Abstract
Psychophysical methods are a cornerstone of vision science, psychology, and neuroscience where they have been used to quantify behavior and its neural correlates for a vast range of mental phenomena. Their power derives from the combination of controlled experiments and rigorous analysis through signal detection theory. Unfortunately, they require many tedious trials and preferably highly trained participants. A recently developed approach, continuous psychophysics, promises to transform the field by abandoning the rigid trial structure involving binary responses and replacing it with continuous behavioral adjustments to dynamic stimuli. However, because behavior now unfolds within the perception and action cycle, classic signal detection theory is not applicable.
In this talk, we present our recently developed computational analysis framework for continuous psychophysics based on Bayesian inverse optimal control. We start by formalizing an ideal observer account of these tasks and then move to ideal actors. In the tradition of rational analysis, we subsequently allow for subjects being influenced by internal cognitive costs and, finally, that subjects potentially possess false beliefs about experimental stimulus dynamics. Carrying out inference over these models and applying rigorous model comparison allows a principled explanation of individuals’ behavior and reconciles descriptive with normative models.
We show via simulations and on previously published data that this recovers perceptual thresholds and additionally estimates subjects’ action variability, internal behavioral costs, and subjective beliefs. Taken together, we provide further evidence for the importance of including acting uncertainties, subjective beliefs, and, crucially, the intrinsic costs of behavior, even in experiments seemingly only investigating perception.