Psychophysical studies on confidence construction are often grounded in bidimensional signal detection theory (SDT) and its relatives. However, these studies often stand on oversimplified assumptions of (1) bidimensional variance-equality and (2) bidimensional statistical independence. The present study simulated two-alternative forced-choice and confidence rating performances, incorporating more empirically plausible variance-covariance structures. One prominent observation is that superior metacognitive accuracy can be achieved when one applies a heuristic in which the response-incongruent dimension of information is ignored. This is because such heuristic takes advantage of the specific unequal-variance structure, which paradoxically cannot be easily exploited if both dimensions are evaluated together. Furthermore, under a variety of internal statistical structures, this simple heuristic predicts dissociations of objective decision and subjective metacognition, which have been empirically observed. Also, it provides a tentative account of some behavioral features of blindsight. Therefore, this surprisingly simple decision heuristic may inspire novel perspectives on metacognition and consciousness.
Previous work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional modeling frameworks, such as signal detection theory or Bayesian inference, leaving open the question of how decision confidence operates in the domain of high-dimensional, naturalistic stimuli. To address this, we developed a deep neural network model optimized to assess decision confidence directly given high-dimensional inputs such as images. The model naturally accounts for a number of puzzling dissociations between decisions and confidence, suggests a principled explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.