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样本里有什么?冒险行为中的认知不确定性和元认知意识。

What's in a sample? Epistemic uncertainty and metacognitive awareness in risk taking.

机构信息

Department of Psychology, University of Basel, Switzerland; Warwick Business School, University of Warwick, United Kingdom.

Department of Economics and Management, Karlsruhe Institute of Technology, Germany.

出版信息

Cogn Psychol. 2024 Mar;149:101642. doi: 10.1016/j.cogpsych.2024.101642. Epub 2024 Feb 23.

DOI:10.1016/j.cogpsych.2024.101642
PMID:38401485
Abstract

In a fundamentally uncertain world, sound information processing is a prerequisite for effective behavior. Given that information processing is subject to inevitable cognitive imprecision, decision makers should adapt to this imprecision and to the resulting epistemic uncertainty when taking risks. We tested this metacognitive ability in two experiments in which participants estimated the expected value of different number distributions from sequential samples and then bet on their own estimation accuracy. Results show that estimates were imprecise, and this imprecision increased with higher distributional standard deviations. Importantly, participants adapted their risk-taking behavior to this imprecision and hence deviated from the predictions of Bayesian models of uncertainty that assume perfect integration of information. To explain these results, we developed a computational model that combines Bayesian updating with a metacognitive awareness of cognitive imprecision in the integration of information. Modeling results were robust to the inclusion of an empirical measure of participants' perceived variability. In sum, we show that cognitive imprecision is crucial to understanding risk taking in decisions from experience. The results further demonstrate the importance of metacognitive awareness as a cognitive building block for adaptive behavior under (partial) uncertainty.

摘要

在一个基本不确定的世界中,可靠的信息处理是有效行为的前提。鉴于信息处理不可避免地存在认知不准确性,决策者在冒险时应该适应这种不准确性和由此产生的认知不确定性。我们在两项实验中测试了这种元认知能力,参与者从连续样本中估计不同数字分布的预期值,然后对自己的估计准确性进行下注。结果表明,估计值不精确,这种不精确性随着分布标准差的增加而增加。重要的是,参与者根据这种不精确性调整了他们的风险承担行为,因此偏离了假设信息完美整合的不确定性贝叶斯模型的预测。为了解释这些结果,我们开发了一个计算模型,该模型将贝叶斯更新与对信息整合中的认知不精确性的元认知意识相结合。建模结果对包括参与者感知变异性的经验衡量标准的纳入具有稳健性。总之,我们表明认知不精确性对于理解经验决策中的风险承担至关重要。结果进一步证明了元认知意识作为(部分)不确定性下适应性行为的认知基石的重要性。

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