Publications in Current Directions in Psychological Science

The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case and that these systems in fact offer new opportunities for Bayesian modeling. Specifically, we argue that artificial neural networks and Bayesian models of cognition lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, in which a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.

Delusions are distressing and disabling symptoms of various clinical disorders. Delusions are associated with an aberrant and apparently contradictory treatment of evidence, characterized by both excessive credulity (adopting unusual beliefs on minimal evidence) and excessive rigidity (holding steadfast to these beliefs in the face of strong counterevidence). Here we attempt to make sense of this contradiction by considering the literature on epistemic vigilance. Although there is little evolutionary advantage to scrutinizing the evidence our senses provide, it pays to be vigilant toward ostensive evidence—information communicated by others. This asymmetry is generally adaptive, but in deluded individuals the scales tip too far in the direction of the sensory and perceptual, producing an apparently paradoxical combination of credulity (with respect to one’s own perception) and skepticism (with respect to the testimony of others). © The Author(s) 2023.