Visual/verbal-analytic reasoning bias as a function of self-reported autistic-like traits: a study of typically developing individuals solving Raven's Advanced Progressive Matrices.
In typical adults, more autistic-like traits predict better visual puzzle solving, not better verbal solving.
01Research in Context
What this study did
The team asked typical college students to fill out the Autism-Spectrum Quotient. Then each student worked through Raven's Advanced Progressive Matrices, a set of tricky picture puzzles.
While the students solved the puzzles, the researchers tracked which items each person got right. They also looked at whether the items needed more visual or more verbal thinking.
What they found
Students who scored high on autistic-like traits solved more puzzles than peers with low traits. The boost showed up only on the puzzles that leaned on visuospatial skill, not on the ones that needed verbal rules.
In plain words, stronger autistic traits went hand-in-hand with better non-verbal reasoning.
How this fits with other research
Dickinson et al. (2014) saw the same pattern with a different task: adults with higher traits were also sharper at spotting tiny tilt differences between lines. Together the two papers build a picture that visuospatial perks sit on the broader autism trait continuum.
Takahashi et al. (2014) added brain data. Using EEG, they showed that high-trait adults process complex visual patterns just as quickly as simple ones. Their neural-efficiency finding helps explain why the Raven scores jumped in the 2011 study.
Baker et al. (2005) once reported weaker spatial working memory in diagnosed autism. That sounds like a clash, but the 2011 study tested typical adults, not a clinical group. The gap reminds us that trait effects in the general population do not always mirror clinical profiles.
Why it matters
If you give visual problem-solving tasks, notice who sails through them. A learner with sub-clinical autistic traits may outscore peers on block design, puzzles, or map reading. You can lean on these strengths when teaching new skills or when choosing reinforcers that fit visual learners. At intake, pair standard language tests with non-verbal measures so you do not miss talent that hides behind quiet speech.
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02At a glance
03Original abstract
People with autism spectrum condition (ASC) perform well on Raven's matrices, a test which loads highly on the general factor in intelligence. However, the mechanisms supporting enhanced performance on the test are poorly understood. Evidence is accumulating that milder variants of the ASC phenotype are present in typically developing individuals, and that those who are further along the autistic-like trait spectrum show similar patterns of abilities and impairments as people with clinically diagnosed ASC. We investigated whether self-reported autistic-like traits in a university student sample, assessed using the Autism-Spectrum Quotient (AQ; Baron-Cohen, Wheelwright, Skinner, et al., 2001), predict performance on Raven's Advanced Progressive Matrices. We found that reporting poorer social skills but better attention switching predicted a higher Advanced matrices score overall. DeShon, Chan, and Weissbein (1995) classified Advanced matrices items as requiring a visuospatial, or a verbal-analytic strategy. We hypothesised that higher AQ scores would predict better performance on visuospatial items than on verbal-analytic items. This prediction was confirmed. These results are consistent with the continuum view and can be explained by the enhanced perceptual functioning theory of performance peaks in ASC. The results also confirm a new prediction about Raven's Advanced Progressive Matrices performance in people with ASC.
Autism : the international journal of research and practice, 2011 · doi:10.1177/1362361310371798