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Attentional dynamics in multi-attribute preferential choices

Authors
Ms. Xiaozhi Yang
Ohio State University ~ Psychology
Prof. Ian Krajbich
Ohio State University ~ Psychology, Economics
Abstract

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.

Tags

Keywords

attention
multi-attribute choices
drift-diffusion model

Topics

Decision Making
Accumulator/Diffusion models
Discussion
New

I really enjoy your talk and wonderful work, Xiaozhi! I have a question about the correlation analysis between omega and probability of looking at the more important attribute. If I understand correctly, omega was estimated from your model, where probs were inputs into the model, so omega estimates will inevitably affected by the gaze probs. The...

Ms. Qingfang Liu 1 comment
Context effects Last updated 3 months ago

Very good talk, and very clear. Except I have one question about fitting the model. You said you " transformed sequence of fixation dependent drift rates into a constant drift rate model" How was this done? And wouldn't this lose some of the power of the theory? Without attention switching and if you used a constant diffusion rate, then your ...

Jerome Busemeyer 1 comment
Nice talk! Last updated 3 months ago

Good talk! And I really enjoy it. I have a question: how can you validate the assumptions for your model? Speciically, is this assumption that the attention depends on gaze locations an agreement among literature? The conclusion in this paper is relatively expected, is there an intuitive explanation about why attention discounts in a multipl...

Yubai Yuan 1 comment