School of Psychology, University of Queensland, Queensland, Australia.
Queensland Brain Institute, University of Queensland, Queensland, Australia.
PLoS Comput Biol. 2023 Jul 14;19(7):e1011245. doi: 10.1371/journal.pcbi.1011245. eCollection 2023 Jul.
The mechanisms that enable humans to evaluate their confidence across a range of different decisions remain poorly understood. To bridge this gap in understanding, we used computational modelling to investigate the processes that underlie confidence judgements for perceptual decisions and the extent to which these computations are the same in the visual and auditory modalities. Participants completed two versions of a categorisation task with visual or auditory stimuli and made confidence judgements about their category decisions. In each modality, we varied both evidence strength, (i.e., the strength of the evidence for a particular category) and sensory uncertainty (i.e., the intensity of the sensory signal). We evaluated several classes of computational models which formalise the mapping of evidence strength and sensory uncertainty to confidence in different ways: 1) unscaled evidence strength models, 2) scaled evidence strength models, and 3) Bayesian models. Our model comparison results showed that across tasks and modalities, participants take evidence strength and sensory uncertainty into account in a way that is consistent with the scaled evidence strength class. Notably, the Bayesian class provided a relatively poor account of the data across modalities, particularly in the more complex categorisation task. Our findings suggest that a common process is used for evaluating confidence in perceptual decisions across domains, but that the parameter settings governing the process are tuned differently in each modality. Overall, our results highlight the impact of sensory uncertainty on confidence and the unity of metacognitive processing across sensory modalities.
人类能够在一系列不同的决策中评估自己的信心的机制仍未得到很好的理解。为了弥合这一理解差距,我们使用计算建模来研究感知决策中信心判断的背后过程,以及这些计算在视觉和听觉模态中是否相同。参与者完成了两个版本的分类任务,其中包括视觉或听觉刺激,并对他们的类别决策做出信心判断。在每种模态中,我们都改变了证据强度(即特定类别的证据强度)和感觉不确定性(即感觉信号的强度)。我们评估了几类计算模型,这些模型以不同的方式将证据强度和感觉不确定性映射到信心上:1)未缩放证据强度模型,2)缩放证据强度模型,3)贝叶斯模型。我们的模型比较结果表明,参与者在不同任务和模态中,以与缩放证据强度类一致的方式考虑证据强度和感觉不确定性。值得注意的是,贝叶斯类在跨模态的情况下,特别是在更复杂的分类任务中,对数据的解释相对较差。我们的研究结果表明,在不同领域的感知决策中,使用了一种共同的过程来评估信心,但在每种模态中,用于控制该过程的参数设置是不同的。总的来说,我们的研究结果突出了感觉不确定性对信心的影响以及跨感觉模态的元认知处理的统一性。