Putting perception into action: Inverse optimal control for continuous psychophysics

Abstract

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 recover perceptual thresholds, which are one order of magnitude 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 on 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.

Causal inference during closed-loop navigation: parsing of self- and object-motion

“…Namely, in future work we hope to leverage the full trajectories generated by participants in attempting to intercept moving, hidden targets. This, however, will require accounting for both idiosyncrasies emanating from path integration (e.g., putatively a slow-speed prior and cost functions that evolve with time and distance travelled [21,23]), as well as derivation of optimal control policies (see [38][39][40] for recent attempts to model continuous behavior as rational, and then invert this model to deduce the dynamics of internal models). Similarly, at risk of losing generalizability, the modelling approach could be expanded to explicitly take flow vectors as input.…”

A Dynamic Bayesian Actor Model explains Endpoint Variability in Homing Tasks

“…More importantly, they only consider a single percept or action, i.e., the selection of a single response location [35, 39] or direction [38]. But navigation is inherently a sequential visuomotor behavior, which unfolds over space and time, and requires the actor to continuously integrate sensory uncertainty, internal model uncertainty, motor variability, and behavioral costs to plan subsequent actions [26, 47–50]. Thus, variability in trajectories and endpoints results from multiple interacting uncertainties and variabilities that accrue over time, which the actor shapes actively through behavior.…”

Quantifying decision-making in dynamic, continuously evolving environments

“…In ‘tracking’ paradigms, for example, not only is the sensory input continuous and time-varying but also the behavioural responses made by participants. This approach has been used to dramatically reduce the length of time needed from individual participants, meaning that experiments that previously required many thousands of trials over several hours to recover psychophysical functions can now be completed in a matter of minutes ( Bonnen et al, 2015 ; Knöll et al, 2018 ; Straub and Rothkopf, 2021 ). In our paradigm, the behavioural responses remained discrete and sparse (as participants were completing a signal detection/discrimination task, rather than a tracking task), but the EEG data is continuous and time-varying, and our analysis approach similarly benefits from being able to relate continuous variations in sensory input to this continuous neural signal.…”

“…Although progress has recently been made in model fitting for decision-making in continuous decision-making paradigms ( Geuzebroek et al, 2022 ), a key feature of our paradigm is that many responses result from the structured noise that we inject into the sensory evidence stream, which complicates the use of aggregate measures such as reaction time quantiles for model fitting. Model estimation could potentially be improved by having continuous behavioural output, as recently demonstrated in tracking paradigms ( Huk et al, 2018 ; Straub and Rothkopf, 2021 ).…”