Dataset Information

Saccade-confounded image statistics explain visual crowding.

ABSTRACT: Processing of shape information in human peripheral visual fields is impeded beyond what can be expected by poor spatial resolution. Visual crowding, the inability to identify objects in clutter, has been shown to be the primary factor limiting shape perception in peripheral vision. Despite the well-documented effects of crowding, its underlying causes remain poorly understood. Given that spatial attention both facilitates learning of image statistics and directs saccadic eye movements, we propose that the acquisition of image statistics in peripheral visual fields is confounded by eye-movement artifacts. Specifically, the image statistics acquired under a peripherally deployed spotlight of attention are systematically biased by saccade-induced image displacements. These erroneously represented image statistics lead to inappropriate contextual interactions in the periphery and cause crowding.

SUBMITTER: Nandy AS

PROVIDER: S-EPMC3288353 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Saccade-confounded image statistics explain visual crowding.

Nandy Anirvan S AS Tjan Bosco S BS

Nature neuroscience 20120108 3

Processing of shape information in human peripheral visual fields is impeded beyond what can be expected by poor spatial resolution. Visual crowding, the inability to identify objects in clutter, has been shown to be the primary factor limiting shape perception in peripheral vision. Despite the well-documented effects of crowding, its underlying causes remain poorly understood. Given that spatial attention both facilitates learning of image statistics and directs saccadic eye movements, we propo ...[more]

PMID: 22231425

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Project description:Visual crowding, the phenomenon in which the ability to distinguish objects is hindered in cluttered environments, has critical implications for various ophthalmic and neurological disorders. Traditional methods for assessing crowding involve time-consuming and attention-demanding psychophysical tasks, making routine examination challenging. This study sought to compare trial-based Alternative Forced-Choice (AFC) paradigms using either manual or eye movement responses and a continuous serial search paradigm employing eye movement responses to evaluate their efficiency in rapidly assessing peripheral crowding. In all paradigms, we manipulated the orientation of a central Gabor patch, which could be presented alone or surrounded by six Gabor patches. We measured participants' target orientation discrimination thresholds using adaptive psychophysics to assess crowding magnitude. Depending on the paradigm, participants either made saccadic eye movements to the target location or responded manually by pressing a key or moving a mouse. We compared these paradigms in terms of crowding magnitude, assessment time, and paradigm demand. Our results indicate that employing eye movement-based paradigms for assessing peripheral visual crowding yields results faster compared to paradigms that necessitate manual responses. Furthermore, when considering similar levels of confidence in the threshold measurements, both a novel serial search paradigm and an eye movement-based 6AFC paradigm proved to be the most efficient in assessing crowding magnitude. Additionally, crowding estimates obtained through either the continuous serial search or the 6AFC paradigms were consistently higher than those obtained using the 2AFC paradigms. Lastly, participants did not report a clear difference between paradigms in terms of their perceived demand. In conclusion, both the continuous serial search and the 6AFC eye movement response paradigms enable a fast assessment of visual crowding. These approaches may potentially facilitate future routine crowding assessment. However, the usability of these paradigms in specific patient populations and specific purposes should be assessed.

Dataset Information

Saccade-confounded image statistics explain visual crowding.

Publications

Saccade-confounded image statistics explain visual crowding.

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