Modeling the dynamics of real-world perceptual expertise

Novices are faster to verify categories at intermediate ("basic") levels of abstraction (bird) than superordinate (animal) or subordinate (blue jay) levels, whereas experts are equally fast at subordinate and intermediate levels. One explanation ("entry-level hypothesis") is that for novices, categorization at intermediate levels is faster because it is the "entry level" into conceptual knowledge; experts become as fast at the subordinate level because the subordinate level becomes an alternative entry level. An alternative explanation ("differentiation hypothesis") is that for novices, categorization at intermediate levels is faster because that level is more differentiated and informative, not that it happens first; experts become as fast at the subordinate level because representations of objects at that level become more differentiated. We evaluated these hypotheses by fitting the diffusion decision model to accuracy and response time data from online participants with various psychometrically measured levels of birding expertise. We identified the alternative hypotheses with diffusion decision model parameters: Differences in nondecision time across category levels are arguably associated with the entry-level hypothesis, whereas differences in drift rate are arguably associated with the differentiation hypothesis. We fitted the diffusion decision model using a Bayesian hierarchical framework to estimate individual differences in model parameters across conditions. Behaviorally, we replicated the entry-level shift online. Theoretically, we found that differences in categorization speed across levels of expertise were captured by differences in both drift rate and nondecision time across levels. These results provide insights into the changes in representations and processes with the development of perceptual expertise. (PsycInfo Database Record (c) 2025 APA, all rights reserved).