Attentional dynamics in multi-attribute preferential choices

When making decisions, how people allocate their attention influences their choices. One empirical finding is that people are more likely to choose the option that they have looked at more. This relation has been formalized with the attentional drift-diffusion model (aDDM; Krajbich et al., 2010). However, options often have multiple attributes. Attention is also thought to govern the relative weighting of those attributes (Roe et al. 2001). However, little is known about how these two distinct features of the choice process interact; we still lack a model (and tests of that model) that incorporate both option and attribute-wise attention. Here, we propose a multi-attribute attentional drift-diffusion model to account for attentional discount factors on both options and attributes. We then use five eye-tracking datasets (two-alternative, two-attribute preferential tasks) from different choice domains to test the model. We find very stable option-level and attribute-level attentional discount factors across datasets, though non-fixated options are consistently discounted more than non-fixated attributes. Additionally, we find that people generally discount the non-fixated attribute of the non-fixated option in a multiplicative way, and so that feature is consistently discounted the most. Finally, we also find that gaze allocation reflects attribute weights, with more gaze to higher-weighted attributes. In summary, our work uncovers an intricate interplay between attribute weights, gaze processes, and preferential choice.