A bounded accumulation 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 (Paton & Buonomano, 2018). 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 as bounded accumulation (Balc & Simen, 2024). So far, from the range of temporal psychophysical tasks, the bounded accumulation model has only been applied to temporal bisection, in which participants are requested to categorize an interval as "long" or "short" (Balc & Simen, 2014; Ofir & Landau, 2022). In this work, we extend the 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 (Wearden, 1992). Previous models of performance in this task focused on either the group level or performance of highly trained animals (Birngruber et al., 2014; Church & Gibbon, 1982; Wearden, 1992). Whether the same models can fit performance from a few hundreds of trials of single participants, necessary for comparing performance across experimental manipulations, has not been tested. A drift-diffusion model with two decision boundaries fits the data of single participants better than the previous models. We ran 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 compared to audition, the lower boundary decreased with learning in the task.