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Estimating the dynamic role of attention via random utility

Published online by Cambridge University Press:  01 January 2025

Stephanie M. Smith ,
Ian Krajbich Open the ORCID record for Ian Krajbich [Opens in a new window]  and
Ryan Webb
Stephanie M. Smith
Affiliation:
Department of Psychology, The Ohio State University, Columbus, USA
Ian Krajbich*
Affiliation:
Department of Psychology, The Ohio State University, Columbus, USA Department of Economics, The Ohio State University, Columbus, USA
Ryan Webb
Affiliation:
Rotman School of Management, University of Toronto, Toronto, Canada
*
e-mail: [email protected]
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Abstract

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.

Keywords

Eye trackingSequential samplingDiffusion modelRandom utilityaDDMAttention

JEL classification

D87: Neuroeconomics C91: Laboratory, Individual Behavior C81: Methodology for Collecting, Estimating, and Organizing Microeconomic Data • Data Access
Type
Original Paper
Information
Journal of the Economic Science Association , Volume 5 , Issue 1: Special Issue: Choice-Process Data in Experimental Economics , August 2019 , pp. 97 - 111
Copyright
Copyright © Economic Science Association 2019

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