Attention as a source of overall value effects in diffusion models

Research has demonstrated that value-based decisions depend not only on the relative difference between options, but also on their overall value. In particular, response times tend to decrease as the overall value of the alternatives 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, attention-based models that incorporate eye-tracking data produce this overall value effect as a direct consequence of the multiplicative relationship between attention and value. Using a non-attentional diffusion model fit to data simulated with an attention-based model, we find that parameters related to decision thresholds or noise vary as a function of overall value, even though these were not features of the data-generating process. We find additional evidence for misidentified parameters in a similar analysis using two empirical datasets. In both datasets, models that incorporated attention-based evidence accumulation provided superiors fits to the empirical data and led to different conclusions about value-based boundaries. Our results indicate that neglecting attentional effects can lead to mistaken conclusions about which decision parameters (e.g., noise or thresholds) are sensitive to overall value.