Rational decision-making in inhibitory control
Rational decision-making in inhibitory control
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DOI：
10.3389/fnhum.2011.00048
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发表时间：
2011-05-27
影响因子：
2.9
通讯作者：
Yu, Angela J.
中科院分区：
文献类型：
--
作者：
Shenoy, Pradeep;Yu, Angela J.
认知灵活性的一个重要方面是抑制控制，即根据感觉环境或任务需求的变化动态修改或取消计划动作的能力。我们为停车信号范式中的抑制控制制定了一个概率的、理性的决策框架。我们的模型假设，受试者保持一种贝叶斯最优的、不断更新的感觉输入表示，并反复评估在精细的时间尺度上停止和继续的相对价值，以便就何时以及是否继续进行每一次试验做出最佳决定。我们进一步假设，他们针对一个全局目标函数实施这种持续评估，该目标函数捕捉与不同行为结果相关的各种奖励和惩罚，例如速度和准确性，或停止错误和围棋错误的相对成本。我们证明，我们的理性决策模型自然会产生这一范式一致观察到的基本行为特征，以及由于背景因素(如奖励偶发因素或动机因素)而产生的更微妙的影响。此外，我们表明，经典的种族模型可以被视为计算上更简单，也许在神经上可信的，最优决策的近似。这一概念链接允许我们预测比赛模型的参数，如停止延迟，应该如何随着任务参数和个人经验/能力的变化而变化。
An important aspect of cognitive flexibility is inhibitory control, the ability to dynamically modify or cancel planned actions in response to changes in the sensory environment or task demands. We formulate a probabilistic, rational decision-making framework for inhibitory control in the stop signal paradigm. Our model posits that subjects maintain a Bayes-optimal, continually updated representation of sensory inputs, and repeatedly assess the relative value of stopping and going on a fine temporal scale, in order to make an optimal decision on when and whether to go on each trial. We further posit that they implement this continual evaluation with respect to a global objective function capturing the various reward and penalties associated with different behavioral outcomes, such as speed and accuracy, or the relative costs of stop errors and go errors. We demonstrate that our rational decision-making model naturally gives rise to basic behavioral characteristics consistently observed for this paradigm, as well as more subtle effects due to contextual factors such as reward contingencies or motivational factors. Furthermore, we show that the classical race model can be seen as a computationally simpler, perhaps neurally plausible, approximation to optimal decision-making. This conceptual link allows us to predict how the parameters of the race model, such as the stopping latency, should change with task parameters and individual experiences/ability.