Jacqueline M. Fulvio

Revealing individual differences in strategy selection through visual motion extrapolation

Humans are constantly challenged to make use of internal models to fill in missing sensory information. We measured human performance in a simple motion extrapolation task where no feedback was provided in order to elucidate the models of object motion incorporated into observers’ extrapolation strategies. There was no “right” model for extrapolation in this task. Observers consistently adopted one of two models, linear or quadratic, but different observers chose different models. We further demonstrate that differences in motion sensitivity impact the choice of internal models for many observers. These results demonstrate that internal models and individual differences in those models can be elicited by unconstrained, predictive-based psychophysical tasks.

Consistency of location and gradient judgments of visually-interpolated contours

We report two experiments assessing how observers interpolate partly-occluded contours induced by pairs of line segments that disappeared behind an occluder. On each trial observers iteratively adjusted the location and orientation of a short line probe that could be moved vertically within the region of occlusion. They were instructed to set the line probe to be tangent to the occluded contour. The line probe could appear at one of six horizontal offsets and the inducer pairs on some trials were relatable (the inducers could be joined by a smooth curve without a point of inflection) and on other trials non-relatable. We interpreted the settings as estimates of the location and gradient (slope) of the contour in the region of occlusion. We tested whether the resulting visual estimates of location and gradient were consistent with any single smooth contour. When inducers were relatable, estimates of location and gradient were mutually consistent for all observers and could be modeled as polynomials of 5^th or lower degree. When the inducers were non-relatable, the consistency of location and gradient settings deteriorated, indicating that there can be no single smooth curve that accounts for observers’ judgments. We discuss the implication of these results for models of human visual interpolation.

Use of cues in virtual reality depends on visual feedback

Abstract3D motion perception is of central importance to daily life. However, when tested in laboratory settings, sensitivity to 3D motion signals is found to be poor, leading to the view that heuristics and prior assumptions are critical for 3D motion perception. Here we explore an alternative: sensitivity to 3D motion signals is context-dependent and must be learned based on explicit visual feedback in novel environments. The need for action-contingent visual feedback is well-established in the developmental literature. For example, young kittens that are passively moved through an environment, but unable to move through it themselves, fail to develop accurate depth perception. We find that these principles also obtain in adult human perception. Observers that do not experience visual consequences of their actions fail to develop accurate 3D motion perception in a virtual reality environment, even after prolonged exposure. By contrast, observers that experience the consequences of their actions improve performance based on available sensory cues to 3D motion. Specifically, we find that observers learn to exploit the small motion parallax cues provided by head jitter. Our findings advance understanding of human 3D motion processing and form a foundation for future study of perception in virtual and natural 3D environments.

Contour extrapolation using probabilistic cue combination

A common approach to the problem of contour interpolation is based on the calculus of variations. The optimal interpolating contour is taken to be one that minimizes a given smoothness functional. Two important such functionals are total curvature (or bending energy) and variation in curvature. We analyzed contours extrapolated by human observers given arcs of Euler spirals that disappeared behind an occluding surface. Irrespective of whether the Euler spirals had increasing or decreasing curvature as they approached the occluding edge, visually-extrapolated contours were found to be characterized by decaying curvature. This curvature decay is modeled in terms of a Bayesian interaction between probabilistically-expressed constraints to minimize curvature and minimize variation in curvature. The analysis suggests that using fixed smoothness functionals is not appropriate for modeling human vision. Rather, the relative weights assigned to different probabilistic shape constraints may vary as a function of distance from the point(s) of occlusion. Implications are discussed for computational models of shape completion.

Systematic misperceptions of 3-D motion explained by Bayesian inference

People make surprising but reliable perceptual errors. Here, we provide a unified explanation for systematic errors in the perception of three-dimensional (3-D) motion. To do so, we characterized the binocular retinal motion signals produced by objects moving through arbitrary locations in 3-D. Next, we developed a Bayesian model, treating 3-D motion perception as optimal inference given sensory noise in the measurement of retinal motion. The model predicts a set of systematic perceptual errors, which depend on stimulus distance, contrast, and eccentricity. We then used a virtual-reality headset as well as a standard 3-D desktop stereoscopic display to test these predictions in a series of perceptual experiments. As predicted, we found evidence that errors in 3-D motion perception depend on the contrast, viewing distance, and eccentricity of a stimulus. These errors include a lateral bias in perceived motion direction and a surprising tendency to misreport approaching motion as receding and vice versa. In sum, we present a Bayesian model that provides a parsimonious account for a range of systematic misperceptions of motion in naturalistic environments.