Skip to main content

Thank you for visiting 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.

Minimizing threat via heuristic and optimal policies recruits hippocampus and medial prefrontal cortex


Jointly minimizing multiple threats over extended time horizons enhances survival. Consequently, many tests of approach–avoidance conflicts incorporate multiple threats for probing corollaries of animal and human anxiety. To facilitate computations necessary for threat minimization, the human brain may concurrently harness multiple decision policies and associated neural controllers, but it is unclear which. We combine a task that mimics foraging under predation with behavioural modelling and functional neuroimaging. Human choices rely on immediate predator probability—a myopic heuristic policy—and on the optimal policy, which integrates all relevant variables. Predator probability relates positively and the associated choice uncertainty relates negatively to activations in the anterior hippocampus, amygdala and dorsolateral prefrontal cortex. The optimal policy is positively associated with dorsomedial prefrontal cortex activity. We thus provide a decision-theoretic outlook on the role of the human hippocampus, amygdala and prefrontal cortex in resolving approach–avoidance conflicts relevant for anxiety and integral for survival.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Task outline.
Fig. 2: Models of choice data in the fMRI sample.
Fig. 3: fMRI results during the choice phase.
Fig. 4: Visualization of the clusters in the hippocampus extending into the amygdala.

Data availability

The behavioural data that support the findings of this study are publicly available at github ( and at figshare ( The neuroimaging data that support the findings of this study are publicly available at neurovault (

Code availability

The code used for the analyses is available at github (


  1. 1.

    Bach, D. R. & Dayan, P. Algorithms for survival: a comparative perspective on emotions. Nat. Rev. Neurosci. 18, 311–319 (2017).

    CAS  Article  Google Scholar 

  2. 2.

    Korn, C. W. & Bach, D. R. Heuristic and optimal policy computations in the human brain during sequential decision-making. Nat. Commun. 9, 325 (2018).

    Article  Google Scholar 

  3. 3.

    Korn, C. W. & Bach, D. R. Maintaining homeostasis by decision-making. PLoS Comput. Biol. 11, e1004301 (2015).

    Article  Google Scholar 

  4. 4.

    Huys, Q. J. M. et al. Interplay of approximate planning strategies. Proc. Natl Acad. Sci. USA 112, 3098–3103 (2015).

    CAS  Article  Google Scholar 

  5. 5.

    Huys, Q. J. M. et al. Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Comput. Biol. 8, e1002410 (2012).

    CAS  Article  Google Scholar 

  6. 6.

    Keramati, M., Smittenaar, P., Dolan, R. J. & Dayan, P. Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum. Proc. Natl Acad. Sci. USA 113, 12868–12873 (2016).

    CAS  Article  Google Scholar 

  7. 7.

    Keramati, M., Dezfouli, A. & Piray, P. Speed/accuracy trade-off between the habitual and the goal-directed processes. PLoS Comput. Biol. 7, e1002055 (2011).

    CAS  Article  Google Scholar 

  8. 8.

    LeDoux, J. & Daw, N. D. Surviving threats: neural circuit and computational implications of a new taxonomy of defensive behaviour. Nat. Rev. Neurosci. 19, 269–282 (2018).

    CAS  Article  Google Scholar 

  9. 9.

    Mobbs, D., Trimmer, P. C., Blumstein, D. T. & Dayan, P. Foraging for foundations in decision neuroscience: insights from ethology. Nat. Rev. Neurosci. 19, 419–427 (2018).

  10. 10.

    Mobbs, D. The ethological deconstruction of fear(s). Curr. Opin. Behav. Sci. 24, 32–37 (2018).

    Article  Google Scholar 

  11. 11.

    Griebel, G. & Holmes, A. 50 years of hurdles and hope in anxiolytic drug discovery. Nat. Rev. Drug Discov. 12, 667–687 (2013).

    CAS  Article  Google Scholar 

  12. 12.

    Haller, J., Aliczki, M. & Gyimesine Pelczer, K. Classical and novel approaches to the preclinical testing of anxiolytics: a critical evaluation. Neurosci. Biobehav. Rev. 37, 2318–2330 (2013).

    CAS  Article  Google Scholar 

  13. 13.

    Cryan, J. F. & Sweeney, F. F. The age of anxiety: role of animal models of anxiolytic action in drug discovery. Br. J. Pharm. 164, 1129–1161 (2011).

    CAS  Article  Google Scholar 

  14. 14.

    Kirlic, N., Young, J. & Aupperle, R. L. Animal to human translational paradigms relevant for approach avoidance conflict decision making. Behav. Res. Ther. 96, 14–29 (2017).

    Article  Google Scholar 

  15. 15.

    Calhoon, G. G. & Tye, K. M. Resolving the neural circuits of anxiety. Nat. Neurosci. 18, 1394–1404 (2015).

    CAS  Article  Google Scholar 

  16. 16.

    Gray, J. A. & McNaughton, N. The Neuropsychology of Anxiety: An Enquiry into the Functions of the Septohippocampal System (Oxford Univ. Press, 2000).

  17. 17.

    Aupperle, R. L., Melrose, A. J., Francisco, A., Paulus, M. P. & Stein, M. B. Neural substrates of approach–avoidance conflict decision-making. Hum. Brain Mapp. 36, 449–462 (2015).

    Article  Google Scholar 

  18. 18.

    Bach, D. R. et al. Human hippocampus arbitrates approach–avoidance conflict. Curr. Biol. 24, 541–547 (2014).

    CAS  Article  Google Scholar 

  19. 19.

    Loh, E. et al. Parsing the role of the hippocampus in approach–avoidance conflict. Cereb. Cortex 27, 201–215 (2016).

  20. 20.

    O’Neil, E. B. et al. Examining the role of the human hippocampus in approach–avoidance decision making using a novel conflict paradigm and multivariate functional magnetic resonance imaging. J. Neurosci. 35, 15039–15049 (2015).

    Article  Google Scholar 

  21. 21.

    Ito, R. & Lee, A. C. H. The role of the hippocampus in approach–avoidance conflict decision-making: evidence from rodent and human studies. Behav. Brain Res. 313, 345–357 (2016).

    Article  Google Scholar 

  22. 22.

    Schlund, M. W. et al. The tipping point: value differences and parallel dorsal–ventral frontal circuits gating human approach–avoidance behavior. Neuroimage 136, 94–105 (2016).

    Article  Google Scholar 

  23. 23.

    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 

  24. 24.

    Qi, S. et al. How cognitive and reactive fear circuits optimize escape decisions in humans. Proc. Natl Acad. Sci. USA 115, 3186–3191 (2018).

    CAS  Article  Google Scholar 

  25. 25.

    Bach, D. R. Anxiety-like behavioural inhibition is normative under environmental threat-reward correlations. PLoS Comput. Biol. 11, 1–20 (2015).

    Article  Google Scholar 

  26. 26.

    Korn, C. W. et al. Amygdala lesions reduce anxiety-like behavior in a human benzodiazepine-sensitive approach–avoidance conflict test. Biol. Psychiatry 82, 522–531 (2017).

    Article  Google Scholar 

  27. 27.

    Khemka, S., Barnes, G., Dolan, R. J. & Bach, D. R. Dissecting the function of hippocampal oscillations in a human anxiety model. J. Neurosci. 37, 6869–6876 (2017).

    CAS  Article  Google Scholar 

  28. 28.

    McNaughton, N. & Corr, P. J. Survival circuits and risk assessment. Curr. Opin. Behav. Sci. 24, 14–20 (2018).

    Google Scholar 

  29. 29.

    Blanchard, D. C. Risk assessment: at the interface of cognition and emotion. Curr. Opin. Behav. Sci. 24, 69–74 (2018).

    Article  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.

    Symmonds, M., Wright, N. D., Bach, D. R. & Dolan, R. J. Deconstructing risk: separable encoding of variance and skewness in the brain. Neuroimage 58, 1139–1149 (2011).

    Article  Google Scholar 

  32. 32.

    Mohr, P. N. C., Biele, G. & Heekeren, H. R. Neural processing of risk. J. Neurosci. 30, 6613–6619 (2010).

    CAS  Article  Google Scholar 

  33. 33.

    Bach, D. R. & Dolan, R. J. Knowing how much you don’t know: a neural organization of uncertainty estimates. Nat. Rev. Neurosci. 13, 572–586 (2012).

    CAS  Article  Google Scholar 

  34. 34.

    Bartra, O., McGuire, J. T. & Kable, J. W. The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage 76, 412–427 (2013).

    Article  Google Scholar 

  35. 35.

    Clithero, J. A. & Rangel, A. Informatic parcellation of the network involved in the computation of subjective value. Soc. Cogn. Affect. Neurosci. 9, 1289–1302 (2014).

    Article  Google Scholar 

  36. 36.

    Rushworth, M. F. S., 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 

  37. 37.

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

    CAS  Article  Google Scholar 

  38. 38.

    Kolling, N., Wittmann, M. & Rushworth, M. F. S. Multiple neural mechanisms of decision making and their competition under changing risk pressure. Neuron 81, 1190–1202 (2014).

    CAS  Article  Google Scholar 

  39. 39.

    Hayden, B. Y., Pearson, J. M. & Platt, M. L. Neuronal basis of sequential foraging decisions in a patchy environment. Nat. Neurosci. 14, 933–939 (2011).

    CAS  Article  Google Scholar 

  40. 40.

    Mata, R., Wilke, A. & Czienskowski, U. Foraging across the life span: is there a reduction in exploration with aging? Front. Neurosci. 7, 53 (2013).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Shenhav, A., Straccia, M. A., Cohen, J. D. & Botvinick, M. M. Anterior cingulate engagement in a foraging context reflects choice difficulty, not foraging value. Nat. Neurosci. 17, 1249–1254 (2014).

    CAS  Article  Google Scholar 

  42. 42.

    Constantino, S. M. & Daw, N. D. Learning the opportunity cost of time in a patch-foraging task. Cogn. Affect. Behav. Neurosci. 15, 837–853 (2015).

    Article  Google Scholar 

  43. 43.

    Pearson, J. M., Watson, K. K. & Platt, M. L. Decision making: the neuroethological turn. Neuron 82, 950–965 (2014).

    CAS  Article  Google Scholar 

  44. 44.

    Gigerenzer, G. & Gaissmaier, W. Heuristic decision making. Annu. Rev. Psychol. 62, 451–482 (2011).

    Article  Google Scholar 

  45. 45.

    Gu, X. & FitzGerald, T. H. B. Interoceptive inference: homeostasis and decision-making. Trends Cogn. Sci. 18, 269–270 (2014).

    Article  Google Scholar 

  46. 46.

    Fawcett, T. W. et al. The evolution of decision rules in complex environments. Trends Cogn. Sci. 18, 153–161 (2014).

    Article  Google Scholar 

  47. 47.

    Dayan, P. Rationalizable irrationalities of choice. Top. Cogn. Sci. 6, 204–228 (2014).

    Article  Google Scholar 

  48. 48.

    Tovote, P., Fadok, J. P. & Lüthi, A. Neuronal circuits for fear and anxiety. Nat. Rev. Neurosci. 16, 317–331 (2015).

    CAS  Article  Google Scholar 

  49. 49.

    Jimenez, J. C. et al. Anxiety cells in a hippocampal–hypothalamic circuit. Neuron 97, 670–683.e6 (2018).

    CAS  Article  Google Scholar 

  50. 50.

    Blanchard, D. C. Translating dynamic defense patterns from rodents to people. Neurosci. Biobehav. Rev. 76, 22–28 (2017).

    Article  Google Scholar 

  51. 51.

    Payzan-LeNestour, E., Dunne, S., Bossaerts, P. & O’Doherty, J. The neural representation of unexpected uncertainty during value-based decision making. Neuron 79, 191–201 (2013).

    CAS  Article  Google Scholar 

  52. 52.

    Rigoli, F., Michely, J., Friston, K. J. & Dolan, R. J. The role of the hippocampus in weighting expectations during inference under uncertainty. Cortex 115, 1–14 (2019).

    Article  Google Scholar 

  53. 53.

    Harrison, L. M., Duggins, A. & Friston, K. J. Encoding uncertainty in the hippocampus. Neural Netw. 19, 535–546 (2006).

    CAS  Article  Google Scholar 

  54. 54.

    Strange, B. A., Duggins, A., Penny, W., Dolan, R. J. & Friston, K. J. Information theory, novelty and hippocampal responses: unpredicted or unpredictable? Neural Netw. 18, 225–230 (2005).

    Article  Google Scholar 

  55. 55.

    Lee, S. W., Shimojo, S. & O’Doherty, J. P. Neural computations underlying arbitration between model-based and model-free learning. Neuron 81, 687–699 (2014).

    CAS  Article  Google Scholar 

  56. 56.

    Boureau, Y.-L., Sokol-Hessner, P. & Daw, N. D. Deciding how to decide: self-control and meta-decision making. Trends Cogn. Sci. 19, 700–710 (2015).

    Article  Google Scholar 

  57. 57.

    Laux, L., Glanzmann, P., Schaffner, P. & Spielberger, C. D. STAI—State-Trait-Angstinventar. Theoretische Grundlagen und Handanweisung in German (Beltz Test GmbH, 1981).

  58. 58.

    Bless, H., Wänke, M., Bohner, G., Fellhauer, R. F. & Schwarz, N. Need for cognition: Eine Skala zur Erfassung von Engagement und Freude bei Denkaufgaben. Z. Sozialpsychol. 25 , 147–154 (1994).

  59. 59.

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

  60. 60.

    Rigoux, L., Stephan, K. E., Friston, K. J. & Daunizeau, J. Bayesian model selection for group studies - revisited. Neuroimage 84, 971–985 (2014).

    CAS  Article  Google Scholar 

  61. 61.

    Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J. & Friston, K. J. Bayesian model selection for group studies. Neuroimage 46, 1004–1017 (2009).

    Article  Google Scholar 

  62. 62.

    Penny, W. D. Comparing dynamic causal models using AIC, BIC and free energy. Neuroimage 59, 319–330 (2012).

    CAS  Article  Google Scholar 

  63. 63.

    Baayen, R. H., Davidson, D. J. & Bates, D. M. Mixed-effects modeling with crossed random effects for subjects and items. J. Mem. Lang. 59, 390–412 (2008).

    Article  Google Scholar 

  64. 64.

    Hutton, C. et al. Image distortion correction in fMRI: a quantitative evaluation. Neuroimage 16, 217–240 (2002).

    Article  Google Scholar 

  65. 65.

    Ashburner, J. & Friston, K. J. Unified segmentation. Neuroimage 26, 839–851 (2005).

    Article  Google Scholar 

  66. 66.

    Eklund, A., Nichols, T. E. & Knutsson, H. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl Acad. Sci. USA 113, 7900–7905 (2016).

    CAS  Article  Google Scholar 

Download references


We thank G. Castegnetti, S. Khemka, M. Staib, A. Tzovara and C. Ioan for discussions and help with data acquisition. The Wellcome Trust Centre for Neuroimaging is supported by a strategic grant from the Wellcome Trust (091593/Z/10/Z). C.W.K. was supported by two grants from the German Research Foundation (DFG) during the final stages of manuscript preparation: the collaborative research centre SFB TRR 169 and an Emmy Noether Research Group (392443797). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information




C.W.K. and D.R.B. designed the experiment, developed the analysis procedures and wrote the paper. C.W.K. collected and analysed the data.

Corresponding author

Correspondence to Christoph W. Korn.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10, Supplementary Tables 1–17, Supplementary References and Supplementary Notes 1–3.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Korn, C.W., Bach, D.R. Minimizing threat via heuristic and optimal policies recruits hippocampus and medial prefrontal cortex. Nat Hum Behav 3, 733–745 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing