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


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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.


  1. 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).

  2. 2

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

  3. 3

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

  4. 4

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

  5. 5

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

  6. 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).

  7. 7

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

  8. 8

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

  9. 9

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

  10. 10

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

  11. 11

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

  12. 12

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

  13. 13

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

  14. 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).

  15. 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).

  16. 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).

  17. 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).

  18. 18

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

  19. 19

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

  20. 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).

  21. 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).

  22. 22

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

  23. 23

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

  24. 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).

  25. 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).

  26. 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).

  27. 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).

  28. 28

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

  29. 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).

  30. 30

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

  31. 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).

  32. 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).

  33. 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).

  34. 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).

  35. 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).

  36. 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).

  37. 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).

  38. 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).

  39. 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).

  40. 40

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

  41. 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).

  42. 42

    Shinners, P. PyGame–python game development (2011).

  43. 43

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

  44. 44

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

  45. 45

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

  46. 46

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

  47. 47

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

  48. 48

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

  49. 49

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

  50. 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).

  51. 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).

  52. 52

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

  53. 53

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

  54. 54

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

  55. 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).

  56. 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


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

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.

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).

Download citation

Further reading