.png){kind=icon}
.png){kind=icon}
Return
Decoding the Brain's Algorithm for Categorization from Its Neural Implementation
.png){kind=icon}
OA
AI
DOI：10.1016/j.cub.2013.08.035.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Acts of cognition can be described at different levels of analysis: what behavior should characterize the act, what algorithms and representations underlie the behavior, and how the algorithms are physically realized in neural activity [1]. Theories that bridge levels of analysis offer more complete explanations by leveraging the constraints present at each level [2-4]. Despite the great potential for theoretical advances, few studies of cognition bridge levels of analysis. For example, formal cognitive models of category decisions accurately predict human decision making [5, 6], but whether model algorithms and representations supporting category decisions are consistent with underlying neural implementation remains unknown. This uncertainty is largely due to the hurdle of forging links between theory and brain [7-9]. Here, we tackle this critical problem by using brain response to characterize the nature of mental computations that support category decisions to evaluate two dominant, and opposing, models of categorization. We found that brain states during category decisions were significantly more consistent with latent model representations from exemplar [5] rather than prototype theory [10, 11]. Representations of individual experiences, not the abstraction of experiences, are critical for category decision making. Holding models accountable for behavior and neural implementation provides a means for advancing more complete descriptions of the algorithms of cognition.
Keywords:
PERCEPTUAL DECISION-MAKING
MODEL-BASED FMRI
PARIETAL CORTEX
FUNCTIONAL MRI
ATTENTIONAL ALLOCATION
EPISODIC MEMORY
REPRESENTATION
PROTOTYPE
RECONSTRUCTION
NEUROSCIENCE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.