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

该项目以决策信心为重点，旨在评估学习期间多种不确定性的独立和交互影响。使用行为任务，贝叶斯计算模型的组合，并有可能扩展到大脑成像范式，这些研究的结果将阐明如何在不同的不确定性水平的信息是优先级，以及是否不同的认知策略，如基于规则或类别学习，是由个人在不确定性。这可能会揭示的过程中，导致报告的信心判断的变化，并最终可以告知正在进行的辩论，人类感知和决策的贝叶斯最优性。对日常判断的检查揭示了许多可能影响个人决策信心的因素，他们相信他们的选择是准确的。在判断天气时，一个观察力敏锐的人可以了解到晴朗的天空通常预示着干燥的天气，而深灰色的天空通常预示着降雨。基于云量的分类可以提供对即将到来的天气的良好估计，但是，它不是绝对的。在晴朗的日子里，可能会发生突然的暴风雨，或者在多云的日子里，它可能会保持干燥。这种简单的判断反映了观察者所面临的多种类型的不确定性：1）基于不完美的感官测量来判断云水平的感知不确定性，2）云水平与特定天气结果的关系有多强的不确定性，以及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。一个天真的观察者必须与这些不确定性作斗争，同时了解这种变化与阳光或雨水的结果之间的关系，以确定一个行动方案，如带伞或把它留在家里。这种形式的不确定性之间的相互作用，但在文献中，特别是在学习期间的过程中，定义不明确，并可能解释在文献中的置信度判断的变化。

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.