Immersion in Modified Reality: Sensitivity to the Slope (α) of the Amplitude Spectrum is Dependent on the α of Recently Viewed Environments

Abstract

AbstractScenes contain many statistical regularities that, if accounted for by the visual system, could greatly benefit visual processing. One such statistic to consider is the orientation-averaged slope (α) of the amplitude spectrum of natural scenes. Human observers are differently sensitive to αs, and they may utilize this statistic when processing natural scenes. Here, we explore whether discrimination sensitivity to α is associated with the recently viewed environment. Observers were immersed, using a Head-Mounted Display, in an environment that was either unaltered or had its average α steepened or shallowed. Discrimination thresholds were affected by the average shift in α: a steeper environment decreased thresholds for very steep reference αs while a shallower environment decreased thresholds for shallow values. We modelled these data with a Bayesian observer model and explored how different prior shapes may influence the ability of the model to fit observer thresholds. We explore three potential prior shapes: unimodal, bimodal and trimodal modified-PERT distributions and found the bimodal prior to best-capture observer thresholds for all experimental conditions. Notably, the prior modes’ position was shifted following adaptation, which suggests that a priori expectations for α are sufficiently malleable to account for changes in the average α of the recently viewed scenes.Author SummaryHuman observers are sensitive to the statistics of natural scenes. Here, we investigated whether differences in discrimination thresholds to the amplitude spectrum slope (α) of natural scenes is tied to the distribution of α in typical visual environments of humans. We measured discrimination sensitivity to α within the environment of observers in a Head-Mounted Display, which overlaid our stimuli onto a real-time recording of the observer’s environment. Observers were then immersed in a modified environment; the α of each image was steepened or shallowed to shift the distribution of α observers were exposed to. Our adaptation procedure altered discrimination thresholds as they showed an additional peak in sensitivity for α values near the new average α of the modified environment. Our Bayesian modelling indicates that the prior for α is bimodal and sufficiently flexible to adapt to novel environments with different α distributions. Overall, our findings demonstrate that the human visual system continuously adapts to the current statistical structure of its natural environment.