Purpose

To examine perceptual adaptation when people wear spectacles that produce unequal retinal image magnification.

Methods

Two groups of 15 participants (10 male; mean age 25.6 ± 4.9 years) wore spectacles with a 3.8% horizontal magnifier over one eye. The continuous-wear group wore the spectacles for 5 hours straight. The intermittent-wear group wore them for five 1-hour intervals. To measure slant and shape distortions produced by the spectacles, participants adjusted visual stimuli until they appeared frontoparallel or equiangular, respectively. Adaptation was quantified as the difference in responses at the beginning and end of wearing the spectacles. Aftereffects were quantified as the difference before and after removing the spectacles. We hypothesized that intermittent wear may lead to visual cue reweighting, so we fit a cue combination model to the data and examined changes in weights given to perspective and binocular disparity slant cues.

Results

Both groups experienced significant shape adaptation and aftereffects. The continuous-wear group underwent significant slant adaptation and the intermittent group did not, but there was no significant difference between groups, suggesting that the difference in adaptation was negligible. There was no evidence for cue reweighting in the intermittent wear group, but unexpectedly, the weight given to binocular disparity cues for slant increased significantly in the continuous-wear group.

Conclusions

We did not find strong evidence that adaptation to spatial distortions differed between the two groups. However, there may be differences in the cue weighting strategies employed when spectacles are worn intermittently or continuously.

A central question in neuroscience is how sensory inputs are transformed into percepts. At this point, it is clear that this process is strongly influenced by prior knowledge of the sensory environment. Bayesian ideal observer models provide a useful link between data and theory that can help researchers evaluate how prior knowledge is represented and integrated with incoming sensory information. However, the statistical prior employed by a Bayesian observer cannot be measured directly, and must instead be inferred from behavioral measurements. Here, we review the general problem of inferring priors from psychophysical data, and the simple solution that follows from assuming a prior that is a Gaussian probability distribution. As our understanding of sensory processing advances, however, there is an increasing need for methods to flexibly recover the shape of Bayesian priors that are not well approximated by elementary functions. To address this issue, we describe a novel approach that applies to arbitrary prior shapes, which we parameterize using mixtures of Gaussian distributions. After incorporating a simple approximation, this method produces an analytical solution for psychophysical quantities that can be numerically optimized to recover the shapes of Bayesian priors. This approach offers advantages in flexibility, while still providing an analytical framework for many scenarios. We provide a MATLAB toolbox implementing key computations described herein.