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Updated: Jun 14, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Published on: November 2, 2012
Differential category learning processes: The neural basis of comparison-based learning and induction
Rubi Hammer1, André Brechmann, Frank Ohl
1Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel. rubih@stanford.edu
Abstract:
Findings from numerous studies suggest that multiple neural systems are involved in category learning. Specifically, it is often argued that acquiring a representation of different category structures (e.g., rule-based vs. prototype-based representation) involves different computational challenges, which are resolved by different neural circuitries in the human brain. Here we present an alternative approach for studying neural mechanisms of category learning: We refer to the idea that any category learning task involves mapping common features shared by same-category members, distinctive features discriminating members of different categories, or both. We argue that since these processes are psychologically and computationally distinct, they differ in their usability for category learning. Our participants learned novel categories of complex visual stimuli by comparing either pairs of objects from the same novel category or pairs of objects from different categories. Object pairs were chosen so that the objective amount of information they contained was identical in the two category learning conditions, equally enabling learning the predefined objective category structure. We find that the neural circuitry involved in detecting important between-categories differences is associated mainly with the dorsal striatum (bilaterally) and the right hippocampus. On the other hand, mapping within-category similarities and differences is restricted to high-level visual brain areas. We suggest that multiple neural mechanisms are involved in category learning enabling us to face different computational challenges associated with different basic types of induction processes that differ in their usability for learning different category structures.
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