September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
Can we predict vividness from the characteristics of imagined images? A novel database featuring vividness judgments of the Natural Scene Dataset
Author Affiliations
  • Catherine Landry
    Cerebrum, Université de Montréal, CA
  • Jasper JF van den Bosch
    School of Psychology, University of Leeds, UK
  • Vincent Taschereau-Dumouchel
    Département de psychiatrie et d'addictologie, Université de Montréal, CA
    Centre de recherche de l’Institut Universitaire en Santé Mentale de Montréal, CA
  • Frédéric Gosselin
    Cerebrum, Université de Montréal, CA
  • Ian Charest
    Cerebrum, Université de Montréal, CA
Journal of Vision September 2024, Vol.24, 1235. doi:https://doi.org/10.1167/jov.24.10.1235
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      Catherine Landry, Jasper JF van den Bosch, Vincent Taschereau-Dumouchel, Frédéric Gosselin, Ian Charest; Can we predict vividness from the characteristics of imagined images? A novel database featuring vividness judgments of the Natural Scene Dataset. Journal of Vision 2024;24(10):1235. https://doi.org/10.1167/jov.24.10.1235.

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

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Abstract

What makes an image easy to imagine? Previous research on mental imagery mostly used very limited samples, which prevented establishing a direct association between image characteristics and vividness ratings (i.e., the clarity and detail level of imagined images). Here, we present a large-scale database of vividness judgments associated with the natural scenes from the Natural Scenes Dataset (NSD; Allen et al., 2021), which consists of 73,000 annotated natural images. During each trial, participants sequentially view two NSD images and are then randomly asked to imagine one or the other (i.e., retro-cued target image) for 4 s. Participants then rate the vividness of their mental image (on a continuous scale from 0 to 100), followed by a test to ensure that they imagined the correct target. Participants (n = 1825), recruited from Prolific, are directed to the Meadows platform for online experiments. Each complete 120 trials of our vividness task, for a total of 219,000 vividness ratings across participants. They also complete the Vividness of Visual Imagery Questionnaire (Marks, 1973) to measure their visual imagery ability. Overall, preliminary data reveals excellent performance on the task (average accuracy of 95.83% correct target identification), as well as substantial interimage (M = 0.65 ± 0.25) and interindividual (M = 0.66 ± 0.17) variability in average vividness scores. This large-scale dataset of vividness ratings will offer invaluable insights into visual imagery by allowing to train predictive models of subjective imagery experiences. It will enable a deeper understanding of the visual and cognitive factors influencing mental image vividness, and serve as a guiding resource for future experiments. Furthermore, integrating these vividness judgments with neural data from the NSD will allow for an exploration of the relationship between subjective experience and objective brain responses.

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