Biology subjects

Adler, W. T.

Publications and source records attributed to Adler, W. T..

3 recordsLinked to original sources

bioRxiv2017

Limitations of proposed signatures of Bayesian confidence

The Bayesian model of confidence posits that confidence is the observers posterior probability that the decision is correct. It has been proposed that researchers can gain evidence in favor of the Bayesian model by deriving qualitative signatures of Bayesian confidence, i.e., patterns that one would expect to see if an observer was Bayesian, and looking for those signatures in human or animal data. We examine two proposed qualitative signatures, showing that their derivations contain hidden assumptions that limit their applicability, and that they are neither necessary nor sufficient conditions for Bayesian confidence. One signature is an average confidence of 0.75 for trials with neutral evidence. This signature only holds when class-conditioned stimulus distributions do not overlap and internal noise is very low. Another signature is that, as stimulus magnitude increases, confidence increases on correct trials but decreases on incorrect trials. This signature is also dependent on stimulus distribution type. There is an alternative form of this signature that has been applied in the literature; we find no indication that it is expected under Bayesian confidence, which resolves an ostensible discrepancy. We conclude that, to determine the nature of the computations underlying confidence reports, there may be no shortcut to quantitative model comparison.

neuroscience↗

bioRxiv2017

Humans flexibly incorporate attention-dependent uncertainty into perceptual decisions and confidence

Perceptual decisions are better when they take uncertainty into account. Uncertainty arises not only from the properties of sensory input but also from cognitive sources, such as different levels of attention. However, it is unknown whether humans appropriately adjust for such cognitive sources of uncertainty during perceptual decision making. Here we show that human categorization and confidence decisions take into account uncertainty related to attention. We manipulated uncertainty in an orientation categorization task from trial to trial using only an attentional cue. The categorization task was designed to disambiguate decision rules that did or did not depend on attention. Using formal model comparison to evaluate decision behavior, we found that category and confidence decision boundaries shifted as a function of attention in an approximately Bayesian fashion. This means that the observers attentional state on each trial contributed probabilistically to the decision computation. This responsiveness of an observers decisions to attention-dependent uncertainty should improve perceptual decisions in natural vision, in which attention is unevenly distributed across a scene.

neuroscience↗

bioRxiv2016

The computations underlying human confidence reports are probabilistic, but not Bayesian

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.

neuroscience↗