Flexible mechanisms underlie the evaluation of visual confidence

Abstract

Visual processing is fraught with uncertainty: The visual system must attempt to estimate physical properties despite missing information and noisy mechanisms. Sometimes high visual uncertainty translates into lack of confidence in our visual perception: We are aware of not seeing well. The mechanism by which we achieve this awareness-how we assess our own visual uncertainty -is unknown, but its investigation is critical to our understanding of visual decision mechanisms. The simplest possibility is that the visual system relies on cues to uncertainty, stimulus features usually associated with visual uncertainty, like blurriness. Probabilistic models of the brain suggest a more sophisticated mechanism, in which visual uncertainty is explicitly represented as probability distributions. In two separate experiments, observers performed a visual discrimination task in which confidence could be determined by the cues available (contrast and crowding or eccentricity and masking) or by their actual performance, the latter requiring a more sophisticated mechanism than cue monitoring. Results show that observers' confidence followed performance rather than cues, indicating that the mechanisms underlying the evaluation of visual confidence are relatively complex. This result supports probabilistic models, which imply the existence of sophisticated mechanisms for evaluating uncertainty.decision making | visual perception P erformance in a visual task cannot be perfect. When we try to infer some property of the physical world from visual data, there is always the chance that we will make a mistake. The possibility of error signals objective visual uncertainty: the more visual uncertainty, the higher the probability of an error. When asked how confident we feel in our visual judgment, we find it natural to estimate how strong that uncertainty is, if only by saying that we do not see well. Thus, the level of confidence actually reported by observers is a measure of subjective visual uncertainty.It has been known for some time that performance and confidence usually correlate in human observers (1), and more recently this has also been observed in certain nonhuman species (2). If one asks observers to recognize blurry letters, both confidence and performance will decrease with the amount of blur. Although this fact seems intuitively obvious, the mechanisms involved in the correlation between performance and confidence have only recently begun to be investigated (3-6). A wide array of mechanisms could account for such a correlation, and finding out which one is actually at work could yield important insights into decision-making processes in the brain (7).In any given visual task, such as letter recognition, performance is determined by a large number of factors: Some have to do with the stimulus (its level of blur, its size, etc.), some have to do with its context (surrounding letters), and some have to do with the internal constraints of the visual system (neural stochasticity, the shape of receptive fields, the availability ...

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“…The two examples above (Barthelmé and Mamassian, 2010;Meyniel et al, 2015) are cases stressing that choice readout and confidence readout can be based on the same information. But it is a corollary of our thesis that the confidence readout may be based only on a subset of the information, compared to the choice readout.…”

Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation.

“…Conversely, if ρ is low, the model operates in a parallel fashion, and as σ act approaches zero, cases of “blind insight” may occur in which the model is aware of making erroneous or correct actions despite performing at or near chance (Scott et al, 2014). Finally, there may be domains or tasks in which confidence reports show a particularly high degree of sophistication in tracking task performance, which would suggest that decision and confidence variables are tightly coupled, with little opportunity for dissociations (e.g., Barthelme & Mamassian, 2010; Meyniel, Sigman, & Mainen, 2015; Peters & Lau, 2015). …”

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“…Ma [33] suggests that strong evidence for Bayesian processing dependent on explicit representation of uncertainty is provided by experiments in which unpredictable changes in the level of uncertainty on individual trials produces appropriate alterations in the weighting. However, if the estimate of uncertainty for a single trial can be based on some aspect of the cue that covaries with cue uncertainty in a consistent manner (a proxy or 'valid cue' [34]) then it is possible that an appropriate mapping to the 'optimal' weighting could have been learnt or evolved. However, it is notable that both here and in previous studies [11,23], ants appear to combine cues even when they indicate very discrepant directions.…”