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Nico Broers, Niko Busch; The intrinsic memorability of an image is associated with familiarity and recollection. Journal of Vision 2018;18(10):1301. doi: https://doi.org/10.1167/18.10.1301.
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Images contain the feature of memorability: across a wide range of contexts and observers, the same pictures tend to be remembered or tend to be forgotten. Most previous studies have investigated memorability using recognition tests, which require a decision as to whether a given image is old or new. In principle, recognition can be based on a vague feeling of familiarity ("I feel like I have seen something like this before.") or on recollection of specific image details ("I remember seeing the house with the green door."). It is currently unknown whether memorable images are more recognizable due to an increased sense of familiarity or due to recollection of more detailed memories. Here, we used Receiver Operating Characteristics (ROCs) to disentangle the contribution of familiarity and recollection to memorability. In a study phase, participants (n = 50, mean age = 29, 31 female) were presented with a stream of images for 500 ms each, and classified each image as indoor/outdoor. In the following test phase, old target items were intermixed with new lure items. Participants decided whether an image was old or new and indicated their confidence on a scale from 1 ("Sure") to 3 ("Not Sure"). Both target and lure images covered the full range from very memorable to very forgettable, as determined by previous studies using these images. As expected, images found to be memorable in previous studies also tended to be more recognizable in our study, as indicated by a larger area under the ROC curves. We fitted a Dual Process Signal Detection Model to our ROC data and found that both familiarity and recollection parameters were significantly increased for memorable images. Thus, memorable images are recognized better due to stronger feelings of familiarity, but also due to recollection of specific image details.
Meeting abstract presented at VSS 2018
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