Contrasting Probabilistic and Intentional Accounts of Confidence in Perceptual Decisions

The ability to evaluate ones own knowledge states is often studied using paradigms in which participants make a decision and subsequently report their confidence. This structure has motivated hierarchical models in which confidence arises from a metacognitive process, distinct from the decision process itself, that estimates the probability that the choice is correct (Meyniel et al., 2015; Pouget et al., 2016; Fleming and Daw, 2017). Here, we contrast this framework with an alternative based on an intentional architecture (Shadlen et al., 2008). In this account, choice and confidence are determined simultaneously through a multidimensional drift-diffusion process, where each dimension represents one choice-confidence combination (Ratcliff and Starns, 2009, 2013). Choice, response time, and confidence jointly emerge when one of these accumulators reaches a decision bound. To adjudicate between these accounts, we fit both models to behavioral data from two perceptual tasks: a random-dots motion discrimination task with incentivized confidence reports, and a luminance discrimination task without feedback or incentives. The integrated model provided a superior fit for the incentivized motion task, whereas the hierarchical model more accurately captured behavior in the un-incentivized luminance task. These results suggest that confidence does not rely on a single computational mechanism, but rather its implementation may adapt to the specific demands and structure of the task.