September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Sampling Human Visual Experience Through Text and Media Messages
Author Affiliations & Notes
  • Jennifer Hart
    Bates College
  • Michelle Greene
    Bates College
  • Footnotes
    Acknowledgements  This material is based upon work supported by the National Science Foundation under grant no. 1920896 and Bates College
Journal of Vision September 2021, Vol.21, 2902. doi:
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      Jennifer Hart, Michelle Greene; Sampling Human Visual Experience Through Text and Media Messages. Journal of Vision 2021;21(9):2902.

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

  • Supplements

How do locations, actions, and goals interact to affect how we categorize our environments? Recent work has shown that scene categorization is mainly a high-level process focusing on high-level properties like objects and attributes that are processed iteratively with their settings. Additional progress on this topic has been limited by a paucity of data on where people spend their time and the types of activities done in these different locations. This study sought to investigate how observers’ locations, actions, and goals influenced the scenes they categorized throughout the course of a month. We uncovered which actions or goals were most commonly associated with certain locations and vice versa. We did this by conducting a virtual study in the month of December 2020 involving ten participants residing in Maine with ages ranging from 20 to 53. Participants received ten daily text messages that asked them to specify their present environment or send a picture of their location, identify the action they were performing, and describe the goal they were trying to achieve. The results suggest that all participants spent the majority of their time inside. The data revealed that 90.9% of all locations participants reported were inside, 3.4% were in a car, and 5.7% were outside. There was a numerical tendency to spend more time indoors with increasing age (R = 0.06). Participants partook in 157 unique activities/goals. The most frequent activity reported was working, followed by relaxing, eating, cooking, and watching videos. The frequency of activities exhibits a power law distribution that obeys Zipf’s law. This study provides more insight into how humans categorize scenes, and the data helps identify which scenes are most commonly experienced by humans on a daily basis.


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