A detailed comparison of optimality and simplicity in perceptual decision making.
A detailed comparison of optimality and simplicity in perceptual decision making.
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DOI：
10.1037/rev0000028
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发表时间：
2016-07
影响因子：
5.4
通讯作者：
Ma WJ
中科院分区：
文献类型：
--
作者：
Shen S;Ma WJ
在决策研究中，有两个突出的观点是，生物体的行为接近最优，它们使用简单的启发式规则。这些原则可能在不同类型的任务中起作用，但如果没有在单一任务中进行直接、严格的比较，就无法充分研究这种可能性。这种比较在大多数以前的研究中是缺乏的，因为a）最优决策规则很简单; B）没有考虑简单的次优规则; c）不清楚什么是最优的，或者d）简单的规则可以非常接近最优规则。在这里，我们使用了一个感性的决策任务，其中的最佳决策规则是明确的和复杂的，并作出定性不同的预测，从许多简单的次优规则。我们发现，所有简单的规则测试无法描述人类的行为，最佳的规则帐户以及数据，几个复杂的次优规则是无法区分的最佳之一。此外，我们发现证据表明最优模型接近真实模型：首先，次优模型的试验间预测与最优模型的预测一致性越好，次优模型拟合得越好;其次，我们对最优模型与真实模型之间的Kullback-Leibler散度的估计与零没有显著差异。当观察者没有收到反馈时，最优模型仍然能最好地描述行为，这表明感官的不确定性被隐含地表示和考虑在内。除了这里研究的任务和模型，我们的研究结果对模型比较的最佳实践有影响。
Two prominent ideas in the study of decision-making have been that organisms behave near-optimally, and that they use simple heuristic rules. These principles might be operating in different types of tasks, but this possibility cannot be fully investigated without a direct, rigorous comparison within a single task. Such a comparison was lacking in most previous studies, because a) the optimal decision rule was simple; b) no simple suboptimal rules were considered; c) it was unclear what was optimal, or d) a simple rule could closely approximate the optimal rule. Here, we used a perceptual decision-making task in which the optimal decision rule is well-defined and complex, and makes qualitatively distinct predictions from many simple suboptimal rules. We find that all simple rules tested fail to describe human behavior, that the optimal rule accounts well for the data, and that several complex suboptimal rules are indistinguishable from the optimal one. Moreover, we found evidence that the optimal model is close to the true model: first, the better the trial-to-trial predictions of a suboptimal model agree with those of the optimal model, the better that suboptimal model fits; second, our estimate of the Kullback-Leibler divergence between the optimal model and the true model is not significantly different from zero. When observers receive no feedback, the optimal model still describes behavior best, suggesting that sensory uncertainty is implicitly represented and taken into account. Beyond the task and models studied here, our results have implications for best practices of model comparison.