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Lester C. Loschky, Sebastien Szaffarczyk, Clement Beugnet, Michael E. Young, Muriel Boucart; The contributions of central and peripheral vision to scene-gist recognition with a 180° visual field. Journal of Vision 2019;19(5):15. doi: 10.1167/19.5.15.
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© ARVO (1962-2015); The Authors (2016-present)
We investigated the relative contributions of central versus peripheral vision in scene-gist recognition with panoramic 180° scenes. Experiment 1 used the window/scotoma paradigm of Larson and Loschky (2009). We replicated their findings that peripheral vision was more important for rapid scene categorization, while central vision was more efficient, but those effects were greatly magnified. For example, in comparing our critical radius (which produced equivalent performance with mutually exclusive central and peripheral image regions) to that of Larson and Loschky, our critical radius of 10° had a ratio of central to peripheral image area that was 10 times smaller. Importantly, we found different functional relationships between the radius of centrally versus peripherally presented imagery (or the proportion of centrally versus peripherally presented image area) and scene-categorization sensitivity. For central vision, stimulus discriminability was an inverse function of image radius, while for peripheral vision the relationship was essentially linear. In Experiment 2, we tested the photographic-bias hypothesis that the greater efficiency of central vision for rapid scene categorization was due to more diagnostic information in the center of photographs. We factorially compared the effects of the eccentricity from which imagery was sampled versus the eccentricity at which imagery was presented. The presentation eccentricity effect was roughly 3 times greater than the sampling eccentricity effect, showing that the central-vision efficiency advantage was primarily due to the greater sensitivity of central vision. We discuss our results in terms of the eccentricity-dependent neurophysiology of vision and discuss implications for computationally modeling rapid scene categorization.
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