Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making.
Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making.
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DOI：
10.1371/journal.pcbi.1004519
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发表时间：
2015-10
影响因子：
4.3
通讯作者：
Latham PE
中科院分区：
文献类型：
--
作者：
Aitchison L;Bang D;Bahrami B;Latham PE
人类从其他动物中脱颖而出，因为他们能够明确地报告自己内部操作的可靠性。这种能力被称为元认知，通常是通过让人们报告他们对某些决定的正确性的信心来进行研究的。然而，信心报告背后的计算方法仍不清楚。本文提出了一种直接比较置信度模型的完全贝叶斯方法。使用视觉双区间强制选择任务，我们测试了置信度报告是否反映了启发式计算(例如，感觉数据的大小)或贝叶斯最优计算(即，给定感觉数据，一个决定正确的可能性有多大)。在标准设计中，受试者首先被要求做出决定，然后才给出他们的信心，受试者大多是贝叶斯最优的。相比之下，在一个不太常用的设计中，受试者同时表明他们的信心和决定，他们使用贝叶斯最优策略或使用启发式但次优策略的可能性大致相同。我们的结果表明，虽然人们的信心报告可以反映贝叶斯最优计算，但即使是一个微小的不寻常的扭曲或额外的复杂性元素也可能阻止最优。信心在群体互动中起着关键作用：当人们表达观点时，他们几乎总是--或暗示或明确--传达他们的信心，而信心的程度对听众有很大的影响。因此，既要了解信心是如何产生的，又要了解信心是如何被解释的，这对于理解群体互动至关重要。在这里，我们问：人们是如何产生信心的？先验地，他们可以使用启发式策略(例如，他们的信心可以或多或少地随感觉数据的大小而变化)，或者我们认为的最佳策略(即，他们的信心是他们观点正确的概率的函数)。我们发现，使用贝叶斯模型选择，置信度报告反映的概率是正确的，至少在更标准的实验设计中是这样。如果这一结果推广到其他领域，它将为信心提供一个相对简单的解释，从而极大地扩展我们对群体互动的理解。
Humans stand out from other animals in that they are able to explicitly report on the reliability of their internal operations. This ability, which is known as metacognition, is typically studied by asking people to report their confidence in the correctness of some decision. However, the computations underlying confidence reports remain unclear. In this paper, we present a fully Bayesian method for directly comparing models of confidence. Using a visual two-interval forced-choice task, we tested whether confidence reports reflect heuristic computations (e.g. the magnitude of sensory data) or Bayes optimal ones (i.e. how likely a decision is to be correct given the sensory data). In a standard design in which subjects were first asked to make a decision, and only then gave their confidence, subjects were mostly Bayes optimal. In contrast, in a less-commonly used design in which subjects indicated their confidence and decision simultaneously, they were roughly equally likely to use the Bayes optimal strategy or to use a heuristic but suboptimal strategy. Our results suggest that, while people’s confidence reports can reflect Bayes optimal computations, even a small unusual twist or additional element of complexity can prevent optimality. Confidence plays a key role in group interactions: when people express an opinion, they almost always communicate—either implicitly or explicitly—their confidence, and the degree of confidence has a strong effect on listeners. Understanding both how confidence is generated and how it is interpreted are therefore critical for understanding group interactions. Here we ask: how do people generate their confidence? A priori, they could use a heuristic strategy (e.g. their confidence could scale more or less with the magnitude of the sensory data) or what we take to be an optimal strategy (i.e. their confidence is a function of the probability that their opinion is correct). We found, using Bayesian model selection, that confidence reports reflect probability correct, at least in more standard experimental designs. If this result extends to other domains, it would provide a relatively simple interpretation of confidence, and thus greatly extend our understanding of group interactions.