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Prediction errors drive dynamic changes in neural patterns that guide behavior
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AI
DOI：10.1016/j.celrep.2023.112931.png)
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Abstract
En 中文
Learning describes the process by which our internal expectation models of the world are updated by surprising outcomes (prediction errors [PEs]) to improve predictions of future events. However, the mechanisms through which error signals dynamically influence existing neural representations are unknown. Here, we use functional magnetic resonance imaging (fMRI) in humans solving a two-step Markov decision task to investigate changes in neural activation patterns following PEs. Using a dynamic multivariate pattern analysis, we can show that PE-related fMRI responses in error-coding regions predict trial-by-trial changes in multivariate neural patterns in the orbitofrontal cortex, the precuneus, and the ventromedial prefrontal cortex (vmPFC). Importantly, the dynamics of these pattern changes in the vmPFC also predicted upcoming changes in choice strategies and thus highlight the importance of these pattern changes for behavior.
Keywords:
ORBITOFRONTAL CORTEX
BASAL GANGLIA
REWARD PREDICTION
UNCERTAINTY
VMPFC
ACC
REPRESENTATIONS
CONNECTIVITY
METAANALYSIS
MODULATION
Journal
IF:
6.9
Papers:
1.7W
Citations:
10.2W
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Cited Papers
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