August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
How do people map colors to concepts? Modeling assignment inference as evidence accumulation
Author Affiliations & Notes
  • Kushin Mukherjee
    University of Wisconsin-Madison
  • Laurent Lessard
    Northeastern University
  • Karen B. Schloss
    University of Wisconsin-Madison
  • Footnotes
    Acknowledgements  NSF award BCS-1945303 to K.B.S.
Journal of Vision August 2023, Vol.23, 5669. doi:
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      Kushin Mukherjee, Laurent Lessard, Karen B. Schloss; How do people map colors to concepts? Modeling assignment inference as evidence accumulation. Journal of Vision 2023;23(9):5669.

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

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When interpreting information visualizations where distinct colors represent different concepts, observers map colors to concepts using a process called assignment inference (Schloss et al., 2018). In assignment inference, people do not simply assign concepts to the most strongly associated color (local assignment). Instead, they make assignments that maximize the relative associations across all color-concept pairs in the encoding system (global assignment). It would be intractable to compare all color-concept pairings to determine the optimal assignment as the number of colors increases, so how might observers perform assignment inference? We propose observers use a heuristic to solve this problem by finding the single color that has the most 'evidence' for being mapped to the target concept relative to other colors in the scope. This approach can be modeled using the Linear Ballistic Accumulator (LBA) model of decision-making where each color is modeled using an accumulator, which independently ‘races’ to reach a response threshold for a target concept. A critical factor in determining the winning accumulator is its rate of evidence accumulation (drift rate). We investigated what factors determine drift rate. First, we collected data on an assignment inference task. Participants saw dot plots with four colored dots (each representing a different concept) and indicated which color represented a target concept (4 target concepts x 15 color palettes x 8 repetitions). Next, we modeled responses/RTs using an LBA model with different accumulators for each color-concept pair. We tested whether patterns of drift rates could be explained by direct associations with the target concept (local) and relative associations across concepts (global). A mixed-effects model showed that both factors explained independent variance in drift rates (ps<.001). Thus, observers used a heuristic that incorporates local and global information to assign colors to concepts, without having to evaluate every possible color-concept assignment in assignment inference.


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