September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Memorability – predicting memory from visual information, and measuring visual information from memory
Author Affiliations
  • Wilma Bainbridge
    National Institute of Mental Health
Journal of Vision September 2018, Vol.18, 1360. doi:10.1167/18.10.1360
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      Wilma Bainbridge; Memorability – predicting memory from visual information, and measuring visual information from memory. Journal of Vision 2018;18(10):1360. doi: 10.1167/18.10.1360.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

While much of memory research focuses on the memory behavior of individual participants, little memory work has looked at the visual attributes of the stimulus that influence future memory. However, in recent work, we have found that there are surprising consistencies to the images people remember and forget, and that the stimulus ultimately plays a large part in predicting later memory behavior. This consistency in performance can then be measured as a perceptual property of any stimulus, which we call memorability. Memorability can be easily measured in the stimuli of any experiment, and thus can be used to determine the degree previously found effects could be explained by the stimulus. I will present an example where we find separate neural patterns sensitive to stimulus memorability and individual memory performance, through re-analyzing the data and stimuli from a previously published fMRI memory retrieval experiment (Rissman et al., 2010). I will also show how memorability can be easily taken into account when designing experiments to ask fundamental questions about memory, such as – are there differences between the types of images people can recognize versus the types of images people can recall? I will present ways for experimenters to easily measure or control for memorability in their own experiments, and also some new ways quantify the visual information existing within a memory.

Meeting abstract presented at VSS 2018

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