A one-boundary drift-diffusion model for time to collision estimation in a simple driving task

Daneshi, Asieh; Azarnoush, Hamed; Towhidkhah, Farzad

doi:10.1080/20445911.2019.1688336

Cited by 4 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, drivers make behavioral responses to factors external to the mechanics of driving all the time, and our modeling addresses this behavior. Recent studies have extended the use of the diffusion model framework to capture the underlying mechanism of various driving actions such as making a left turn across traffic in the opposite direction (Zgonnikov et al, 2020); steering and gear shift paddle behavior in response to a decelerating car (Markkula, 2014;Markkula et al, 2016;Xue et al, 2018); and estimating the time to collision (Daneshi et al, 2019(Daneshi et al, , 2020. For instance, Xue et al (2018) proposed a model of driving behavior to explain the response of drivers to a decelerating lead car.…”

Section: Discussionmentioning

confidence: 99%

The effect of aging on decision-making while driving: A diffusion model analysis.

Ratcliff¹,

Vanunu²

2022

Psychology and Aging

View full text Add to dashboard Cite

We present a diffusion model analysis of the effect of aging on decision processes during driving. Our goal was to examine the changes in the underlying components as a function of age and both task and environment difficulty. Younger and older adults performed each of three decision-making tasks while operating a computer-based driving simulator in which the task required a driving action. The first task was a one-choice task in which the response to brake lights turning on was to drive around a lead car. The second and third tasks were two-choice brightness-discrimination tasks in which participants were asked to drive the car to the left/right if there were more black/white pixels in an array of black and white pixels. Results showed that older adults were slower in the one-choice task and made more errors in the two-choice tasks than younger adults. The behavioral data were fitted well by one- and two-choice diffusion models, showing lower evidence accumulation rates (drift rates) in older than younger adults. Moreover, in the two-choice tasks under higher environmental demands, older adults showed a lower decision criterion (boundary separation) to compensate for a slower decision process. Together, the differences we found in the decision components between age groups provided an example of a subtle interaction between speed and accuracy in older versus younger adults, and this demonstrates the utility of this modeling approach in studying age effects in driving.

show abstract