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.

Neural encoding of opposing strategy values in anterior and posterior cingulate cortex

Subjects

Abstract

Humans, and animals, often encounter ambiguous situations that require a decision on whether to take an offense or a defense strategy. Behavioral studies suggest that a strategy decision is frequently made before concrete options are evaluated. It remains enigmatic, however, how a strategy is determined without exploration of options. Here we investigated neural correlates of quick offense-versus-defense strategy decision in a board game, shogi. We found that the rostral anterior cingulate cortex and the posterior cingulate cortex complementally encoded the defense and attack strategy values, respectively. The dorsolateral prefrontal cortex compared the two strategy values. Several brain regions were activated during decision of concrete moves under an instructed strategy, whereas none of them showed correlation with defense or attack strategy values in their activities during strategy decision. These findings suggest that values of alternative strategies represented in different parts of the cingulate cortex have essential roles in intuitive strategy decision-making.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Experimental task and subjects' performance.
Figure 2: Brain activities during strategy decision.
Figure 3: Encoding of subjective strategy values in rACC and PCC, and comparison of strategy values in DLPFC.
Figure 4: rACC and PCC activities encoded strategy values, but not strategy selections.
Figure 5: Response bias to attack and its association with rACC and PCC activities.
Figure 6: Functional connectivity among the brain regions specifically activated during strategy decision.

References

  1. 1

    Simon, H.A. Administrative Behavior: A Study of Decision-Making Processes in Administrative Organizations fourth edition. (The Free Press, New York, 1997).

  2. 2

    Mintzberg, H., Raisinghani, D. & Theoret, A. The structure of “unstructured” decision processes. Adm. Sci. Q. 21, 246–275 (1976).

    Article  Google Scholar 

  3. 3

    Dutton, J.E. & Jackson, S.E. Categorizing strategic issues: links to organizational action. Acad. Manage. Rev. 12, 76–90 (1987).

    Article  Google Scholar 

  4. 4

    von Neumann, J. & Morgenstern, O. Theory of Games and Economic Behavior 3rd edn. (Princeton University Press, 1953).

  5. 5

    Sutton, R.S. & Barto, A.G. Reinforcement Learning: An Introduction (MIT Press, 1998).

  6. 6

    Mintzberg, H. Mintzberg on Management: Inside Our Strange World of Organizations (The Free Press, 1989).

  7. 7

    Rangel, A., Camerer, C.F. & Montague, P.R. A framework for studying the neurobiology of value-based decision making. Nat. Rev. Neurosci. 9, 545–556 (2008).

    CAS  Article  Google Scholar 

  8. 8

    Rushworth, M.F., Noonan, M.P., Boorman, E.D., Walton, M.E. & Behrens, T.E. Frontal cortex and reward-guided learning and decision-making. Neuron 70, 1054–1069 (2011).

    CAS  Article  Google Scholar 

  9. 9

    Blanchard, D.C. & Blanchard, R.J. Ethoexperimental approaches to the biology of emotion. Annu. Rev. Psychol. 39, 43–68 (1988).

    CAS  Article  Google Scholar 

  10. 10

    McNaughton, N. & Corr, P.J. A two-dimensional neuropsychology of defense: fear/anxiety and defensive distance. Neurosci. Biobehav. Rev. 28, 285–305 (2004).

    Article  Google Scholar 

  11. 11

    Deakin, J.W. & Graeff, F.G. 5-HT and mechanisms of defense. J. Psychopharmacol. 5, 305–315 (1991).

    CAS  Article  Google Scholar 

  12. 12

    Mobbs, D. et al. When fear is near: threat imminence elicits prefrontal-periaqueductal gray shifts in humans. Science 317, 1079–1083 (2007).

    CAS  Article  Google Scholar 

  13. 13

    Mobbs, D. et al. From threat to fear: the neural organization of defensive fear systems in humans. J. Neurosci. 29, 12236–12243 (2009).

    CAS  Article  Google Scholar 

  14. 14

    von Neumann, J. Zur theorie der gesellschaftsspiele. Mathematische Annalen 100, 295–300 (1928).

    Article  Google Scholar 

  15. 15

    de Groot, A. Thought and Choice in Chess (Mouton, 1965).

  16. 16

    Tesauro, G. Comparison training of chess evaluation functions. in Machines That Learn To Play Games 117–130 (Nova Science Publishers, 2001).

  17. 17

    Wan, X. et al. The neural basis of intuitive best next-move generation in board game experts. Science 331, 341–346 (2011).

    CAS  Article  Google Scholar 

  18. 18

    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 

  19. 19

    Heekeren, H.R., Marrett, S., Bandettini, P.A. & Ungerleider, L.G. A general mechanism for perceptual decision-making in the human brain. Nature 431, 859–862 (2004).

    CAS  Article  Google Scholar 

  20. 20

    Boorman, E.D., Behrens, T.E., Woolrich, M.W. & Rushworth, M.F. 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).

    CAS  Article  Google Scholar 

  21. 21

    FitzGerald, T.H., Seymour, B. & Dolan, R.J. The role of human orbitofrontal cortex in value comparison for incommensurable objects. J. Neurosci. 29, 8388–8395 (2009).

    CAS  Article  Google Scholar 

  22. 22

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

    CAS  Article  Google Scholar 

  23. 23

    Strait, C.E., Blanchard, T.C. & Hayden, B.Y. Reward value comparison via mutual inhibition in ventromedial prefrontal cortex. Neuron 82, 1357–1366 (2014).

    CAS  Article  Google Scholar 

  24. 24

    Kahneman, D. & Tversky, A. Intuitive prediction: biases and corrective procedures. Manage. Sci. 12, 313–327 (1979).

    Google Scholar 

  25. 25

    Weinstein, N.D. Unrealistic optimism about future life events. J. Pers. Soc. Psychol. 39, 806–820 (1980).

    Article  Google Scholar 

  26. 26

    Sharot, T., Riccardi, A.M., Raio, C.M. & Phelps, E.A. Neural mechanisms mediating optimism bias. Nature 450, 102–105 (2007).

    CAS  Article  Google Scholar 

  27. 27

    Wagner, A.D., Shannon, B.J., Kahn, I. & Buckner, R.L. Parietal lobe contributions to episodic memory retrieval. Trends Cogn. Sci. 9, 445–453 (2005).

    Article  Google Scholar 

  28. 28

    Bush, G., Luu, P. & Posner, M.I. Cognitive and emotional influences in anterior cingulated cortex. Trends Cogn. Sci. 4, 215–222 (2000).

    CAS  Article  Google Scholar 

  29. 29

    Kolling, N., Behrens, T.E.J., Mars, R.B. & Rushworth, M.F.S. Neural mechanisms of Foraging. Science 336, 95–98 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Amemori, K. & Graybiel, A.M. Localized microstimulation of primate pregenual cingulate cortex induces negative decision-making. Nat. Neurosci. 15, 776–785 (2012).

    CAS  Article  Google Scholar 

  31. 31

    Pearson, J.M., Heilbronner, S.R., Barack, D.L., Hayden, B.Y. & Platt, M.L. Posterior cingulate cortex: adapting behavior to a changing world. Trends Cogn. Sci. 15, 143–151 (2011).

    Article  Google Scholar 

  32. 32

    McCoy, A.N. & Platt, M.L. Risk-sensitive neurons in macaque posterior cingulate cortex. Nat. Neurosci. 8, 1220–1227 (2005).

    CAS  Article  Google Scholar 

  33. 33

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

    CAS  Article  Google Scholar 

  34. 34

    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).

    CAS  Article  Google Scholar 

  35. 35

    Heilbronner, S.R., Hayden, B.Y. & Platt, M.L. Decision salience signals in posterior cingulate cortex. Front. Neurosci. 5, 55 (2011).

    Article  Google Scholar 

  36. 36

    Levy, D.J. & Glimcher, P.W. The root of all value: a neural common currency for choice. Curr. Opin. Neurobiol. 22, 1027–1038 (2012).

    CAS  Article  Google Scholar 

  37. 37

    Fletcher, P.C. et al. Other minds in the brain: a functional imaging study of “theory of mind” in story comprehension. Cognition 57, 109–128 (1995).

    CAS  Article  Google Scholar 

  38. 38

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

    CAS  Article  Google Scholar 

  39. 39

    Schiller, D., Freeman, J.B., Mitchell, J.P., Uleman, J.S. & Phelps, E.A. A neural mechanism of first impressions. Nat. Neurosci. 12, 508–514 (2009).

    CAS  Article  Google Scholar 

  40. 40

    Philiastides, M.G., Biele, G. & Heekeren, H.R. A mechanistic account of value computation in the human brain. Proc. Natl. Acad. Sci. USA 107, 9430–9435 (2010).

    CAS  Article  Google Scholar 

  41. 41

    Kim, S., Hwang, J. & Lee, D. Prefrontal coding of temporally discounted values during intertemporal choice. Neuron 59, 161–172 (2008).

    CAS  Article  Google Scholar 

  42. 42

    Kim, S. & Lee, D. Prefrontal cortex and impulsive decision making. Biol. Psychiatry 69, 1140–1146 (2011).

    Article  Google Scholar 

  43. 43

    Vogt, B.A. & Pandya, D.N. Cingulate cortex of the rhesus monkey: II. Cortical afferents. J. Comp. Neurol. 262, 271–289 (1987).

    CAS  Article  Google Scholar 

  44. 44

    Tversky, A. & Kahneman, D. Judgment under uncertainty: heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty. Science 185, 1124–1131 (1974).

    CAS  Article  Google Scholar 

  45. 45

    Wan, X. et al. Developing intuition: neural correlates of cognitive-skill learning in caudate nucleus. J. Neurosci. 32, 17492–17501 (2012).

    CAS  Article  Google Scholar 

  46. 46

    Kellman, P., Epstein, F.H. & McVeigh, E.R. Adaptive sensitivity encoding incorporating temporal filtering (TSENS). Magn. Reson. Med. 45, 846–852 (2001).

    CAS  Article  Google Scholar 

  47. 47

    Macmillan, N.A. & Creelman, C.D. Response bias: characteristics of detection theory, threshold theory, and “nonparametric” indexes. Psychol. Bull. 107, 401–413 (1990).

    Article  Google Scholar 

  48. 48

    Talairach, J. & Tournoux, P. Co-Planar Stereotaxic Atlas of the Human Brain (Thieme, 1988).

  49. 49

    Jekins, G.M. & Watts, D.G. Spectral Analysis and Its Applications (Holden-Day, 1968).

Download references

Acknowledgements

We thank N. Nakatani, T. Asamizuya, K. Ueno and C. Suzuki for technical help, T. Ito and Y. Tsuruoka for invaluable discussions of the computational shogi program, Japan Shogi Association for advice on the task, and K. Matsumoto and H. Nakahara for valuable comments on an earlier version of the manuscript. This work was partially supported by the Fujitsu Laboratories and the 111 Project (B07008) in China.

Author information

Affiliations

Authors

Contributions

X.W., K.C. and K.T. designed the experiments, analyzed the results and wrote the manuscript. X.W. conducted the experiments.

Corresponding author

Correspondence to Keiji Tanaka.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Behavioral performance in the strategy and option decision tasks conducted with two choices for both tasks.

The tasks were similar to those used in the fMRI experiments, except that 1) the number of choices was two for both strategy and option decision tasks; 2) the choices were shown together with the board position from the beginning; 3) the subjects could press a button even during the presentation of board position; and 4) the time up occurred 4 s after the termination of the board position. Eight experienced amateur players (27–55 y/o, amateur 3–4 dan) participated in these psychological experiments. The subjects were instructed to make a response as soon as they reached a high self-confidence (~80%) on their choices. During debriefing, one subject reported that he made his responses towards the end of each trial after carefully confirming his choices. Another subject reported that he largely gave up evaluating the two choices in the option decision task and selected one just by gut feeling. We removed these two subjects from the analyses. (a) Response times in the strategy and option decision tasks. A circle represents a subject. The response time in the strategy decision task (on average 4.0 s) was significantly shorter than that in the option decision task (on average 4.4 s) (P = 0.0098, two-tailed paired t test; Cohen’s d = 3.2). (b) The percentages of correct responses in the strategy and option decision tasks. The response accuracy in the strategy decision task (on average 68%) was significantly higher than that in the option decision task (on average 48%) (P = 0.00054 by two-tailed paired t test; Cohen’s d = 5.9). These psychological results demonstrate that making a strategy selection was faster than making an option selection and that the subjects made fairly good strategy decisions without knowing the best options of next move (close to the chance level, 50%).

Supplementary Figure 2 Strategy values as independent variables of the logistic regression of subjects’ selections.

(a) Coefficients for values of the n-th best moves (n = 1–10) in the two-dimensional model when each pair of attack and defense values of the same rank alone was entered into the model. Coefficients for attack (a1) and defense (a2) are shown in the upper and lower halves, respectively. The ordinate of the lower half is inverted. (b) Coefficients for mean values of the best n moves (n = 1–10) in two-dimensional model when each pair of mean values was entered into the model. (c) The Akaike’s information criterion (AIC = –2logL + K, where logL is maximum log-likelihood function for the estimated model, and K is the number of parameters) for mean values of the best n moves. Mean values of the best three moves gave the best fitting (the minimum AIC). (d) The Akaike’s information criterion shows that the 2-dimensional model was better than the 1-dimensional model (P = 0.000081). The Bayesian information criterion (BIC = –2logL + K × logN, where N is the number of observation) also showed that that the 2-dimensional model was better than the 1-dimensional model: the BIC for the fitting by the two-dimensional model was 2.40 less than that for the fitting by the one-dimensional model (P = 0.0080). Mean values of the best three moves were used in these comparisons. Error bars indicate S.E.M. across subjects.

Supplementary Figure 3 Response time was longer when differences between subjective values of chosen and unchosen strategies were smaller (r = 0.41, P = 0.039, z test).

This suggests that the task was more difficult when difference between subjective values of alternative strategies was small. Error bars indicate S.E.M. across subjects.

Supplementary Figure 4 Response bias was not associated with response times.

(a) Response times were not different between attack and defense selections (P = 0.37, two-tailed paired t test). (b) The difference between mean response times for attack and defense selections in each subject was uncorrelated with his response bias (r = –0.076, P = 0.39, Z-test). Each data point indicates a subject.

Supplementary Figure 5 Mean activities in rACC and PCC during defense and attack strategy selections in the three subjects who had response bias toward defense.

The data of the three subjects were pooled using a fixed-effect analysis. rACC activities were higher during defense selection than during attack selection (P = 0.032, two-tailed unpaired t test) and PCC activities were higher during attack selection than during defense selection (P = 0.0017). Error bars indicate S.E.M. across trials. *, P < 0.05; **, P < 0.01.

Supplementary Figure 6 The diagram of functional interaction among rACC, PCC and DLPFC during strategy decision.

Supplementary Figure 7 Activities in the ventral PCC (vPCC).

(a) Activations determined by the contrast of decision-making period versus “Gold”-piece detection period in all task trials (yellow). The cyan circle circumscribes vPCC. The dorsal activation was located in the precuneus. (b) vPCC was comparably activated in all trial types (F[1,102] = 2.1, P = 0.15, one-way ANOVA). Error bars indicate S.E.M. across subjects.

Supplementary Figure 8 The strategy decision was not associated with affective responses.

(a) The heart rates in attack and defense selections in the strategy selection task were not different from each other (P = 0.26, two-tailed paired t test). The heart rate was normalized by the values during the inter-trial intervals of all trials in each subject. (b) The regions (ventral striatum and amygdala) thought to be associated with affective responses were not activated during the strategy task (P = 0.32 in ventral striatum and P = 0.24 in amygdala, two-tailed paired t test, the minimum P value among the six conditions). Error bars indicate S.E.M. across subjects.

Supplementary Figure 9 The dorsal ACC (dACC) was not activated during decision-making in either the strategy decision task or the option decision task.

The ROI for the analyses here was defined by the peak coordinate for encoding of search value (peak Tal: [–4, 32, 17], radius: 10 voxels) in Kolling et al. (2012). (a) dACC was neither activated during strategy decision nor during option decision (P = 0.35, two-tailed paired t test, the minimum P value among the six conditions). (b) fMRI activity time courses in dACC during trials of the strategy and option tasks. There was a significant activation in trials of the option task, but it occurred after the selection was completed. The grey bar indicates the problem presentation time.

Supplementary Figure 10 The ventromedial PFC (VMPFC) was neither correlated with subjective strategy values in the strategy decision task, nor with the chosen value in the option decision task.

The regressor encoding the value of chosen option during the option decision task was analyzed only in the trials where the subjects chose one of the three concrete options other than “the other options” (about 60% trials). The VMPFC ROI (blue) for the analyses here was defined by the peak coordinates for encoding of subjective values (peak Tal: right, [3, 32, –3]; left, [–8, 35, –2], radius: 10 voxels) in Levy & Glimcher (2012). (a) The location of VMPFC ROI, which is ventral to the rACC region (red). (b) The estimated parameters of fMRI signals in VMPFC associated with SASV, SDSV and SchosenSunchosen in the strategy decision task, and associated with the chosen values in the option decision task. None of them were significantly different from zero (P = 0.21, two-tailed paired t test, the minimum P value among the four conditions). Error bars indicate S.E.M. across subjects.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wan, X., Cheng, K. & Tanaka, K. Neural encoding of opposing strategy values in anterior and posterior cingulate cortex. Nat Neurosci 18, 752–759 (2015). https://doi.org/10.1038/nn.3999

Download citation

Further reading

Search

Quick links