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.

  • Article
  • Published:

Changing value through cued approach: an automatic mechanism of behavior change

Abstract

It is believed that choice behavior reveals the underlying value of goods. The subjective values of stimuli can be changed through reward-based learning mechanisms as well as by modifying the description of the decision problem, but it has yet to be shown that preferences can be manipulated by perturbing intrinsic values of individual items. Here we show that the value of food items can be modulated by the concurrent presentation of an irrelevant auditory cue to which subjects must make a simple motor response (i.e., cue-approach training). Follow-up tests showed that the effects of this pairing on choice lasted at least 2 months after prolonged training. Eye-tracking during choice confirmed that cue-approach training increased attention to the cued items. Neuroimaging revealed the neural signature of a value change in the form of amplified preference-related activity in ventromedial prefrontal cortex.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Task procedure.
Figure 2: Behavioral results for cue-approach and cue-avoidance studies.
Figure 3: Imaging results from the probe phase.
Figure 4: Imaging results from the last training run.

Similar content being viewed by others

References

  1. Samuelson, P.A. A note on the pure theory of consumer's behaviour. Economica New Ser. 5, 61–71 and addendum 353–354 (1938).

    Article  Google Scholar 

  2. Thorndike, E.L. Animal Intelligence: Experimental Studies (Macmillan, 1911).

  3. O'Doherty, J. et al. Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304, 452–454 (2004).

    Article  CAS  Google Scholar 

  4. Tversky, A. & Kahneman, D. Rational choice and the framing of decisions. J. Bus. 59, S251–S278 (1986).

    Article  Google Scholar 

  5. Slovic, P. The construction of preference. Am. Psychol. 50, 364 (1995).

    Article  Google Scholar 

  6. De Martino, B., Kumaran, D., Seymour, B. & Dolan, R.J. Frames, biases, and rational decision-making in the human brain. Science 313, 684–687 (2006).

    Article  CAS  Google Scholar 

  7. Fischhoff, B. Value elicitation: is there anything in there? Am. Psychol. 46, 835–847 (1991).

    Article  Google Scholar 

  8. Payne, J.W., Bettman, J.R. & Schkade, D.A. Measuring constructed preferences: towards a building code. J. Risk Uncertain. 19, 243–270 (1999).

    Article  Google Scholar 

  9. Brehm, J.W. Post-decision changes in the desirability of choice alternatives. J. Abnorm. Soc. Psychol. 52, 384–389 (1956).

    Article  CAS  Google Scholar 

  10. Sharot, T., De Martino, B. & Dolan, R.J. How choice reveals and shapes expected hedonic outcome. J. Neurosci. 29, 3760–3765 (2009).

    Article  CAS  Google Scholar 

  11. Izuma, K. et al. Neural correlates of cognitive dissonance and choice-induced preference change. Proc. Natl. Acad. Sci. USA 107, 22014–22019 (2010).

    Article  CAS  Google Scholar 

  12. Zajonc, R.B. Attitudinal effects of mere exposure. J. Pers. Soc. Psychol. 9, 1–6 (1968).

    Article  Google Scholar 

  13. Zajonc, R.B. Mere exposure: a gateway to the subliminal. Curr. Dir. Psychol. Sci. 10, 224–228 (2001).

    Article  Google Scholar 

  14. Cacioppo, J.T., Priester, J.R. & Berntson, G.G. Rudimentary determinants of attitudes. II. Arm flexion and extension have differential effects on attitudes. J. Pers. Soc. Psychol. 65, 5–17 (1993).

    Article  CAS  Google Scholar 

  15. Fishbach, A. & Shah, J.Y. Self-control in action: implicit dispositions toward goals and away from temptations. J. Pers. Soc. Psychol. 90, 820–832 (2006).

    Article  Google Scholar 

  16. Wiers, R.W., Eberl, C., Rinck, M., Becker, E.S. & Lindenmeyer, J. Retraining automatic action tendencies changes alcoholic patients' approach bias for alcohol and improves treatment outcome. Psychol. Sci. 22, 490–497 (2011).

    Article  Google Scholar 

  17. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Google Scholar 

  20. Lin, J.Y., Pype, A.D., Murray, S.O. & Boynton, G.M. Enhanced memory for scenes presented at behaviorally relevant points in time. PLoS Biol. 8, e1000337 (2010).

    Article  Google Scholar 

  21. Swallow, K.M. & Jiang, Y.V. The attentional boost effect: transient increases in attention to one task enhance performance in a second task. Cognition 115, 118–132 (2010).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  23. Plassmann, H., O'Doherty, J. & Rangel, A. Orbitofrontal cortex encodes willingness to pay in everyday economic transactions. J. Neurosci. 27, 9984–9988 (2007).

    Article  CAS  Google Scholar 

  24. Sharot, T., Fleming, S.M., Yu, X., Koster, R. & Dolan, R.J. Is choice-induced preference change long lasting? Psychol. Sci. 23, 1123–1129 (2012).

    Article  Google Scholar 

  25. Logan, G.D. & Cowan, W.B. On the ability to inhibit thought and action: a theory of an act of control. Psychol. Rev. 91, 295–327 (1984).

    Article  Google Scholar 

  26. Verbruggen, F. & Logan, G.D. Automatic and controlled response inhibition: associative learning in the go/no-go and stop-signal paradigms. J. Exp. Psychol. Gen. 137, 649–672 (2008).

    Article  Google Scholar 

  27. Lenartowicz, A., Verbruggen, F., Logan, G.D. & Poldrack, R.A. Inhibition-related activation in the right inferior frontal gyrus in the absence of inhibitory cues. J. Cogn. Neurosci. 23, 3388–3399 (2011).

    Article  Google Scholar 

  28. Yagi, Y., Ikoma, S. & Kikuchi, T. Attentional modulation of the mere exposure effect. J. Exp. Psychol. Learn. Mem. Cogn. 35, 1403–1410 (2009).

    Article  Google Scholar 

  29. Huang, Y.F. & Hsieh, P.J. The mere exposure effect is modulated by selective attention but not visual awareness. Vision Res. 91, 56–61 (2013).

    Article  Google Scholar 

  30. Anderson, B.A., Laurent, P.A. & Yantis, S. Value-driven attentional capture. Proc. Natl. Acad. Sci. USA 108, 10367–10371 (2011).

    Article  CAS  Google Scholar 

  31. Lim, S.L., O'doherty, J.P. & Rangel, A. The decision value computations in the vmpfc and striatum use a relative value code that is guided by visual attention. J. Neurosci. 31, 13214–13223 (2011).

    Article  CAS  Google Scholar 

  32. 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).

    Article  CAS  Google Scholar 

  33. Tom, S.M., Fox, C.R., Trepel, C. & Poldrack, R.A. The neural basis of loss aversion in decision-making under risk. Science 315, 515–518 (2007).

    Article  CAS  Google Scholar 

  34. Chib, V.S., Rangel, A., Shimojo, S. & O'Doherty, J.P. Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex. J. Neurosci. 29, 12315–12320 (2009).

    Article  CAS  Google Scholar 

  35. McNamee, D., Rangel, A. & O'Doherty, J.P. Category-dependent and category-independent goal-value codes in human ventromedial prefrontal cortex. Nat. Neurosci. 16, 479–485 (2013).

    Article  CAS  Google Scholar 

  36. Kang, M.J., Rangel, A., Camus, M. & Camerer, C.F. Hypothetical and real choice differentially activate common valuation areas. J. Neurosci. 31, 461–468 (2011).

    Article  CAS  Google Scholar 

  37. Levy, I., Lazzaro, S.C., Rutledge, R.B. & Glimcher, P.W. Choice from non-choice: predicting consumer preferences from blood oxygenation level–dependent signals obtained during passive viewing. J. Neurosci. 31, 118–125 (2011).

    Article  CAS  Google Scholar 

  38. Veling, H., Aarts, H. & Papies, E.K. Using stop signals to inhibit chronic dieters' responses toward palatable foods. Behav. Res. Ther. 49, 771–780 (2011).

    Article  Google Scholar 

  39. Veling, H., Aarts, H. & Stroebe, W. Using stop signals to reduce impulsive choices for palatable unhealthy foods. Br. J. Health Psychol. 18, 354–368 (2013).

    Article  Google Scholar 

  40. Thaler, R.H. & Sunstein, C.R. Nudge: Improving Decisions About Health, Wealth, and Happiness (Yale University Press, 2008).

  41. Marteau, T.M., Hollands, G.J. & Fletcher, P.C. Changing human behavior to prevent disease: the importance of targeting automatic processes. Science 337, 1492–1495 (2012).

    Article  CAS  Google Scholar 

  42. Shinners, P. PyGame–python game development http://www.pygame.org/ (2011).

  43. Brainard, D.H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).

    Article  CAS  Google Scholar 

  44. Pelli, D.G.D. The videotoolbox software for visual psychophysics: transforming numbers into movies. Spat. Vis. 10, 437–442 (1997).

    Article  CAS  Google Scholar 

  45. Kleiner, M., Brainard, D., Pelli, D., Ingling, A. & Murray, R. What's new in Psychtoolbox-3. Perception 36, ECVP Abstract Supplement (2007).

  46. Patton, J.H., Stanford, M.S. & Barratt, E.S. Factor structure of the Barratt impulsiveness scale. J. Clin. Psychol. 51, 768–774 (1995).

    Article  CAS  Google Scholar 

  47. de Boeck, P. & Wilson, M. Explanatory Item Response Models (Springer, 2004).

  48. Kenward, M.G. & Roger, J.H. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53, 983–997 (1997).

    Article  CAS  Google Scholar 

  49. Deichmann, R., Gottfried, J.A., Hutton, C. & Turner, R. Optimized EPI for fMRI studies of the orbitofrontal cortex. Neuroimage 19, 430–441 (2003).

    Article  CAS  Google Scholar 

  50. Moeller, S. et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn. Reson. Med. 63, 1144–1153 (2010).

    Article  Google Scholar 

  51. Smith, S.M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 (suppl. 1), S208–S219 (2004).

    Article  Google Scholar 

  52. Ségonne, F. et al. A hybrid approach to the skull stripping problem in MRI. Neuroimage 22, 1060–1075 (2004).

    Article  Google Scholar 

  53. Dale, A.M., Fischl, B. & Sereno, M.I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).

    Article  CAS  Google Scholar 

  54. Greve, D.N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48, 63–72 (2009).

    Article  Google Scholar 

  55. Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L. & Petersen, S.E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).

    Article  Google Scholar 

  56. Siegel, J.S. et al. Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points. Hum. Brain Mapp. 10.1002/hbm.22307 (17 July 2013).

Download references

Acknowledgements

We thank N. Malecek for assistance with eye-tracking, C. Leuker for assistance with data collection, and A. Aron, C. Fox, C. Trepel and C. White for comments on an earlier version of this manuscript. This research was supported by a grant from US National Institutes of Health (1R01AG041653).

Author information

Authors and Affiliations

Authors

Contributions

T.S., A.B. and R.A.P. designed the experiment, T.S., A.B., A.H.M., L.N. and J.P. conducted the experiment, T.S., A.B., A.H.M. and J.A.M. analyzed the data, and T.S., A.B. and R.A.P. discussed the results and wrote the paper.

Corresponding author

Correspondence to Tom Schonberg.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Sorting and pairing procedure used for studies 1–6, 8 and 9.

Diagram of the sorting and pairing procedure used for studies 1 through 6, 8 and 9.

Supplementary Figure 2 Sorting and pairing procedure used for study 7.

Diagram of the sorting and pairing procedure used for Study 7.

Supplementary Figure 3 Proportion of choices of the Go item for studies 7 and 8.

Retest of Probe after 1 week and 1 month for Study 7. Proportion of choices of the GO item in pairs of high value Go versus NoGo (dark grey) and low value Go versus NoGo (light grey) items for each of Study 7, Study 7 Retest 1 (1 week after original training), Study 7 Retest 2 (1 month after original training) as well as Study 8 (where participants heard a tone, but were not required to press a button). The larger effect size in Study 7 may be due to the fact that in this study only 30 items were presented during training. This will need to be examined in future studies to control for the difference in chosen items below the median.

Supplementary Figure 4 Proportion of total gaze time during retest probe of study 4.

Proportion of total choice time during retest probe that gaze position was on the high Go (black) or high NoGo (white) item in a pair for trials when Go or NoGo items were chosen separately. The sample is a subset of Study 4 Retest. Seventeen participants had their eye positions recorded with an eye tracker while performing a probe on average two months after cue-approach training. Effects are discussed in the text. Error bars reflect within subject SEM.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 and Supplementary Tables 1–5 (PDF 852 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schonberg, T., Bakkour, A., Hover, A. et al. Changing value through cued approach: an automatic mechanism of behavior change. Nat Neurosci 17, 625–630 (2014). https://doi.org/10.1038/nn.3673

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3673

This article is cited by

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