Perception & Action

The cognitive processes underlying Go/No-Go performance may be explained by two plausible evidence accumulation models: Two-Boundary (2-B) and One-Boundary (1-B) decision drift models (DDMs). While both embed a Go decision, the 2-B DDM embeds a definitive No-Go decision, whereas the 1-B DDM embeds a response window for Go. Using simulations, we found that model comparison methods like leave-one-out cross-validation (LOO), coupled with Bayesian hierarchical modeling, can correctly identify the underlying model. Additionally, using the correct model reduces the risk of missing true effects or detecting spurious findings. Therefore, we recommend researchers implement and compare both models for Go/No-Go studies to reduce misleading results. Lastly, we implemented these models to investigate race effects in the decision to shoot during police training. We found that the accumulated evidence needed to reach the Shoot decision is lower for Black suspects, which explains the heightened error rates for shooting unarmed Black suspects in data.

Go/no-go tasks are used extensively in neuropsychological testing to assess attention and inhibitory control. But because go/no-go tasks have only one response, it is possible that some responses for some subjects are fast guesses. This is critically important because fast responding can be used as a sign of impaired attention or inhibitory processing. In two-choice tasks, fast guesses can be identified using short cutoffs and examining accuracy of responses below that cutoff. When accuracy, even in the easiest conditions, is at chance, then responses are almost certainly guesses. A similar solution can be used for the go/no-go task using conditional accuracy functions. We used conditional accuracy functions to eliminate fast guesses and fit diffusion models to data from 4 go/no-go tasks and standard two-choice tasks tested on the same subjects. Nondecision time and starting points differed between the tasks but unlike earlier modeling, drift rates and the other model parameters were the same for both versions of the task. Anticipations are used in diagnosis for ADHD for example, but we found that the proportion of fast guesses for ADHD children, controls, and undergraduates were similar which shows potentially serious problems for the use of go/no-go tasks in neuropsychological testing.

Pedestrian decision-making in road-crossing scenarios involves real-time sensory integration, risk assessment, and adaptive learning based on past experiences. Traditional models such as Drift Diffusion Models (DDMs) effectively capture momentary evidence accumulation but overlook learning mechanisms, while Reinforcement Learning (RL) frameworks excel at modeling experience-driven adaptations but lack real-time decision dynamics. In this study, we introduce a novel framework, Reinforcement Learning-Drift Diffusion Model (RL-DDM) that unifies these approaches, providing a comprehensive mathematical framework. We apply this to model pedestrian crossing decisions under uncertainty. Our model incorporates dynamically modulated drift rates, urgency-dependent decision boundaries, and feedback-driven learning mechanisms, capturing how individuals adapt crossing strategies based on trial-by-trial experiences and time-to-arrival (TTA) evaluations. Using hierarchical Bayesian inference, we fit the RL-DDM to experimental pedestrian crossing data, demonstrating superior predictive accuracy over standard DDM and RL models. Results reveal that learning-based drift adjustments and collapsing boundaries significantly improve alignment with observed crossing behaviors, highlighting the interplay between real-time evidence accumulation and long-term adaptive learning. Our findings offer new insights into cognitive models of decision-making in dynamic environments, bridging mathematical psychology with transportation research and pedestrian safety interventions.