Extraction and generalisation of category-level information during visual statistical learning in autistic people

Abstract

Background: We examined whether information extracted during a visual statistical learning task could be generalised from specific exemplars to semantically similar ones. We then looked at whether performance in autistic people differed to non-autistic people during a visual statistical learning task and specifically examined whether differences in performance between groups occurred when sequential information was presented at a semantic level. We did this by assessing recall performance using a two-alternative forced choice paradigm after presenting participants with a sequence of naturalistic scene images.

Methods: 125 adult participants (61 participants with an autism diagnosis and 64 non-autistic controls) were presented with a fast serial presentation sequence of images and given a cover task to avoid attention being explicitly drawn to patterns in the underlying sequences. This was followed by a two-alternative forced choice task to assess participants' implicit recall. Participants were presented with 1 of 3 unique versions of the task, in which the presentation and assessment of statistical regularities was done at either a low feature-based level or a high semantic-based level.

Results: Participants were able to generalise statistical information from specific exemplars to semantically similar ones. There was an overall significant reduction in visual statistical learning in the autistic group but we were unable to determine whether group differences occurred specifically in conditions where the learning of semantic information was required.

Conclusions: These results provide evidence that participants are able to extract statistical information that is presented at the level of specific exemplars and generalise it to semantically similar contexts. We also showed a modest but statistically significant reduction in recall performance in the autistic participants relative to the non-autistic participants.

Fig 1. Example sequence.

Diagram showing an example sequence of images. Two separate triplets are shown, highlighted in blue and green respectively. A duplicate trial is demonstrated, highlighted in red.

Fig 2. Example stimuli.

Two examples of stimuli for the same triplet in the category-level condition. The triplet shown, forest—kitchen—mountain is the same as the blue triplet in Fig 1.

Fig 3. Distributions of scores for training phase.

Distributions of scores (proportion correct responses) in the training phase of the task. Distributions are shown separately for the two diagnosis groups and individual data points are shown on top in ascending order from left to right. Group means are shown by the two vertical lines.

Fig 4. Distributions of sensitivity index and decision criterion.

Box plots showing the signal detection performance measures during the training phase. Box plots are shown separately for the two diagnosis groups (control: CTR and autism: ASC), for both the sensitivity index (a) and the decision criterion (b). Individual data from all participants are also displayed.

Fig 5. Distributions of scores from the recall phase.

Scores in the recall phase (proportion correct) stratified by diagnosis group and condition type. Distributions are shown as violin plots (top) and as individual data points (bottom). The expected score based on chance guessing (0.5) is shown as a horizontal grey dashed line.

Fig 6. Correlation between training phase and recall phase performance.

Scatter plot showing the correlations between performance during the training phase and recall phase for both diagnosis groups. Lines of best fit are shown for each group with the 95% confidence interval displayed by the shaded regions.