December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
EZ Diffusion Modeling of Visual Search with Positive, Negative, and Neutral Cues
Author Affiliations & Notes
  • Nancy Carlisle
    Lehigh University
  • Ziyao Zhang
    UT Austin
  • Footnotes
    Acknowledgements  R15EY030247
Journal of Vision December 2022, Vol.22, 3223. doi:
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      Nancy Carlisle, Ziyao Zhang; EZ Diffusion Modeling of Visual Search with Positive, Negative, and Neutral Cues. Journal of Vision 2022;22(14):3223.

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

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Recent evidence suggests that participants may be able to guide attention towards potential targets after receiving a positive color cue that indicates the upcoming target color, and also guide attention away from distractors after receiving a negative color cue that signals the upcoming distractor color. Both positive and negative cues lead to RT benefits compared to a neutral uninformative cue, but negative cues are reliably less beneficial than positive cues. It remains unclear how the processes of attention change when participants use these different cue types. In this work, we used the EZ diffusion model to examine how attentional processing changes depending on the type of cue guiding attention. Note that we are making a simplifying assumption that the entire search process can be modeled as a single decision. We calculated drift, boundary, and non-decision time for each participant in an existing data set of 96 participants for each cue condition. We then compared the values for each parameter based on cue type. We found a main effect of cue type on drift rate, with the slowest drift rate for neutral, followed by negative, and then positive. We found a main effect of cue type on boundary, with the positive cue boundary being significantly larger than the negative or neutral cue boundaries. Finally, we also found a main effect of cue type on non-decision time, with the positive cue non-decision time being significantly shorter than the negative or neutral cue non-decision time. These results suggest negative cues lead to benefits mainly through a shift in drift rate, while positive cues lead to benefits due to a combination of drift rate and reduced non-decision time.


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