Article | Published:

Decoding subjective decisions from orbitofrontal cortex

Nature Neuroscience volume 19, pages 973980 (2016) | Download Citation

Abstract

When making a subjective choice, the brain must compute a value for each option and compare those values to make a decision. The orbitofrontal cortex (OFC) is critically involved in this process, but the neural mechanisms remain obscure, in part due to limitations in our ability to measure and control the internal deliberations that can alter the dynamics of the decision process. Here we tracked these dynamics by recovering temporally precise neural states from multidimensional data in OFC. During individual choices, OFC alternated between states associated with the value of two available options, with dynamics that predicted whether a subject would decide quickly or vacillate between the two alternatives. Ensembles of value-encoding neurons contributed to these states, with individual neurons shifting activity patterns as the network evaluated each option. Thus, the mechanism of subjective decision-making involves the dynamic activation of OFC states associated with each choice alternative.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from $8.99

All prices are NET prices.

References

  1. 1.

    Neurobiology of economic choice: a good-based model. Annu. Rev. Neurosci. 34, 333–359 (2011).

  2. 2.

    , & A framework for studying the neurobiology of value-based decision making. Nat. Rev. Neurosci. 9, 545–556 (2008).

  3. 3.

    , & General mechanisms for making decisions? Curr. Opin. Neurobiol. 19, 75–83 (2009).

  4. 4.

    , & Visual fixations and the computation and comparison of value in simple choice. Nat. Neurosci. 13, 1292–1298 (2010).

  5. 5.

    & A probabilistic, dynamic, and attribute-wise model of intertemporal choice. J. Exp. Psychol. Gen. 143, 1489–1514 (2014).

  6. 6.

    Cross-species studies of orbitofrontal cortex and value-based decision-making. Nat. Neurosci. 15, 13–19 (2012).

  7. 7.

    & The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron 84, 1143–1156 (2014).

  8. 8.

    et al. Orbitofrontal cortex supports behavior and learning using inferred but not cached values. Science 338, 953–956 (2012).

  9. 9.

    Orbitofrontal contributions to value-based decision making: evidence from humans with frontal lobe damage. Ann. NY Acad. Sci. 1239, 51–58 (2011).

  10. 10.

    Neuronal origins of choice variability in economic decisions. Neuron 80, 1322–1336 (2013).

  11. 11.

    Modulators of decision making. Nat. Neurosci. 11, 410–416 (2008).

  12. 12.

    , & Matching behavior and the representation of value in the parietal cortex. Science 304, 1782–1787 (2004).

  13. 13.

    et al. Orbitofrontal cortex is required for optimal waiting based on decision confidence. Neuron 84, 190–201 (2014).

  14. 14.

    , , & Gaze bias both reflects and influences preference. Nat. Neurosci. 6, 1317–1322 (2003).

  15. 15.

    & Ventromedial and orbital prefrontal neurons differentially encode internally and externally driven motivational values in monkeys. J. Neurosci. 30, 8591–8601 (2010).

  16. 16.

    , , & Techniques for extracting single-trial activity patterns from large-scale neural recordings. Curr. Opin. Neurobiol. 17, 609–618 (2007).

  17. 17.

    & Absence of spatial tuning in the orbitofrontal cortex. PLoS One 9, e112750 (2014).

  18. 18.

    & Medial-lateral organization of the orbitofrontal cortex. J. Cogn. Neurosci. 26, 1347–1362 (2014).

  19. 19.

    & Neuronal activity in primate dorsolateral and orbital prefrontal cortex during performance of a reward preference task. Eur. J. Neurosci. 18, 2069–2081 (2003).

  20. 20.

    , , & Single-neuron mechanisms underlying cost-benefit analysis in frontal cortex. J. Neurosci. 33, 17385–17397 (2013).

  21. 21.

    & Neurons in the orbitofrontal cortex encode economic value. Nature 441, 223–226 (2006).

  22. 22.

    & A neuro-computational model of economic decisions. J. Neurophysiol. 114, 1382–1398 (2015).

  23. 23.

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

  24. 24.

    , , & A mechanism for value-guided choice based on the excitation-inhibition balance in prefrontal cortex. Nat. Neurosci. 15, 960–961 (2012).

  25. 25.

    Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955–968 (2002).

  26. 26.

    & The time course of perceptual choice: the leaky, competing accumulator model. Psychol. Rev. 108, 550–592 (2001).

  27. 27.

    , & Neuronal population coding of movement direction. Science 233, 1416–1419 (1986).

  28. 28.

    , , , & Differential coding of conspecific vocalizations in the ventral auditory cortical stream. J. Neurosci. 34, 4665–4676 (2014).

  29. 29.

    , , & Improving brain-machine interface performance by decoding intended future movements. J. Neural Eng. 10, 026011 (2013).

  30. 30.

    & Memory of sequential experience in the hippocampus during slow wave sleep. Neuron 36, 1183–1194 (2002).

  31. 31.

    , , & Attentional filtering of visual information by neuronal ensembles in the primate lateral prefrontal cortex. Neuron 85, 202–215 (2015).

  32. 32.

    & Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. J. Neurosci. 27, 12176–12189 (2007).

  33. 33.

    , , , & Theta-paced flickering between place-cell maps in the hippocampus. Nature 478, 246–249 (2011).

  34. 34.

    , , , & Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task. J. Neurosci. 16, 752–768 (1996).

  35. 35.

    & Contributions of orbitofrontal and lateral prefrontal cortices to economic choice and the good-to-action transformation. Neuron 81, 1140–1151 (2014).

  36. 36.

    , , , & Capturing the temporal evolution of choice across prefrontal cortex. Elife 4, e11945 (2015).

  37. 37.

    et al. Model-based learning and the contribution of the orbitofrontal cortex to the model-free world. Eur. J. Neurosci. 35, 991–996 (2012).

  38. 38.

    , & Online evaluation of novel choices by simultaneous representation of multiple memories. Nat. Neurosci. 16, 1492–1498 (2013).

  39. 39.

    et al. Neural estimates of imagined outcomes in the orbitofrontal cortex drive behavior and learning. Neuron 80, 507–518 (2013).

  40. 40.

    , , & Orbitofrontal cortex as a cognitive map of task space. Neuron 81, 267–279 (2014).

  41. 41.

    , , & Does the orbitofrontal cortex signal value? Ann. NY Acad. Sci. 1239, 87–99 (2011).

  42. 42.

    & Choice coding in frontal cortex during stimulus-guided or action-guided decision-making. J. Neurosci. 33, 1864–1871 (2013).

  43. 43.

    et al. Neuronal selectivity for spatial position of offers and choices in five reward regions. J. Neurophysiol. 115, 1098–1111 (2016).

  44. 44.

    & Executive control processes underlying multi-item working memory. Nat. Neurosci. 17, 876–883 (2014).

  45. 45.

    et al. Orbitofrontal cortex encodes memories within value-based schemas and represents contexts that guide memory retrieval. J. Neurosci. 35, 8333–8344 (2015).

  46. 46.

    et al. Hippocampal representation of related and opposing memories develop within distinct, hierarchically organized neural schemas. Neuron 83, 202–215 (2014).

  47. 47.

    & Limbic connections of the orbital and medial prefrontal cortex in macaque monkeys. J. Comp. Neurol. 363, 615–641 (1995).

  48. 48.

    & Sensory and premotor connections of the orbital and medial prefrontal cortex of macaque monkeys. J. Comp. Neurol. 363, 642–664 (1995).

  49. 49.

    , , & Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. PLoS Comput. Biol. 7, e1002211 (2011).

  50. 50.

    , & Stochastic computations in cortical microcircuit models. PLoS Comput. Biol. 9, e1003311 (2013).

  51. 51.

    & A flexible software tool for temporally-precise behavioral control in Matlab. J. Neurosci. Methods 174, 245–258 (2008).

  52. 52.

    , & Encoding of gustatory working memory by orbitofrontal neurons. J. Neurosci. 29, 765–774 (2009).

  53. 53.

    , , , & Single-trial decoding of intended eye movement goals from lateral prefrontal cortex neural ensembles. J. Neurophysiol. 115, 486–499 (2016).

  54. 54.

    , , & Neural activity in prefrontal cortex during copying geometrical shapes. II. Decoding shape segments from neural ensembles. Exp. Brain Res. 150, 142–153 (2003).

  55. 55.

    , , , & Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat. Neurosci. 5, 805–811 (2002).

  56. 56.

    et al. Prefrontal neurons transmit signals to parietal neurons that reflect executive control of cognition. Nat. Neurosci. 16, 1484–1491 (2013).

Download references

Acknowledgements

The authors thank M. Rangel-Gomez for comments on the manuscript. This work was funded by NIDA R01 DA19028 and NIMH R01 MH097990 (to J.D.W.) and by the Hilda and Preston Davis Foundation and NIDA K08 DA039051 to (E.L.R.). This research was partially funded by the Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number W911NF-14-2-0043, issued by the Army Research Office contracting office in support of DARPA'S SUBNETS program. The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.

Author information

Affiliations

  1. Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, California, USA.

    • Erin L Rich
    •  & Jonathan D Wallis
  2. Department of Psychology, University of California at Berkeley, Berkeley, California, USA.

    • Jonathan D Wallis

Authors

  1. Search for Erin L Rich in:

  2. Search for Jonathan D Wallis in:

Contributions

E.L.R. and J.D.W. designed the experiment and wrote the manuscript. E.L.R. collected and analyzed the data. J.D.W. supervised the project.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Erin L Rich.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–10

  2. 2.

    Supplementary Methods Checklist

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nn.4320