Klinger and colleagues (Klinger & Dawson, 2005; Klinger, Klinger, & Pohlig, 2007) proposed that individuals with ASD have impaired categorization abilities that lead to difficulties in learning and generalization. Typically, when individuals encounter natural categories that have “fuzzy” boundaries, they abstract a best example (or prototype) that represents that category and use this prototype to generalize to new experiences. Specifically, prototype formation is a type of implicit learning in which statistical averages are abstracted from complex stimuli (e.g., if you try to think of the most representative table, it will probably be a statistical average of all the tables you have ever seen). Behavioral studies have suggested that prototype formation may be impaired in individuals with ASD (e.g., Gastgeb, Dundas, Minshew, & Strauss, 2009; Gastgeb, Strauss, & Minshew, 2006; Klinger & Dawson; Klinger, Klinger, & Pohlig). In this study, fMRI technology was used to investigate the cognitive and neural responses to prototypes in persons with ASD compared to typically developing individuals.
Objectives:
The present study sought to examine whether different patterns of fMRI-measured brain activation occurred for prototype category learning in persons with ASD compared to typically developing individuals.
Methods:
Fourteen high-functioning children and adolescents with ASD and 14 age and verbal raw matched neurotypical controls participated in this study. The data were collected using a Siemens 3T Allegra fMRI scanner at the Baylor College of Medicine (Houston, TX). In the scanner, participants passively viewed eight drawings of an imaginary animal, whose features varied in size along a scale from one to five. After the viewing, a test phase occurred, in which participants saw more drawings of that animal and were asked to indicate via button press if they had seen that drawing before. The drawings in the test phase included previously seen animals, new animals, and prototype animals (e.g., animals whose features were the mathematical average of the previously seen animals). This procedure was repeated across four sets of imaginary animals.
Results:
The prototype effect was calculated as the proportion of prototype animals selected as “previously seen” minus the proportion of new animals selected as previously seen. This provided of evidence of whether participants viewed the prototype as familiar, suggesting that they had formed this mental representation. Behavioral results showed that typically developing participants showed a larger prototype effect (M=.48) than ASD participants (M=.24). Both groups showed significant prototype learning (TD t(13) = 7.79, p < .001; ASD t(13) = 2.92, p = .01). However, the ASD participants showed significantly less prototype learning than typically developing controls, F(1, 26) = 5.25, p = .03. All imaging data have been collected and preprocessed, but fMRI individual and group contrast analyses are ongoing.
Conclusions:
Behaviorally, prototype learning may be less robust in persons with ASD. We hypothesize that, at the neural level, individuals with ASD may show less activation in areas typically associated with implicit learning (e.g., basal ganglia) during prototype formation tasks. fMRI data analysis will be forthcoming in the next month.