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Value signals guide abstraction during learning
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DOI：10.7554/eLife.68943.png)
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The human brain excels at constructing and using abstractions, such as rules, or concepts. Here, in two fMRI experiments, we demonstrate a mechanism of abstraction built upon the valuation of sensory features. Human volunteers learned novel association rules based on simple visual features. Reinforcement-learning algorithms revealed that, with learning, high-value abstract representations increasingly guided participant behaviour, resulting in better choices and higher subjective confidence. We also found that the brain area computing value signals - the ventromedial prefrontal cortex - prioritised and selected latent task elements during abstraction, both locally and through its connection to the visual cortex. Such a coding scheme predicts a causal role for valuation. Hence, in a second experiment, we used multivoxel neural reinforcement to test for the causality of feature valuation in the sensory cortex, as a mechanism of abstraction. Tagging the neural representation of a task feature with rewards evoked abstraction-based decisions. Together, these findings provide a novel interpretation of value as a goal-dependent, key factor in forging abstract representations.
Keyword:
DECODED FMRI NEUROFEEDBACK
ORBITOFRONTAL CORTEX
CONCEPTUAL KNOWLEDGE
COGNITIVE MAP
MECHANISMS
ATTENTION
REPRESENTATIONS
ARCHITECTURE
HIPPOCAMPUS
INTEGRATION
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0
论文数:
1.8W
被引数:
16
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分享收藏Orbitofrontal control of visual cortex gain promotes visual associative learning视皮层增益的眶额控制促进视觉联想学习.png)
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