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Dorsolateral prefrontal neurons mediate subjective decisions and their variation in humans

Nature Neuroscience volume 22, pages 1010–1020 (2019)Cite this article

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Abstract

Subjective decisions play a vital role in human behavior because, while often grounded in fact, they are inherently based on personal beliefs that can vary broadly within and between individuals. While these properties set subjective decisions apart from many other sensorimotor processes and are of wide sociological impact, their single-neuronal basis in humans is unknown. Here we find cells in the dorsolateral prefrontal cortex (dlPFC) that reflect variations in the subjective decisions of humans when performing opinion-based tasks. These neurons changed their activities gradually as the participants transitioned between choice options but also reflected their unique point of conversion at equipoise. Focal disruption of the dlPFC, by contrast, diminished gradation between opposing decisions but had little effect on sensory perceptual choices or their motor report. These findings suggest that the human dlPFC plays an important role in subjective decisions and propose a mechanism for mediating their variation during opinion formation.

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Fig. 1: Single-neuronal recordings from the dlPFC during performance of a naturalistic situational assessment task.
Fig. 2: Trial structure, psychometric performances and variation in subjective decisions within and between the participants.
Fig. 3: Neuronal responses in the dlPFC correlate with variations in the participant’s subjective decisions.
Fig. 4: Neuronal modulation in relation to equipoise and voting profile.
Fig. 5: Neuronal responses and the effect of dlPFC lesions on sensory perceptual decisions.
Fig. 6: Temporal dynamic of neuronal response.
Fig. 7: Changes in neuronal activity in the dlPFC correlate with interpersonal variations in opinion.
Fig. 8: Focal dysfunction of the dlPFC leads to a loss of gradation between opposing decisions but does not influence the participant’s motor report.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The primary codes used to analyze the data are available from the corresponding author upon reasonable request.

References

  1. Zaller, J. The Nature and Origins of Mass Opinion (Cambridge Univ. Press, 1992).

  2. Dryzek, J. S. Discursive Democracy: Politics, Policy, and Political Science (Cambridge Univ. Press, 1990).

  3. Mara, G. M. Socrates’ Discursive Democracy: Logos and Ergon in Platonic Political Philosophy (State Univ. of New York Press, 1997).

  4. Turner, C. F., Martin, E. & National Research Council (U.S.). Panel on Survey Measurement of Subjective Phenomena. Surveying Subjective Phenomena (Russell Sage Foundation, 1984).

  5. Pesaran, B., Nelson, M. J. & Andersen, R. A. Free choice activates a decision circuit between frontal and parietal cortex. Nature 453, 406–409 (2008).

    Article  CAS  Google Scholar 

  6. Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).

    Article  CAS  Google Scholar 

  7. Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).

    Article  CAS  Google Scholar 

  8. Williams, Z. M. & Eskandar, E. N. Selective enhancement of associative learning by microstimulation of the anterior caudate. Nat. Neurosci. 9, 562–568 (2006).

    Article  CAS  Google Scholar 

  9. Yartsev, M. M., Hanks, T. D., Yoon, A. M. & Brody, C. D. Causal contribution and dynamical encoding in the striatum during evidence accumulation. eLife 7, e34929 (2018).

    Article  Google Scholar 

  10. Kim, J. N. & Shadlen, M. N. Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nat. Neurosci. 2, 176–185 (1999).

    Article  Google Scholar 

  11. Odegaard, B. et al. Superior colliculus neuronal ensemble activity signals optimal rather than subjective confidence. Proc. Natl Acad. Sci. USA 115, E1588–E1597 (2018).

    Article  CAS  Google Scholar 

  12. Romo, R. & de Lafuente, V. Conversion of sensory signals into perceptual decisions. Prog. Neurobiol. 103, 41–75 (2013).

    Article  Google Scholar 

  13. de Lafuente, V. & Romo, R. Dopamine neurons code subjective sensory experience and uncertainty of perceptual decisions. Proc. Natl Acad. Sci. USA 108, 19767–19771 (2011).

    Article  Google Scholar 

  14. Padoa-Schioppa, C. & Assad, J. A. Neurons in the orbitofrontal cortex encode economic value. Nature 441, 223–226 (2006).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  16. Koenigs, M. et al. Damage to the prefrontal cortex increases utilitarian moral judgements. Nature 446, 908–911 (2007).

    Article  CAS  Google Scholar 

  17. Chung, D., Christopoulos, G. I., King-Casas, B., Ball, S. B. & Chiu, P. H. Social signals of safety and risk confer utility and have asymmetric effects on observers’ choices. Nat. Neurosci. 18, 912–916 (2015).

    Article  CAS  Google Scholar 

  18. Kable, J. W. & Glimcher, P. W. The neural correlates of subjective value during intertemporal choice. Nat. Neurosci. 10, 1625–1633 (2007).

    Article  CAS  Google Scholar 

  19. Shenhav, A. & Greene, J. D. Moral judgments recruit domain-general valuation mechanisms to integrate representations of probability and magnitude. Neuron 67, 667–677 (2010).

    Article  CAS  Google Scholar 

  20. Sternberg, R. J. Cognitive Psychology 3rd edn (Thomson/Wadsworth, 2003).

  21. Weekley, J. A. & Ployhart, R. E. Situational Judgment Tests: Theory, Measurement, and Application (Lawrence Erlbaum Associates, 2006).

  22. Nowinski, W. L., Gupta, V., Qian, G., Ambrosius, W. & Kazmierski, R. Population-based stroke atlas for outcome prediction: method and preliminary results for ischemic stroke from CT. PLoS One 9, e102048 (2014).

    Article  Google Scholar 

  23. Ndode-Ekane, X. E., Kharatishvili, I. & Pitkanen, A. Unfolded maps for quantitative analysis of cortical lesion location and extent after traumatic brain injury. J. Neurotrauma 34, 459–474 (2017).

    Article  Google Scholar 

  24. Patel, S. R. et al. Studying task-related activity of individual neurons in the human brain. Nat. Protoc. 8, 949–957 (2013).

    Article  CAS  Google Scholar 

  25. Zedeck, S. & American Psychological Association. APA Handbook of Industrial and Organizational Psychology 1st edn (American Psychological Association, 2011).

  26. Rainer, G., Asaad, W. F. & Miller, E. K. Selective representation of relevant information by neurons in the primate prefrontal cortex. Nature 393, 577–579 (1998).

    Article  CAS  Google Scholar 

  27. Haroush, K. & Williams, Z. M. Neuronal prediction of opponent’s behavior during cooperative social interchange in primates. Cell 160, 1233–1245 (2015).

    Article  CAS  Google Scholar 

  28. Kepecs, A., Uchida, N., Zariwala, H. A. & Mainen, Z. F. Neural correlates, computation and behavioural impact of decision confidence. Nature 455, 227–231 (2008).

    Article  CAS  Google Scholar 

  29. Hanes, D. P., Patterson, W. F. II & Schall, J. D. Role of frontal eye fields in countermanding saccades: visual, movement, and fixation activity. J. Neurophysiol. 79, 817–834 (1998).

    Article  CAS  Google Scholar 

  30. Freeman, E., Sagi, D. & Driver, J. Lateral interactions between targets and flankers in low-level vision depend on attention to the flankers. Nat. Neurosci. 4, 1032–1036 (2001).

    Article  CAS  Google Scholar 

  31. Padoa-Schioppa, C. & Assad, J. A. The representation of economic value in the orbitofrontal cortex is invariant for changes of menu. Nat. Neurosci. 11, 95–102 (2008).

    Article  CAS  Google Scholar 

  32. Summerfield, C., Behrens, T. E. & Koechlin, E. Perceptual classification in a rapidly changing environment. Neuron 71, 725–736 (2011).

    Article  CAS  Google Scholar 

  33. Williams, Z. M., Elfar, J. C., Eskandar, E. N., Toth, L. J. & Assad, J. A. Parietal activity and the perceived direction of ambiguous apparent motion. Nat. Neurosci. 6, 616–623 (2003).

    Article  CAS  Google Scholar 

  34. Mian, M. K. et al. Encoding of rules by neurons in the human dorsolateral prefrontal cortex. Cereb. Cortex 24, 807–816 (2014).

    Article  Google Scholar 

  35. Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).

    Article  CAS  Google Scholar 

  36. Fusi, S., Asaad, W. F., Miller, E. K. & Wang, X. J. A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales. Neuron 54, 319–333 (2007).

    Article  CAS  Google Scholar 

  37. Zuckerman, M. Psychobiology of Personality 2nd edn (Cambridge Univ. Press, 2005).

  38. Porter, K. R., McCarthy, B. J., Freels, S., Kim, Y. & Davis, F. G. Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology. Neuro Oncol. 12, 520–527 (2010).

    Article  Google Scholar 

  39. Sofer, C., Dotsch, R., Wigboldus, D. H. & Todorov, A. What is typical is good: the influence of face typicality on perceived trustworthiness. Psychol. Sci. 26, 39–47 (2015).

    Article  Google Scholar 

  40. Sheth, S. A. et al. Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation. Nature 488, 218–221 (2012).

    Article  CAS  Google Scholar 

  41. Freiwald, W. A., Tsao, D. Y. & Livingstone, M. S. A face feature space in the macaque temporal lobe. Nat. Neurosci. 12, 1187–1196 (2009).

    Article  CAS  Google Scholar 

  42. Hirabayashi, T., Takeuchi, D., Tamura, K. & Miyashita, Y. Microcircuits for hierarchical elaboration of object coding across primate temporal areas. Science 341, 191–195 (2013).

    Article  CAS  Google Scholar 

  43. Siddique, Z., Anand, S. & Lewis-Greene, H. Situational Judgment Tests for Dentists: the DF1 Guidebook (Wiley, 2017).

  44. Eysenck, H. J. The Psychology of Politics (Routledge and Kegan Paul, 1954).

  45. Rushworth, M. F., Hadland, K. A., Gaffan, D. & Passingham, R. E. The effect of cingulate cortex lesions on task switching and working memory. J. Cogn. Neurosci. 15, 338–353 (2003).

    Article  CAS  Google Scholar 

  46. Williams, Z. M., Bush, G., Rauch, S. L., Cosgrove, G. R. & Eskandar, E. N. Human anterior cingulate neurons and the integration of monetary reward with motor responses. Nat. Neurosci. 7, 1370–1375 (2004).

    Article  CAS  Google Scholar 

  47. Wenzel, A., Chapman, J. E., Newman, C. F., Beck, A. T. & Brown, G. K. Hypothesized mechanisms of change in cognitive therapy for borderline personality disorder. J. Clin. Psychol. 62, 503–516 (2006).

    Article  Google Scholar 

  48. Eysenck, H. J. The Psychology of Politics (Transaction Publishers, 1999).

  49. Asaad, W. F., Rainer, G. & Miller, E. K. Neural activity in the primate prefrontal cortex during associative learning. Neuron 21, 1399–1407 (1998).

    Article  CAS  Google Scholar 

  50. Green, D. M. & Swets, J. A. Signal Detection Theory and Psychophysics (Krieger, 1974).

  51. Amirnovin, R., Williams, Z. M., Cosgrove, G. R. & Eskandar, E. N. Experience with microelectrode guided subthalamic nucleus deep brain stimulation. Neurosurgery 58, ONS96–ONS102 (2006).

    PubMed  Google Scholar 

  52. Gross, R. E., Krack, P., Rodriguez-Oroz, M. C., Rezai, A. R. & Benabid, A. L. Electrophysiological mapping for the implantation of deep brain stimulators for Parkinson’s disease and tremor. Mov. Disord. 21, S259–S283 (2006).

    Article  Google Scholar 

  53. Theodosopoulos, P. V., Marks, W. J. Jr., Christine, C. & Starr, P. A. Locations of movement-related cells in the human subthalamic nucleus in Parkinson’s disease. Mov. Disord. 18, 791–798 (2003).

    Article  Google Scholar 

  54. Asaad, W. F. & Eskandar, E. N. Achieving behavioral control with millisecond resolution in a high-level programming environment. J. Neurosci. Methods 173, 235–240 (2008).

    Article  Google Scholar 

  55. Wasserman, L. All of Statistics: A Concise Course in Statistical Inference (Springer Texts, 2005).

Download references

Acknowledgements

Z.M.W. is supported by NIH grant nos. R01HD059852 and R01NS091390, the Presidential Early Career Award for Scientists and Engineers and the Whitehall Foundation. M.J. is supported by the Banting Foundation, B.G. is supported by the NREF and Z.B.M. is supported by the NREF and NIH NRSA. K.H. is supported by the Simmons’s foundation. E.N.E. is supported by grant nos. NIH R01NS086422 and NIH UH3NS100548.

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Author notes

  1. These authors contributed equally: Mohsen Jamali, Ben Grannan, Keren Haroush.

Authors and Affiliations

  1. Department of Neurosurgery, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Mohsen Jamali, Ben Grannan & Ziv M. Williams

  2. Department of Neurobiology, Stanford University School of Medicine, Palo Alto, CA, USA

    Keren Haroush

  3. Department of Neurosurgery, Brigham and Women’s Hospital, Boston, MA, USA

    Ziev B. Moses

  4. Department of Neurosurgery, Albert Einstein University, Bronx, NY, USA

    Emad N. Eskandar

  5. Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Todd Herrington & Shaun Patel

  6. Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA

    Ziv M. Williams

  7. Harvard Medical School, Program in Neuroscience, Boston, MA, USA

    Ziv M. Williams

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Contributions

M.J. performed the analyses, helped obtain neuronal recordings and co-wrote the manuscript. B.G., Z.B.M., K.H., S.P., T.H. and E.N.E. helped perform the neuronal recordings and task set up. S.P. and T.H. helped program the task presentation. Z.M.W. conceived and designed the study, performed the neuronal recordings and wrote the manuscript.

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Correspondence to Ziv M. Williams.

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The authors declare no competing interests.

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Journal peer review information: Nature Neuroscience thanks Rony Paz, Philip Starr, and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Integrated supplementary information

Supplementary Figure 1 Recording stability, waveform morphology and single-unit isolation.

(a) The first principal component of the waveform morphology for a single-unit as a function of time. The lack of angular distortion in the centroid (red oval) indicates that there is no drift in waveform morphology over the course of recordings. This procedure was performed for all recorded neurons, and only neurons with stable waveform morphology over time were included. (b) Example of waveform morphologies and their isolations. Waveform morphologies and associated principal component distributions are displayed on the left and right, respectively. In the top panel, a single unit’s activity (red) was isolated from the electrode (number waveforms: 2,882). In the bottom panel, two units (green and red) were isolated (number waveforms: 7,876 green, 432 red). Light lines and dots denote individual waveforms. Dark lines represent average waveform. The horizontal bar indicates a 500 μs interval for scale. The gray areas in the PC space represent noise.

Supplementary Figure 2 Example neurons modulated by safe or unsafe choice selections during scene presentation in the situational assessment task.

Peri-stimulus spike histogram and trial heatmap of three example neurons during task performance. The shaded bands represent standard error of the mean. The first two neurons modulate more during unsafe scenes whereas the activity of the last neuron increases during safe trials. The first vertical dashed line (0 s) represents scene presentation onset and the second vertical dashed line (3 s) represents the choice option presentation. The grayed area represents the time period considered for neuronal analysis, and was chosen to reflect the corresponding stimulus-response delay and scene presentation times.

Supplementary Figure 3 Correlation between neuronal activity and choice.

The average (a) and the distribution (b) of correlation coefficients between neural activity and single-trial choice responses.

Supplementary Figure 4 Neuronal modulation in relation to equipoise and choice report.

Distribution of population firing rates near equipoise (that is, ordinal positions −1/+1) compared to firing rates far from equipoise (that is, ordinal positions −4/+4). Inset: average population firing activity when choices were made near equipoise compared to when they were not.

Supplementary Figure 5 Evaluating the relation between neural activity and motor response reaction time.

Scatter plot displaying the mean firing activity of all cells across all trials and their relation to the participant’s reaction times (the participants were given up to 10 s to make their choice). The red line indicates the degree of correlation and is non-significant (Pearson’s correlation r = 0.004, p = 0.78). Similar results were obtained if examining only activity between 100 and 101 (note that given the sparsity of spiking on individual trials, some data points reflect very low rates). Here, the negative log scale (for example, 10−8) indicate that the firing rate approaches zero for those individual trials.

Supplementary Figure 6 Example of a neuron modulated by safe versus unsafe choice selections during object or item presentation.

Peri-stimulus spike histogram and trial heatmap of an example neuron responding to the items (left) but not the scenes (right). The shaded bands represent s.e.m. The first vertical dashed line (0 s) represents scene presentation onset and the second vertical dashed line (3 s) represents the choice option presentation. The grayed area represents the time period considered for neuronal analysis, and was chosen to reflect the corresponding stimulus-response delay and scene presentation times.

Supplementary Figure 7 Psychometric profiles on the subjective situational assessment task for participants with dlPFC lesions and control.

(a) Psychometric profile and the corresponding raw data points for participants with dlPFC lesions (n = 4). (b) Psychometric profile and the corresponding raw data points for control participants who underwent surgery (n = 11). Here, the individual participants are additionally color coded.

Supplementary Figure 8 Psychometric profiles on the subjective trustworthiness assessment task for participants with dlPFC lesions and control based on pathology.

(a) Examples of faces generated by a data-driven computational model for the subjective assessment of trustworthiness. (b) Averaged voting profiles of participants with dlPFC lesions (n = 5), control participants (that is, patients which have undergone neuronal recording from the dlPFC but no lesioning; n = 2) and healthy participants (n = 12). Overall, the participants with dlPFC lesions displayed a significant increase in slope of their psychometric curves when compared to either the healthy participants (* p = 0.04; one-sided Wilcoxon test) or the control participants that had undergone neuronal recordings (** p = 0.01; one-sided Wilcoxon test), whereas the latter groups display no difference in their slopes of psychometric curves (p = 0.25; two-sided Wilcoxon test). The boxplots represent the median (thick lines), quartiles (boxes) and the range (whiskers) of the slopes for different subject groups.

Supplementary Figure 9 Evaluating the relation between neuronal response and underlying pathology.

The participants were divided into those that had PD (n = 2), ET (n = 7) or other disorders (n = 2; dystonia and pain). Overall, the participants appeared to possess a similar proportion of neurons that responded to opinion across the three different disease states. The boxplots represent the median (red lines), quartiles (boxes) and the range (whiskers) of the percentage of significant neurons across patients with different underlying pathology.

Supplementary Figure 10 Psychometric profiles on the subjective situational assessment task for participants with dlPFC lesions and control based on pathology.

Psychometric profiles for participants with gliomas (n = 6) non-glioma (n = 3) lesions, and control subjects (n = 11) are displayed in red, green, and gray respectively. Overall, subjects with non-glioma lesions displayed a similar slope compared to glioma patients (mean ± s.e.m.: 109 ± 9%Δ versus 115 ± 4%Δ respectively; two-sided Wilcoxon test, p = 0.99) and a significantly higher slope compared to control. Furthermore, both the glial (one-sided Wilcoxon test, p = 0.026) and non-glial (one-sided Wilcoxon test, p = 0.028) lesion participants displayed a significantly steeper voting profile slope compared to control. The boxplots represent the median (thick lines), quartiles (boxes) and the range (whiskers) of the slopes for different subject groups.

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Jamali, M., Grannan, B., Haroush, K. et al. Dorsolateral prefrontal neurons mediate subjective decisions and their variation in humans. Nat Neurosci 22, 1010–1020 (2019). https://doi.org/10.1038/s41593-019-0378-3

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