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Jiri Lukavsky, Filip Dechterenko, Andrea Dally; Scene categorisation in the presence of a distractor. Journal of Vision 2017;17(10):557. doi: https://doi.org/10.1167/17.10.557.
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People can recognize the content of a real-world scene (gist) within a single fixation. This ability is not limited to the central vision. Larson et al. (2014) showed how retinal eccentricity affects the time course of the gist acquisition. Here, we tested how allocating attention affects gist acquisition in central vision and in periphery. We presented participants with composite images featuring one scene circularly cropped in the center (radius 5.54 deg) and another in surrounding ring (radius 11.8 deg). The participants' task was to pay attention to either a center or periphery (varied across blocks) and decide whether the scene presented there was a natural or man-made scene. The stimulus was shown for 33 ms, followed by 167 ms ISI and 33 ms pixel mask. We used eye tracker to check that the central fixation was maintained throughout a trial. Each of 28 participants completed 100 central and 100 peripheral trials. Participants were successful in categorizing scenes (accuracy 96.5%). The categorizing natural scenes was easier (98.2% vs 94.9%). We found no difference in accuracy when categorizing scenes in fovea/periphery (97.0% vs 96.0%). The concurrent presence of incongruent scenes led to a small but significant drop in accuracy (96.9% to 96.1%). As an indirect measure of confidence, we analyzed the response times. Participants responded to natural scenes significantly faster (445 ms vs 527 ms), but neither allocation of attention (center: 466 ms, periphery: 507 ms) or presence of scene conflict (conflict: 475 ms, congruent: 497 ms) had a significant effect. We found that in this particular settings people were able to successfully categorize both central and peripheral parts of the composite scenes. The presence of the conflict did not affect the performance suggesting that the other scene is successfully suppressed
Meeting abstract presented at VSS 2017
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