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Temporal Evolution of Rapid-Scene Perception

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poster
posted on 12.11.2019, 07:52 by Zhao Hui KohZhao Hui Koh, Regan GallagherRegan Gallagher, Shinji Nishimoto, Nao TsuchiyaNao Tsuchiya
Abstract


Upon a brief presentation, we can rapidly understand the gist of natural images. We can usually do this with high confidence. With comparable duration of presentation, we sometimes find it difficult to perceive some aspects of the artificial images (e.g., Gabor-patches and letters) typically used in laboratory settings. These and other evidence have resulted in opposing views on the accuracy of visual perception with its associated subjective confidence, which may or may not reflect the accuracy of perception. To examine these issues, we devised a novel online task, where 640 participants were tested with naturalistic (N=420) and artificial (N=8) images. Participants viewed each image briefly (67, 133 or 267ms) and described their impression with five English words together with confidence ratings (0-4). For naturalistic images, we found that 133ms and 267ms presentation time resulted in similarly detailed reports, with similar degrees of high confidence. At 67ms, participants reported basic-category words more often, with lower confidence than 133 and 267ms presentation times. For artificial images, descriptions often contained many non-veridical reports and low confidence. These results caution us on what we can conclude about how human conscious visual experience evolves over short time-scales from typical lab stimuli; each naturalistic image can give us distinguishable and unique impressions, implying massive differentiability, while artificial images are less distinguishable. We conclude that usage of naturalistic stimuli to probe broader aspects of a moment of conscious experience leads to higher estimates of consciously available and differentiable information.


References

Haun, A. M., Tononi, G., Koch, C., & Tsuchiya, N. (2017). Are we underestimating the richness of visual experience? Neuroscience of Consciousness, 2017(1), 817–4.

Nishimoto, S. (2015). CiNet VideoBlocks movie library. Unpublished dataset.

Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012). A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron, 76(6), 1210-1224.

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