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.
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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.
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.
Von Neumann, J. & Morgenstern, O. Theory of Games and Economic Behavior (Princeton Univ. Press, 1944).
Luce, R. D. & Raiffa, H. Games and Decisions: Introduction and Critical Survey (Wiley, 1957).
Armel, K. C., Beaumel, A. & Rangel, A. Biasing simple choices by manipulating relative visual attention. Judgm. Decis. Mak. 3, 396–403 (2008).
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).
Fiedler, S. & Glöckner, A. The dynamics of decision making in risky choice: an eye-tracking analysis. Front. Psychol. 3, 335 (2012).
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).
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).
Konovalov, A. & Krajbich, I. Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning. Nat. Commun. 7, 12438 (2016).
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).
Krajbich, I., Armel, C. & Rangel, A. Visual fixations and the computation and comparison of value in simple choice. Nat. Neurosci. 13, 1292–1298 (2010).
Krajbich, I., Lu, D., Camerer, C. & Rangel, A. The attentional drift-diffusion model extends to simple purchasing decisions. Front. Pyschol. 3, 193 (2012).
Pärnamets, P. et al. Biasing moral decisions by exploiting the dynamics of eye gaze. Proc. Natl Acad. Sci. USA 112, 4170–4175 (2015).
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).
Shimojo, S., Simion, C., Shimojo, E. & Scheier, C. Gaze bias both reflects and influences preference. Nat. Neurosci. 6, 1317–1322 (2003).
Stewart, N., Hermens, F. & Matthews, W. J. Eye movements in risky choice. J. Behav. Decis. Mak. 29, 116–136 (2016).
Stewart, N., Gächter, S., Noguchi, T. & Mullett, T. L. Eye movements in strategic choice. J. Behav. Decis. Mak. 29, 137–156 (2016).
Vaidya, A. R. & Fellows, L. K. Testing necessary regional frontal contributions to value assessment and fixation-based updating. Nat. Commun. 6, 10120 (2015).
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).
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).
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).
Tavares, G., Perona, P. & Rangel, A. The attentional drift diffusion model of simple perceptual decision-making. Front. Neurosci. 11, 468 (2017).
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).
Fisher, G. An attentional drift diffusion model over binary-attribute choice. Cognition 168, 34–45 (2017).
Gluth, S., Spektor, M. S. & Rieskamp, J. Value-based attentional capture affects multi-alternative decision making. eLife 7, e39659 (2018).
Ratcliff, R. A theory of memory retrieval. Psychol. Rev. 85, 59–108 (1978).
Ratcliff, R., Smith, P. L., Brown, S. D. & McKoon, G. Diffusion decision model: current issues and history. Trends Cogn. Sci. 20, 260–281 (2016).
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).
Lewandowsky, S. & Farrell, S. Computational Modeling in Cognition: Principles and Practice (SAGE Publications, 2010).
Hayes, K. J. The backward curve: a method for the study of learning. Psychol. Rev. 60, 269–275 (1953).
Itti, L. & Koch, C. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Res. 40, 1489–1506 (2000).
Becker, G. M., DeGroot, M. H. & Marschak, J. Measuring utility by a single-response sequential method. Behav. Sci. 9, 226–232 (1964).
Tillman, G. The racing diffusion model of speeded decision making. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/xuwbk (2017).
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).
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).
Lopez-Persem, A., Domenech, P. & Pessiglione, M. How prior preferences determine decision-making frames and biases in the human brain. eLife 5, e20317 (2016).
Krajbich, I. Accounting for attention in sequential sampling models of decision making. Curr. Opin. Psychol. 29, 6–11 (2019).
Smith, S. M. & Krajbich, I. Gaze amplifies value in decision making. Psychol. Sci. 30, 116–128 (2019).
Ratcliff, R., Thapar, A. & McKoon, G. Individual differences, aging, and IQ in two-choice tasks. Cognit. Psychol. 60, 127–157 (2010).
Ratcliff, R., Thapar, A. & McKoon, G. Aging and individual differences in rapid two-choice decisions. Psychon. Bull. Rev. 13, 626–635 (2006).
Smith, S. M. & Krajbich, I. Attention and choice across domains. J. Exp. Psychol. Gen. 147, 1810–1826 (2018).
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).
Nunez, M. D., Srinivasan, R. & Vandekerckhove, J. Individual differences in attention influence perceptual decision making. Front. Psychol. 8, 18 (2015).
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).
Hunt, L. T. et al. Triple dissociation of attention and decision computations across prefrontal cortex. Nat. Neurosci. 21, 1471 (2018).
McGinty, V. B., Rangel, A. & Newsome, W. T. Orbitofrontal cortex value signals depend on fixation location during free viewing. Neuron 90, 1299–1311 (2016).
Wald, A. Sequential Analysis (Courier Corp., 1973).
Salvatier, J., Wiecki, T. V. & Fonnesbeck, C. Probabilistic programming in Python using PyMC3. PeerJ Comput. Sci. 2, e55 (2016).
Wiecki, T. V., Sofer, I. & Frank, M. J. HDDM: hierarchical Bayesian estimation of the drift-diffusion model in Python. Front. Neuroinform. 7, 14 (2013).
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).
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).
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).
Westfall, J. Statistical details of the default priors in the Bambi library. Preprint at arXiv https://arxiv.org/abs/1702.01201 (2017).
Kruschke, J. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (Academic Press, 2014).
Oliphant, T. E. Python for scientific computing. Comput. Sci. Eng. 9, 10–20 (2007).
McKinney, W. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (O’Reilly Media, 2012).
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).
The Theano Development Team. Theano: a Python framework for fast computation of mathematical expressions. Preprint at arXiv https://arxiv.org/abs/1605.02688 (2016).
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Folke, T. Explicit representations of confidence informs future value-based decisions. Figshare https://doi.org/10.6084/m9.figshare.3756144.v2(2016).
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.
The authors declare no competing interests.
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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
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