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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Advances in mobile eye-tracking have enabled field research in the area. In a typical study, participants wear glasses with embedded eye-tracking technology while they shop in stores. The glasses record both eye movements and the environment, allowing researchers to investigate the role of attention in a field setting (e.g., Harwood and Jones 2014; Bagdziunaite et al. 2014). While academic research in this area is nascent, a number of commercial enterprises are already collecting and using this data. Three examples include: (1) market research firms like Nielsen are currently using eye tracking to measure consumer responses to advertisements; (2) Consumer-grade eye tracking is being tested and implemented by a number of firms, for both general use (Google Glass) as well as specific uses (VR headsets); and (3) Retailors like Amazon are implementing high-definition, high-density, camera technology in brick-and-mortar locations that can track a shopper’s attention to specific products on the shelf. We expect further technological advances to be made in this area. For instance, researchers can take advantage of front-facing cameras on phones, tablets, and computers to better understand how consumers interact with and attend to the information on their screens.
For an example of how components of value can be estimated from observable attributes in the DDM framework, see Chiong et al. (2018).
In many applications, an “initial point” Zi,0 can be specified to allow for any priors that decision makers might have.
It should be noted that the distribution of choice probabilities is not completely independent of the order of attention in this model. For example, a process with high autocorrelation in the gaze pattern will lead to an earlier boundary crossing than a process with low autocorrelation, even with equal ex ante division of attention in both cases. This will be captured by the distribution of ηi implied by Proposition 1 and how it depends on time.
For a related derivation of θ , though using a DDM-based method rather than Logit, see Cavanagh et al. 2014.
References
Amasino, D. R., Sullivan, N. J., Kranton, R. E., & Huettel, S. A. (2019). Amount and time exert independent influences on intertemporal choice. Nature Human Behaviour. https://doi.org/10.1038/s41562-019-0537-2
Arieli, A., Ben-Ami, Y., & Rubinstein, A. (2011). Tracking decision makers under uncertainty. American Economic Journal: Microeconomics, 3(4), 68–76.
Armel, K. C., Beaumel, A., & Rangel, A. (2008). Biasing simple choices by manipulating relative visual attention. Judgment and Decision Making, 3(5), 396–403.
Ashby, N. J. S., Jekel, M., Dickert, S., & Glöckner, A. (2016). Finding the right fit: A comparison of process assumptions underlying popular drift-diffusion models. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10.1037/xlm0000279.
Bagdziunaite, D., Nassri, K., Clement, J., & Ramsøy, T. Z. (2014). An added value of neuroscientific tools to understand consumers’ in-store behaviour. In EMAC 2014.
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 13(1), 281–305.
Cavanagh, J. F., Wiecki, T. V., Kochar, A., & Frank, M. J. (2014). Eye tracking and pupillometry are indicators of dissociable latent decision processes. Journal of Experimental Psychology: General, 143(4), 1476–1488. https://doi.org/10.1037/a0035813.
Chen, W. J., & Krajbich, I. (2017). Computational modeling of epiphany learning. Proceedings of the National Academy of Sciences, 114(18), 4637–4642. https://doi.org/10.1073/pnas.1618161114.
Chiong, K., Shum, M., Webb, R., & Chen, R. (2018). Split-second decision-making in the field: Response times in mobile advertising. SSRN Working Paper.
Devetag, G., Di Guida, S., & Polonio, L. (2016). An eye-tracking study of feature-based choice in one-shot games. Experimental Economics, 19(1), 177–201.
Echenique, F., & Saito, K. (2017). Response time and utility. Journal of Economic Behavior & Organization, 139, 49–59. https://doi.org/10.1016/j.jebo.2017.04.008.
Fehr, E., & Rangel, A. (2011). Neuroeconomic foundations of economic choice—Recent advances. The Journal of Economic Perspectives, 25(4), 3–30.
Fisher, G. (2017). An attentional drift diffusion model over binary-attribute choice. Cognition, 168, 34–45. https://doi.org/10.1016/j.cognition.2017.06.007.
Fudenberg, D., Strack, P., & Strzalecki, T. (2018). Speed, accuracy, and the optimal timing of choices. American Economic Review, 108(12), 3651–3684. https://doi.org/10.1257/aer.20150742.
Gabaix, X. (2017). Behavioral Inattention (No. w24096). Cambridge, MA: National Bureau of Economic Research. https://doi.org/10.3386/w24096.
Gossner, C., Steiner, J., & Stewart, C. (2018). Attention Please!*, 34. Working paper, University of Toronto Department of Economics
Harwood, T., & Jones, M. (2014). Mobile eye-tracking in retail research. In M. Horsley, M. Eliot, B. A. Knight, & R. Reilly (Eds.), Current trends in eye tracking research (pp. 183–199). Cham: Springer. https://doi.org/10.1007/978-3-319-02868-2_14.
Knoepfle, D. T., Tao-yi Wang, J., & Camerer, C. F. (2009). Studying learning in games using eye-tracking. Journal of the European Economic Association, 7(2–3), 388–398. https://doi.org/10.1162/JEEA.2009.7.2-3.388.
Konovalov, A., & Krajbich, I. (2016). Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning. Nature Communications, 7, 12438. https://doi.org/10.1038/ncomms12438.
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292–1298. https://doi.org/10.1038/nn.2635.
Krajbich, I., Lu, D., Camerer, C., & Rangel, A. (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology, 3. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3374478/.
Krajbich, I., Oud, B., & Fehr, E. (2014). Benefits of neuroeconomic modeling: New policy interventions and predictors of preference. The American Economic Review, 104(5), 501–506.
Krajbich, I., & Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Sciences, 108(33), 13852–13857. https://doi.org/10.1073/pnas.1101328108.
Lahey, J. N., & Oxley, D. (2016). The power of eye tracking in economics experiments. American Economic Review, 106(5), 309–313. https://doi.org/10.1257/aer.p20161009.
Pärnamets, P., Johansson, P., Hall, L., Balkenius, C., Spivey, M. J., & Richardson, D. C. (2015). Biasing moral decisions by exploiting the dynamics of eye gaze. Proceedings of the National Academy of Sciences, 112(13), 4170–4175.
Polonio, L., Di Guida, S., & Coricelli, G. (2015). Strategic sophistication and attention in games: An eye-tracking study. Games and Economic Behavior, 94, 80–96. https://doi.org/10.1016/j.geb.2015.09.003.
Reutskaja, E., Nagel, R., Camerer, C. F., & Rangel, A. (2011). Search dynamics in consumer choice under time pressure: An eye-tracking study. American Economic Review, 101(2), 900–926. https://doi.org/10.1257/aer.101.2.900.
Shi, S. W., Wedel, M., & Pieters, F. G. M. (2013). Information acquisition during online decision making: A model-based exploration using eye-tracking data. Management Science, 59(5), 1009–1026.
Sims, C. A. (2003). Implications of rational inattention. Journal of Monetary Economics, 50(3), 665–690. https://doi.org/10.1016/S0304-3932(03)00029-1.
Smith, S. M., & Krajbich, I. (2018). Attention and choice across domains. Journal of Experimental Psychology: General. https://doi.org/10.1037/xge0000482.
Smith, S. M., & Krajbich, I. (2019). Gaze amplifies value in decision making. Psychological Science, 30(1), 116–128. https://doi.org/10.1177/0956797618810521.
Stewart, N., Hermens, F., & Matthews, W. J. (2015). Eye movements in risky choice. Journal of Behavioral Decision Making. Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/bdm.1854/full.
Towal, R. B., Mormann, M., & Koch, C. (2013). Simultaneous modeling of visual saliency and value computation improves predictions of economic choice. Proceedings of the National Academy of Sciences, 110(40), E3858–E3867.
Vaidya, A. R., & Fellows, L. K. (2015). Testing necessary regional frontal contributions to value assessment and fixation-based updating. Nature Communications, 6, 10120. https://doi.org/10.1038/ncomms10120.
Wang, J. T., Spezio, M., & Camerer, C. F. (2010). Pinocchio’s pupil: Using eye tracking and pupil dilation to understand truth telling and deception in sender-receiver games. American Economic Review, 100(3), 984–1007. https://doi.org/10.1257/aer.100.3.984.
Webb, R. (2019). The (neural) dynamics of stochastic choice. Management Science, 65(1), 230–255. https://doi.org/10.1287/mnsc.2017.2931.
Woodford, M. (2014). Stochastic choice: An optimizing neuroeconomic model. The American Economic Review, 104(5), 495–500.
Acknowledgements
Funding was provided by the National Science Foundation Division of Social and Economic Sciences (Grant No. 1554837) and National Science Foundation (GRFP DGE-1343012).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Smith, S.M., Krajbich, I. & Webb, R. Estimating the dynamic role of attention via random utility. J Econ Sci Assoc 5, 97–111 (2019). https://doi.org/10.1007/s40881-019-00062-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40881-019-00062-4