Decision confidence as a function of multiple uncertainty judgements: An investigation of Bayesian inference in decision-making and learning

决策置信度作为多个不确定性判断的函数：决策和学习中贝叶斯推理的调查

• 批准号: 2273714
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2273714
• 关键词: Decision; confidence; function; multiple; uncertainty

Focusing on decision confidence, this project seeks to assess the independent and interactive effects of multiple uncertainties over the course of the learning period. Using a combination of behavioural tasks, Bayesian computational modelling, and with potential to extend to brain-imaging paradigms, the results of these studies will elucidate how information across different uncertainty levels is prioritised and whether different cognitive strategies, such as rule-based or category learning, are employed by individuals under uncertainty. This may shed light on the processes leading to the variability of reported confidence judgements and ultimately could inform the ongoing debate regarding the Bayesian optimality of human perception and decision-making. Examination of everyday judgements reveals a multitude of factors which may influence an individuals' sense of decision-confidence, their belief that their choices are accurate. When judging the weather, an observant individual can learn that a clear sky is usually predictive of dry weather and that darker grey skies usually predict rainfall. Categorising based on cloud cover can provide a good estimate of the upcoming weather, however, it is not absolute. On a clear day, a sudden storm could occur or on a cloudy day it could remain dry. This simple judgement reflects the multiple types of uncertainty faced by an observer 1) a perceptual uncertainty in judging cloud level based on imperfect sensory measurements, 2) an uncertainty in terms of how strongly the level of cloud relates to a certain weather outcome and 3) how likely rain or sunshine are in this new environment over time. A naïve observer must grapple with these uncertainties, while simultaneously learning how this variation relates to outcomes of sunshine or rain, in settling on a course of action such as bringing an umbrella or leaving it at home. This kind of interaction between forms of uncertainty is, of yet, poorly defined in the literature, particularly over the course of the learning period, and may explain the reported variability of confidence judgements in the literature.

本项目以决策信心为重点，旨在评估学习过程中多种不确定因素的独立和互动影响。结合行为任务、贝叶斯计算模型，并有可能扩展到脑成像范式，这些研究的结果将阐明不同不确定性水平的信息是如何优先排序的，以及不确定性下个体是否采用了不同的认知策略，如基于规则的学习或类别学习。这可能会揭示导致报告的信心判断可变性的过程，并最终可以为正在进行的关于人类感知和决策的贝叶斯最优性的辩论提供信息。对日常判断的考察揭示了许多可能影响个人决策感的因素——自信，即他们相信自己的选择是准确的。在判断天气时，一个善于观察的人可以了解到，晴朗的天空通常预示着天气干燥，而较暗的灰色天空通常预示着降雨。基于云量的分类可以很好地估计即将到来的天气，然而，这不是绝对的。在晴朗的日子里，可能会突然出现暴风雨，而在阴天，天气可能会保持干燥。这个简单的判断反映了观测者所面临的多种不确定性：1)基于不完善的感官测量判断云层水平时的感知不确定性；2)云层水平与某种天气结果的关系有多强的不确定性；3)随着时间的推移，在这个新环境中下雨或阳光的可能性有多大。naïve观测者必须与这些不确定性作斗争，同时了解这种变化与晴天或雨天的结果之间的关系，以确定一个行动过程，例如带伞或把伞留在家里。这种形式的不确定性之间的相互作用，迄今为止，在文献中定义不清，特别是在学习期间，这可能解释了文献中报道的信心判断的可变性。