Attention as a source of variability in decision-making: Accounting for overall-value effects with diffusion models

Abstract

Research has demonstrated that value-based decisions depend on not only the relative value difference between options, but also their overall value. In particular, response times tend to decrease as the overall (summed) value of the options increase. Standard sequential sampling models such as the diffusion model can account for this fact by assuming that decision thresholds or noise vary with overall value. Alternatively, gaze-based models that incorporate eye-tracking data can accommodate this overall-value effect directly as a consequence of the multiplicative relationship between gaze and option value. We compare the fit of non-gaze diffusion models to data simulated with a multiplicative-gaze model. The results show how parameters related to decision thresholds or noise will vary as a function of overall value, even when there is no such variability in the data generating process. In empirical data, we find similar patterns where decision thresholds, noise, or non-decision time seem to vary with overall value. Our results reveal that specific patterns in standard diffusion model parameters can arise from a latent process of gaze-dependent evidence accumulation.