Quantifying Motor Task Performance by Bounded Rational Decision Theory
Quantifying Motor Task Performance by Bounded Rational Decision Theory
复制标题
通过有界理性决策理论量化运动任务表现
DOI：
10.3389/fnins.2018.00932
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发表时间：
2018
影响因子：
4.3
通讯作者：
Daniel A. Braun
中科院分区：
文献类型：
--
作者：
S. Schach;Sebastian Gottwald;Daniel A. Braun
预期效用模型通常被用作人类运动任务表现的标准基线。然而，这个基线忽略了搜索最优策略时产生的计算成本。相比之下，有限理性决策理论提供了一个考虑计算努力的规范基线，因为它描述了具有有限信息处理能力的代理的最佳行为，将先前的运动策略（信息处理之前）改变为后验策略（信息处理之后）。在这里，我们设计了一个指向的任务，受试者有限制的反应和运动时间。特别是，我们操纵了允许的反应时间，作为规划动作所允许的计算量的代理。此外，我们测试了三种不同的分布在目标位置，以诱导不同的先验策略，这将影响所需的信息处理量。我们发现，运动终点精度一般会随着规划时间的限制而降低，并且非均匀的先验概率允许向高概率目标进行更精确的运动。在有限理性决策模型中考虑这些约束，我们发现受试者通常接近有限最优。我们的结论是，有限理性决策理论可能是一个有前途的规范框架来分析人类的感觉运动性能。
Expected utility models are often used as a normative baseline for human performance in motor tasks. However, this baseline ignores computational costs that are incurred when searching for the optimal strategy. In contrast, bounded rational decision-theory provides a normative baseline that takes computational effort into account, as it describes optimal behavior of an agent with limited information-processing capacity to change a prior motor strategy (before information-processing) into a posterior strategy (after information-processing). Here, we devised a pointing task where subjects had restricted reaction and movement time. In particular, we manipulated the permissible reaction time as a proxy for the amount of computation allowed for planning the movements. Moreover, we tested three different distributions over the target locations to induce different prior strategies that would influence the amount of required information-processing. We found that movement endpoint precision generally decreases with limited planning time and that non-uniform prior probabilities allow for more precise movements toward high-probability targets. Considering these constraints in a bounded rational decision model, we found that subjects were generally close to bounded optimal. We conclude that bounded rational decision theory may be a promising normative framework to analyze human sensorimotor performance.
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