September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
Getting more out of response time measures: Delineating separable motor and cognitive subcomponents of response time via drift diffusion modeling
Author Affiliations & Notes
  • Justin Grady
    The George Washington University
  • Audrey Siqi-Liu
    The George Washington University
  • Sarah Malykke
    The George Washington University
  • Patrick Cox
    Lehigh University
  • Dwight Kravitz
    The George Washington University
  • Stephen Mitroff
    The George Washington University
  • Footnotes
    Acknowledgements  US Army Research Laboratory Cooperative Agreements #W911NF-21-2-0179, #W911NF-23-2-0210, & #W911NF-23-2-0097
Journal of Vision September 2024, Vol.24, 942. doi:https://doi.org/10.1167/jov.24.10.942
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      Justin Grady, Audrey Siqi-Liu, Sarah Malykke, Patrick Cox, Dwight Kravitz, Stephen Mitroff; Getting more out of response time measures: Delineating separable motor and cognitive subcomponents of response time via drift diffusion modeling. Journal of Vision 2024;24(10):942. https://doi.org/10.1167/jov.24.10.942.

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

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Abstract

Cognitive psychology studies have used a wide range of experimental designs to make important contributions to the understanding of cognitive processing. Most studies, however, have relied on a rather coarse measure of response speed—how long it takes participants to press a key on a keyboard. While straightforward, this measure may confound cognitive processes with motor initiation, stimulus encoding, and other potential sources of noise. Recent work from our lab (Kramer et al., 2021) introduced a “touch and swipe” response as part of an object sorting task. In this task, participants tap on objects as they appear on the screen (touch time) and then make a swiping motion to sort them into one of two bins based on category membership (swipe time). Critically, this work suggests that touch time may reflect a prepotent motor response or impulsivity, while swipe time may better reflect cognitive processing time. The current study aimed to further characterize the nature of the touch and swipe time measures using drift diffusion modeling, which derives separable decision making components from response time distributions. Specifically, this study assessed whether estimates of non-decision time and drift rate (speed of evidence accumulation) scale differentially with touch and swipe time measures. Non-decision times scaled positively with relatively longer touch times, while drift rate scaled positively with relatively longer swipe times. These modeling results provide converging evidence for the hypothesis that touch and swipe times are meaningful subcomponents of response time that map onto separable underlying processes—swipe time relates to evidence accumulation, which is likely more informative for psychological experiments of cognitive performance, while touch time relates to non-decision time which is more reflective of motor components. Taken together, these results indicate that the touch and swipe method for recording responses is a potentially powerful experimental design.

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