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Confidence in value-based choice

Nature Neuroscience volume 16, pages 105110 (2013) | Download Citation

Abstract

Decisions are never perfect, with confidence in one's choices fluctuating over time. How subjective confidence and valuation of choice options interact at the level of brain and behavior is unknown. Using a dynamic model of the decision process, we show that confidence reflects the evolution of a decision variable over time, explaining the observed relation between confidence, value, accuracy and reaction time. As predicted by our dynamic model, we show that a functional magnetic resonance imaging signal in human ventromedial prefrontal cortex (vmPFC) reflects both value comparison and confidence in the value comparison process. Crucially, individuals varied in how they related confidence to accuracy, allowing us to show that this introspective ability is predicted by a measure of functional connectivity between vmPFC and rostrolateral prefrontal cortex. Our findings provide a mechanistic link between noise in value comparison and metacognitive awareness of choice, enabling us both to want and to express knowledge of what we want.

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References

  1. 1.

    & Neural computations associated with goal-directed choice. Curr. Opin. Neurobiol. 20, 262–270 (2010).

  2. 2.

    , & The role of human orbitofrontal cortex in value comparison for incommensurable objects. J. Neurosci. 29, 8388–8395 (2009).

  3. 3.

    & The neural correlates of subjective value during intertemporal choice. Nat. Neurosci. 10, 1625–1633 (2007).

  4. 4.

    , , & How the brain integrates costs and benefits during decision making. Proc. Natl. Acad. Sci. USA 107, 21767–21772 (2010).

  5. 5.

    et al. Distinct value signals in anterior and posterior ventromedial prefrontal cortex. J. Neurosci. 30, 2490–2495 (2010).

  6. 6.

    et al. Mechanisms underlying cortical activity during value-guided choice. Nat. Neurosci. 15, 470–476 (2012).

  7. 7.

    , , & Neural correlates, computation and behavioural impact of decision confidence. Nature 455, 227–231 (2008).

  8. 8.

    , & Choice, difficulty, and confidence in the brain. Neuroimage 53, 694–706 (2010).

  9. 9.

    , , , & Relating introspective accuracy to individual differences in brain structure. Science 329, 1541–1543 (2010).

  10. 10.

    & Two-stage dynamic signal detection: A theory of choice, decision time, and confidence. Psychol. Rev. 117, 864–901 (2010).

  11. 11.

    & A computational framework for the study of confidence in humans and animals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 1322–1337 (2012).

  12. 12.

    Decision Processes in Visual Perception (Academic Press, 1979).

  13. 13.

    , , , & The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev. 113, 700–765 (2006).

  14. 14.

    , & Prefrontal contributions to metacognition in perceptual decision making. J. Neurosci. 32, 6117–6125 (2012).

  15. 15.

    , & Measuring utility by a single-response sequential method. Behav. Sci. 9, 226–232 (1964).

  16. 16.

    , & Orbitofrontal cortex encodes willingness to pay in everyday economic transactions. J. Neurosci. 27, 9984–9988 (2007).

  17. 17.

    , , & The neurobiology of reference-dependent value computation. J. Neurosci. 29, 3833–3842 (2009).

  18. 18.

    , , , & Dissociating the role of the orbitofrontal cortex and the striatum in the computation of goal values and prediction errors. J. Neurosci. 28, 5623–5630 (2008).

  19. 19.

    , , & How green is the grass on the other side? Frontopolar cortex and the evidence in favor of alternative courses of action. Neuron 62, 733–743 (2009).

  20. 20.

    , & Choosing the greater of two goods: neural currencies for valuation and decision making. Nat. Rev. Neurosci. 6, 363–375 (2005).

  21. 21.

    Evidence for an accumulator model of psychophysical discrimination. Ergonomics 13, 37–58 (1970).

  22. 22.

    , , & Confidence-related decision making. J. Neurophysiol. 104, 539–547 (2010).

  23. 23.

    & Empirical support for higher-order theories of conscious awareness. Trends Cogn. Sci. 15, 365–373 (2011).

  24. 24.

    , & Know thyself: metacognitive networks and measures of consciousness. Cognition 117, 182–190 (2010).

  25. 25.

    et al. Right frontopolar cortex activity correlates with reliability of retrospective rating of confidence in short-term recognition memory performance. Neurosci. Res. 68, 199–206 (2010).

  26. 26.

    & Comparing signal detection models of perceptual decision confidence. J. Vis. 10, 213 (2010).

  27. 27.

    & Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324, 759–764 (2009).

  28. 28.

    & Building bridges between perceptual and economic decision-making: neural and computational mechanisms. Front. Neurosci. 6, 70 (2012).

  29. 29.

    & Attention, uncertainty, and free-energy. Front. Hum. Neurosci. 4, 215 (2010).

  30. 30.

    & The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27, 712–719 (2004).

  31. 31.

    The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010).

  32. 32.

    , & Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324, 646–648 (2009).

  33. 33.

    Indeterminacy in brain and behavior. Annu. Rev. Psychol. 56, 25–56 (2005).

  34. 34.

    et al. Optimally interacting minds. Science 329, 1081–1085 (2010).

  35. 35.

    Bayes factor. J. Am. Stat. Assoc. 430, 773–795 (1995).

  36. 36.

    et al. Relating inter-individual differences in metacognitive performance on different perceptual tasks. Conscious. Cogn. 20, 1787–1792 (2011).

  37. 37.

    Visualization of group inference data in functional neuroimaging. Neuroinformatics 7, 73–82 (2009).

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Acknowledgements

We thank T. Fitzgerald, D. Kumaran and T. Sharot for comments on a previous draft of this manuscript, and T. Behrens and N. Daw for discussions. This work was supported by a Wellcome Trust Senior Investigator Award, 098362/Z/12/Z to R.J.D.; S.M.F. and B.D.M. are supported by Sir Henry Wellcome Fellowships (B.D.M., 082674/Z/07/Z; S.M.F., WT096185). The Wellcome Trust Centre for Neuroimaging is supported by core funding from the Wellcome Trust, 091593/Z/10/Z.

Author information

Author notes

    • Benedetto De Martino
    •  & Stephen M Fleming

    These authors contributed equally to this work.

Affiliations

  1. Psychology and Language Sciences, University College London, London, UK.

    • Benedetto De Martino
    •  & Neil Garrett
  2. Wellcome Trust Center for Neuroimaging, at University College London, UK.

    • Benedetto De Martino
    • , Stephen M Fleming
    •  & Raymond J Dolan
  3. Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA.

    • Benedetto De Martino
  4. Center for Neural Science, New York University, New York, New York, USA.

    • Stephen M Fleming
  5. Department of Experimental Psychology, University of Oxford, Oxford, UK.

    • Stephen M Fleming

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Contributions

B.D.M. and S.M.F. conceived and designed the study. B.D.M., N.G. and S.M.F. developed stimuli and gathered and analyzed behavioral and fMRI data. B.D.M., S.M.F. and R.J.D. interpreted the data and wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Benedetto De Martino or Stephen M Fleming.

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DOI

https://doi.org/10.1038/nn.3279

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