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Modeling the dynamics of real-world perceptual expertise
Jianhong Shen1, Thomas J Palmeri1
1Department of Psychology, Vanderbilt University.
Summary
Novices categorize birds faster at basic levels, while experts are equally fast at basic and subordinate levels. This study used the diffusion decision model to show expertise changes how quickly we process information, affecting categorization speed.
Area of Science:
- Cognitive Psychology
- Perceptual Expertise Development
Background:
- Novices categorize at basic levels faster than superordinate or subordinate levels.
- Experts show equal speed for subordinate and basic categorization.
- Two hypotheses explain this: the "entry-level hypothesis" and the "differentiation hypothesis".
Purpose of the Study:
- To evaluate the entry-level and differentiation hypotheses of categorization speed.
- To investigate how perceptual expertise influences categorization processes.
- To model changes in cognitive representations with expertise development.
Main Methods:
- Diffusion decision model (DDM) fitted to accuracy and response time data.
- Online participants with varying levels of birding expertise.
- Bayesian hierarchical framework for parameter estimation.
Main Results:
- Replicated the "entry-level shift" in categorization speed online.
- Found that both "drift rate" (differentiation) and "nondecision time" (entry-level) parameters explained expertise differences.
- Categorization speed changes with expertise are linked to both representational differentiation and processing efficiency.
Conclusions:
- Both entry-level and differentiation mechanisms contribute to expertise-related changes in categorization.
- The diffusion decision model effectively captures cognitive processes underlying expertise.
- Findings offer insights into how perceptual learning reshapes cognitive representations and decision-making speed.
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