Motion Coherence Processing with High Autistic Traits
This study assessed the accuracy and reaction times of two groups of typically-developed (individuals that have met all the developmental milestones) adult participants, using a motion coherence task. The 116 participants were split into two groups based on their autism trait score (high vs. low), determined using the Autism Spectrum Quotient. The motion coherence task required the participants to determine which way the majority of a group of dots were moving in a random dot Kinematogram. The high autism trait group showed significantly less accuracy than the low autism trait group (meaning they have a high motion coherence threshold), but no difference in reaction times was detected. The findings suggest that individuals with autistic traits may struggle to process/detect coherent motion. Future work should investigate where on the autistic spectrum motion coherence begins to deteriorate and whether the change is abrupt or gradual, so to better provide educational support for autistic individuals.
Soorya, Carpenter and El-Ghoroury (2017) define autism as a severe neurodevelopmental disorder that characterizes itself by affecting a person’s social functioning and communication, and causing repetitive behaviour patterns.
One of the drivers of these effects is a decrease in visual perception ability, which can present itself as a form of agnosia or the inability to detect whole shapes. The perception difference of autistic individuals and ‘normal’ individuals was investigated using various visual stimuli in either whole or partial form e.g. whole shapes and shapes in multiple parts (Shah and Frith, 1993). The participants with autism performed significantly better than the control group (normal) when examining the whole shapes, suggesting that they can see the finer details in whole shapes better than those without autism. This finding was the catalyst for the development of the Weak Central Coherence Theory (Firth, 2003), which states that individuals with autism focus on the local information (finer details) rather than global information (overall picture).
One way to measure how well individuals view global and local information is using a motion coherence task (i.e. random dot Kinematogram). During a motion coherence task, participants view a group of moving dots and must judge which direction the majority are moving in. A participant’s motion coherence threshold is determined from the minimum number of moving dots that they can still correctly identify as moving in a single direction, amongst a large group of randomly/erratically moving dots. Milne et al. (2002) conducted a motion coherence task with a control group and a group of autistic children. The autistic group had a higher motion coherence threshold than the control group, leading to the suggestion that individuals with autism perform worse in motion coherence tasks than non-autistic individuals. Manning (2015), however, has challenged this theory after conducting a similar experiment (with a similar age group) to that of `Milne et al. (2002), which concluded that children with autism could tolerate wider variability when judging the direction of the majority of the dots. Meaning that autistic children should perform better when participating in tasks connected to motion coherence than typically-developed children.
Previous studies have focused on comparing formally diagnosed autistic individuals with a control (non-autistic) group. To date no study has looked for perception differences in a typically-developed population sample, which left a gap in the knowledge for the current study to fill, by starting to determine where significant perception differences begin to appear on the autistic spectrum. In accordance with the majority of previous studies using autistic individuals, the current study predicted that motion coherence discrimination will be significantly worse for typically-developed individuals who have higher autism trait scores. The two specific hypotheses tested were that accuracy will be significantly lower for individuals with higher autism scores, and reaction times will be significantly longer for individuals with higher autism scores. Both hypotheses were tested using an Autism Spectrum Quotient and a Motion Coherence Task.
The participants consisted of 116 typically-developed, first-year-psychology students from a UK university, with a mean age of 20.7 years (SD = 5.3), who were required to participate as a condition of their psychology degree course. Each participant used the last three digits of their student number so the experimenter could track the results, and therefore the results for each test were anonymised.
Participants took the Autism Spectrum Quotient (ASQ) using an online server, and a motion coherence task (random dot Kinematogram) conducted using a piece of experimental software called PsychoPy (Peirce, 2007).
The experiment was a between subject’s design, and had one independent variable (the group of participants) with two levels (high and low autism traits), and two dependent variables (test accuracy and reaction time). Both dependent variables were measured simultaneously.
First, all participants completed the ASQ and then were given the instructions for the Motion Coherence Task prior to the practice trials. All participants were instructed to press the left or right arrow keys of a computer keyboard, corresponding to the direction they see the majority of a large (40+) group of dots moving in. Each participant completed 80 practice trials. Each trial began with a brief fixation cross (present for 200 μs) to focus the participant’s attention, before the dot stimuli were presented (for 1500 μs per trial).
The participants then moved on to the experimental task, which consisted of five groups of 40 trials (200 trials in total). The software recorded whether the participant was correct (accuracy) and how much time it took them to respond to the stimuli (in μs).
The scores from the Autism Spectrum Quotient (ASQ) were ordered and divided into three groups: lowest 1/3 of scores, middle 1/3 of scores and highest 1/3of scores. The middle 1/3 of the scores were then discarded, leaving the high autism trait group (highest 1/3 of scores) and low autism trait group (lowest 1/3 of scores). Outlying data points, determined as those laying too far from the mean, were excluded. Independent samples two-tailed t-tests were used to determine whether the high and low autism trait groups scored differently in both their accuracy and response times. All means are expressed ± standard deviation.
There was a significant decrease in accuracy for the high autism trait group (74.86 ± 12.33) compared to that of the low autism trait group (81.10 ± 11.25) conditions; t(114) = 2.83, p = 0.005. In contrast, no significant difference in response time scores was found between groups; low autism trait group (623.14 ± 112.54 μs) and high autism trait group (654.44 ± 134.03 μs) conditions; t(114) = 1.35, p = 0.117.
The participants with higher autism traits had less accuracy than the lower autism trait group, and therefore have a higher motion coherence threshold. These findings support the first hypothesis; accuracy will be lower for individuals with higher autism scores. There was, however, no detectable effect of autism score on reaction times, which is contrary to the second hypothesis tested (reaction times will be significantly longer for individuals with higher autism scores). The results support previous work that found children with autism had less accuracy when completing a motion coherence task than the typically-developed children (Milne et al, 2002), but expands the concept, and therefore doesn’t support Manning’s (2015) theory that children with autism perform better. Specifically, motion coherence thresholds may be affected by autism score within typically developed individuals and not just a trait of autistic individuals per se.
Αn effect on response time, specifically an increase in response time for the high autism trait group, would have indicated a difference in attention span (Robertson et al, 2014). The reason why autism scores were only found to affect accuracy and not response times here could be because both of the groups tested were typically-developed, meaning there should be no major difference between the two (Robertson et al, 2014). The results could suggest that response is binary, whereas accuracy is a continuous spectrum.
The findings of this study could also be the result of the methodology used, for example, it is relatively unknown how reliable the ASQ is when differentiating between Autism, Asperger’s, and Attention Deficit and Hyperactivity Disorder (Sizoo et al, 2009). It was found that out of 129 participants tested, using the Autism Spectrum Quotient, only 73% of classifications were correct (when the results were compared to the participant’s former diagnosis’s), indicating that the ASQ may lack resolution (Sizoo et al, 2009). However, as the participants in the current experiment were all typically-developed and the ASQ wasn’t being used as a diagnostic tool, and the distance between the ASQ scores of the two groups was broad (achieved by disregarding participants that produced the middle third of scores), the results are less likely to be impacted by the resolution limitations of the ASQ.
Future research could expand on this study by comparing a typically-developed, high autism trait group with a diagnosed autistic group to try and determine where on the autistic spectrum accuracy dramatically changes. This could be useful knowledge in providing adequate support for children with autism in education settings, by broadening the understanding of how they view the world. In summary, a significant decrease in accuracy was found for the high autism trait group, but no effect of ASQ scores was found for the reaction times. Therefore, the first hypothesis is supported, but the second experimental hypothesis must be rejected. The data collected supports previous theories e.g. Milne et al. (2002), but the ASQ may not always accurately define autism level. Nonetheless, the data collected provides a good foundation for future research to build on.
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