Abstract
Psychophysical methods are the gold standard in vision science because they provide precise quantitative measurements of the relationship between the physical world and mental processes. This is due to the combination of highly controlled experimental paradigms such as forced-choice tasks and rigorous mathematical analysis through signal detection theory. However, they require a large number of tedious trials involving binary responses, preferably by highly trained participants. A recently developed approach, named continuous psychophysics, abandons the rigid trial structure and replaces it with continuous behavioral adjustments to dynamic stimuli, for example tracking tasks (Bonnen et al., 2015). Because these continuous tasks are more intuitive and require much less time, they promise experiments with untrained participants and more efficient data collection. What has precluded wide adoption of continuous psychophysics is that current analysis methods based on ideal observers recover perceptual thresholds an order of magnitude larger compared to equivalent forced-choice experiments. This discrepancy can be explained by additional sources of variability in these tasks as a result of continuous actions involving motor-variability and internal behavioral costs, which classical psychophysics eliminates by experimental design. Here, we account for these factors by modeling a continuous target tracking task using optimal control under uncertainty. To infer parameters from observed data, we invert the model using Bayesian inverse optimal control. We show via simulations and on previously published data that this allows estimating perceptual thresholds in closer agreement with classical psychophysics compared to previous analyses based on ideal observers. Additionally, our method estimates participants’ action variability, internal behavioral costs, and possibly mistaken assumptions about the stimulus dynamics. Taken together, we introduce a computational analysis framework for continuous psychophysics and provide further evidence for the importance of including sensory and acting uncertainties, subjective beliefs, and the intrinsic costs of behavior, even in experiments seemingly only investigating perception.