Bayesian versus heuristic-based choice under sleep restriction and suboptimal times of day
Bayesian versus heuristic-based choice under sleep restriction and suboptimal times of day
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DOI：
10.1016/j.geb.2019.02.011
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发表时间：
2019-05-01
影响因子：
1.1
通讯作者：
McElroy, Todd
中科院分区：
文献类型：
--
作者：
Dickinson, David L.;McElroy, Todd
这篇论文考察了在贝叶斯任务中，一种常见的认知状态--困倦--如何影响选择。我们实施了一种生态上有效的睡眠操作，我们的主要结果是在困倦时增加了对简单强化启发式睡眠的使用。虽然在贝叶斯规则和强化冲突的困难决策环境中使用这种启发式算法会导致错误，但在贝叶斯规则和强化重合的较容易环境中它不会影响选择的准确性。我们的结果总体上符合这样的假设，即在常见的不良睡眠状态下，个人以相对不那么深思熟虑的方式做出决定，这会导致自动或启发式决策的相对增加。虽然这种由昏昏欲睡推动的基于启发式的决策不一定在所有情况下都是次优的，但我们的结果表明，在更复杂的决策环境中，贝叶斯评估的准确性在昏昏欲睡时可能会受到影响。(C)2019 Elsevier Inc.保留所有权利。
This paper examines how a commonly experienced cognitive state, sleepiness, impacts choice in a Bayesian task. We implement an ecologically valid sleep manipulation, and our main result is the increased use of a simple reinforcement-heuristic when sleepy. While use of this heuristic leads to errors in difficult decision environments where Bayes rule and reinforcement conflict, it does not harm choice accuracy in easier environments where Bayes rule and reinforcement coincide. Our results overall are consistent with the hypothesis that individuals make decisions in a relatively less deliberative manner under common adverse sleep states, which gives rise to a relative increase in automatic or heuristic-based decision making. While such heuristic-based decisions that are promoted by sleepiness need not be suboptimal in all contexts, our results show that the accuracy of a Bayesian assessment in more complex decision environments will likely suffer when sleepy. (C) 2019 Elsevier Inc. All rights reserved.