Advertisement

Cognitive, Affective, & Behavioral Neuroscience

, Volume 19, Issue 6, pp 1492–1508 | Cite as

Neural signatures underlying deliberation in human foraging decisions

  • Samantha V. Abram
  • Michael Hanke
  • A. David RedishEmail author
  • Angus W. MacDonaldIIIEmail author
Article

Abstract

Humans have a remarkable capacity to mentally project themselves far ahead in time. This ability, which entails the mental simulation of events, is thought to be fundamental to deliberative decision making, as it allows us to search through and evaluate possible choices. Many decisions that humans make are foraging decisions, in which one must decide whether an available offer is worth taking, when compared to unknown future possibilities (i.e., the background). Using a translational decision-making paradigm designed to reveal decision preferences in rats, we found that humans engaged in deliberation when making foraging decisions. A key feature of this task is that preferences (and thus, value) are revealed as a function of serial choices. Like rats, humans also took longer to respond when faced with difficult decisions near their preference boundary, which was associated with prefrontal and hippocampal activation, exemplifying cross-species parallels in deliberation. Furthermore, we found that voxels within the visual cortices encoded neural representations of the available possibilities specifically following regret-inducing experiences, in which the subject had previously rejected a good offer only to encounter a low-valued offer on the subsequent trial.

Keywords

Deliberation Episodic simulation Foraging Regret fMRI Neural decoding 

Notes

Supplementary material

13415_2019_733_MOESM1_ESM.pdf (7.5 mb)
ESM 1 (PDF 7.53 MB)

References

  1. Abram, S. V., Breton, Y.-A., Schmidt, B., Redish, A. D., & MacDonald, A. W., III. (2016). The Web-Surf Task: A translational model of human decision-making. Cognitive, Affective, & Behavioral Neuroscience, 16, 37–50.  https://doi.org/10.3758/s13415-015-0379-y CrossRefGoogle Scholar
  2. Addis, D. R., Wong, A. T., & Schacter, D. L. (2007). Remembering the past and imagining the future: Common And distinct neural substrates during event construction and elaboration. Neuropsychologia, 45, 1363–1377.  https://doi.org/10.1016/j.neuropsychologia.2006.10.016 CrossRefPubMedGoogle Scholar
  3. Addis, D. R., Wong, A. T., & Schacter, D. L. (2008). Age-related changes in the episodic simulation of future events. Psychological Science, 19, 33–41.  https://doi.org/10.1111/j.1467-9280.2008.02043.x CrossRefPubMedGoogle Scholar
  4. Andersson, J. L. R., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging. NeuroImage, 20, 870–888.  https://doi.org/10.1016/S1053-8119(03)00336-7 CrossRefPubMedGoogle Scholar
  5. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). lme4: Linear mixed-effects models using “Eigen” and S4. Retrieved from http://cran.r-project.org/package=lme4
  6. Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10, 1214–1221.  https://doi.org/10.1038/nn1954 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Bell, D. E. (1982). Regret in decision making under uncertainty. Operations Research, 30, 961–981.CrossRefGoogle Scholar
  8. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57, 289–300.Google Scholar
  9. Bett, D., Murdoch, L. H., Wood, E. R., & Dudchenko, P. A. (2015). Hippocampus, delay discounting, and vicarious trial-and-error. Hippocampus, 25, 643–654.  https://doi.org/10.1002/hipo.22400 CrossRefPubMedGoogle Scholar
  10. Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences, 8, 539–546.  https://doi.org/10.1016/j.tics.2004.10.003 CrossRefPubMedGoogle Scholar
  11. Buckner, R. L., & Carroll, D. C. (2007). Self-projection and the brain. Trends in Cognitive Sciences, 11, 49–57.  https://doi.org/10.1016/j.tics.2006.11.004 CrossRefPubMedGoogle Scholar
  12. Busby, J., & Suddendorf, T. (2005). Recalling yesterday and predicting tomorrow. Cognitive Development, 20, 362–372.  https://doi.org/10.1016/j.cogdev.2005.05.002 CrossRefGoogle Scholar
  13. Camerer, C. (1997). Taxi drivers and beauty contests. Engineering and Science, 60, 11–19.Google Scholar
  14. Cazé, R., Khamassi, M., Aubin, L., & Girard, B. (2018). Hippocampal replays under the scrutiny of reinforcement learning models. Journal of Neurophysiology, 120, 2877–2896.  https://doi.org/10.1152/jn.00145.2018 CrossRefPubMedGoogle Scholar
  15. Charnov, E. L. (1976). Optimal foraging, the marginal value theorem. Theoretical Population Biology, 9, 129–136.CrossRefGoogle Scholar
  16. Doll, B. B., Duncan, K. D., Simon, D. A., Shohamy, D., & Daw, N. D. (2015). Model-based choices involve prospective neural activity. Nature Neuroscience, 18, 767–772.  https://doi.org/10.1038/nn.3981 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Foster, D. J., & Wilson, M. A. (2006). Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature, 440, 680–683.  https://doi.org/10.1038/nature04587 CrossRefPubMedGoogle Scholar
  18. Gilbert, D. T., & Wilson, T. D. (2007). Prospection: Experiencing the future. Science, 317, 1351–1354.  https://doi.org/10.1126/science.1144161 CrossRefPubMedGoogle Scholar
  19. Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54, 493–503.  https://doi.org/10.1037/0003-066X.54.7.493 CrossRefGoogle Scholar
  20. Grill-Spector, K., & Weiner, K. S. (2014). The functional architecture of the ventral temporal cortex and its role in categorization. Nature Reviews Neuroscience, 15, 536–548.  https://doi.org/10.1038/nrn3747 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., & Pollmann, S. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37–53.  https://doi.org/10.1007/s12021-008-9041-y CrossRefPubMedPubMedCentralGoogle Scholar
  22. Hassabis, D., Kumaran, D., Vann, S. D., & Maguire, E. A. (2007). Patients with hippocampal amnesia cannot imagine new experiences. Proceedings of the National Academy of Sciences, 104, 1726–1731.  https://doi.org/10.1073/pnas.0610561104 CrossRefGoogle Scholar
  23. Hassabis, D., & Maguire, E. A. (2007). Deconstructing episodic memory with construction. Trends in Cognitive Sciences, 11, 299–306.  https://doi.org/10.1016/j.tics.2007.05.001 CrossRefPubMedGoogle Scholar
  24. Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., … Ramadge, P. J. (2011). A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72, 404–416.  https://doi.org/10.1016/j.neuron.2011.08.026 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Haxby, J. V, Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425–2430.  https://doi.org/10.1126/science.1063736 CrossRefPubMedGoogle Scholar
  26. Hoffer, L. D., Bobashev, G., & Morris, R. J. (2009). Researching a local heroin market as a complex adaptive system. American Journal of Community Psychology, 44, 273–286.  https://doi.org/10.1007/s10464-009-9268-2 CrossRefPubMedGoogle Scholar
  27. Ito, H. T., Zhang, S.-J., Witter, M. P., Moser, E. I., & Moser, M.-B. (2015). A prefrontal–thalamo–hippocampal circuit for goal-directed spatial navigation. Nature, 522, 50–55.  https://doi.org/10.1038/nature14396 CrossRefPubMedGoogle Scholar
  28. Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62, 782–790.  https://doi.org/10.1016/j.neuroimage.2011.09.015 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Johnson, A., & Redish, A. D. (2005). Hippocampal replay contributes to within session learning in a temporal difference reinforcement learning model. Neural Networks, 18, 1162–1171.  https://doi.org/10.1016/j.neunet.2005.08.009 CrossRefGoogle Scholar
  30. Johnson, A., & Redish, A. D. (2007). Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. Journal of Neuroscience, 27, 12176–12189.  https://doi.org/10.1523/JNEUROSCI.3761-07.2007 CrossRefPubMedGoogle Scholar
  31. Kane, J., Van Boven, L., & Mcgraw, A. P. (2012). Prototypical prospection: Future events are more prototypically represented and simulated than past events. European Journal of Social Psychology, 42, 352–362.  https://doi.org/10.1002/ejsp.1866 CrossRefGoogle Scholar
  32. Kaplan, R., Schuck, N. W., & Doeller, C. F. (2017). The role of mental maps in decision-making. Trends in Neurosciences, 40, 256–259.  https://doi.org/10.1016/j.tins.2017.03.002 CrossRefPubMedGoogle Scholar
  33. Killcross, S., & Coutureau, E. (2003). Coordination of actions and habits in the medial prefrontal cortex of rats. Cerebral Cortex, 13, 400–408.  https://doi.org/10.1093/cercor/13.4.400 CrossRefPubMedGoogle Scholar
  34. Kolling, N., Behrens, T. E. J., Mars, R. B., & Rushworth, M. F. S. (2012). Neural mechanisms of foraging. Science, 336, 95–98.  https://doi.org/10.1126/science.1216930 CrossRefPubMedPubMedCentralGoogle Scholar
  35. Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. Nature Reviews Neuroscience, 2, 635–642.  https://doi.org/10.1038/35090055 CrossRefPubMedGoogle Scholar
  36. Krishnapuram, B., Carin, L., Figueiredo, M. A. T., & Hartemink, A. J. (2005). Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 957–968.  https://doi.org/10.1109/TPAMI.2005.127 CrossRefPubMedGoogle Scholar
  37. Kurth-Nelson, Z., Bickel, W., & Redish, A. D. (2012). A theoretical account of cognitive effects in delay discounting. European Journal of Neuroscience, 35, 1052–1064.  https://doi.org/10.1111/j.1460-9568.2012.08058.x CrossRefPubMedGoogle Scholar
  38. Kwan, D., Carson, N., Addis, D. R., & Rosenbaum, R. S. (2010). Deficits in past remembering extend to future imagining in a case of developmental amnesia. Neuropsychologia, 48, 3179–3186.  https://doi.org/10.1016/j.neuropsychologia.2010.06.011 CrossRefPubMedGoogle Scholar
  39. Lindquist, M. A., Meng Loh, J., Atlas, L. Y., & Wager, T. D. (2009). Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling. NeuroImage, 45, S187–S198.  https://doi.org/10.1016/j.neuroimage.2008.10.065 CrossRefPubMedGoogle Scholar
  40. Marsh, R. L., Hicks, J. L., & Cook, G. I. (2006). Task interference from prospective memories covaries with contextual associations of fulfilling them. Memory & Cognition, 34, 1037–1045.  https://doi.org/10.3758/BF03193250 CrossRefGoogle Scholar
  41. Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, 16, 1936–1947.CrossRefGoogle Scholar
  42. Mullally, S. L., & Maguire, E. A. (2014). Memory, imagination, and predicting the future: A common brain mechanism? Neuroscientist, 20, 220–234.  https://doi.org/10.1177/1073858413495091 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Mushiake, H., Saito, N., Sakamoto, K., Itoyama, Y., & Tanji, J. (2006). Activity in the lateral prefrontal cortex reflects multiple steps of future events in action plans. Neuron, 50, 631–641.  https://doi.org/10.1016/j.neuron.2006.03.045 CrossRefPubMedGoogle Scholar
  44. Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10, 424–430.  https://doi.org/10.1016/j.tics.2006.07.005 CrossRefPubMedGoogle Scholar
  45. Pearson, J., Naselaris, T., Holmes, E. A., & Kosslyn, S. M. (2015). Mental imagery: Functional mechanisms and clinical applications. Trends in Cognitive Sciences, 19, 590–602.  https://doi.org/10.1016/j.tics.2015.08.003 CrossRefPubMedPubMedCentralGoogle Scholar
  46. Peirce, J. W. (2009). Generating stimuli for neuroscience using PsychoPy. Frontiers in Neuroinformatics, 2, 10.  https://doi.org/10.3389/neuro.11.010.2008 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45(1 Suppl), S199–S209.  https://doi.org/10.1016/j.neuroimage.2008.11.007 CrossRefPubMedGoogle Scholar
  48. Peters, J., & Büchel, C. (2010). Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal–mediotemporal interactions. Neuron, 66, 138–148.  https://doi.org/10.1016/j.neuron.2010.03.026 CrossRefPubMedGoogle Scholar
  49. Reddy, L., Tsuchiya, N., & Serre, T. (2010). Reading the mind’s eye: Decoding category information during mental imagery. NeuroImage, 50, 818–825.  https://doi.org/10.1016/j.neuroimage.2009.11.084 CrossRefPubMedGoogle Scholar
  50. Redish, A. D. (2013). The mind within the brain: How we make decisions and how those decisions go wrong. New York, NY: Oxford University Press.Google Scholar
  51. Redish, A. D. (2016). Vicarious trial and error. Nature Reviews Neuroscience, 17, 147–159.  https://doi.org/10.1038/nrn.2015.30 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Riefer, P. S., Prior, R., Blair, N., Pavey, G., & Love, B. C. (2017). Coherency-maximizing exploration in the supermarket. Nature Human Behaviour, 1, 0017.  https://doi.org/10.1038/s41562-016-0017 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Satterthwaite, T. D., Green, L., Myerson, J., Parker, J., Ramaratnam, M., & Buckner, R. L. (2007). Dissociable but inter-related systems of cognitive control and reward during decision making: Evidence from pupillometry and event-related fMRI. NeuroImage, 37, 1017–1031.  https://doi.org/10.1016/j.neuroimage.2007.04.066 CrossRefPubMedGoogle Scholar
  54. Schacter, D. L., & Addis, D. R. (2007a). The cognitive neuroscience of constructive memory: Remembering the past and imagining the future. Philosophical Transactions of the Royal Society B, 362, 773–786.  https://doi.org/10.1098/rstb.2007.2087 CrossRefGoogle Scholar
  55. Schacter, D. L., & Addis, D. R. (2007b). Constructive memory: The ghosts of past and future. Nature, 445, 27.  https://doi.org/10.1038/445027a CrossRefPubMedGoogle Scholar
  56. Schacter, D. L., & Addis, D. R. (2011). On the nature of medial temporal lobe contributions to the constructive simulation of future events. In M. Bar (Ed.), Predictions in the brain: Using our past to generate a future (pp. 1245–1253). New York, NY: Oxford University Press.  https://doi.org/10.1093/acprof:oso/9780195395518.003.0024 CrossRefGoogle Scholar
  57. Schacter, D. L., Addis, D. R., & Buckner, R. L. (2008). Episodic simulation of future events: Concepts, data, and applications. Annals of the New York Academy of Sciences, 1124, 39–60.  https://doi.org/10.1196/annals.1440.001 CrossRefPubMedGoogle Scholar
  58. Schacter, D. L., Addis, D. R., Hassabis, D., Martin, V. C., Spreng, R. N., & Szpunar, K. K. (2012). The future of memory: Remembering, imagining, and the brain. Neuron, 76, 677–694.  https://doi.org/10.1016/j.neuron.2012.11.001 CrossRefPubMedGoogle Scholar
  59. Schmidt, B., Duin, A. A., & Redish, A. D. (2019). Disrupting the medial prefrontal cortex alters hippocampal sequences during deliberative decision making. Journal of Neurophysiology, 121, 1981–2000.  https://doi.org/10.1152/jn.00793.2018 CrossRefPubMedGoogle Scholar
  60. Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1599.  https://doi.org/10.1126/science.275.5306.1593 CrossRefGoogle Scholar
  61. Shenhav, A., Straccia, M. A., Cohen, J. D., & Botvinick, M. M. (2014). Anterior cingulate engagement in a foraging context reflects choice difficulty, not foraging value. Nature Neuroscience, 17, 1249–1254.  https://doi.org/10.1038/nn.3771 CrossRefPubMedPubMedCentralGoogle Scholar
  62. Slotnick, S. D., Thompson, W. L., & Kosslyn, S. M. (2012). Visual memory and visual mental imagery recruit common control and sensory regions of the brain. Cognitive Neuroscience, 3, 14–20.  https://doi.org/10.1080/17588928.2011.578210 CrossRefPubMedGoogle Scholar
  63. Smith, E. A. (1991). Inujjuamiunt foraging strategies: Evolutionary ecology of an Arctic hunting economy. New York, NY: Routledge.Google Scholar
  64. Smith, K. S., & Graybiel, A. M. (2013). A dual operator view of habitual behavior reflecting cortical and striatal dynamics. Neuron, 79, 361–374.  https://doi.org/10.1016/j.neuron.2013.05.038 CrossRefPubMedPubMedCentralGoogle Scholar
  65. Smith, R. E. (2003). The cost of remembering to remember in event-based prospective memory: Investigating the capacity demands of delayed intention performance. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 347–361.  https://doi.org/10.1037/0278-7393.29.3.347 CrossRefPubMedGoogle Scholar
  66. Spellman, T., Rigotti, M., Ahmari, S. E., Fusi, S., Gogos, J. A., & Gordon, J. A. (2015). Hippocampal–prefrontal input supports spatial encoding in working memory. Nature, 522, 309–314.  https://doi.org/10.1038/nature14445 CrossRefPubMedPubMedCentralGoogle Scholar
  67. Spreng, R. N., Gerlach, K. D., Turner, G. R., & Schacter, D. L. (2015). Autobiographical planning and the brain: Activation and its modulation by qualitative features. Journal of Cognitive Neuroscience, 27, 2147–2157.  https://doi.org/10.1162/jocn_a_00846 CrossRefPubMedPubMedCentralGoogle Scholar
  68. Steiner, A. P., & Redish, A. D. (2014). Behavioral and neurophysiological correlates of regret in rat decision-making on a neuroeconomic task. Nature Neuroscience, 17, 995–1002.  https://doi.org/10.1038/nn.3740 CrossRefPubMedPubMedCentralGoogle Scholar
  69. Stephens, D. W. (2008). Decision ecology: Foraging and the ecology of animal decision making. Cognitive, Affective, & Behavioral Neuroscience, 8, 475–484.  https://doi.org/10.3758/CABN.8.4.475 CrossRefGoogle Scholar
  70. Stott, J. J., & Redish, A. D. (2014). A functional difference in information processing between orbitofrontal cortex and ventral striatum during decision-making behaviour. Philosophical Transactions of the Royal Society B, 369, 20130472.  https://doi.org/10.1098/rstb.2013.0472 CrossRefGoogle Scholar
  71. Suddendorf, T. (2013). The gap: The science of what separates us from other animals. New York, NY: Basic Books.Google Scholar
  72. Sun, D., van Erp, T. G. M., Thompson, P. M., Bearden, C. E., Daley, M., Kushan, L., … Cannon, T. D. (2009). Elucidating a magnetic resonance imaging-based neuroanatomic biomarker for psychosis: Classification analysis using probabilistic brain atlas and machine learning algorithms. Biological Psychiatry, 66, 1055–1060.  https://doi.org/10.1016/j.biopsych.2009.07.019 CrossRefPubMedPubMedCentralGoogle Scholar
  73. Sweis, B. M., Abram, S. V., Schmidt, B. J., Seeland, K. D., MacDonald, A. W., Thomas, M. J., & Redish, A. D. (2018a). Sensitivity to “sunk costs” in mice, rats, and humans. Science, 361, 178–181.  https://doi.org/10.1126/science.aar8644 CrossRefPubMedPubMedCentralGoogle Scholar
  74. Sweis, B. M., Redish, A. D., & Thomas, M. J. (2018b). Prolonged abstinence from cocaine or morphine disrupts separable valuations during decision conflict. Nature Communications, 9, 2521.  https://doi.org/10.1038/s41467-018-04967-2 CrossRefPubMedPubMedCentralGoogle Scholar
  75. Sweis, B. M., Thomas, M. J., & Redish, A. D. (2018c). Mice learn to avoid regret. PLoS Biology, 16, e2005853.  https://doi.org/10.1371/journal.pbio.2005853 CrossRefPubMedPubMedCentralGoogle Scholar
  76. Szpunar, K. K., Spreng, R. N., & Schacter, D. L. (2014). A taxonomy of prospection: Introducing an organizational framework for future-oriented cognition. Proceedings of the National Academy of Sciences, 111, 18414–18421.  https://doi.org/10.1073/pnas.1417144111 CrossRefGoogle Scholar
  77. Tolman, E. C. (1939). Prediction of vicarious trial and error by means of the schematic sowbug. Psychological Review, 46, 318–336.  https://doi.org/10.1037/h0057054 CrossRefGoogle Scholar
  78. Tong, F., & Pratte, M. S. (2012). Decoding patterns of human brain activity. Annual Review of Psychology, 63, 483–509.  https://doi.org/10.1146/annurev-psych-120710-100412 CrossRefPubMedGoogle Scholar
  79. van der Meer, M. A. A., Johnson, A., Schmitzer-Torbert, N. C., & Redish, A. D. (2010). Triple dissociation of information processing in dorsal striatum, ventral striatum, and hippocampus on a learned spatial decision task. Neuron, 67, 25–32.  https://doi.org/10.1016/j.neuron.2010.06.023 CrossRefPubMedPubMedCentralGoogle Scholar
  80. Wang, J. X., Cohen, N. J., & Voss, J. L. (2015). Covert rapid action–memory simulation (CRAMS): A hypothesis of hippocampal–prefrontal interactions for adaptive behavior. Neurobiology of Learning and Memory, 117, 22–33.  https://doi.org/10.1016/j.nlm.2014.04.003 CrossRefPubMedGoogle Scholar
  81. Wikenheiser, A. M., Stephens, D. W., & Redish, A. D. (2013). Subjective costs drive overly patient foraging strategies in rats on an intertemporal foraging task. Proceedings of the National Academy of Sciences, 110, 8308–8313.  https://doi.org/10.1073/pnas.1220738110 CrossRefGoogle Scholar
  82. Zeithamova, D., Schlichting, M. L., & Preston, A. R. (2012). The hippocampus and inferential reasoning: Building memories to navigate future decisions. Frontiers in Human Neuroscience, 6, 70.  https://doi.org/10.3389/fnhum.2012.00070 CrossRefPubMedPubMedCentralGoogle Scholar
  83. Zentall, T. R. (2010). Coding of stimuli by animals: Retrospection, prospection, episodic memory and future planning. Learning and Motivation, 41, 225–240.  https://doi.org/10.1016/j.lmot.2010.08.001 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of MinnesotaMinneapolisUSA
  2. 2.Sierra Pacific Mental Illness Research Education and Clinical Centers, San Francisco VA Medical Center, and the University of CaliforniaSan FranciscoUSA
  3. 3.Psychoinformatics Laboratory, Institute of PsychologyOtto-von-Guericke UniversityMagdeburgGermany
  4. 4.Center for Behavioral Brain SciencesMagdeburgGermany
  5. 5.Department of NeuroscienceUniversity of MinnesotaMinneapolisUSA
  6. 6.Department of PsychiatryUniversity of MinnesotaMinneapolisUSA

Personalised recommendations