IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0226428.html

GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour

Author

Listed:
  • Felix Molter
  • Armin W Thomas
  • Hauke R Heekeren
  • Peter N C Mohr

Abstract

Recent empirical findings have indicated that gaze allocation plays a crucial role in simple decision behaviour. Many of these findings point towards an influence of gaze allocation onto the speed of evidence accumulation in an accumulation-to-bound decision process (resulting in generally higher choice probabilities for items that have been looked at longer). Further, researchers have shown that the strength of the association between gaze and choice behaviour is highly variable between individuals, encouraging future work to study this association on the individual level. However, few decision models exist that enable a straightforward characterization of the gaze-choice association at the individual level, due to the high cost of developing and implementing them. The model space is particularly scarce for choice sets with more than two choice alternatives. Here, we present GLAMbox, a Python-based toolbox that is built upon PyMC3 and allows the easy application of the gaze-weighted linear accumulator model (GLAM) to experimental choice data. The GLAM assumes gaze-dependent evidence accumulation in a linear stochastic race that extends to decision scenarios with many choice alternatives. GLAMbox enables Bayesian parameter estimation of the GLAM for individual, pooled or hierarchical models, provides an easy-to-use interface to predict choice behaviour and visualize choice data, and benefits from all of PyMC3’s Bayesian statistical modeling functionality. Further documentation, resources and the toolbox itself are available at https://glambox.readthedocs.io.

Suggested Citation

  • Felix Molter & Armin W Thomas & Hauke R Heekeren & Peter N C Mohr, 2019. "GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-23, December.
    • Handle: RePEc:plo:pone00:0226428
    DOI: 10.1371/journal.pone.0226428
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0226428
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226428&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0226428?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. K. Carrie Armel & Aurelie Beaumel & Antonio Rangel, 2008. "Biasing simple choices by manipulating relative visual attention," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 3, pages 396-403, June.
    2. Arkady Konovalov & Ian Krajbich, 2016. "Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning," Nature Communications, Nature, vol. 7(1), pages 1-11, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Geoffrey Fisher, 2023. "Measuring the Factors Influencing Purchasing Decisions: Evidence From Cursor Tracking and Cognitive Modeling," Management Science, INFORMS, vol. 69(8), pages 4558-4578, August.
    2. Fisher, Geoffrey, 2021. "A multiattribute attentional drift diffusion model," Organizational Behavior and Human Decision Processes, Elsevier, vol. 165(C), pages 167-182.
    3. Molter, Felix & Thomas, Armin W. & Heekeren, Hauke R. & Mohr, Peter N. C., 2019. "GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 14(12), pages 1-23.
    4. Amasino, Dianna R. & Dolgin, Jack & Huettel, Scott A., 2023. "Eyes on the account size: Interactions between attention and budget in consumer choice," Journal of Economic Psychology, Elsevier, vol. 97(C).
    5. Zhi-Wei Li & Wei Ji Ma, 2021. "An uncertainty-based model of the effects of fixation on choice," PLOS Computational Biology, Public Library of Science, vol. 17(8), pages 1-17, August.
    6. Kremena Valkanova, 2024. "Markov Stochastic Choice," Papers 2410.22001, arXiv.org.
    7. Vandenbroele, Jolien & Slabbinck, Hendrik & Van Kerckhove, Anneleen & Vermeir, Iris, 2021. "Mock meat in the butchery: Nudging consumers toward meat substitutes," Organizational Behavior and Human Decision Processes, Elsevier, vol. 163(C), pages 105-116.
    8. Bauer, Jan M. & Aarestrup, Simon C. & Hansen, Pelle G. & Reisch, Lucia A., 2022. "Nudging more sustainable grocery purchases: Behavioural innovations in a supermarket setting," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    9. Devdeepta Bose & Henning Cordes & Sven Nolte & Judith Christiane Schneider & Colin Farrell Camerer, 2022. "Decision Weights for Experimental Asset Prices Based on Visual Salience," The Review of Financial Studies, Society for Financial Studies, vol. 35(11), pages 5094-5126.
    10. Xiaoling Zhang & Ke Zhang & Shibo Li & Damien Koenitz, 2023. "Effects of store fixture shape at retail checkout: Evidence from field and online studies," Production and Operations Management, Production and Operations Management Society, vol. 32(10), pages 3158-3173, October.
    11. Alicia L. Rihn & Chengyan Yue, 2016. "Visual Attention's Influence on Consumers’ Willingness‐to‐Pay for Processed Food Products," Agribusiness, John Wiley & Sons, Ltd., vol. 32(3), pages 314-328, July.
    12. Fischbacher, Urs & Hausfeld, Jan & Renerte, Baiba, 2022. "Strategic incentives undermine gaze as a signal of prosocial motives," Games and Economic Behavior, Elsevier, vol. 136(C), pages 63-91.
    13. Stephanie M. Smith & Ian Krajbich & Ryan Webb, 2019. "Estimating the dynamic role of attention via random utility," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 97-111, August.
    14. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    15. Frederick Callaway & Antonio Rangel & Thomas L Griffiths, 2021. "Fixation patterns in simple choice reflect optimal information sampling," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-29, March.
    16. Nathaniel J. S. Ashby & Stephan Dickert & Andreas Glockner, 2012. "Focusing on what you own: Biased information uptake due to ownership," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 7(3), pages 254-267, May.
    17. Zuschke, Nick, 2020. "The impact of task complexity and task motivation on in-store marketing effectiveness: An eye tracking analysis," Journal of Business Research, Elsevier, vol. 116(C), pages 337-350.
    18. Jacob L Orquin & Sonja Perkovic & Klaus G Grunert, 2018. "Visual Biases in Decision Making," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 40(4), pages 523-537, December.
    19. Phillips, Aryn Z. & Rodriguez, Hector P., 2020. "U.S. county “food swamp” severity and hospitalization rates among adults with diabetes: A nonlinear relationship," Social Science & Medicine, Elsevier, vol. 249(C).
    20. Geoffrey Fisher, 2021. "Intertemporal Choices Are Causally Influenced by Fluctuations in Visual Attention," Management Science, INFORMS, vol. 67(8), pages 4961-4981, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0226428. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.