Should I stay or should I go? Evidence accumulation drives decision making in human drivers

Abstract

<p>Laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. Yet it is unclear whether the cognitive processes implicated in simple, isolated decisions in the lab are as paramount to decisions that are ingrained in more complex behaviors, such as driving. Here we aim to address the gap between modern cognitive models of decision making and studies of naturalistic decision making in drivers, which so far have provided only limited insight into the underlying cognitive processes. We investigate drivers' decision making during unprotected left turns, and model the cognitive process driving these decisions. Our model builds on the classical drift-diffusion model, and emphasizes, first, the drift rate linked to the relevant perceptual quantities dynamically sampled from the environment, and, second, collapsing decision boundaries reflecting the dynamic constraints imposed on the decision maker’s response by the environment. We show that the model explains the observed decision outcomes and response times, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions, effectively providing a way to predict human drivers’ behavior in real time. Our results reveal the cognitive mechanisms of gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help us to understand human behavior in complex real-world tasks.</p>

Variable-drift diffusion models of pedestrian road-crossing decisions

“…These preliminary results were promising, but we concluded that the tested models were overly complex in relation to the adopted fitting methods and the relatively small data set; the simplest model with just a single drift diffusion unit performed essentially as well as the more complex alternatives. This type of simplified model was then tested by Zgonnikov et al [73] on a related traffic scenario-drivers deciding to turn across oncoming traffic-and was found capable of reproducing response time distributions of "turn" versus "wait" decisions, thus confirming that existing discrete gap acceptance/rejection models of turning drivers can be generalised, using drift diffusion models with time-varying input, to model also the timing of these decisions. This study was however limited to only constant speed, non-yielding oncoming traffic.…”

“…The work by Zgonnikov et al [73] demonstrated that, for car drivers turning across oncoming non-yielding traffic, variable-drift diffusion models allow modeling of not only the frequency of gap acceptance as a function of vehicle TTA and distance, but also the distributions of timing of these decisions. Our results replicate this finding for a pedestrian road-crossing scenario.…”

“…We and others have investigated the application of drift diffusion-type models in the road traffic context, with promising results initially for low-level locomotion decisions on applying braking or steering control [39,48,71], more recently also extending to multi-agent interaction situations [2,24,31,41,73].…”

“…Here, we pursue two main objectives: First, to determine whether the type of variable-drift diffusion model successfully applied to gap acceptance decisions of turning drivers by Zgonnikov et al [73] can also account for pedestrian road-crossing decisions. Second, we attempt to go beyond pure gap acceptance, generalizing to situations where vehicles may be yielding to the decision-maker, with or without additional implicit or explicit communicative cues.…”

Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection

“…Third, we show that established evidence accumulation models of decision-making can be extended beyond typical laboratory paradigms with static or intermittently changing abstract stimuli, to a task with clear real-world relevance, and continuously time-varying sensory evidence. We and others have reported that evidence accumulation models show promise for modelling decisions in real-world tasks, e.g., when to apply brakes in response to a developing collision threat [52], [53], [61], or on whether and when to cross a road with oncoming traffic [62], [63]. However, in these types of naturalistic tasks it has not been possible to fit full response time probability distributions per participant, a minimum expectation in evidence accumulation modelling of more typical, abstract laboratory tasks.…”