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Rapid scene categorization: From coarse peripheral vision to fine central vision

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Studies on scene perception have shown that the rapid extraction of low spatial frequencies (LSF) allows a coarse parsing of the scene, prior to the analysis of high spatial frequencies… Click to show full abstract

Studies on scene perception have shown that the rapid extraction of low spatial frequencies (LSF) allows a coarse parsing of the scene, prior to the analysis of high spatial frequencies (HSF) containing details. Many studies suggest that scene gist recognition can be achieved with only the low resolution of peripheral vision. Our study investigated the advantage of peripheral vision on central vision during a scene categorization task (indoor vs. outdoor). In Experiment 1, we used large scene photographs from which we built one central disk and four circular rings of different eccentricities. The central disk either contained or not an object semantically related to the scene category. Results showed better categorization performances for the peripheral rings, despite the presence of an object in central vision that was semantically related to the scene category that significantly improved categorization performances. In Experiment 2, the central disk and rings were assembled from Central to Peripheral vision (CtP sequence) or from Peripheral to Central vision (PtC sequence). Results revealed better performances for PtC than CtP sequences, except when no central object was present under rapid categorization constraints. As Experiment 3 suggested that the PtC advantage was not explained by a reduction of the visibility of the object in the central disk by the surrounding peripheral rings (CtP sequence), results are interpreted in the context of a predominant coarse-to-fine processing during scene categorization, with greater efficiency and utility of coarse peripheral vision relative to fine central vision during rapid scene categorization.

Keywords: central vision; scene; peripheral vision; scene categorization; vision

Journal Title: Vision Research
Year Published: 2020

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