Perceptual decision-making in autism as assessed by “spot the difference” visual cognition tasks

Discriminating between similar figures proves to be a remarkably demanding task due to the limited capacity of our visual cognitive processes. Here we examine how perceptual inference and decision-making are modulated by differences arising from neurodiversity. A large sample of autistic (n = 140) and typical (n = 147) participants completed two forced choice similarity judgement tasks online. Each task consisted of “match” (identical figures) and “mismatch” (subtle differences between figures) conditions. Signal detection theory analyses indicated a response bias by the autism group during conditions of uncertainty. More specifically, autistic participants were more likely to choose the “mismatch” option, thus leading to more hits on the “mismatch” condition, but also more false alarms on the “match” condition. These results suggest differences in response strategies during perceptual decision-making in autism.

www.nature.com/scientificreports/ Optimal performance on a perceptual decision task requires filtering out of external noise and a reduction in internal noise [29][30][31][32][33] . While external noise encompasses environmental factors, such as task-relevant or taskirrelevant distractors, internal noise refers to variability in neuronal signals or random neuronal fluctuations that pose a challenge during perceptual inference and decision-making 34 . Neural models of autism suggest that, due to a possible imbalance of excitatory and inhibitory neurotransmitters, variable levels of internal noise may contribute to the cognitive features characteristic of the condition [35][36][37][38][39] .
In this investigation, we aimed to expand upon previous findings of figure disembedding in autism by investigating how autistic and typical individuals make perceptual decisions about two similar or differing figures.

Methods
Participants. Participants with normal or corrected-to-normal vision were recruited online via an email notification sent to individuals registered to two University of Cambridge databases: (1) the Autism Research Centre database (accessible at www. autis mrese archc entre. com) was used to recruit autistic adults and (2) a second database (accessible at www. cambr idgep sycho logy. com) was used to recruit the non-autistic adult controls. The first database collects information on formal autism diagnoses by asking participants to choose their diagnoses from a drop-down menu. This is followed up by questions about the year of diagnosis, the professional who diagnosed them, and the facility where they were diagnosed. Participants were entered into a prize draw for the chance to win £50. After excluding participants with missing/incomplete data, the dataset contained 140 autistic (82 females) and 147 non-autistic (118 females) adults aged 18-60 years. There were no significance group differences in age (t(283) = −0.55, p = 0.579) for autism (Mean = 35.1, SD = 9.85) and controls (Mean = 35.8, SD = 9.85).
Procedure. This study was approved by and conducted in accordance with the regulations of the Psychology Research Ethics Committee in Cambridge (PREC. 2015.018). Written informed consent was obtained from all participants. Participants completed behavioural tasks probing working memory and visual perception via Cambridge Brain Sciences (www. cambr idgeb rains cienc es. com), a web-based platform for cognitive assessments. Participants were instructed to complete the tasks on a desktop computer while seated comfortably and with a clear view of the screen. Verbal and visuospatial working memory were assessed using the standardised Digit Span test, which measures the ability to recall a sequence of digits, and the Monkey Ladder test, which measures the ability to recall the location of digits 40,41 . Stimuli were scaled to size to account for differences in browsers, devices, and screen size. All tasks were adapted for online computerized testing and validated in large samples 42 .
For each task, participants were given 90 s to complete as many trials as possible, with a timer and the score displayed on one side of the screen. The stimulus presentation was pseudo-randomized such that equal numbers of "match" and "mismatch" trials were administered over 90 s. The difficulty level of each trial increased or decreased based on the participant's performance on the previous trial. More specifically, there were two trials at each level of difficulty. If the participant got both trials right, the difficulty level increased by one and if they got both wrong, it reduced by one. The following visual scene discrimination tasks were implemented: Task 1: Interlocking polygons. The Interlocking Polygons task is based on pen-and-paper tasks used in clinical neuropsychological tests 43 . In this task, a pair of interlocked polygons is displayed on one side of the screen. Participants were instructed to indicate whether a polygon displayed on the other side of the screen is identical ("match") or not identical ("mismatch") to one of the interlocking polygons (Fig. 1A). Difficulty on each trial corresponded to more subtle differences in the polygons. www.nature.com/scientificreports/ Task 2: Feature match. The Feature Match task is a visual search task based on the feature integration theory of visual attention 5 . Arrays of abstract shapes were displayed on either side of the screen. Participants were instructed to indicate whether the arrays' contents were identical ("match") or differed by a single shape ("mismatch") ( Fig. 1B). Difficulty on each trial corresponded to an increase in the number of shapes in the array.
Data analysis. Data were analysed in R version 4.0.3 (R Core Team, 2020) and RStudio (RStudio Team, 2020) with the help of the "tidyverse" package 44 . For Bayesian statistics, we used the "Bayes Factor" R package and report Bayes factors (BF) which quantify the strength of evidence for the alternative hypothesis (BF 10 ) over the null (BF 01 ) [45][46][47] . The magnitude of this strength increases with deviation from 1, with BF 10 > 3 considered as moderate evidence and BF 10 > 10 as strong evidence for the alternative hypothesis, while BF 10 < 3 is insufficient evidence for or against the alternative hypothesis [48][49][50] . For t-tests, we report t-statistics, p-values, 95% confidence interval (CI) values, and effect sizes in addition to the Bayes factors. The R package "psycho" was used for the signal detection theory analyses 51 .
To help address the heterogeneity within our online sample, we first excluded participants whose working memory scores were less than 2 standard deviations from the overall mean. We then conducted exploratory t-tests to measure the extent to which the Autism and Control groups differed in working memory abilities.
As accuracy rates do not adequately capture the participants' decision criteria, we employed a Signal Detection Theory (SDT) approach to examine the response biases/strategies used by the groups 52,53 . In this approach, we considered the "mismatch" trials as the signal and the "match" trials as noise.
We calculated the sensitivity/discriminability index (d′) of signal from noise using the following formula: The response criterion (C) which measures participant bias in choosing one response was calculated using the following: where hits and false alarms are expressed as the proportion of responses in each category, and Z(.) is the inverse of the cumulative distribution function of the given Gaussian distribution. We then assessed group differences in sensitivity indices (d´) and response criteria (C) by means of t-tests. Additional analyses of group differences in accuracy rates on each condition are reported in the Supplementary Material.

Results
Working memory. After excluding participants whose working memory performance was below the cut-off,  (Fig. 2B). This suggests a response bias by the Autism group in choosing the "mismatch" option when uncertain, thus leading to more false alarms on the "match" trials (Fig. 2B).  Table 1). The SDT analyses showed no evidence of group differences in the sensitivity index (d′) (BF 10 (Fig. 3B). This suggests more false alarms by the autism group during "match" trials.

Discussion
Using a large sample of autistic and typical participants, we conducted two visual cognition tasks to test figure discrimination ability and perceptual decision-making. In Task 1: Interlocking Polygons, participants indicated whether a target polygon was present in the comparison figure of interlocking polygons. In Task 2: Feature Match, participants indicated whether two arrays of shapes differed by a single item. Investigations using Signal Detection Theory indicated no group differences in visual perceptual sensitivity. At the same time, we found group differences in the decision criterion used by the groups. Specifically, autistic participants on average tended to choose the "mismatch" option when faced with uncertainty during "match" trials. We found no group differences in the sensitivity index (d′), suggesting no clear differences in visual perception between the groups (Figs. 2A, 3A). This contradicts our initial hypotheses of differential visual perception in autism. At the same time, we found a notable response bias by the autism group while making decisions. More specifically, autistic participants were more likely to choose the "mismatch" option, thus leading to more hits on  www.nature.com/scientificreports/ the "mismatch" condition, but also more false alarms on the "match" condition (Figs. 2B, 3B). To measure the extent of this response bias, additional one-sample t-tests on the response criterion (C) were computed separately for each group, which indicated that the autism group tended to choose "mismatch" more often than the ideal observer (Supplementary Material). It has been hypothesized that the perceptual features of autism may be due to variable levels of internal noise, thus leading to difficulties in signal-to-noise separation 39,[54][55][56][57][58][59] . Internal noise in autism has been attributed to atypical neural connectivity of sensory brain networks 35,37,60,61 and an imbalance in excitatory and inhibitory neurotransmitters 38,[62][63][64] . However, in this study, we found no differences between autistic and control groups in their discriminability indices in both tasks (Figs. 2A, 3A). While our findings indicate group differences in the decision criteria used by the groups (Figs. 2B, 3B), it is unclear why autistic individuals use a lower decision criterion while choosing between signal and noise responses. Future research using computational models and neuroimaging methods may shed more light on these findings. Our study has its limitations: the less-controlled nature of the online task set-up, the sampling bias of participants with access to computers and internet, and the unbalanced sex ratio within our study sample. A greater percentage of female participants reflects what is the norm with online research 65 . However, due to possible sex differences in autism and visual cognition, we acknowledge this as an important caveat. Our findings may be more generalizable to females, however it is ultimately unclear whether the same effects would be observed in a sex-balanced or male-only sample. Future research taking these caveats into account may answer more questions about perceptual inference and decision-making in autism.
In conclusion, contrary to findings from previous research, when compared to typical people, autistic individuals show no differences in visual perceptual sensitivity on two variations of figure disembedding tasks. On the other hand, autistic individuals show a response bias when faced with uncertainty during these tasks, suggesting differences in perceptual decision-making. Taken together, our findings shed light on how autistic individuals make perceptual choices on similarity judgement tasks and provide clear directions for future research.

Data availability
The raw datasets generated and/or analysed during the current study are not publicly available as volunteers in the Cambridge Autism Research Database (CARD) did not consent for their data to be deposited in an Open Access archive. However, the CARD Management Committee considers requests by researchers for specific parts of the database (in anonymised form) to test specific hypotheses (please contact: research@autismresearchcentre.com).