A growing body of research has shown that simple choices involve the construction and comparison of values at the time of decision. These processes are modulated by attention in a way that leaves decision makers susceptible to attentional biases. Here, we studied the role of peripheral visual information on the choice process and on attentional choice biases. We used an eye-tracking experiment in which participants (N = 50 adults) made binary choices between food items that were displayed in marked screen “shelves” in two conditions: (a) where both items were displayed, and (b) where items were displayed only when participants fixated within their shelves. We found that removing the nonfixated option approximately doubled the size of the attentional biases. The results show that peripheral visual information is crucial in facilitating good decisions and suggest that individuals might be influenceable by settings in which only one item is shown at a time, such as e-commerce.
ArmelK. C.BeaumelA.RangelA. (2008). Biasing simple choices by manipulating relative visual attention. Judgment and Decision Making, 3(5), 396–403. https://doi.org/10.1017/S1930297500000413
2.
ArmelK. C.RangelA. (2008). The impact of computation time and experience on decision values. American Economic Review, 98(2), 163–168. https://doi.org/10.1257/aer.98.2.163
3.
BürknerP.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80, 1–28. https://doi.org/10.18637/jss.v080.i01
4.
BürknerP.-C. (2018). Advanced Bayesian multilevel modeling with the R package brms. The R Journal, 10(1), 395–411.
5.
CallawayF.RangelA.GriffithsT. L. (2021). Fixation patterns in simple choice reflect optimal information sampling. PLOS Computational Biology, 17(3), Article e1008863. https://doi.org/10.1371/journal.pcbi.1008863
CavanaghJ. F.WieckiT. V.KocharA.FrankM. 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
8.
CavanaghS. E.MalalasekeraW. M. N.MirandaB.HuntL. T.KennerleyS. W. (2019). Visual fixation patterns during economic choice reflect covert valuation processes that emerge with learning. Proceedings of the National Academy of Sciences, USA, 116(45), 22795–22801. https://doi.org/10.1073/pnas.1906662116
FolkeT.JacobsenC.FlemingS. M.De MartinoB. (2016). Explicit representation of confidence informs future value-based decisions. Nature Human Behaviour, 1(1), Article 1. https://doi.org/10.1038/s41562-016-0002
11.
Franco-WatkinsA. M.JohnsonJ. G. (2011). Applying the decision moving window to risky choice: Comparison of eye-tracking and mouse-tracing methods. Judgment and Decision Making, 6(8), 740–749. https://doi.org/10.1017/S1930297500004174
12.
GhaffariM.FiedlerS. (2018). The power of attention: Using eye gaze to predict other-regarding and moral choices. Psychological Science, 29(11), 1878–1889. https://doi.org/10.1177/0956797618799301
13.
GluthS.KernN.KortmannM.VitaliC. L. (2020). Value-based attention but not divisive normalization influences decisions with multiple alternatives. Nature Human Behaviour, 4(6), 634–645. https://doi.org/10.1038/s41562-020-0822-0
HareT. A.MalmaudJ.RangelA. (2011). Focusing attention on the health aspects of foods changes value signals in vmPFC and improves dietary choice. Journal of Neuroscience, 31(30), 11077–11087. https://doi.org/10.1523/JNEUROSCI.6383-10.2011
17.
IttiL.KochC. (2000). A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40(10), 1489–1506. https://doi.org/10.1016/S0042-6989(99)00163-7
18.
JangA. I.SharmaR.DrugowitschJ. (2021). Optimal policy for attention-modulated decisions explains human fixation behavior. eLife, 10, Article e63436. https://doi.org/10.7554/eLife.63436
19.
JohnsonE. J.ShuS. B.DellaertB. G. C.FoxC.GoldsteinD. G.HäublG.LarrickR. P.PayneJ. W.PetersE.SchkadeD.WansinkB.WeberE. U. (2012). Beyond nudges: Tools of a choice architecture. Marketing Letters, 23(2), 487–504. https://doi.org/10.1007/s11002-012-9186-1
20.
KrajbichI.ArmelC.RangelA. (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
21.
KrajbichI.LuD.CamererC.RangelA. (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology, 3, Article 193. https://doi.org/10.3389/fpsyg.2012.00193
22.
KrajbichI.RangelA. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Sciences, USA, 108(33), 13852–13857. https://doi.org/10.1073/pnas.1101328108
23.
KunarM. A.WatsonD. G.TsetsosK.ChaterN. (2017). The influence of attention on value integration. Attention, Perception, & Psychophysics, 79(6), 1615–1627. https://doi.org/10.3758/s13414-017-1340-7
24.
LiZ.-W.MaW. J. (2021). An uncertainty-based model of the effects of fixation on choice. PLOS Computational Biology, 17(8), Article e1009190. https://doi.org/10.1371/journal.pcbi.1009190
25.
LombardiG.HareT. (2021). Piecewise constant averaging methods allow for fast and accurate hierarchical Bayesian estimation of drift diffusion models with time-varying evidence accumulation rates [Preprint]. https://doi.org/10.31234/osf.io/5azyx
26.
MachnerB.DorrM.SprengerA.von der GablentzJ.HeideW.BarthE.HelmchenC. (2012). Impact of dynamic bottom-up features and top-down control on the visual exploration of moving real-world scenes in hemispatial neglect. Neuropsychologia, 50(10), 2415–2425. https://doi.org/10.1016/j.neuropsychologia.2012.06.012
27.
MachnerB.LencerM. C.MöllerL.von der GablentzJ.HeideW.HelmchenC.SprengerA. (2020). Unbalancing the attentional priority map via gaze-contingent displays induces neglect-like visual exploration. Frontiers in Human Neuroscience, 14, Article 41. https://doi.org/10.3389/fnhum.2020.00041
28.
MormannM.RussoJ. E. (2021). Does attention increase the value of choice alternatives?Trends in Cognitive Sciences, 25(4), 305–315. https://doi.org/10.1016/j.tics.2021.01.004
29.
PärnametsP.JohanssonP.HallL.BalkeniusC.SpiveyM. J.RichardsonD. C. (2015). Biasing moral decisions by exploiting the dynamics of eye gaze. Proceedings of the National Academy of Sciences, USA, 112(13), 4170–4175. https://doi.org/10.1073/pnas.1415250112
30.
PerkovicS.SchoemannM.LagerkvistC.-J.OrquinJ. L. (2022). Covert attention leads to fast and accurate decision-making. Journal of Experimental Psychology: Applied. Advance online publication. https://doi.org/10.1037/xap0000425
31.
PeschelA. O.OrquinJ. L.Mueller LooseS. (2019). Increasing consumers’ attention capture and food choice through bottom-up effects. Appetite, 132, 1–7. https://doi.org/10.1016/j.appet.2018.09.015
32.
RatcliffR.McKoonG. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. https://doi.org/10.1162/neco.2008.12-06-420
33.
RatcliffR.SmithP. L.BrownS. D.McKoonG. (2016). Diffusion decision model: Current issues and history. Trends in Cognitive Sciences, 20(4), 260–281. https://doi.org/10.1016/j.tics.2016.01.007
34.
R Core Team. (2022). R: A language and environment for statistical computing (Version 4.1.3) [Computer software]. R Foundation for Statistical Computing. http://www.R-project.org
35.
SepulvedaP.UsherM.DaviesN.BensonA. A.OrtolevaP.De MartinoB. (2020). Visual attention modulates the integration of goal-relevant evidence and not value. eLife, 9, Article e60705. https://doi.org/10.7554/eLife.60705
36.
ShimojoS.SimionC.ShimojoE.ScheierC. (2003). Gaze bias both reflects and influences preference. Nature Neuroscience, 6(12), 1317–1322. https://doi.org/10.1038/nn1150
37.
SimionC.ShimojoS. (2006). Early interactions between orienting, visual sampling and decision making in facial preference. Vision Research, 46(20), 3331–3335. https://doi.org/10.1016/j.visres.2006.04.019
38.
SmithS. M.KrajbichI. (2018). Attention and choice across domains. Journal of Experimental Psychology: General, 147(12), 1810–1826. https://doi.org/10.1037/xge0000482
TavaresG.PeronaP.RangelA. (2017). The attentional drift diffusion model of simple perceptual decision-making. Frontiers in Neuroscience, 11, Article 468. https://doi.org/10.3389/fnins.2017.00468
TowalR. B.MormannM.KochC. (2013). Simultaneous modeling of visual saliency and value computation improves predictions of economic choice. Proceedings of the National Academy of Sciences, USA, 110(40), E3858–E3867. https://doi.org/10.1073/pnas.1304429110
43.
WästlundE.ShamsP.OtterbringT. (2018). Unsold is unseen . . . or is it? Examining the role of peripheral vision in the consumer choice process using eye-tracking methodology. Appetite, 120, 49–56. https://doi.org/10.1016/j.appet.2017.08.024
44.
WeilbächerR. A.KrajbichI.RieskampJ.GluthS. (2021). The influence of visual attention on memory-based preferential choice. Cognition, 215, Article 104804. https://doi.org/10.1016/j.cognition.2021.104804
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
