People can infer the relative distances of objects reliably from their visual size and position in a two-dimensional (2D) scene (e.g., viewed on a computer screen) but not their absolute simulated distance. Might this be an artifact of picture viewing? In a naturally sized indoor corridor scene, projected onto a wall and aligned with the viewer's eye level, participants adjusted the size of the image of an object based on its position in the scene or its position based on its visual size to match the size of an actual object held in their hands (Experiment 1). The adjusted position was consistent with the visual size of the object, but the adjusted size was consistently larger than its veridical size. Such discrepancy demonstrates that the distance and the size of the object in the scene were judged differently, where the visual size of the object relative to its absolute distance may have been judged erroneously. However, expressing the data as the horizon ratio suggested that people might do these tasks using this ratio rather than considering the simulated absolute distance of the object. Measuring people's perceptions of the position of the horizon (Experiment 2) revealed that people are generally accurate at judging the elevation of the horizon in a scene, but people misperceived their eye level when looking at a monitor or when the horizon in the scene was misaligned with their eye level. The misperceived eye level may be due to the misjudged distance between their eyes and the ground (i.e., the eye height) which may lead to errors when computing distance to the object on the ground based on its visual angular declination. Therefore, we concluded that the horizon position and the visually perceived eye level play important role in people's distance and size perception in 2D scenes and the objects in it.
Contrast determines how strong visual stimuli drive neural responses and, in turn, how well we can see. This contrast-response relationship is nonlinear, with gain control mechanisms acting to compress responses to high-contrast stimuli, effectively setting the contrast range that neural responses are most sensitive to. Yet, capturing these saturating responses noninvasively in human functional magnetic resonance imaging (fMRI) has proven challenging. Here, we introduce a model-based fMRI approach to more flexibly and efficiently estimate saturating contrast responses: population contrast response function (CRF) mapping. To vet this technique, we benchmarked pCRF mapping against traditional deconvolved BOLD responses for stimuli presented with a fast event-related design. We found tight correspondence in parameter estimates between the deconvolution-derived CRF and pCRF estimates, validating the model-based approach. We then asked whether pCRF mapping could push beyond conventional paradigms by employing a continuous, highly condition-rich stimulation design that affords greater efficiency and flexibility but does not lend itself to traditional fMRI analyses. As predicted by the model-based approach, voxel-wise pCRF estimates could still be recovered from this unconventional design. In sum, we demonstrated that model-based pCRF mapping dissociates CRF estimation from strict design constraints and broadens the space of usable experimental paradigms while still capturing the saturating nonlinearities that are fundamental to neural population responses.
