Comparing Bayesian and non-Bayesian accounts of human confidence reports

Abstract

Humans can meaningfully report their confidence in a perceptual or cognitive decision. It is widely believed that these reports reflect the Bayesian probability that the decision is correct, but this hypothesis has not been rigorously tested against non-Bayesian alternatives. We use two perceptual categorization tasks in which Bayesian confidence reporting requires subjects to take sensory uncertainty into account in a specific way. We find that subjects do take sensory uncertainty into account when reporting confidence, suggesting that brain areas involved in reporting confidence can access low-level representations of sensory uncertainty. However, behavior is not fully consistent with the Bayesian hypothesis and is better described by simple heuristic models. Both conclusions are robust to changes in the uncertainty manipulation, task, response modality, model comparison metric, and additional flexibility in the Bayesian model. Our results suggest that adhering to a rational account of confidence behavior may require incorporating implementational constraints.

Nonlinear Neural Network Dynamics Accounts for Human Confidence in a Sequence of Perceptual Decisions

“…Here we have shown, for the first time, that the network accounts for sequences of decisions and reproduce response times, accuracy and confidence individually for each participant. In addition, the model accounts for other effects reported in the literature, among which some effects which were believed to be specific signatures of Bayesian confidence Sanders et al [2016], Adler and Ma [2018]: confidence increases with the orientation for correct trials but decreased for error trials.…”

“…A general understanding of the notion of confidence is that it quantifies the degree of belief we have in a decision Meyniel et al [2015b], Mamassian [2015]. Many cognitive and psychology studies have tackled the problem of confidence estimation either by directly requiring participants to provide an estimation of their confidence Peirce and Jastrow [1884], Zylberberg et al [2012], Adler and Ma [2018], or by using postdecision wagering (subjects can choose a safe option, with low reward regardless of the correct choice) Vickers [1979Vickers [ (reeditited in 2014, Kepecs and Mainen [2012], Fleming et al [2010], Seth [2008], Massoni [2014]. Postdecision wagering has been used in behaving animals in order to study the neural basis of confidence estimation Smith et al [2003], Kepecs et al [2008], Kiani and Shadlen [2009], Komura et al [2013], Lak et al [2014].…”

Performance Monitoring for Sensorimotor Confidence: A Visuomotor Tracking Study

“…Perceptual confidence is a metacogni-59 tive process, which corresponds to the subjective sense of the correctness of our perceptual 60 decisions (Galvin et al, 2003;Pouget et al, 2016). Human observers exhibit considerable 61 sensitivity to the quality of the processing of sensory information and the resulting abil-62 ity to predict the correctness of the perceptual choice (Barthelmé and Mamassian, 2010;63 Kiani et al, 2014; Adler and Ma, 2018). However this so-called Type-2 judgement often 64 incurs additional noise, on top of the sensory noise that reduces perceptual performance 65 (Type-1 decisions) (Maniscalco and Lau, 2016).…”

An insula-driven network computes decision uncertainty and promotes abstinence in chronic cocaine users

“…A key computational process involved in decision-making is the estimation of action-outcome 66 probabilities (De Martino et al, 2013;Payzan-LeNestour et al, 2013;Meyniel et al, 2015). That 67 is, an individual must be able to accurately represent how likely her chosen action is to lead to a 68 desirable result: we defined this quantity as choice uncertainty, following previous literature 69 (Meyniel et al, 2015;Adler & Ma, 2018;Atiya et al, 2020). Previous studies have shown the 70 involvement of several brain regions in the estimation of such probabilities, including the 71 anterior insular cortex (AIC) (Clark et al, 2008;Preuschoff et al, 2008), dorsal anterior 72 cingulate cortex (dACC) (Rushworth & Behrens, 2008;Stolyarova et al, 2019), and ventral 73 striatum (VS) (Berns et al, 2001).…”

“…In Bayesian terms, newly acquired information, such as the 111 symbolic visual stimuli of the stop signal task, is used to update prior beliefs (Friston, 2010;112 Friston et al, 2016), which can be consolidated or weakened. Thus, each choice selection can be 113 associated with a degree of uncertainty, representing the estimated probability the chosen action 114 might not be correct (Meyniel et al, 2015;Adler & Ma, 2018;Atiya et al, 2020). A few studies 115 have adopted a similar Bayesian approach to examine prediction error encoding in 116 methamphetamine-dependent individuals using the stop signal task (Harle et al, 2015;Harle et 117 al., 2016).…”