Occipitotemporal Category Representations Are Sensitive to Abstract Category Boundaries Defined by Generalization...

Area of Science:

• Cognitive Neuroscience
• Neuroimaging
• Perception and Categorization

Background:

• Categorization relies on organizing perceptual information to highlight class-predictive features.
• Neural representations are thought to encode learned category structures.
• The influence of generalization demands on these neural representations remains underexplored.

Purpose of the Study:

• To investigate how neural representations of learned category structure change based on generalization demands.
• To examine the roles of frontoparietal and occipitotemporal regions in categorization under varying decisional contexts.

Main Methods:

• Human participants performed a categorization task under two distinct rules: 'strict' (limited generalization) and 'lax' (broad generalization).
• Functional magnetic resonance imaging (fMRI) was used to measure brain activity.
• Decoding models analyzed neural representations of stimulus typicality and decisional demands.

Main Results:

• Frontoparietal regions reflected decisional demands (distance from category boundary).
• Occipitotemporal representations encoded stimulus typicality, but this varied significantly between the strict and lax rules.
• Decoding accuracy decreased when models were trained and tested across different rules, indicating rule-dependent representations.

Conclusions:

• Occipitotemporal cortex representations are not invariant but are flexibly modulated by abstract decisional factors and generalization rules.
• The discriminability of neural signals increased as stimuli approached the category boundary.
• These findings challenge the notion of fixed visual representations, highlighting their dynamic nature in response to cognitive demands.