Abstract

Visual crowding is the inability to identify visible features when they are surrounded by other structure in the peripheral field. Since natural environments are replete with structure and most of our visual field is peripheral, crowding represents the primary limit on vision in the real world. However, little is known about the characteristics of crowding under natural conditions. Here we examine where crowding occurs in natural images. Observers were required to identify which of four locations contained a patch of "dead leaves'' (synthetic, naturalistic contour structure) embedded into natural images. Threshold size for the dead leaves patch scaled with eccentricity in a manner consistent with crowding. Reverse correlation at multiple scales was used to determine local image statistics that correlated with task performance. Stepwise model selection revealed that local RMS contrast and edge density at the site of the dead leaves patch were of primary importance in predicting the occurrence of crowding once patch size and eccentricity had been considered. The absolute magnitudes of the regression weights for RMS contrast at different spatial scales varied in a manner consistent with receptive field sizes measured in striate cortex of primate brains. Our results are consistent with crowding models that are based on spatial averaging of features in the early stages of the visual system, and allow the prediction of where crowding is likely to occur in natural images.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.