Modality Independent or Modality Specific? Common Computations Underlie Confidence Judgements in Visual and Auditory Decisions

Humans possess the ability to evaluate their confidence in a range of different decisions. In this study, we investigated the computational processes that underlie confidence judgements and the extent to which these computations are the same for perceptual decisions 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 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. Author Summary In this study, we investigated the computational processes that describe how people derive a sense of confidence in their decisions. In particular, we determined whether the computations that underlie the evaluation of confidence for a visual decision are the same as those for an auditory decision. We tested a range of different models from 3 distinct classes which make different predictions about the computations that are used. We found that a single class of models provided the best account of confidence, suggesting a common process for evaluating confidence across sensory modalities. Even though these computations are governed by the same general process, our results suggest that the process is still fine-tuned within each modality.