Neurally constrained modeling of perceptual decision making.

Neurally constrained modeling of perceptual decision making.
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感知决策的神经约束建模。

DOI:
10.1037/a0020311
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发表时间:
2010-10
影响因子:
5.4
通讯作者:
Palmeri, Thomas J.
Palmeri, Thomas J.
中科院分区:
心理学1区
文献类型:
--
作者:
Purcell, Braden A.;Heitz, Richard P.;Cohen, Jeremiah Y.;Schall, Jeffrey D.;Logan, Gordon D.;Palmeri, Thomas J.

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随机累加器模型通过假设感知证据累积到阈值来解释感知决策任务中的响应时间。本研究将额眼场 (FEF) 视觉神经元的放电率映射到感知证据,并将 FEF 运动神经元的放电率映射到证据积累,以测试证据在积累过程中如何组合的替代模型。对模型预测响应时间分布和在执行视觉搜索任务的猴子中观察到的运动神经元活动的能力进行了评估。假设将感知证据门控到累积单元的模型提供了行为和神经数据的最佳解释。这些结果确定了具有解剖学上不同的神经群体的离散处理阶段,并排除了几种替代架构。结果还说明了神经生理学数据作为模型选择工具的用途,并建立了一个新的框架来连接计算和神经层面的解释。
Stochastic accumulator models account for response time in perceptual decision-making tasks by assuming that perceptual evidence accumulates to a threshold. The present investigation mapped the firing rate of frontal eye field (FEF) visual neurons onto perceptual evidence and the firing rate of FEF movement neurons onto evidence accumulation to test alternative models of how evidence is combined in the accumulation process. The models were evaluated on their ability to predict both response time distributions and movement neuron activity observed in monkeys performing a visual search task. Models that assume gating of perceptual evidence to the accumulating units provide the best account of both behavioral and neural data. These results identify discrete stages of processing with anatomically distinct neural populations and rule out several alternative architectures. The results also illustrate the use of neurophysiological data as a model selection tool and establish a novel framework to bridge computational and neural levels of explanation.
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