TY - JOUR TI - Putting perception into action with inverse optimal control for continuous psychophysics AU - Straub, Dominik AU - Rothkopf, Constantin A A2 - Diedrichsen, Jörn A2 - Gold, Joshua I A2 - Diedrichsen, Jörn VL - 11 PY - 2022 DA - 2022/09/29 SP - e76635 C1 - eLife 2022;11:e76635 DO - 10.7554/eLife.76635 UR - https://doi.org/10.7554/eLife.76635 AB - Psychophysical methods are a cornerstone of psychology, cognitive science, 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, what has precluded wide adoption of this approach is that current analysis methods do not account for the additional variability introduced by the motor component of the task and therefore recover perceptual thresholds that are larger compared to equivalent traditional psychophysical experiments. Here, we introduce a computational analysis framework for continuous psychophysics based on Bayesian inverse optimal control. We show via simulations and previously published data that this not only recovers the perceptual thresholds but additionally estimates subjects’ action variability, internal behavioral costs, and subjective beliefs about the experimental stimulus dynamics. 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. KW - continuous psychophysics KW - optimal control KW - inverse reinforcement learning KW - rational analysis KW - perception and action JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -