Suboptimality in perceptual decision making

Abstract

Human perceptual decisions are often described as optimal. Critics of this view have argued that claims of optimality are overly flexible and lack explanatory power. Meanwhile, advocates for optimality have countered that such criticisms single out a few selected papers. To elucidate the issue of optimality in perceptual decision making, we review the extensive literature on suboptimal performance in perceptual tasks. We discuss eight different classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria, inadequate tradeoff between speed and accuracy, inappropriate confidence ratings, misweightings in cue combination, and findings related to various perceptual illusions and biases. In addition, we discuss conceptual shortcomings of a focus on optimality, such as definitional difficulties and the limited value of optimality claims in and of themselves. We therefore advocate that the field drop its emphasis on whether observed behavior is optimal and instead concentrate on building and testing detailed observer models that explain behavior across a wide range of tasks. To facilitate this transition, we compile the proposed hypotheses regarding the origins of suboptimal perceptual decisions reviewed here. We argue that verifying, rejecting, and expanding these explanations for suboptimal behavior - rather than assessing optimality per se - should be among the major goals of the science of perceptual decision making.

Young children combine sensory cues with learned information in a statistically efficient manner: But task complexity matters

“…Notably, children exhibited this behavior despite learning the mean locations of the underlying distributions, and having access to the reliability of the sensory information on each trial. This pattern of behavior is consistent with an inability to appropriately take each available source of information into account, which could be due to deficits in the computation of task-relevant variables (including likelihoods, priors, objective functions and decision rules), or limitations in the neural implementation or approximation of Bayesian inference (Drugowitsch, Wyart, Devauchelle, & Koechlin, 2016;Rahnev & Denison, 2018). Note that one can only combine information across multiple cues if one can keep track of which aspects of that information should be combined and under what conditions.…”

Alpha Activity Reflects the Magnitude of an Individual Bias in Human Perception

“…These priors may vary between individuals, but are stable for a given individual, and reflect idiosyncratic biases. These are often unknown to the experimenter and hence may be shadowed by experimental manipulations or may be regarded as interindividual noise that is eliminated during data analysis (Kanai & Rees, 2011;Wexler, Duyck, & Mamassian, 2015;Rahnev & Denison, 2018;Lebovich, Darshan, Lavi, Hansel, & Loewenstein, 2019). We here argue that such inter-individual variability in temporary and idiosyncratic biases provides key perspectives on the neural mechanisms underlying perception.…”

The suboptimality of perceptual decision making with multiple alternatives

“…The level of activity can then be used as a measure of uncertainty, and confidence levels can be based on this level. Such confidence ratings will be less informative than the perceptual decision, which is exactly what has been observed in a number of studies 2,28,29 . In addition, this type of confidence generation may explain findings that confidence tends to be biased towards the level of the evidence for the chosen stimulus category and tends to ignore the level of evidence against the chosen category [30][31][32][33][34][35] .…”

“…We sought to confirm and generalize these findings in two additional, pre-registered experiments. For Experiment 2, we made several modifications: (1) we changed the stimulus from color to symbols, (2) we raised the number of stimulus categories from four to six, and (3) we significantly increased the number of trials per subject in order to obtain stronger results on the individual-subject level. Specifically, we presented the six symbols '?…”