No 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 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.

Which strategy people use to guide locomotor interception remains unclear despite considerable research and the importance of an answer with ramification into the heuristics and biases debate. Because the constant bearing (CB) strategy corresponds to the target-heading (CTH) strategy with an additional constraint, these two strategies can be confounded experimentally. But, the two strategies are distinct in the information they require: while the CTH strategy only requires access to the relative angle between the direction of motion and the target, the CB strategy requires access to a stable allocentric reference frame. Here, we manipulated the visual information about allocentric reference frames in three virtual environments and asked participants to steer a car to intercept a moving target. Participants’ interception paths showed different degrees of curvature and their target-heading angles were approximately constant, consistent with the CTH strategy. By contrast, the target’s bearing angle continuously changed in all participants except one. This particular participant produced linear interception paths with little change in the target’s bearing angle, seemingly consistent with both strategies. This participant continued this pattern of steering even in the environment without any visual information about allocentric reference frames. Therefore, this pattern of steering is attributed to the CTH strategy rather than the CB strategy. The overall results add important evidence for the conclusion that locomotor interception is better accounted for by the CTH strategy and that experimentally observing a straight interception trajectory with a constant bearing angle is not sufficient evidence for the CB strategy.

The efficient coding hypothesis posits that sensory systems are tuned to the regularities of their natural input. The statistics of natural image databases have been the topic of many studies, which have revealed biases in the distribution of orientations that are related to neural representations as well as behavior in psychophysical tasks. However, commonly used natural image databases contain images taken with a camera with a planar image sensor and limited field of view. Thus, these images do not incorporate the physical properties of the visual system and its active use reflecting body and eye movements. Here, we investigate quantitatively, whether the active use of the visual system influences image statistics across the visual field by simulating visual behaviors in an avatar in a naturalistic virtual environment. Images with a field of view of 120° were generated during exploration of a virtual forest environment both for a human and cat avatar. The physical properties of the visual system were taken into account by projecting the images onto idealized retinas according to models of the eyes' geometrical optics. Crucially, different active gaze behaviors were simulated to obtain image ensembles that allow investigating the consequences of active visual behaviors on the statistics of the input to the visual system. In the central visual field, the statistics of the virtual images matched photographic images regarding their power spectra and a bias in edge orientations toward cardinal directions. At larger eccentricities, the cardinal bias was superimposed with a gradually increasing radial bias. The strength of this effect depends on the active visual behavior and the physical properties of the eye. There were also significant differences between the upper and lower visual field, which became stronger depending on how the environment was actively sampled. Taken together, the results show that quantitatively relating natural image statistics to neural representations and psychophysical behavior requires not only to take the structure of the environment into account, but also the physical properties of the visual system, and its active use in behavior.