results obtained confirm the possibility of recognizing progress in particular areas of development. The recognition accuracy exceeds 80%. Moreover, the study identifies a subset of parameters which appear to be better predictors of therapy progress than others. The proposed method - consisting of data recording, parameter calculation formulas and prediction models - might be implemented in a tool to support both therapists and parents of autistic children. Such a tool might be used to monitor the course of the therapy, modify it and report its results.


Subject(s)
Autism Spectrum Disorder/therapy , Computational Biology/methods , Machine Learning , Child , Child, Preschool , Data Collection , Female , Humans , Male , Treatment Outcome
7.
Sci Rep ; 6: 31107, 2016 08 24.
Article in English | MEDLINE | ID: mdl-27553971

ABSTRACT

Autism is a developmental disorder evident from infancy. Yet, its clinical identification requires expert diagnostic training. New evidence indicates disruption to motor timing and integration may underpin the disorder, providing a potential new computational marker for its early identification. In this study, we employed smart tablet computers with touch-sensitive screens and embedded inertial movement sensors to record the movement kinematics and gesture forces made by 37 children 3-6 years old with autism and 45 age- and gender-matched children developing typically. Machine learning analysis of the children's motor patterns identified autism with up to 93% accuracy. Analysis revealed these patterns consisted of greater forces at contact and with a different distribution of forces within a gesture, and gesture kinematics were faster and larger, with more distal use of space. These data support the notion disruption to movement is core feature of autism, and demonstrate autism can be computationally assessed by fun, smart device gameplay.


Subject(s)
Autistic Disorder/diagnosis , Autistic Disorder/physiopathology , Diagnosis, Computer-Assisted/methods , Gestures , Psychomotor Performance/physiology , Video Games , Algorithms , Autistic Disorder/psychology , Biomechanical Phenomena , Case-Control Studies , Child , Child, Preschool , Computers, Handheld , Early Diagnosis , Female , Humans , Machine Learning , Male
8.
Conscious Cogn ; 36: 1-11, 2015 Nov.
Article in English | MEDLINE | ID: mdl-26057402

ABSTRACT

Recently, Windey, Gevers, and Cleeremans (2013) proposed a level of processing (LoP) hypothesis claiming that the transition from unconscious to conscious perception is influenced by the level of processing imposed by task requirements. Here, we carried out two experiments to test the LoP hypothesis. In both, participants were asked to classify briefly presented pairs of letters as same or different, based either on the letters' physical features (a low-level task), or on a semantic rule (a high-level task). Stimulus awareness was measured by means of the four-point Perceptual Awareness Scale (PAS). The results showed that low or moderate stimulus visibility was reported more frequently in the low-level task than in the high-level task, suggesting that the transition from unconscious to conscious perception is more gradual in the former than in the latter. Therefore, although alternative interpretations remain possible, the results of the present study fully support the LoP hypothesis.


Subject(s)
Awareness/physiology , Consciousness/physiology , Unconscious, Psychology , Visual Perception/physiology , Adult , Female , Humans , Male , Pattern Recognition, Visual , Semantics , Young Adult
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