Journal of the Economic Science Association

, Volume 5, Issue 1, pp 97–111 | Cite as

Estimating the dynamic role of attention via random utility

  • Stephanie M. Smith
  • Ian KrajbichEmail author
  • Ryan Webb
Original Paper


When making decisions, people tend to look back and forth between the alternatives until they eventually make a choice. Eye-tracking research has established that these shifts in attention are strongly linked to choice outcomes. A predominant framework for understanding the dynamics of the choice process, and thus the effects of attention, is sequential sampling of information. However, existing methods for estimating the attention parameters in these models are computationally costly and overly flexible, and yield estimates with unknown precision and bias. Here we propose an estimation method that relies on a link between sequential sampling models and random utility models (RUM). This method uses familiar econometric tools (i.e., logistic regression) and yields estimates that appear to be unbiased and relatively precise compared to existing methods, in a small fraction of the computation time. The RUM thus appears to be a useful tool for estimating the effects of attention on choice.


Eye tracking Sequential sampling Diffusion model Random utility aDDM Attention 

JEL Classification

C81 C91 D87 



Funding was provided by the National Science Foundation Division of Social and Economic Sciences (Grant No. 1554837) and National Science Foundation (GRFP DGE-1343012).


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Copyright information

© Economic Science Association 2019

Authors and Affiliations

  1. 1.Department of PsychologyThe Ohio State UniversityColumbusUSA
  2. 2.Department of EconomicsThe Ohio State UniversityColumbusUSA
  3. 3.Rotman School of ManagementUniversity of TorontoTorontoCanada

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