Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Gaze bias differences capture individual choice behaviour

Abstract

How do we make simple choices such as deciding between an apple and an orange? Recent empirical evidence suggests that choice behaviour and gaze allocation are closely linked at the group level, whereby items looked at longer during the decision-making process are more likely to be chosen. However, it is unclear how variable this gaze bias effect is between individuals. Here we investigate this question across four different simple choice experiments and using a computational model that can be easily applied to individuals. We show that an association between gaze and choice is present for most individuals, but differs considerably in strength. Generally, individuals with a strong association between gaze and choice behaviour are worse at choosing the best item from a choice set compared with individuals with a weak association. Accounting for individuals’ variability in gaze bias in the model can explain and accurately predict individual differences in choice behaviour.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Experimental paradigms.
Fig. 2: Individual differences in the three studied behavioural metrics and their associations.
Fig. 3: The GLAM.
Fig. 4: Individual relative model comparison between the full GLAM and a restricted no-gaze-bias GLAM variant.
Fig. 5: Individual out-of-sample predictions of behavioural metrics for all odd-numbered trials.
Fig. 6: Associations between individuals’ response behaviour in the odd-numbered trials and the model parameters estimated from the even-numbered trials.

Data availability

All datasets are available at http://www.github.com/glamlab/gaze-bias-differences. The Folke 2016 dataset59 is originally available at figshare: https://doi.org/10.6084/m9.figshare.3756144.v2.

Code availability

All analyses and figures can be reproduced using the datasets, scripts and GLAM resources that are available at http://www.github.com/glamlab/gaze-bias-differences.

References

  1. 1.

    Von Neumann, J. & Morgenstern, O. Theory of Games and Economic Behavior (Princeton Univ. Press, 1944).

  2. 2.

    Luce, R. D. & Raiffa, H. Games and Decisions: Introduction and Critical Survey (Wiley, 1957).

  3. 3.

    Armel, K. C., Beaumel, A. & Rangel, A. Biasing simple choices by manipulating relative visual attention. Judgm. Decis. Mak. 3, 396–403 (2008).

    Google Scholar 

  4. 4.

    Cavanagh, J. F., Wiecki, T. V., Kochar, A. & Frank, M. J. Eye tracking and pupillometry are indicators of dissociable latent decision processes. J. Exp. Psychol. Gen. 143, 1476–1488 (2014).

    Article  Google Scholar 

  5. 5.

    Fiedler, S. & Glöckner, A. The dynamics of decision making in risky choice: an eye-tracking analysis. Front. Psychol. 3, 335 (2012).

    Article  Google Scholar 

  6. 6.

    Folke, T., Jacobsen, C., Fleming, S. M. & De Martino, B. Explicit representation of confidence informs future value-based decisions. Nat. Hum. Behav. 1, 0002 (2017).

    Article  Google Scholar 

  7. 7.

    Glöckner, A. & Herbold, A.-K. An eye-tracking study on information processing in risky decisions: evidence for compensatory strategies based on automatic processes. J. Behav. Decis. Mak. 24, 71–98 (2011).

    Article  Google Scholar 

  8. 8.

    Konovalov, A. & Krajbich, I. Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning. Nat. Commun. 7, 12438 (2016).

    CAS  Article  Google Scholar 

  9. 9.

    Krajbich, I. & Rangel, A. Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proc. Natl Acad. Sci. USA 108, 13852–13857 (2011).

    CAS  Article  Google Scholar 

  10. 10.

    Krajbich, I., Armel, C. & Rangel, A. Visual fixations and the computation and comparison of value in simple choice. Nat. Neurosci. 13, 1292–1298 (2010).

    CAS  Article  Google Scholar 

  11. 11.

    Krajbich, I., Lu, D., Camerer, C. & Rangel, A. The attentional drift-diffusion model extends to simple purchasing decisions. Front. Pyschol. 3, 193 (2012).

    Google Scholar 

  12. 12.

    Pärnamets, P. et al. Biasing moral decisions by exploiting the dynamics of eye gaze. Proc. Natl Acad. Sci. USA 112, 4170–4175 (2015).

    Article  Google Scholar 

  13. 13.

    Roe, R. M., Busemeyer, J. R. & Townsend, J. T. Multialternative decision field theory: a dynamic connectionist model of decision making. Psychol. Rev. 108, 370 (2001).

    CAS  Article  Google Scholar 

  14. 14.

    Shimojo, S., Simion, C., Shimojo, E. & Scheier, C. Gaze bias both reflects and influences preference. Nat. Neurosci. 6, 1317–1322 (2003).

    CAS  Article  Google Scholar 

  15. 15.

    Stewart, N., Hermens, F. & Matthews, W. J. Eye movements in risky choice. J. Behav. Decis. Mak. 29, 116–136 (2016).

    Article  Google Scholar 

  16. 16.

    Stewart, N., Gächter, S., Noguchi, T. & Mullett, T. L. Eye movements in strategic choice. J. Behav. Decis. Mak. 29, 137–156 (2016).

    Article  Google Scholar 

  17. 17.

    Vaidya, A. R. & Fellows, L. K. Testing necessary regional frontal contributions to value assessment and fixation-based updating. Nat. Commun. 6, 10120 (2015).

    CAS  Article  Google Scholar 

  18. 18.

    Tsetsos, K., Chater, N. & Usher, M. Salience driven value integration explains decision biases and preference reversal. Proc. Natl Acad. Sci. USA 109, 9659–9664 (2012).

    CAS  Article  Google Scholar 

  19. 19.

    Milosavljevic, M., Navalpakkam, V., Koch, C. & Rangel, A. Relative visual saliency differences induce sizable bias in consumer choice. J. Consum. Psychol. 22, 67–74 (2012).

    Article  Google Scholar 

  20. 20.

    Towal, R. B., Mormann, M. & Koch, C. Simultaneous modeling of visual saliency and value computation improves predictions of economic choice. Proc. Natl Acad. Sci. USA 110, E3858–E3867 (2013).

    CAS  Article  Google Scholar 

  21. 21.

    Tavares, G., Perona, P. & Rangel, A. The attentional drift diffusion model of simple perceptual decision-making. Front. Neurosci. 11, 468 (2017).

    Article  Google Scholar 

  22. 22.

    Ashby, N. J. S., Jekel, M., Dickert, S. & Glöckner, A. Finding the right fit: a comparison of process assumptions underlying popular drift-diffusion models. J. Exp. Psychol. Learn. Mem. Cogn. 42, 1982–1993 (2016).

    Article  Google Scholar 

  23. 23.

    Fisher, G. An attentional drift diffusion model over binary-attribute choice. Cognition 168, 34–45 (2017).

    Article  Google Scholar 

  24. 24.

    Gluth, S., Spektor, M. S. & Rieskamp, J. Value-based attentional capture affects multi-alternative decision making. eLife 7, e39659 (2018).

    Article  Google Scholar 

  25. 25.

    Ratcliff, R. A theory of memory retrieval. Psychol. Rev. 85, 59–108 (1978).

    Article  Google Scholar 

  26. 26.

    Ratcliff, R., Smith, P. L., Brown, S. D. & McKoon, G. Diffusion decision model: current issues and history. Trends Cogn. Sci. 20, 260–281 (2016).

    Article  Google Scholar 

  27. 27.

    Grandy, T. H., Lindenberger, U. & Werkle-Bergner, M. When group means fail: can one size fit all? Preprint at biorXiv https://doi.org/10.1101/126490 (2017).

  28. 28.

    Lewandowsky, S. & Farrell, S. Computational Modeling in Cognition: Principles and Practice (SAGE Publications, 2010).

  29. 29.

    Hayes, K. J. The backward curve: a method for the study of learning. Psychol. Rev. 60, 269–275 (1953).

    CAS  Article  Google Scholar 

  30. 30.

    Itti, L. & Koch, C. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Res. 40, 1489–1506 (2000).

    CAS  Article  Google Scholar 

  31. 31.

    Becker, G. M., DeGroot, M. H. & Marschak, J. Measuring utility by a single-response sequential method. Behav. Sci. 9, 226–232 (1964).

    CAS  Article  Google Scholar 

  32. 32.

    Tillman, G. The racing diffusion model of speeded decision making. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/xuwbk (2017).

  33. 33.

    Usher, M., Olami, Z. & McClelland, J. L. Hick’s Law in a stochastic race model with speed–accuracy tradeoff. J. Math. Psychol. 46, 704–715 (2002).

    Article  Google Scholar 

  34. 34.

    Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).

    Article  Google Scholar 

  35. 35.

    Lopez-Persem, A., Domenech, P. & Pessiglione, M. How prior preferences determine decision-making frames and biases in the human brain. eLife 5, e20317 (2016).

    Article  Google Scholar 

  36. 36.

    Krajbich, I. Accounting for attention in sequential sampling models of decision making. Curr. Opin. Psychol. 29, 6–11 (2019).

    Article  Google Scholar 

  37. 37.

    Smith, S. M. & Krajbich, I. Gaze amplifies value in decision making. Psychol. Sci. 30, 116–128 (2019).

    Article  Google Scholar 

  38. 38.

    Ratcliff, R., Thapar, A. & McKoon, G. Individual differences, aging, and IQ in two-choice tasks. Cognit. Psychol. 60, 127–157 (2010).

    Article  Google Scholar 

  39. 39.

    Ratcliff, R., Thapar, A. & McKoon, G. Aging and individual differences in rapid two-choice decisions. Psychon. Bull. Rev. 13, 626–635 (2006).

    Article  Google Scholar 

  40. 40.

    Smith, S. M. & Krajbich, I. Attention and choice across domains. J. Exp. Psychol. Gen. 147, 1810–1826 (2018).

    Article  Google Scholar 

  41. 41.

    Reutskaja, E., Nagel, R., Camerer, C. F. & Rangel, A. Search dynamics in consumer choice under time pressure: an eye-tracking study. Am. Econ. Rev. 101, 900–926 (2011).

    Article  Google Scholar 

  42. 42.

    Nunez, M. D., Srinivasan, R. & Vandekerckhove, J. Individual differences in attention influence perceptual decision making. Front. Psychol. 8, 18 (2015).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Nunez, M. D., Vandekerckhove, J. & Srinivasan, R. How attention influences perceptual decision making: single-trial EEG correlates of drift-diffusion model parameters. J. Math. Psychol. 76, 117–130 (2017).

    Article  Google Scholar 

  44. 44.

    Hunt, L. T. et al. Triple dissociation of attention and decision computations across prefrontal cortex. Nat. Neurosci. 21, 1471 (2018).

    CAS  Article  Google Scholar 

  45. 45.

    McGinty, V. B., Rangel, A. & Newsome, W. T. Orbitofrontal cortex value signals depend on fixation location during free viewing. Neuron 90, 1299–1311 (2016).

    CAS  Article  Google Scholar 

  46. 46.

    Wald, A. Sequential Analysis (Courier Corp., 1973).

  47. 47.

    Salvatier, J., Wiecki, T. V. & Fonnesbeck, C. Probabilistic programming in Python using PyMC3. PeerJ Comput. Sci. 2, e55 (2016).

    Article  Google Scholar 

  48. 48.

    Wiecki, T. V., Sofer, I. & Frank, M. J. HDDM: hierarchical Bayesian estimation of the drift-diffusion model in Python. Front. Neuroinform. 7, 14 (2013).

    Article  Google Scholar 

  49. 49.

    Ratcliff, R. & Tuerlinckx, F. Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychon. Bull. Rev. 9, 438–481 (2002).

    Article  Google Scholar 

  50. 50.

    Hoffman, M. D. & Gelman, A. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15, 1593–1623 (2014).

    Google Scholar 

  51. 51.

    Yarkoni, T. & Westfall, J. Bambi: a simple interface for fitting Bayesian mixed effects models. Preprint at OSF Preprints https://doi.org/10.31219/osf.io/rv7sn (2016).

  52. 52.

    Westfall, J. Statistical details of the default priors in the Bambi library. Preprint at arXiv https://arxiv.org/abs/1702.01201 (2017).

  53. 53.

    Kruschke, J. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (Academic Press, 2014).

  54. 54.

    Oliphant, T. E. Python for scientific computing. Comput. Sci. Eng. 9, 10–20 (2007).

    CAS  Article  Google Scholar 

  55. 55.

    McKinney, W. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (O’Reilly Media, 2012).

  56. 56.

    Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. In Proc. 9th Python in Science Conference (Eds van der Walt, S. & Millman, J.) 57–61 (SciPy, 2010).

  57. 57.

    The Theano Development Team. Theano: a Python framework for fast computation of mathematical expressions. Preprint at arXiv https://arxiv.org/abs/1605.02688 (2016).

  58. 58.

    Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Article  Google Scholar 

  59. 59.

    Folke, T. Explicit representations of confidence informs future value-based decisions. Figshare https://doi.org/10.6084/m9.figshare.3756144.v2(2016).

Download references

Acknowledgements

The Junior Professorship of P.N.C.M. as well as the associated Dahlem International Network Junior Research Group Neuroeconomics is supported by Freie Universität Berlin within the Excellence Initiative of the German Research Foundation (DFG). Further support for P.N.C.M. is provided by the WZB Berlin Social Science Center. F.M. is supported by the International Max Planck Research School on the Life Course (LIFE). I.K. is funded by the National Science Foundation Career Award 1554837. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

Authors

Contributions

A.W.T. and F.M. contributed equally to the manuscript and share first authorship. A.W.T. and F.M. conceived of the GLAM, implemented all visualizations of the experimental procedures and performed all formal data analyses. A.W.T. and F.M. also co-wrote all software that was used in the data analyses that underlies the GLAM. A.W.T. and F.M. wrote the original draft of the manuscript, and I.K., H.R.H. and P.N.C.M. reviewed and edited the manuscript. Funding for this work was acquired by P.N.C.M. The work was supervised by H.R.H. and P.N.C.M.

Corresponding author

Correspondence to Peter N. C. Mohr.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods 1–3, Supplementary Figures 1–7, Supplementary Table 1, and Supplementary References.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Thomas, A.W., Molter, F., Krajbich, I. et al. Gaze bias differences capture individual choice behaviour. Nat Hum Behav 3, 625–635 (2019). https://doi.org/10.1038/s41562-019-0584-8

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing