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抑制控制的理性决策框架

A rational decision making framework for inhibitory control
课程网址: http://videolectures.net/nips2010_yu_rdm/  
主讲教师: Angela J. Yu
开课单位: 加州大学圣地亚哥分校
开课时间: 2011-01-12
课程语种: 英语
中文简介:
智能代理经常面临着选择具有不确定后果的行动的需要,并根据不断进行的感官处理和不断变化的任务需求修改这些行动。动态地改变或取消计划行为的必要能力在心理学中被称为抑制控制。我们将抑制控制形式化为一个合理的决策问题,并将其应用到经典的停止信号任务中。利用贝叶斯推理和随机控制工具,我们发现最优策略系统地依赖于问题的各种参数,如不同行动选择的相对成本、感官输入的噪声水平以及环境需求变化的动力学。我们的标准模型解释了人类和动物在停止信号任务中的一系列行为数据,表明大脑在抑制控制问题的背景下实现了统计最优、动态适应性和奖励敏感决策。
课程简介: Intelligent agents are often faced with the need to choose actions with uncertain consequences, and to modify those actions according to ongoing sensory processing and changing task demands. The requisite ability to dynamically modify or cancel planned actions is known as inhibitory control in psychology. We formalize inhibitory control as a rational decision-making problem, and apply to it to the classical stop-signal task. Using Bayesian inference and stochastic control tools, we show that the optimal policy systematically depends on various parameters of the problem, such as the relative costs of different action choices, the noise level of sensory inputs, and the dynamics of changing environmental demands. Our normative model accounts for a range of behavioral data in humans and animals in the stop-signal task, suggesting that the brain implements statistically optimal, dynamically adaptive, and reward-sensitive decision-making in the context of inhibitory control problems.
关 键 词: 计算机科学; 决策支持; 贝叶斯推理; 随机控制
课程来源: 视频讲座网
最后编审: 2021-02-03:nkq
阅读次数: 25