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Verena Willenbockel, Sandro Wiesmann, Frédéric Gosselin, Melissa Vo; Spatial frequency tuning for outdoor scene categorization. Journal of Vision 2018;18(10):137. doi: https://doi.org/10.1167/18.10.137.
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Which spatial frequencies (SFs) are used for the efficient categorization of real-world scenes? Previous results obtained with the SF Bubbles technique have shown that two SF bands are diagnostic for quick and accurate basic-level categorization of indoor scenes — one around 0.50 cycles per degree of visual angle (cpd) and one around 4.67 cpd (Willenbockel, Gosselin, & Võ, VSS 2017). In the present study, we employed the same technique and paradigm to examine SF tuning for outdoor scene categorization with four natural basic-level categories (coasts, fields, forests, and mountains). The base stimulus set comprised 200 typical gray-scale images per category, all matched in luminance. On each trial, observers saw an image filtered using 20 randomly distributed Gaussian "bubbles". Stimuli were presented in randomized order and remained on the screen until response. Observers were asked to press the space bar as soon as they recognized the scene category, and upon stimulus offset, press the respective key for the correct category. Performance feedback was provided. Mean accuracy across observers was 91.32% correct (SD = 3.64), and mean RT was 451 ms (SD = 107). A multiple linear regression on the transformed RTs from the space bar press and the respective SF filters revealed a significant SF band around 2 cycles per image (cpi; 0.33 cpd) and another one around 26 cpi (4.33 cpd). When using the transformed RTs from the category key press as regressor, only the high-SF band attained significance (27 cpi; 4.50 cpd). Interestingly, the significant SFs closely match those found for fast indoor scene categorization. Additional second-order analyses on both studies' data sets indicate that the significant low- and high-SF bands were used conjunctively. Our results show that people rely on a combination of coarse and fine scales for the efficient basic-level categorization of both indoor and outdoor scenes.
Meeting abstract presented at VSS 2018
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