Reinforcement learning can account for associative and perceptual learning on a visual-decision task.
Reinforcement learning can account for associative and perceptual learning on a visual-decision task.
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DOI:
10.1038/nn.2304
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发表时间:
2009-05
影响因子:
25
通讯作者:
Gold, Joshua I.
中科院分区:
文献类型:
--
作者:
Law, Chi-Tat;Gold, Joshua I.
We recently showed that improved perceptual performance on a visual motion direction-discrimination task corresponds to changes not in how sensory information is represented in the brain but rather how that information is interpreted to form a decision that guides behaviour. Here we show that these changes can be accounted for using a reinforcement learning rule to shape functional connectivity between the sensory and decision neurons. We modelled performance based on the readout of simulated responses of direction-selective sensory neurons in the middle temporal area (MT) of monkey cortex. A reward prediction error guided changes in connections between these sensory neurons and the decision process, first establishing the association between motion direction and response direction and then gradually improving perceptual sensitivity by selectively strengthening the connections from the most sensitive neurons in the sensory population. The results suggest a common, feedback-driven mechanism for some forms of associative and perceptual learning.
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影响因子:
64.8
作者:
Ahissar, M;Hochstein, S
通讯作者:
Hochstein, S
影响因子:
1.9
作者:
BRITTEN, KH;SHADLEN, MN;MOVSHON, JA
通讯作者:
MOVSHON, JA
影响因子:
1.9
作者:
Britten, KH;Newsome, WT;Movshon, JA
通讯作者:
Movshon, JA
影响因子:
1.8
作者:
BALL, K;SEKULER, R
通讯作者:
SEKULER, R
DOI:
10.1109/tsmc.1985.6313371
发表时间:
1985-01-01
期刊:
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS
影响因子:
--
作者:
BARTO, AG;ANANDAN, P
通讯作者:
ANANDAN, P