Perceptual similarity in autism

Abstract

People with autism have consistently been found to outperform controls on visuo-spatial tasks such as block design, embedded figures, and visual search tasks. Plaisted, O'Riordan, and others (Bonnel et al., 2003; O'Riordan & Plaisted, 2001; O'Riordan, Plaisted, Driver, & Baron-Cohen, 2001; Plaisted, O'Riordan, & Baron-Cohen, 1998a, 1998b) have suggested that these findings might be explained in terms of reduced perceptual similarity in autism, and that reduced perceptual similarity could also account for the difficulties that people with autism have in making generalizations to novel situations. In this study, high-functioning adults with autism and ability-matched controls performed a low-level categorization task designed to examine perceptual similarity. Results were analysed using standard statistical techniques and modelled using a quantitative model of categorization. This analysis revealed that participants with autism required reliably longer to learn the category structure than did the control group but, contrary to the predictions of the reduced perceptual similarity hypothesis, no evidence was found of more accurate performance by the participants with autism during the generalization stage. Our results suggest that when all participants are attending to the same attributes of an object in the visual domain, people with autism will not display signs of enhanced perceptual similarity.

Acknowledgments

Research was supported by an RDTF grant to Lamberts, Boucher, and Bott from the University of Warwick. Lewis Bott was supported by a studentship from the Brain and Behavioral Sciences Research Council and by a grant from the Centre National de la Recherche Scientifique (France) as part of Action Thematique et Incitative. Jon Brock was supported by a studentship from the Williams Syndrome Foundation.

Notes

¹Plaisted and O'Riordan generally express their theory in terms of “enhanced discrimination” rather than “reduced perceptual similarity”. Although these two expressions are not identical, there is general agreement in the categorization literature that the perceptual similarity of two objects is monotonically related to the extent to which they can be discriminated (see, for example, Medin & Schaffer, 1978; Nosofsky, 1986, 1987; Shepherd, 1987). Put simply, an individual who finds it relatively easy to discriminate between two objects would also consider them relatively dissimilar. However, the concepts of discrimination and (dis)similarity are not used interchangeably in the categorization literature, and the theory described by Plaisted et al. (1998a) corresponds better to the categorization expression “reduced perceptual similarity” than to the term “enhanced discrimination”. Because the techniques and theory employed in this article are based on standard paradigms used in perceptual categorization, we adopt the term “reduced perceptual similarity” for the remainder of the article.

²If a participant tried to classify the rectangles on the basis of height alone, then it would not be possible to correctly classify rectangles B7 and A4 (amongst others) because they have the same height but are in different categories. Similar arguments hold for width (e.g., rectangles B9, B8, and A2), area (rectangles A1 and B8), and shape (B8 and A4 are both square).

³ Referred to as the “weirdness” index by Young and Harris (1994).

⁴An alternative to analysing the participant weights might have been to fit two completely different solutions, one for the HFA group and another for the control group, and to examine the degree to which each solution fitted the data. One might have expected that, if the HFA group were basing their judgements on fewer dimensions, then the most optimal dimensionality for the HFA group would be lower than that of the controls. However, there are two problems with this method, which would make any result difficult to interpret. The first is that formal methods for deciding dimensionality, such as Lee's (2001) BIC, favour a low dimensional solution for data that has a high variance. Thus, different ideal dimensionalities could be the result of differences in the variance across groups, rather than a difference in the perceived rectangle space. The second problem is that even if the HFA solution requires a high-dimension solution, it could be because different participants within the group choose to base their judgements on different dimensions. Hence, a high-dimensional solution is required to account for between-participant variation in the choice of dimensions, even though any single participant might be best modelled with a low-dimensional solution. The INDSCAL technique that we used avoids both of these potential problems.

⁵The removal of these two HFA participants did not substantially alter the matching between our two groups. The new means and ranges for the HFA group were: 31.2 (20–62), 27.3 (20–35), and 22 (17–31) years for age, verbal mental age, and performance mental age, respectively. As before, there were no reliable differences between groups on the verbal and performance mental age measures, t(25)s < 1, ps > .83, and the difference on age remained, t(25) = 2.6, p = .014.

⁶One participant in the control group had an extremely high c value (the maximum possible value tested by our optimization algorithm). This was because he had a near-perfect score for the training items during the testing phase, which the GCM fits best by using an infinitely high c parameter value.

Additional information

Notes on contributors

Lewis Bott

Lewis Bott is now at the Department of Psychology, New York University

Jon Brock

Jon Brock is now at the Department of Experimental Psychology, University of Oxford

Noellie Brockdorff

Noellie Brockdorff is now at the Centre for Communication Technology, University of Malta, Malta