A drift-diffusion model of temporal generalization outperforms existing models and captures modality differences and learning effects.

Multiple systems in the brain track the passage of time and can adapt their activity to temporal requirements. While the neural implementation of timing varies widely between neural substrates and behavioral tasks, at the algorithmic level, many of these behaviors can be described using drift-diffusion models of decision-making. In this work, wedevelop a drift-diffusion model to fit performance in the temporal generalization task, in which participants are required to categorize an interval as being the same or different compared to a standard, or reference, duration. The model includes a drift-diffusion process which starts with interval onset, representing the internal estimate of elapsed duration, and two boundaries. If the drift-diffusion process at interval offset is between the boundaries, the interval is categorized as equal to the standard. If it is below the lower boundary or above the upper boundary, the interval is categorized as different. This model outperformed previous models in fitting the data of single participants and in parameter recovery analyses. We also used the drift-diffusion model to analyze data from two experiments, one comparing performance between vision and audition and another examining the effect of learning. We found that decision boundaries can be modified independently: While the upper boundary was higher in vision than in audition, the lower boundary decreased with learning in the task. In both experiments, timing noise was positively correlated with upper boundaries across participants, which reflects an accuracy-maximizing strategy in the task.