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Adaptation to the Speed of Biological Motion in Autism

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Title: Adaptation to the Speed of Biological Motion in Autism
Language: English
Authors: Karaminis, Themis (ORCID 0000-0003-2977-5451), Arrighi, Roberto, Forth, Georgia, Burr, David, Pellicano, Elizabeth
Source: Journal of Autism and Developmental Disorders. Feb 2020 50(2):373-385.
Availability: Springer. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 13
Publication Date: 2020
Document Type: Journal Articles
Reports - Evaluative
Education Level: Elementary Education
Secondary Education
Descriptors: Foreign Countries, Elementary School Students, Secondary School Students, Autism, Pervasive Developmental Disorders, Sensory Experience, Motion, Eye Movements, Attention, Stimuli, Visual Discrimination, Reaction Time
Geographic Terms: United Kingdom (London)
DOI: 10.1007/s10803-019-04241-4
ISSN: 0162-3257
Abstract: Autistic individuals often present atypicalities in adaptation--the continuous recalibration of perceptual systems driven by recent sensory experiences. Here, we examined such atypicalities in human biological motion. We used a dual-task paradigm, including a running-speed discrimination task ('comparing the speed of two running silhouettes') and a change-detection task ('detecting fixation-point shrinkages') assessing attention. We tested 19 school-age autistic and 19 age- and ability-matched typical participants, also recording eye-movements. The two groups presented comparable speed-discrimination abilities and, unexpectedly, comparable adaptation. Accuracy in the change-detection task and the scatter of eye-fixations around the fixation point were also similar across groups. Yet, the scatter of fixations reliably predicted the magnitude of adaptation, demonstrating the importance of controlling for attention in adaptation studies.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1242049
Database: ERIC
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  Value: <anid>AN0141512418;aut01feb.20;2020Feb04.03:52;v2.2.500</anid> <title id="AN0141512418-1">Adaptation to the Speed of Biological Motion in Autism </title> <p>Autistic individuals often present atypicalities in adaptation—the continuous recalibration of perceptual systems driven by recent sensory experiences. Here, we examined such atypicalities in human biological motion. We used a dual-task paradigm, including a running-speed discrimination task ('comparing the speed of two running silhouettes') and a change-detection task ('detecting fixation-point shrinkages') assessing attention. We tested 19 school-age autistic and 19 age- and ability-matched typical participants, also recording eye-movements. The two groups presented comparable speed-discrimination abilities and, unexpectedly, comparable adaptation. Accuracy in the change-detection task and the scatter of eye-fixations around the fixation point were also similar across groups. Yet, the scatter of fixations reliably predicted the magnitude of adaptation, demonstrating the importance of controlling for attention in adaptation studies.</p> <p>Keywords: Autism; Perception; Adaptation; Biological motion; Running speed</p> <p>Electronic supplementary material The online version of this article (10.1007/s10803-019-04241-4) contains supplementary material, which is available to authorized users.</p> <hd id="AN0141512418-2">Introduction</hd> <p>Perceptual adaptation refers to the continuous recalibration of the response properties of perceptual and sensory systems driven by recent sensory experiences (Clifford and Rhodes [<reflink idref="bib9" id="ref1">9</reflink>]). For example, a quiet and continuous pure tone will be perceived to decrease in loudness over time (adaptation to loudness; see Lawson et al. [<reflink idref="bib36" id="ref2">36</reflink>]), while prolonged exposure to a face identity will cause a bias to perceive subsequently presented faces as dissimilar to it (adaptation to face identity; see Pellicano et al. [<reflink idref="bib44" id="ref3">44</reflink>]). Such adaptation is a ubiquitous property of perception and is thought to offer many functional advantages (e.g., Kohn [<reflink idref="bib32" id="ref4">32</reflink>]), in particular with regards to the efficiency with which sensory systems distinguish relevant from irrelevant stimuli. Limitations in adaptation should imply increases in the transmission of redundant information and should render individuals less able to distinguish relevant from irrelevant stimuli (Barlow [<reflink idref="bib4" id="ref5">4</reflink>]; Clifford et al. [<reflink idref="bib10" id="ref6">10</reflink>]; Webster et al. [<reflink idref="bib65" id="ref7">65</reflink>]). Such limitations could therefore have profound effects on how individuals perceive and interpret incoming sensory information.</p> <p>Adaptation is also pertinent to theoretical accounts of autistic perception aiming to account for a range of sensory atypicalities and symptoms in the condition (DSM-5; American Psychiatric Association [<reflink idref="bib1" id="ref8">1</reflink>]). Atypicalities in perceptual adaptation have been thought to reflect difficulties of autistic[<reflink idref="bib1" id="ref9">1</reflink>] individuals in deriving or using prior knowledge representations accrued from recent sensory experiences (Pellicano and Burr [<reflink idref="bib43" id="ref10">43</reflink>]). Within the Bayesian inference, or predictive-coding theoretical frameworks, which, in broad terms, suggest that the brain continually exploits the statistics of the world to predict current sensory input using a hierarchical and bidirectional processing system which aims to minimise prediction error within a cascade of cortical processing (Clark [<reflink idref="bib8" id="ref11">8</reflink>]; Friston [<reflink idref="bib21" id="ref12">21</reflink>]), adaptation may relate to the atypical encoding of precision in the perceptual hierarchy in autism (Lawson et al. [<reflink idref="bib37" id="ref13">37</reflink>]) or the inability to process flexibly prediction errors (Van de Cruys et al. [<reflink idref="bib59" id="ref14">59</reflink>]).</p> <p>Given the ubiquitous presence of adaptation in perception, an intriguing possibility is that autistic individuals' atypicalities in adaptation are pervasive across perceptual domains. The presence of domain-general atypicalities in adaptation could account for sensory issues in autistic people (e.g., why they might find certain sounds particularly disturbing), as well as core social difficulties, on the basis of a common neural mechanism (Lawson et al. [<reflink idref="bib35" id="ref15">35</reflink>]).</p> <p>With regard to social stimuli, attenuated adaptation in autism has been observed consistently within the face-processing domain, including, for example, for facial identity in autistic children (Ewing et al. [<reflink idref="bib17" id="ref16">17</reflink>]; Pellicano et al. [<reflink idref="bib44" id="ref17">44</reflink>]) and relatives of autistic children (Fiorentini et al. [<reflink idref="bib19" id="ref18">19</reflink>]), for facial configuration (Ewing et al. [<reflink idref="bib18" id="ref19">18</reflink>], [<reflink idref="bib17" id="ref20">17</reflink>]) and eye-gaze direction in children (Pellicano et al. [<reflink idref="bib45" id="ref21">45</reflink>]) and adults (Lawson et al. [<reflink idref="bib35" id="ref22">35</reflink>]), and for emotional expressions in children (Rhodes et al. [<reflink idref="bib46" id="ref23">46</reflink>]) and adults (Rutherford et al. [<reflink idref="bib48" id="ref24">48</reflink>]). van Boxtel et al. ([<reflink idref="bib58" id="ref25">58</reflink>]) also found that autistic children show reduced adaptation to action discrimination in biological motion (walking vs. running).</p> <p>Turning to the processing of non-social stimuli, autistic children have been found to present attenuated adaptation to numerosity (Turi et al. [<reflink idref="bib54" id="ref26">54</reflink>]) and, in the auditory domain, autistic adults have been found to present attenuated adaptation to loudness (Lawson et al. [<reflink idref="bib36" id="ref27">36</reflink>]) and audiovisual integration (Turi et al. [<reflink idref="bib55" id="ref28">55</reflink>]). Three studies, however, have failed to find evidence of atypical adaptive-coding abilities, including Cook et al. ([<reflink idref="bib11" id="ref29">11</reflink>]), who reported intact adaptation to facial expression and identity in autistic adults, Karaminis et al. ([<reflink idref="bib28" id="ref30">28</reflink>]), who found that autistic and typical children did not differ in the degree of adaptation of perceptual causality, and Maule et al. ([<reflink idref="bib39" id="ref31">39</reflink>]), who found that autistic and typical adults did not differ in the degree of adaptation to colour.</p> <p>In this study, we contribute new evidence about the adaptive coding of the speed of biological motion in autistic children and adolescents. The examination of the adaptive coding of biological motion in autism is important for two reasons. First, the processing of biological motion is key for a wide range of social competencies, such as inferring other people's emotions, mood, and intentions (e.g., Brooks et al. [<reflink idref="bib7" id="ref32">7</reflink>]). Previous research on the abilities of autistic individuals to process biological motion stimuli has produced mixed results. Autistic individuals have been found to present reduced sensitivity to biological motion and atypical brain activation patterns following the presentation of relevant biological stimuli in some studies (Annaz et al. [<reflink idref="bib2" id="ref33">2</reflink>]; Blake et al. [<reflink idref="bib5" id="ref34">5</reflink>]; Freitag et al. [<reflink idref="bib20" id="ref35">20</reflink>]; Klin and Jones [<reflink idref="bib31" id="ref36">31</reflink>]; Koldewyn et al. [<reflink idref="bib33" id="ref37">33</reflink>]; Nackaerts et al. [<reflink idref="bib41" id="ref38">41</reflink>]; Wang et al. [<reflink idref="bib61" id="ref39">61</reflink>]; see also Wang et al. [<reflink idref="bib62" id="ref40">62</reflink>], for a recent behavioural genetics approach), but other studies have found no such difficulties (Cusack et al. [<reflink idref="bib14" id="ref41">14</reflink>]; Edey et al. [<reflink idref="bib15" id="ref42">15</reflink>]; Jones et al. [<reflink idref="bib26" id="ref43">26</reflink>]; Murphy et al. [<reflink idref="bib40" id="ref44">40</reflink>]; Saygin et al. [<reflink idref="bib50" id="ref45">50</reflink>]; van Boxtel et al. [<reflink idref="bib58" id="ref46">58</reflink>]). With regard to the adaptive coding of biological motion in autism, van Boxtel et al. ([<reflink idref="bib58" id="ref47">58</reflink>]) found attenuated adaptation to action discrimination in autistic children while action discrimination (per se) was intact. There are (to our knowledge) no other studies examining the adaptive coding of biological motion in autism beyond action discrimination (van Boxtel et al. [<reflink idref="bib58" id="ref48">58</reflink>]).</p> <p>Second, it is important to examine the adaptive coding of biological motion in autism to establish whether findings for attenuated adaptation in autism during the processing of social stimuli are specific to faces or extend to other, high-level social stimuli. This could be likely as biological motion is supported by high-level neuronal mechanisms within the superior temporal gyrus (STS) and the fusiform and the lingua gyri (Gobbini et al. [<reflink idref="bib23" id="ref49">23</reflink>]; Vaina et al. [<reflink idref="bib56" id="ref50">56</reflink>]), that is, brain areas that are also involved in the processing of faces (Grossman et al. [<reflink idref="bib24" id="ref51">24</reflink>]), as well as the extrastriate and fusiform body areas (EBA and FBA; Jastorff and Orban [<reflink idref="bib25" id="ref52">25</reflink>]).</p> <p>In this study, we used a different paradigm for biological motion from that used in the study by van Boxtel et al. ([<reflink idref="bib58" id="ref53">58</reflink>]). Our paradigm focuses on adaptive coding of the speed of running silhouettes presented with point light displays (PLDs). We employed child- and autism-friendly methodologies and we also aimed to account for participants' attention to the stimuli. This was important as earlier studies have shown that attention modulates the size of adaptation (Kreutzer et al. [<reflink idref="bib34" id="ref54">34</reflink>]; Rhodes et al. [<reflink idref="bib47" id="ref55">47</reflink>]). Controlling for attention was achieved by employing a dual-task paradigm, in which the primary task measured the perception of biological motion and adaptive coding, while the secondary task motivated participants to attend to the middle of the screen and assessed their attention (see also Ewing et al. [<reflink idref="bib17" id="ref56">17</reflink>]; Karaminis et al. [<reflink idref="bib28" id="ref57">28</reflink>]; Lawson et al. [<reflink idref="bib35" id="ref58">35</reflink>]; Rhodes et al. [<reflink idref="bib46" id="ref59">46</reflink>]). We also collected eye-movement data to quantify participants' looking preferences during the task.</p> <hd id="AN0141512418-3">Method</hd> <p></p> <hd id="AN0141512418-4">Participants</hd> <p>Participants demographics are shown in Table 1.</p> <p>Descriptive statistics for developmental variables for autistic and typical participants</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Measures</p></th><th align="left"><p>Autistic participants</p></th><th align="left"><p>Typical participants</p></th><th align="left"><p>Statistical comparison</p></th></tr></thead><tbody><tr><td align="left"><p>N</p></td><td align="left"><p>19</p></td><td align="left"><p>19</p></td><td align="left" /></tr><tr><td align="left"><p>Gender (n females:n males)</p></td><td align="left"><p>6:13</p></td><td align="left"><p>11:8</p></td><td align="left"><p>X<sup>2</sup>(2, N = 38) = 1.72, p = 0.18</p></td></tr><tr><td align="left" colspan="4"><p>Age (years)</p></td></tr><tr><td align="left"><p> Mean (SD)</p></td><td align="left"><p>14.15 (2.84)</p></td><td align="left"><p>13.93 (3.80)</p></td><td align="left"><p>t(36) = 0.23, p = 0.87</p></td></tr><tr><td align="left"><p> Range</p></td><td align="left"><p>8.68–19.37</p></td><td align="left"><p>7.40–18.75</p></td><td align="left" /></tr><tr><td align="left" colspan="4"><p>Verbal IQ<sup>a</sup></p></td></tr><tr><td align="left"><p> Mean (SD)</p></td><td align="left"><p>104.68 (14.21)</p></td><td align="left"><p>105.47 (10.91)</p></td><td align="left"><p>t(36) = 0.19, p = 0.85</p></td></tr><tr><td align="left"><p> Range</p></td><td align="left"><p>70–126</p></td><td align="left"><p>83–130</p></td><td align="left" /></tr><tr><td align="left" colspan="4"><p>Performance IQ<sup>a</sup></p></td></tr><tr><td align="left"><p> Mean (SD)</p></td><td align="left"><p>103.21 (18.94)</p></td><td align="left"><p>103.21 (18.95)</p></td><td align="left"><p>t(36) = 0.20, p = 0.85</p></td></tr><tr><td align="left"><p> Range</p></td><td align="left"><p>75–132</p></td><td align="left"><p>76–139</p></td><td align="left" /></tr><tr><td align="left" colspan="4"><p>Full-Scale IQ<sup>a</sup></p></td></tr><tr><td align="left"><p> Mean (SD)</p></td><td align="left"><p>104.32 (16.57)</p></td><td align="left"><p>105.58 (12.65)</p></td><td align="left"><p>t(33.66) = 0.26, p = 0.79</p></td></tr><tr><td align="left"><p> Range</p></td><td align="left"><p>80–132</p></td><td align="left"><p>77–138</p></td><td align="left" /></tr><tr><td align="left" colspan="4"><p>ADOS-2 calibrated severity score<sup>b</sup></p></td></tr><tr><td align="left"><p> Mean (SD)</p></td><td align="left"><p>(N = 16) 4.75 (1.48)</p></td><td align="left"><p>n/a</p></td><td align="left"><p>n/a</p></td></tr><tr><td align="left"><p> Range</p></td><td align="left"><p>3–7</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" colspan="4"><p>SCQ score</p></td></tr><tr><td align="left"><p> Mean (SD)</p></td><td align="left"><p>N = 17 21.24 (8.41)</p></td><td align="left"><p>N = 15 2.87 (3.35)</p></td><td align="left"><p>t(21.44) = 8.29, p <.001</p></td></tr><tr><td align="left"><p> Range</p></td><td align="left"><p>5–37</p></td><td align="left"><p>0–12</p></td><td align="left" /></tr></tbody></table> </ephtml> </p> <p> <emph>SCQ</emph> Social Communication Questionnaire (score out of 40; Rutter et al. [<reflink idref="bib49" id="ref60">49</reflink>]) <sups>a</sups>Verbal, Performance and Full-Scale IQ were measured using the Wechsler Abbreviated Scales of Intelligence-2nd edition (WASI-II; [<reflink idref="bib66" id="ref61">66</reflink>]) <sups>b</sups>ADOS-2 calibrated severity scores obtained from Autism Diagnostic Observation Schedule-2 (Lord et al. [<reflink idref="bib38" id="ref62">38</reflink>]), scores range from 1 to 10, higher scores reflect greater autism severity</p> <hd id="AN0141512418-5">Autistic Participants</hd> <p>Nineteen autistic participants (6 girls) aged between 8.8 and 19.5 years (M = 14.15; SD = 2.84) were recruited via schools in London and community contacts. All autistic participants had an independent clinical diagnosis of an autism spectrum disorder and met the criteria for autism on the Autism Diagnostic Observation Schedule-2 (ADOS-2) (Lord et al. [<reflink idref="bib38" id="ref63">38</reflink>]; cut-off score = 7) or the Social Communication Questionnaire-Lifetime (SCQ; Rutter et al. [<reflink idref="bib49" id="ref64">49</reflink>]; cut-off score = 15) (see Corsello et al. [<reflink idref="bib13" id="ref65">13</reflink>]). All autistic participants were considered to be cognitively able, achieving scores ≥ 70 in the Wechsler Abbreviated Scales of Intelligence-2nd edition (WASI-II; [<reflink idref="bib66" id="ref66">66</reflink>]).</p> <hd id="AN0141512418-6">Typical Participants</hd> <p>Nineteen typically developing participants (10 girls), recruited from local London schools, were selected from a pool of 63 participants to match the group of autistic participants for chronological age, t(<reflink idref="bib36" id="ref67">36</reflink>) = 0.23, p = 0.87, gender, X<sups>2</sups>(<reflink idref="bib2" id="ref68">2</reflink>, N = 38) = 1.72, p = 0.18, as well as for performance IQ, t(<reflink idref="bib36" id="ref69">36</reflink>) = 0.20, p = 0.85; verbal IQ, t(<reflink idref="bib36" id="ref70">36</reflink>) = 0.19, p = 0.85; and full-scale IQ, t(33.66) = 0.26, p = 0.79, as measured by the Wechsler Abbreviated Scales of Intelligence-2nd edition (WASI-II; [<reflink idref="bib66" id="ref71">66</reflink>]). Parents of typical participants also completed the SCQ (N = 11). SCQ scores of typical participants ranged between 0 and 12 (M = 2.64, SD = 3.50), below the cut-off point for autism (score of 15; Rutter et al. [<reflink idref="bib49" id="ref72">49</reflink>]).</p> <hd id="AN0141512418-7">Exclusions</hd> <p>Seven additional participants (3 autistic, 4 typical) were tested but excluded because of poorly-fitting psychometric curves, as judged by 2 observers who were blind to any demographic details of the participants (exclusion criterion #1). One additional typical child was excluded due to an IQ score lower than the threshold of 70 in the WASI-II (Wechsler [<reflink idref="bib66" id="ref73">66</reflink>]) (exclusion criterion #2). Five additional autistic and two additional typical participants were excluded due to poor performance on the attentional task (exclusion criterion #3, see "Measurements and Analysis" section). Finally, one additional autistic boy was excluded because he did not fixate centre-screen during the experimental task (exclusion criterion #4, see "Measurements and Analysis" section).</p> <hd id="AN0141512418-8">General Procedure and Ethics</hd> <p>The study was conducted in accordance to the principles laid down in the Declaration of Helsinki. The UCL Institute of Education Research Ethics Committee approved all procedures. Parents of all participants gave their informed written consent prior to their child's participation in the study and participants gave their verbal assent. Participants were tested individually in a quiet room at the Institute of Education. The WASI-II was administered on the same day, before or after the session. The ADOS-2 was administered either on the same day or on a separate occasion.</p> <hd id="AN0141512418-9">Stimuli and Apparatus</hd> <p>Adaptor and test stimuli (see Fig. 1; see also Arrighi et al. [<reflink idref="bib3" id="ref74">3</reflink>]) were PLDs comprising 10 dots of diameter 0.75° of visual angle and simulating running human figures. An original version of PLDs stimulus representing the running human silhouette was downloaded from an online database (http://astro.temple.edu/~tshipley/ptltarchive.html; Shipley [<reflink idref="bib51" id="ref75">51</reflink>]). This movie displayed a complete running cycle (starting with the left foot on the floor and ending with the left foot landing again) in 20 frames. Using customised interpolation scripts, we created 6000 points within each running cycle. We defined running speed as the number of running cycles completed within a second (in Hz).</p> <p>Graph: Fig. 1 Trial structure and task design</p> <p>Adaptor and test stimuli appeared on the left- or the right-hand side of the screen (centred 10° from the centre of the screen). The adaptor stimuli were two PLDs which appeared in grey colour and in pairs, simultaneously on the right- and left-hand side of the screen for 4.0 s. The adaptor stimuli fitted a 10° height × 5° width frame ('medium-sized') and moved at a speed of either 0.5 Hz or 2 Hz.</p> <p>The test stimuli were two PLDs, the Reference stimulus and the Test stimulus (see Fig. 1). The Reference stimulus appeared in red colour on the left-hand side of the screen for 2.0 s. It fitted a 10° × 5° frame ('medium-sized') and moved at a speed of 1 Hz. The Test stimulus appeared in blue colour on the right-hand side of the screen for 2.0 s. It appeared in three possible sizes: small (within a frame of 8° height × 4° width), medium (10° × 5° frame), or large (12° × 6° frame) and at different speeds at the range 0.5–2 Hz.</p> <p>For the change-detection task, the main stimulus was a round dot subtending 1.0° in the centre of the screen, which occasionally shrank to a diameter of 0.75° twice during each adaptation period.</p> <p>All stimuli were displayed on a 60 Hz TFT monitor measuring 50° × 28° when viewed at a distance of 57 cm, controlled by a Dell Desktop computer. The experiments were written in MatLab using routines of the Psychophysics Toolbox 3 (Brainard [<reflink idref="bib6" id="ref76">6</reflink>]; Pelli [<reflink idref="bib42" id="ref77">42</reflink>]; Kleiner et al. [<reflink idref="bib30" id="ref78">30</reflink>]). Eye-tracking data were collected using a Tobii-X300 eye tracker at 120 Hz and were processed with the Tobii Analytics Software Development Kit (SDK).</p> <hd id="AN0141512418-10">Procedure</hd> <p>We measured perceptual adaptation to the speed of biological motion using a developmentally-sensitive computer game, which combined a speed-discrimination task, assessing adaptation to the speed of biological motion, and a change-detection task, motivating participants to attend to the centre of the screen. The general theme of the game was that participants were 'Space Running Trainers' aiming to form a winning team for the 'Space Olympics'. To do so, participants should choose the fastest runners using a 'specialised viewing machine' (which provided the PLDs). The task structure and the trial structure are presented in Fig. 1.</p> <hd id="AN0141512418-11">Speed-Discrimination Task</hd> <p>The speed-discrimination task comprised two conditions, Right and Left ('rounds', counterbalanced across participants), each consisting of 40 trials presented in blocks ('Levels') of 13, 13, and 14 trials. Each trial included an adaptation phase, in which participants were exposed to adaptor stimuli, followed by a testing phase, in which participants judged the speed of test stimuli. The adaptation phase was differentiated in the Right and the Left condition so as to elicit adaptation aftereffects in two opposite directions (see also "Measurements and Analysis" section). The two conditions of the speed-discrimination task thus implemented a so-called 'push–pull' adaptation protocol.</p> <p>In the adaptation phase, which lasted 4.0 s, participants watched the adaptor PLDs while they were encouraged to attend to the fixation point centre-screen (see also "Change-Detection Task" section). In the Right condition, the speed of the right adaptor PLD was 2 Hz, four times faster than the left adaptor (0.5 Hz). Conversely, in the Left condition, the right adaptor that ran at 0.5 Hz and the left at 2 Hz.</p> <p>In the test phase, participants were presented with the two test PLDs, first the Reference stimulus on the left-hand-side of the screen and then the Test stimulus on the right-hand-side of the screen, for 2.0 s each. They were asked to indicate which runner they thought was the fastest by pressing a corresponding red or blue key on the keyboard. Responses were not registered until both runners had finished running.</p> <p>The speed of the Reference PLD always was set at 1.0 Hz. The speed of the Test PLD was chosen using two QUEST functions (Watson and Pelli [<reflink idref="bib64" id="ref79">64</reflink>]), one starting at 0.5 Hz and ascending and one starting at 2.0 Hz and descending. The two QUESTs homed in on the point where the speed of the two test stimuli appeared equal; to ensure a good distribution of durations to estimate discrimination thresholds, a random jitter of SD = 0.1 log units was also added to the QUEST estimates (Watson and Pelli [<reflink idref="bib64" id="ref80">64</reflink>]).</p> <p>The Test stimulus appeared in three possible sizes, small (8° × 4°), medium (10° × 5°), and large (12° × 6°). This manipulation ensured that our participants could not solve the discrimination task by relying on the local speed of the dots constituting the PLDs (see also van Boxtel and Lu [<reflink idref="bib57" id="ref81">57</reflink>]). For example, let's assume that the two test stimuli (Reference and Test) moved at the same speed (say, a gait cycle per second) and that the Test stimulus was small. Because of this size difference, the distance covered by the individual dots of the Test stimulus (e.g., the feet) during a cycle gait would be shorter than the distance covered by the corresponding dots of the Reference stimulus. Based on this difference, if participants relied on a local-speed response strategy, they should present a bias to respond that the Reference stimulus would be faster. By contrast, if participants relied on a global response strategy, they should not present this bias.</p> <hd id="AN0141512418-12">Change-Detection Task</hd> <p>In the change-detection task, participants were asked to respond to changes of the fixation ('viewing machine losing power') point by pressing the spacebar ('powering up the machine'). The fixation point returned to normal after a response. The change-detection task took place during the adaptation phase of the trials of the speed discrimination task. There were zero, one or two shrinkage events in each trial, each lasting 1 s.</p> <hd id="AN0141512418-13">Practice Trials and Motivation</hd> <p>Participants were given visual and verbal instructions for both tasks at the start of the game, including practice on pressing the spacebar when the dot in the centre of the screen shrank. They also completed eight practice trials, in which the speed of each of the running figures in the testing phase were very clearly different from each other (0.5 Hz vs. 1.5 Hz or 2.0 Hz). Practice trials were repeated if participants made more than three mistakes or if they responded that they needed more practice to proceed to the actual game. This happened only for two autistic participants and never more than once. Participants had the opportunity to take short breaks at the end of the testing blocks. They were regularly praised for their performance and, at the end of each round, they were shown a leaderboard. The experimenter encouraged them to attend to the centre of the screen throughout testing and monitored their attention.</p> <hd id="AN0141512418-14">Measurements and Analysis</hd> <p></p> <hd id="AN0141512418-15">Speed-Discrimination Task</hd> <p>Figure 2 shows example data from two of our participants from the speed-discrimination task. We fitted individual data from participants with cumulative Gaussian functions using bootstrapping (Efron and Tibshirani [<reflink idref="bib16" id="ref82">16</reflink>]) with 10 repetitions and a 'maximum likelihood' fitting method (Watson [<reflink idref="bib63" id="ref83">63</reflink>]). First, two observers, blind to any demographic details, judged the quality of the fitted curves. Participants with poorly fitting curves were excluded from the analysis. From the fitted curves, and for each condition, we derived Weber Fractions [the standard deviations of the fitted Gaussians or just noticeable difference (JND) divided by the Points of Subjective Equality (PSE)] and the PSEs (the mean of the fitted Gaussians).</p> <p>Graph: Fig. 2 Sample data from an autistic and a typical participant and fitted psychometric curves. Adaptation is measured as the difference between the Points of Subjective Equality (PSE) in the Right and the Left condition</p> <p>Weber Fractions provided an estimate of the precision with which participants judged the speed of the PLDs. We compared Weber Fractions using a repeated-measures ANOVA with Condition ('Left' vs. 'Right') as a between-participants factor and Group ('Autistic' vs. 'Typical').</p> <p>The PSE corresponded to the value of the Test stimulus intensity (more precisely, the value of the log-transformed ratio <emph>speed of Test stimulus</emph>: <emph>speed of Reference stimulus</emph>) for which the judgements of participants in the speed-discrimination task were at chance levels, that is, participants responded that the Test PLD was faster than the Reference PLD with a probability of 0.5. For each participant, we derived <emph>PSE_Right</emph> and the <emph>PSE_Left</emph> using data from the Right and the Left condition, correspondingly. In our data, due to adaptation, <emph>PSE_Right</emph> tended to be higher than <emph>PSE_Left.</emph> This was as in the Left (Right) condition, the Test stimulus was presented after exposure to a slow (fast) adaptor and was thus perceived to be faster (slower), pushing (pulling) the psychometric curve to the left (right) (see Fig. 2). To estimate the magnitude of the adaptation effect we calculated the distance <emph>PSE_Right </emph>− <emph>PSE_Left</emph>. We compared the magnitude of adaptation in the two matched groups with an independent samples t-tests. We also performed a complementary Bayesian independent samples <emph>t</emph> test for this difference.</p> <hd id="AN0141512418-16">Change-Detection Task</hd> <p>For the change-detection task, we calculated mean accuracy (the proportion of detected shrinkages) in the change-detection task across both conditions. Participants with accuracy scores lower than 25% were excluded from the analysis. We also examined reaction times in the change-detection task (online measure).</p> <hd id="AN0141512418-17">Eye-Tracking Data</hd> <p>From the eye tracking data, we calculated the scatter of fixations around the centre of the screen (the standard deviation of average distance from the centre of the screen) during the adaptation and the testing phase. One autistic participant, with a scatter of fixation of 15.0° of the visual angle was excluded from the analysis. We also calculated correlations between the scatter of fixations and adaptation in the speed-discrimination task.</p> <hd id="AN0141512418-18">Correlational Analysis</hd> <p>In a secondary analysis, we examined correlations between adaptation to the speed of biological motion and precision in speed discrimination, as well as correlations between adaptation and demographic and eye-tracking variables.</p> <hd id="AN0141512418-19">Results</hd> <p></p> <hd id="AN0141512418-20">Similar Speed-Discrimination Precision and Similar Adaptation to the Speed of Biological Moti...</hd> <p>First, we looked at precision in discriminating the speed of biological motion, expressed as Weber Fractions. Figure 3 shows Weber Fractions in the two conditions of the speed discrimination tasks (Left, autistic: M = 0.40, SD = 0.23; typical: M = 0.37, SD = 0.22; Right, autistic: M = 0.42, SD = 0.36; typical: M = 0.37, SD = 0.18). We conducted a mixed-design ANOVA with Group ('Autistic' vs. 'Typical') as a between-participants factor and Condition ('Left' vs. 'Right') as a within-participants factor. There were no significant effects of Group, F(<reflink idref="bib1" id="ref84">1</reflink>, 36) = 0.39, p = 0.54, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups> = 0.01; Condition, F(<reflink idref="bib1" id="ref85">1</reflink>, 36) = 0.01, p = 0.93, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups> < 0.01; and no significant interaction between the two factors, F(<reflink idref="bib1" id="ref86">1</reflink>, 36) = 0.04, p = 0.84, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups> = 0.001. Our analysis therefore suggested that autistic and typical participants presented similar precision in speed-discrimination.</p> <p>Graph: Fig. 3 Speed-discrimination abilities of autistic and typical participants in the 'Left' and the 'Right' condition. Boxplots show group averages (green triangles) and medians (horizontal lines), dots show the performance of individual participants</p> <p>Next, we examined the magnitude of adaptation, shown in Fig. 4 (autistic participants: M = 0.60, SD = 0.20; typical participants: M = 0.55, SD = 0.26). The magnitude of the adaptation effect was significantly higher than 0 in both groups of participants, as revealed by one-sample t-test [autistic participants: t(<reflink idref="bib18" id="ref87">18</reflink>) = 13.36, p < 0.001; typical participants: t(<reflink idref="bib18" id="ref88">18</reflink>) = 10.77, p < 0.001]. Importantly, and contrary to our prediction, there were no differences in adaptation between autistic and typical participants, t(<reflink idref="bib36" id="ref89">36</reflink>) = 0.50, p = 0.48, <emph>d</emph> = 0.20.</p> <p>Graph: Fig. 4 Adaptation to the speed of biological motion as measured by the difference between the Points of Subjective Equality (PSE) in the left and the right. Boxplots show group averages (green triangles) and medians (horizontal lines), dots show performance of individual participants</p> <p>We also performed a Bayesian independent samples t-test using JASP software (Version 0.8.0.0; JASP Team 2016) and estimated a Bayes factor using Bayesian information criteria (Wagenmakers [<reflink idref="bib60" id="ref90">60</reflink>]), which allowed for a comparison of the fit of our data under the null hypothesis that there are no differences between autistic and typical children in the magnitude of the adaptation to the speed of biological motion, and the alternative hypothesis that adaptation differs in the two groups of participants. The Bayes factor (null/alternative-estimated using a Cauchy distribution prior with a scaling factor of 1) was 3.38, suggesting that our results were 3.38 times more likely to occur under the null hypothesis than under the alternative hypothesis. Our data, therefore, provided substantial evidence (Wetzels et al. [<reflink idref="bib67" id="ref91">67</reflink>]) that autistic and typical participants adapted to the speed of biological motion to a comparable degree.</p> <hd id="AN0141512418-21">Similar Performance in the Change-Detection Task</hd> <p>Turning to the change-detection task, Fig. 5 shows accuracy rates in the two conditions of the task (Left, autistic: M = 0.78, SD = 0.17; typical: M = 0.79, SD = 0.17; Right, autistic: M = 0.75, SD = 0.22; typical: M = 0.72, SD = 0.21). A mixed-design ANOVA with Group ('Autistic' vs. 'Typical') as a between-participants factor and Condition ('Left' vs. 'Right') as a within-participants factor showed no effects of Group, F(<reflink idref="bib1" id="ref92">1</reflink>, 36) = 0.27, p = 0.87, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups>= 0.001, a significant effect of Condition, F(<reflink idref="bib1" id="ref93">1</reflink>, 36) = 6.16, p = 0.02, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups>= 0.15, and no significant interaction between Condition and Group, F(<reflink idref="bib1" id="ref94">1</reflink>, 36) = 0.99, p = 0.32, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups>= 0.00. Autistic and typical participants performed similarly on the secondary task.</p> <p>Graph: Fig. 5 Accuracy in the change-detection task, in the two conditions. Boxplots show group averages (green triangles) and medians (horizontal lines), dots show performance of individual participants</p> <hd id="AN0141512418-22">Similar Reaction Times in the Change-Detection Task</hd> <p>For the change-detection task, we examined mean reaction times, shown in Fig. 6 (Left, autistic: M = 2.84, SD = 0.94; typical: M = 2.74, SD = 0.83; Right, autistic: M = 2.74, SD = 0.83; typical: M = 2.47, SD = 0.52). A mixed-design ANOVA with Group ('Autistic' vs. 'Typical') as the between-participants factor and Condition ('Left' vs. 'Right') as the within-participants factor showed no significant effects of Group, F(<reflink idref="bib1" id="ref95">1</reflink>, 36) = 2.40, p = 0.13, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups> = 0.06, or Condition, F(<reflink idref="bib1" id="ref96">1</reflink>, 36) = 0.12, p = 0.73, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups> = 0.003, or condition × group interaction, F(<reflink idref="bib1" id="ref97">1</reflink>, 36) = 0.36, p = 0.54, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups>= 0.01. The results therefore suggested that autistic and typical participants did not differ in their reaction times.</p> <p>Graph: Fig. 6 Reaction times in the speed-discrimination task. Boxplots show group averages (green triangles) and medians (horizontal lines), dots show the performance of individual participants</p> <hd id="AN0141512418-23">Similar Eye-Movement Data</hd> <p>We also examined eye-tracking data to obtain an objective measure of the extent to which participants attended to the centre of the screen (as motivated by the change-detection task, as well as by the experimenter during the testing session). Figure 7 shows the scatter of fixations around centre-screen in the two conditions (in degrees of the visual angle) (Left, autistic: M = 0.035, SD = 0.014; typical: M = 0.039, SD = 0.025; Right, autistic: M = 0.038, SD = 0.020; typical: M = 0.044, SD = 0.032). Again, a mixed-design ANOVA with Group ('Autistic' vs. 'Typical') as a between-participants factor and Condition ('Left' vs. 'Right') as a within-participants factor and showed no significant effects of Group, F(<reflink idref="bib1" id="ref98">1</reflink>, 36) = 0.54, p = 0.47, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups> = 0.02, Condition, F(<reflink idref="bib1" id="ref99">1</reflink>, 36) = 1.08, p = 0.31, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups> = 0.03, and no significant interaction, F(<reflink idref="bib1" id="ref100">1</reflink>, 36) = 0.11, p = 0.74, <emph>n</emph><subs><emph>p</emph></subs><sups>2</sups> = 0.00. Autistic and typical participants fixated to centre-screen to a comparable extent.</p> <p>Graph: Fig. 7 Scatter of fixations in the two conditions of the speed-discrimination task. Boxplots show group averages (green triangles) and medians (horizontal lines), dots show performance of individual participants</p> <hd id="AN0141512418-24">Correlational Analysis</hd> <p>In a secondary correlational analysis, we examined the relationship between adaptation to the speed of biological motion and precision in speed discrimination, as well as between and adaptation demographic and eye-tracking variables (Fig. 8). Correlations between adaptation to the speed of biological motion and precision were non-significant in either group of participants [autistic: r(<reflink idref="bib19" id="ref101">19</reflink>) = − 0.17, p = 0.55; typical: r(<reflink idref="bib19" id="ref102">19</reflink>) = − 0.36, p = 0.13]. Furthermore, in either group of participants, there were no significant correlations between adaptation and age [autistic: r (<reflink idref="bib19" id="ref103">19</reflink>) = 0.08, p = 0.78; typical: r(<reflink idref="bib19" id="ref104">19</reflink>) = 0.04, p = 0.99], and Performance-IQ [autistic: r (<reflink idref="bib19" id="ref105">19</reflink>) = 0.06, p = 0.79; typical: r(<reflink idref="bib19" id="ref106">19</reflink>) = 0.22, p = 0.36] and Verbal-IQ [autistic: r(<reflink idref="bib19" id="ref107">19</reflink>) = 0.36, p = 0.13; typical: r(<reflink idref="bib19" id="ref108">19</reflink>) = − 0.18, p = 0.46]. Within the group autistic participants, there were also no significant correlations between the magnitude of adaptation and autistic features, as indexed by ADOS-2 calibrated severity scores, r(<reflink idref="bib16" id="ref109">16</reflink>) = − 0.35, p = 0.18, or SCQ scores, r(<reflink idref="bib17" id="ref110">17</reflink>) = 0.23, p = 0.38. Correlations between the magnitude of adaptation and SCQ scores were also not significant when autistic and typical participants were considered as one group, r(<reflink idref="bib28" id="ref111">28</reflink>) = 0.08, p = 0.66.</p> <p>Graph: Fig. 8 Results of the secondary correlational analysis of individual variability. Panels show correlations between the magnitude of adaptation and age (a), Performance IQ (b), Verbal IQ (c), scores on the SCQ (d), ADOS severity scores (e) and precision in the speed-discrimination task (f), as well as correlations between precision in the speed-discrimination task and the scatter of fixations (g) and correlations between adaptation and the scatter of fixations (h). The analysis suggested that in both groups of participants, the magnitude of adaptation was smaller for participants with more scattered fixations (panel h). Note that this relationship remained significant when the extreme value in the typical group was removed</p> <p>Interestingly, there was a significant correlation between the magnitude of adaptation and the eye-tracking variable of the scatter of fixations in both autistic, r(<reflink idref="bib19" id="ref112">19</reflink>) = − 0.62, p = 0.005, and typical participants, r(<reflink idref="bib19" id="ref113">19</reflink>) = − 0.61, p = 0.01. As shown in Fig. 8h, the adaptation effect is less pronounced for participants who attended to a lesser extent to centre-screen. Note that correlations between the eye-movement measure and precision in speed-discrimination [autistic: r(<reflink idref="bib19" id="ref114">19</reflink>) = 0.15, p = 0.54; typical: r(<reflink idref="bib19" id="ref115">19</reflink>) = 0.34, p = 0.16] were non-significant.</p> <hd id="AN0141512418-25">Discussion</hd> <p>In this study, we compared autistic and typical participants, of similar age and ability, on the adaptive coding of the speed of biological motion. We hypothesised that autistic individuals' atypicalities in the adaptive coding of facial stimuli (Ewing et al. [<reflink idref="bib18" id="ref116">18</reflink>]; Lawson et al. [<reflink idref="bib35" id="ref117">35</reflink>]; Pellicano et al. [<reflink idref="bib45" id="ref118">45</reflink>]; Rhodes et al. [<reflink idref="bib46" id="ref119">46</reflink>]; Rutherford et al. [<reflink idref="bib48" id="ref120">48</reflink>]) should generalise to non-facial social stimuli and predicted that autistic participants should show less adaptation to the speed of the PLDs of our task than the typical comparison participants. We found that both groups showed significant adaptation effects—but, contrary to our prediction, that the magnitude of adaptation was comparable in autistic and typical participants. This finding could not be attributed to group differences in attention or to looking differences, as both accuracy on the change-detection task and the scatter-of-fixations measure were similar across groups.</p> <p>Furthermore, the lack of differences in adaptation between autistic and typical participants could not be due to differences in precision in speed discrimination. We found that the two groups were equally precise. This latter result is consistent with studies that do not find differences in the processing of biological motion in autism (Cusack et al. [<reflink idref="bib14" id="ref121">14</reflink>]; Jones et al. [<reflink idref="bib26" id="ref122">26</reflink>]; Murphy et al. [<reflink idref="bib40" id="ref123">40</reflink>]; Saygin et al. [<reflink idref="bib50" id="ref124">50</reflink>]; van Boxtel et al. [<reflink idref="bib58" id="ref125">58</reflink>]) rather than those that report reduced sensitivity and differences in the brain activation patterns to biological stimuli (Annaz et al. [<reflink idref="bib2" id="ref126">2</reflink>]; Blake et al. [<reflink idref="bib5" id="ref127">5</reflink>]; Freitag et al. [<reflink idref="bib20" id="ref128">20</reflink>]; Klin and Jones [<reflink idref="bib31" id="ref129">31</reflink>]; Koldewyn et al. [<reflink idref="bib33" id="ref130">33</reflink>]; Nackaerts et al. [<reflink idref="bib41" id="ref131">41</reflink>]).</p> <p>Our results are also inconsistent with the study on adaptation to biological motion by van Boxtel et al. ([<reflink idref="bib58" id="ref132">58</reflink>]), which examined a similar number of autistic and typical children. It is possible that this discrepancy is due to the focus on different aspects of biological motion ("running speed" vs. discrimination of type of movement in van Boxtel et al. [<reflink idref="bib58" id="ref133">58</reflink>]). It is difficult to understand the origin of these discrepancies without further investigation of performance in different types of biological motion within the same individual. It would be interesting to replicate our and van Boxtel et al.'s methods, also considering other biological motion characteristics such as gender, which is more explicitly social and to which adaptation has previously been shown in non-autistic adults (Jordan et al. [<reflink idref="bib27" id="ref134">27</reflink>]; Troje et al. [<reflink idref="bib53" id="ref135">53</reflink>]).</p> <p>Another factor that could be considered in future studies is the likely correspondence between the kinematics of the test stimuli and the kinematics of participants. One study has reported that autistic adults present atypical kinematics and that the degree of such atypicalities predicts performance in a biological motion perception task (Cook et al. [<reflink idref="bib12" id="ref136">12</reflink>]). It is possible that the perceptual similarity or dissimilarity between the kinematics of stimuli and participants could also affect the adaptive coding of biological motion.</p> <p>One important methodological feature of our study is that it carefully examined differences in attention. This was achieved by including the secondary change-detection task and using eye-tracking. By contrast, in van Boxtel et al. ([<reflink idref="bib58" id="ref137">58</reflink>]), where autistic children were found to present attenuated adaptation, "the experimenter monitored fixation throughout the experiment, providing reminders as deemed necessary" (p. 4). Arguably, the use of a change-detection task is a more robust method for directing participants' attention to the fixation point. Interestingly, the post hoc analysis of the eye-tracking data showed that the more participants attended to the fixation point, the larger the magnitude of adaptation. Therefore, even though autistic participants did not differ on average from typical participants on the degree of adaptation, the scatter of fixation accounted for adaptation performance. This result raises the possibility that differences in adaptation in many studies could result from attention differences. It is thus also very important to control for attention in adaptation studies (see also gaze-contingent paradigms; e.g., Wilms et al. [<reflink idref="bib68" id="ref138">68</reflink>]). To our knowledge, controlling for attention has been employed in earlier studies on adaptation in autism by Ewing et al. ([<reflink idref="bib17" id="ref139">17</reflink>]) on face identity, Karaminis et al. ([<reflink idref="bib28" id="ref140">28</reflink>]) on perceptual causality, Lawson et al. ([<reflink idref="bib35" id="ref141">35</reflink>]) on eye-gaze direction and Rhodes et al. ([<reflink idref="bib46" id="ref142">46</reflink>]) on facial expression. Our study on adaptation to the running speed of biological motion in autism is novel in combining the use of a secondary attention task with eye-tracking.</p> <p>Our study is not without its shortcomings. We applied four exclusion criteria and thus excluded a considerable number of participants from our initial dataset to obtain a dataset that would allow measuring the adaptive coding of biological motion. The dual-task paradigm was also demanding, especially for younger participants. Finally, adaptation to biological motion in participants who were not able to attend to stimuli was also not explored in this study.</p> <hd id="AN0141512418-26">Conclusion</hd> <p>Sensory differences have been included in the latest diagnostic criteria for autism (DSM-5; APA [<reflink idref="bib1" id="ref143">1</reflink>]) and represent some of the most puzzling features of the condition. The renewed interest in autistic sensory differences by researchers is prompted largely by the possibility that these and other non-social features of autism might be caused by fundamental differences in sensation and perception. Our results provide evidence that diminished adaptation, proposed to be one such fundamental difference, is not pervasive in autistic perception. Our findings demonstrate that more nuanced accounts of adaptation in autism are warranted, which address the potentially uneven adaptation profile in autism and its developmental implications (cf. Karaminis et al. [<reflink idref="bib28" id="ref144">28</reflink>]). The interplay between adaptation and attention is also important for a fuller understanding of autistic perception.</p> <hd id="AN0141512418-27">Funding</hd> <p>This work was generously supported by a Grant from the UK's Medical Research Council awarded to Elizabeth Pellicano and David Burr (MR/J013145/1) and also by the European Research Council (ERC Advanced Grants "STANIB" and "ECSPLAIN"). This work has also received funding from the EU Horizon 2020 Research And Innovation Programme under Grant Agreement No. 832813 'Spatio-temporal mechanisms of generative perception—GenPercept' (to David Burr). Furthermore, this project has received funding from Italian Ministry of Education, University, and Research under the PRIN2017 programme Grant number 2017XBJN4F—'EnvironMag'.</p> <hd id="AN0141512418-28">Acknowledgments</hd> <p>We are very grateful to the young participants, families and school staff who kindly took part in this research. Thanks also to Lorcan Kenny, Katy Warren, and Hannah White for their help in collecting the data.</p> <hd id="AN0141512418-29">Author Contributions</hd> <p>TK, RA, DB, and EP conceived the study and the experiments. TK, RA, and GF developed the experimental materials. TK and GF conducted the experiments. TK and RA analysed the results. TK wrote the first draft of the manuscript and GF contributed to the Methods section. All authors reviewed the manuscript.</p> <hd id="AN0141512418-30">Compliance with Ethical Standards</hd> <p></p> <hd id="AN0141512418-31">Conflict of interest</hd> <p>All authors declare that they have no conflict of interest of which they are aware.</p> <hd id="AN0141512418-32">Ethical Approval</hd> <p>All procedures performed in the study involving human participants were in accordance with the Ethical Standards of the Institutional and/or National Research Committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.</p> <hd id="AN0141512418-33">Informed Consent</hd> <p>Parents of all child/adolescent participants gave their informed written consent prior to their child's participation in the study and young participants gave their informed verbal assent.</p> <hd id="AN0141512418-34">Electronic supplementary material</hd> <p>Graph: Supplementary material 1 (PDF 255 kb)</p> <hd id="AN0141512418-35">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0141512418-36"> <title> References </title> <blist> <bibl id="bib1" idref="ref8" type="bt">1</bibl> <bibtext> . 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  Label: Title
  Group: Ti
  Data: Adaptation to the Speed of Biological Motion in Autism
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  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Karaminis%2C+Themis%22">Karaminis, Themis</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-2977-5451">0000-0003-2977-5451</externalLink>)<br /><searchLink fieldCode="AR" term="%22Arrighi%2C+Roberto%22">Arrighi, Roberto</searchLink><br /><searchLink fieldCode="AR" term="%22Forth%2C+Georgia%22">Forth, Georgia</searchLink><br /><searchLink fieldCode="AR" term="%22Burr%2C+David%22">Burr, David</searchLink><br /><searchLink fieldCode="AR" term="%22Pellicano%2C+Elizabeth%22">Pellicano, Elizabeth</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Autism+and+Developmental+Disorders%22"><i>Journal of Autism and Developmental Disorders</i></searchLink>. Feb 2020 50(2):373-385.
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  Label: Availability
  Group: Avail
  Data: Springer. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 13
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2020
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Evaluative
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  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+School+Students%22">Elementary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Autism%22">Autism</searchLink><br /><searchLink fieldCode="DE" term="%22Pervasive+Developmental+Disorders%22">Pervasive Developmental Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Sensory+Experience%22">Sensory Experience</searchLink><br /><searchLink fieldCode="DE" term="%22Motion%22">Motion</searchLink><br /><searchLink fieldCode="DE" term="%22Eye+Movements%22">Eye Movements</searchLink><br /><searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink><br /><searchLink fieldCode="DE" term="%22Stimuli%22">Stimuli</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+Discrimination%22">Visual Discrimination</searchLink><br /><searchLink fieldCode="DE" term="%22Reaction+Time%22">Reaction Time</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22United+Kingdom+%28London%29%22">United Kingdom (London)</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s10803-019-04241-4
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0162-3257
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Autistic individuals often present atypicalities in adaptation--the continuous recalibration of perceptual systems driven by recent sensory experiences. Here, we examined such atypicalities in human biological motion. We used a dual-task paradigm, including a running-speed discrimination task ('comparing the speed of two running silhouettes') and a change-detection task ('detecting fixation-point shrinkages') assessing attention. We tested 19 school-age autistic and 19 age- and ability-matched typical participants, also recording eye-movements. The two groups presented comparable speed-discrimination abilities and, unexpectedly, comparable adaptation. Accuracy in the change-detection task and the scatter of eye-fixations around the fixation point were also similar across groups. Yet, the scatter of fixations reliably predicted the magnitude of adaptation, demonstrating the importance of controlling for attention in adaptation studies.
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  Label: Entry Date
  Group: Date
  Data: 2020
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  Label: Accession Number
  Group: ID
  Data: EJ1242049
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        Value: 10.1007/s10803-019-04241-4
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 373
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Secondary School Students
        Type: general
      – SubjectFull: Autism
        Type: general
      – SubjectFull: Pervasive Developmental Disorders
        Type: general
      – SubjectFull: Sensory Experience
        Type: general
      – SubjectFull: Motion
        Type: general
      – SubjectFull: Eye Movements
        Type: general
      – SubjectFull: Attention
        Type: general
      – SubjectFull: Stimuli
        Type: general
      – SubjectFull: Visual Discrimination
        Type: general
      – SubjectFull: Reaction Time
        Type: general
      – SubjectFull: United Kingdom (London)
        Type: general
    Titles:
      – TitleFull: Adaptation to the Speed of Biological Motion in Autism
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Karaminis, Themis
      – PersonEntity:
          Name:
            NameFull: Arrighi, Roberto
      – PersonEntity:
          Name:
            NameFull: Forth, Georgia
      – PersonEntity:
          Name:
            NameFull: Burr, David
      – PersonEntity:
          Name:
            NameFull: Pellicano, Elizabeth
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Type: published
              Y: 2020
          Identifiers:
            – Type: issn-print
              Value: 0162-3257
          Numbering:
            – Type: volume
              Value: 50
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Autism and Developmental Disorders
              Type: main
ResultId 1