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Assaf Harel, Emily Artz; Perceptual properties of scenes determine their subsequent memory. Journal of Vision 2017;17(10):555. doi: 10.1167/17.10.555.
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© ARVO (1962-2015); The Authors (2016-present)
Recent years have seen an advent of research on the perception of visual scenes; however, much less is known about how scenes as a unique category are memorized, particularly regarding the relationship between long-term memory of scenes and their perceptual encoding. In order to address this question, we conducted a study to determine whether global image properties found to be diagnostic for the perception of scenes will also be useful for successful memory recognition of the same scenes. The study consisted of two phases: a study phase and a test phase. In the study phase, participants were presented with 96 scene images varying along two dimensions: spatial boundary (closed and open scenes) and naturalness (manmade and natural scenes). In the test phase, participants were presented with the same 96 scenes embedded along a new set of 96 images, which spanned the same dimensions as the studied images, and overall were highly similar to the original stimulus set. The participants were asked to indicate which scenes were presented to them in the study phase, and which were new scenes. We found that across varying stimulus presentation rates, recognition performance significantly varied as a function of the naturalness of the scenes, with man-made scenes better remembered than natural scenes. An image-based computational analysis of the memorability of the scene images revealed that the manmade and natural scenes did not differ in their memorability and that the majority of scenes used in our study were of low to medium memorability, indicating the current results are not likely to be explained by individual image-based memorability. We suggest that the current findings emphasize a close correspondence between the perception of scenes, exemplified in their global scene properties, and their subsequent memory.
Meeting abstract presented at VSS 2017
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