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.
Gold, Joshua I.
中科院分区:
医学1区
文献类型:
--
作者:
Law, Chi-Tat;Gold, Joshua I.

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我们最近表明,在视觉运动方向辨别任务中,感知表现的提高不是与感觉信息在大脑中的表现方式有关,而是与如何解释这些信息以形成指导行为的决策有关。在这里,我们表明这些变化可以用强化学习规则来解释,以形成感觉神经元和决策神经元之间的功能连接。我们基于猴子皮层中颞区(MT)方向选择感觉神经元的模拟反应的读数来模拟表现。奖励预测错误引导了这些感觉神经元与决策过程之间连接的变化,首先建立了运动方向和反应方向之间的关联,然后通过选择性地加强来自感觉群体中最敏感神经元的连接,逐渐提高了感知灵敏度。研究结果表明,某些形式的联想学习和知觉学习存在一种共同的、反馈驱动的机制。
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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发表时间: 1997-05-22
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