Modelling cognitive load using drift-diffusion models in pedestrian street-crossing: a method supported by neural evidence

When pedestrians are cognitively loaded, this influences their street-crossing behaviour, leading to negative impact on overall road safety. However, the mechanisms underpinning this impact remain debated, and this study seeks to further investigate them through modelling and electroencephalography. We conducted a computer-based pedestrian crossing experiment, and employed drift-diffusion models to quantitatively analyse how cognitive load impacts pedestrian decision-making. To further test the models' validity, we analysed centro-parietal positive potential (CPP), a neural signal associated with evidence accumulation, to investigate whether this neural evidence aligned with the evidence accumulation predicted by the models. In our experiment, participants encountered a simulated scenario with a car approaching under four different time-to-arrival (TTA) conditions. In half the trials, participants performed cognitive tasks while deciding when to cross the street. Results showed that cognitive load weakened the effect of TTA on the probability of crossing before the car, increased response times, raised the probability of collision, and attenuated CPP amplitude. The best-performing model, which captured all of these effects, accumulated evidence based on utility estimates, but with a lower responsiveness to these utilities during cognitive load. This model also showed the strongest correlation between its evidence traces and the CPP amplitude, both with and without cognitive load. These findings support the hypothesis that cognitive load reduces responsiveness to perceptual evidence (at least in non-automatised tasks), making it a strong candidate for explaining both our results and existing research on the effects of cognitive load in other tasks.