Trial-by-trial feedback fails to improve the consideration of acceleration in visual time-to-collision estimation

Marlene Wessels, Heiko Hecht, Thirsa Huisman and Daniel Oberfeld

PLOS ONE, 2023, vol. 18, issue 8, 1-16

Abstract: When judging the time-to-collision (TTC) of visually presented accelerating vehicles, untrained observers do not adequately account for acceleration (second-order information). Instead, their estimations only rely on vehicle distance and velocity (first-order information). As a result, they systemically overestimate the TTC for accelerating objects, which represents a potential risk for pedestrians in traffic situations because it might trigger unsafe road-crossing behavior. Can training help reduce these estimation errors? In this study, we tested whether training with trial-by-trial feedback about the signed deviation of the estimated from the actual TTC can improve TTC estimation accuracy for accelerating vehicles. Using a prediction-motion paradigm, we measured the estimated TTCs of twenty participants for constant-velocity and accelerated vehicle approaches, from a pedestrian’s perspective in a VR traffic simulation. The experiment included three blocks, of which only the second block provided trial-by-trial feedback about the TTC estimation accuracy. Participants adjusted their estimations during and after the feedback, but they failed to differentiate between accelerated and constant-velocity approaches. Thus, the feedback did not help them account for acceleration. The results suggest that a safety training program based on trial-by-trial feedback is not a promising countermeasure against pedestrians’ erroneous TTC estimation for accelerating objects.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0288206

DOI: 10.1371/journal.pone.0288206

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