Nature Neuroscience

Inferences on a multidimensional social hierarchy use a grid-like code

  • 1.

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

  • 2.

    Behrens, T. E. J. et al. What Is a cognitive map? Organizing knowledge for flexible behavior. Neuron 100, 490–509 (2018).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 3.

    Tolman, E. C. Cognitive maps in rats and men. Psychol. Rev. 55, 189–208 (1948).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 4.

    O’Keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Oxford University Press, 1978).

  • 5.

    Stachenfeld, K. L., Botvinick, M. M. & Gershman, S. J. The hippocampus as a predictive map. Nat. Neurosci. 20, 1643–1653 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 6.

    Ekstrom, A. D. & Ranganath, C. Space, time, and episodic memory: the hippocampus is all over the cognitive map. Hippocampus 28, 680–687 (2018).

    PubMed 
    Article 

    Google Scholar
     

  • 7.

    Hafting, T., Fyhn, M., Molden, S., Moser, M.-B. B. & Moser, E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 8.

    Welinder, P. E., Burak, Y. & Fiete, I. R. Grid cells: the position code, neural network models of activity, and the problem of learning. Hippocampus 18, 1283–1300 (2008).

    PubMed 
    Article 

    Google Scholar
     

  • 9.

    Bush, D., Barry, C., Manson, D. & Burgess, N. Using grid cells for navigation. Neuron 87, 507–520 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 10.

    Banino, A. et al. Vector-based navigation using grid-like representations in artificial agents. Nature 557, 429–433 (2018).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 11.

    Whittington, J. C. R. et al. The Tolman–Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation. Cell 183, 1249–1263 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 12.

    Kriete, T., Noelle, D. C., Cohen, J. D. & O’Reilly, R. C. Indirection and symbol-like processing in the prefrontal cortex and basal ganglia. Proc. Natl Acad. Sci. USA 110, 16390–16395 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 13.

    Wang, J. X. et al. Prefrontal cortex as a meta-reinforcement learning system. Nat. Neurosci. 21, 860–868 (2018).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 14.

    Constantinescu, A. O., O’Reilly, J. X. & Behrens, T. E. J. Organizing conceptual knowledge in humans with a gridlike code. Science 352, 1464–1468 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 15.

    Schuck, N. W., Cai, M. B., Wilson, R. C. & Niv, Y. Human orbitofrontal cortex represents a cognitive map of state space. Neuron 91, 1402–1412 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 16.

    Bennett, A. T. D. Do animals have cognitive maps? J. Exp. Biol. 199, 219–224 (1996).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 17.

    Aronov, D., Nevers, R. & Tank, D. W. Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit. Nature 543, 719–722 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 18.

    Bao, X. et al. Grid-like neural representations support olfactory navigation of a two-dimensional odor space. Neuron 102, 1066–1075 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 19.

    Eichenbaum, H. & Cohen, N. J. Can we reconcile the declarative memory and spatial navigation views on hippocampal function? Neuron 83, 764–770 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 20.

    Parkinson, C., Kleinbaum, A. M. & Wheatley, T. Spontaneous neural encoding of social network position. Nat. Hum. Behav. 1, 0072 (2017).

    Article 

    Google Scholar
     

  • 21.

    Stolier, R. M., Hehman, E. & Freeman, J. B. A dynamic structure of social trait space. Trends Cogn. Sci. 22, 197–200 (2018).

    PubMed 
    Article 

    Google Scholar
     

  • 22.

    Tavares, R. M. et al. A map for social navigation in the human brain. Neuron 87, 231–243 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 23.

    Park, S. A., Miller, D. S., Nili, H., Ranganath, C. & Boorman, E. D. Map making: constructing, combining, and inferring on abstract cognitive maps. Neuron 107, 1226–1238 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 24.

    Doeller, C. F., Barry, C. & Burgess, N. Evidence for grid cells in a human memory network. Nature 463, 657–661 (2010).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 25.

    Nau, M., Navarro Schröder, T., Bellmund, J. L. S. & Doeller, C. F. Hexadirectional coding of visual space in human entorhinal cortex. Nat. Neurosci. 21, 188–190 (2018).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 26.

    Fiske, S. T., Cuddy, A. J. C. & Glick, P. Universal dimensions of social cognition: warmth and competence. Trends Cogn. Sci. 11, 77–83 (2007).

    PubMed 
    Article 

    Google Scholar
     

  • 27.

    Hampton, A. N., Bossaerts, P. & O’Doherty, J. P. Neural correlates of mentalizing-related computations during strategic interactions in humans. Proc. Natl Acad. Sci. USA 105, 6741–6746 (2008).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 28.

    Wittmann, M. K., Lockwood, P. L. & Rushworth, M. F. S. Neural mechanisms of social cognition in primates. Annu. Rev. Neurosci. 41, 99–118 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 29.

    Behrens, T. E. J. J., Hunt, L. T., Woolrich, M. W. & Rushworth, M. F. S. S. Associative learning of social value. Nature 456, 245–249 (2008).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 30.

    Nicolle, A. et al. An agent independent axis for executed and modeled choice in medial prefrontal cortex. Neuron 75, 1114–1121 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 31.

    Boorman, E. D., Behrens, T. E. J., Woolrich, M. W. & Rushworth, M. F. S. 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 
    PubMed 
    Article 

    Google Scholar
     

  • 32.

    Boorman, E. D., Behrens, T. E. & Rushworth, M. F. Counterfactual choice and learning in a neural network centered on human lateral frontopolar cortex. PLoS Biol. 9, e1001093 (2011).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 33.

    Park, S. A., Sestito, M., Boorman, E. D. & Dreher, J. C. Neural computations underlying strategic social decision-making in groups. Nat. Commun. 10, 5287 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 34.

    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 
    Article 

    Google Scholar
     

  • 35.

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

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 36.

    De Martino, B., Fleming, S. M., Garrett, N. & Dolan, R. J. Confidence in value-based choice. Nat. Neurosci. 16, 105–110 (2013).

    PubMed 
    Article 
    CAS 

    Google Scholar
     

  • 37.

    Witter, M. P., Wouterlood, F. G., Naber, P. A. & Van Haeften, T. Anatomical organization of the parahippocampal–hippocampal network. Ann. N. Y. Acad. Sci. 911, 1–24 (2000).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 38.

    Bakkour, A. et al. The hippocampus supports deliberation during value-based decisions. eLife 8, e46080 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 39.

    Ferreira-Fernandes, E., Pinto-Correia, B., Quintino, C. & Remondes, M. A Gradient of hippocampal inputs to the medial mesocortex. Cell Rep. 29, 3266–3279 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 40.

    Jacobs, J. et al. Direct recordings of grid-like neuronal activity in human spatial navigation. Nat. Neurosci. 16, 1188–1190 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 41.

    Eichenbaum, H. Prefrontal–hippocampal interactions in episodic memory. Nat. Rev. Neurosci. 18, 547–558 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 42.

    Preston, A. R. & Eichenbaum, H. Interplay of hippocampus and prefrontal cortex in memory. Curr. Biol. 23, R764–R773 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 43.

    Long, X. & Zhang, S.-J. A novel somatosensory spatial navigation system outside the hippocampal formation. Cell Res. 31, 649–663 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 44.

    Piva, M. et al. The dorsomedial prefrontal cortex computes task-invariant relative subjective value for self and other. eLife 8, e44939 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 45.

    Maidenbaum, S., Miller, J., Stein, J. M. & Jacobs, J. Grid-like hexadirectional modulation of human entorhinal theta oscillations. Proc. Natl Acad. Sci. USA 115, 10798–10803 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 46.

    Staudigl, T. et al. Hexadirectional modulation of high-frequency electrophysiological activity in the human anterior medial temporal lobe maps visual space. Curr. Biol. 28, 3325–3329 (2018).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 47.

    Knudsen, E. B. & Wallis, J. D. Closed-loop theta stimulation in the orbitofrontal cortex prevents reward-based learning. Neuron 106, 537–547 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 48.

    Piazza, M., Izard, V., Pinel, P., Le Bihan, D. & Dehaene, S. Tuning curves for approximate numerosity in the human intraparietal sulcus. Neuron 44, 547–555 (2004).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 49.

    Frank, M. J., Rudy, J. W. & O’Reilly, R. C. Transitivity, flexibility, conjunctive representations, and the hippocampus. II. A computational analysis. Hippocampus 13, 341–354 (2003

    PubMed 
    Article 

    Google Scholar
     

  • 50.

    von Fersen, L., Wynne, C. D. L., Delius, J. D. & Staddon, J. E. R. Transitive inference formation in pigeons. J. Exp. Psychol. Anim. Behav. Process. 17, 334–341 (1991).

    Article 

    Google Scholar
     

  • 51.

    Park, S. A., Miller, D. S. & Boorman, E. D. Protocol for building a cognitive map of structural knowledge in humans by integrating abstract relationships from separate experiences. STAR Protoc. 2, 100423 (2021).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 52.

    Strohminger, N. et al. The MR2: a multi-racial, mega-resolution database of facial stimuli. Behav. Res. Methods 48, 1197–1204 (2016).

    PubMed 
    Article 

    Google Scholar
     

  • 53.

    Kumaran, D., Banino, A., Blundell, C., Hassabis, D. & Dayan, P. Computations underlying social hierarchy learning: distinct neural mechanisms for updating and representing self-relevant information. Neuron 92, 1135–1147 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 54.

    Kumaran, D., Melo, H. L. & Duzel, E. The emergence and representation of knowledge about social and nonsocial hierarchies. Neuron 76, 653–666 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 55.

    Jones, B. & Mishkin, M. Limbic lesions and the problem of stimulus-reinforcement associations. Exp. Neurol. 36, 362–377 (1972).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 56.

    Barron, H. C. et al. Neuronal computation underlying inferential reasoning in humans and mice. Cell 183, 228–243 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 57.

    Wang, F., Schoenbaum, G. & Kahnt, T. Interactions between human orbitofrontal cortex and hippocampus support model-based inference. PLoS Biol. 18, e3000578 (2020).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 58.

    Weiskopf, N., Hutton, C., Josephs, O. & Deichmann, R. Optimal EPI parameters for reduction of susceptibility-induced BOLD sensitivity losses: a whole-brain analysis at 3 T and 1.5 T. Neuroimage 33, 493–504 (2006).

    PubMed 
    Article 

    Google Scholar
     

  • 59.

    Mikl, M. et al. Effects of spatial smoothing on fMRI group inferences. Magn. Reson. Imaging 26, 490–503 (2008).

    PubMed 
    Article 

    Google Scholar
     

  • 60.

    Kriegeskorte, N. Representational similarity analysis—connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 61.

    Yushkevich, P. A. et al. Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol. Neuroimage 111, 526–541 (2015).

    PubMed 
    Article 

    Google Scholar
     

  • 62.

    Amunts, K. et al. Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat. Embryol. (Berl.) 210, 343–352 (2005).

    CAS 
    Article 

    Google Scholar
     

  • 63.

    Zilles, K. & Amunts, K. Centenary of Brodmann’s map conception and fate. Nat. Rev. Neurosci. 11, 139–145 (2010).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 64.

    Glasser, M. F. et al. The Human Connectome Project’s neuroimaging approach. Nat. Neurosci. 19, 1175–1187 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • 65.

    Walther, A. et al. Reliability of dissimilarity measures for multi-voxel pattern analysis. Neuroimage 137, 188–200 (2016).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  • 66.

    Kendall, M. G. A new measure of rank correlation. Biometrika 30, 81 (1938).

    Article 

    Google Scholar
     

  • 67.

    Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).


    Google Scholar
     

  • 68.

    Smith, S. M. & Nichols, T. E. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44, 83–98 (2009).

    Article 

    Google Scholar
     

  • 69.

    Fyhn, M., Molden, S., Witter, M. P., Moser, E. I. & Moser, M. B. Spatial representation in the entorhinal cortex. Science 305, 1258–1264 (2004).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • 70.

    Julian, J. B., Keinath, A. T., Frazzetta, G. & Epstein, R. A. Human entorhinal cortex represents visual space using a boundary-anchored grid. Nat. Neurosci. 21, 191–194 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 71.

    Jenkinson, M. & Woolrich, M. Asymptotic T to Z and F to Z statistic transformations. FMRIB Technical Report TR00MJ1. https://www.fmrib.ox.ac.uk/datasets/techrep/tr00mj1/tr00mj1/ (2004).

  • 72.

    Neubert, F.-X., Mars, R. B., Sallet, J. & Rushworth, M. F. S. Connectivity reveals relationship of brain areas for reward-guided learning and decision making in human and monkey frontal cortex. Proc. Natl Acad. Sci. USA 112, 1–10 (2015).

    Article 
    CAS 

    Google Scholar
     

  • 73.

    Berens, P. CircStat: a MATLAB toolbox for circular statistics. J. Stat. Softw. http://www.jstatsoft.org/v31/i10/ (2009).

  • 74.

    Mars, R. B. et al. Connectivity-based subdivisions of the human right ‘temporoparietal junction area’: evidence for different areas participating in different cortical networks. Cereb. Cortex 22, 1894–1903 (2012).

    PubMed 
    Article 

    Google Scholar
     

  • 75.

    Park, S. A., Miller, D. S. & Boorman, E. D. Inferences on a multidimensional social hierarchy use a grid-like code. Open Science Framework https://osf.io/w96yk/ (2020).

  • 76.

    Park, S., Miller, D. & Boorman, E. Inferences on a multidimensional social hierarchy use a grid-like code. NeuroVault https://neurovault.org/collections/9352/ (2020).

  • 77.

    Park, S. A. & Miller, D. S. Behavioral training schedule for learning social hierarchy. Open Science Framework https://osf.io/bnc3w/ (2020).

  • 78.

    Peirce, J. et al. PsychoPy2: experiments in behavior made easy. Behav. Res. Methods 51, 195–203 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 79.

    Penny, W., Friston, K., Ashburner, J., Kiebel, S. & Nichols, T. Statistical Parametric Mapping: The Analysis of Functional Brain Images (Academic Press, 2007).

  • 80.

    Brett, M., Anton, J.-L., Valabregue, R. & Poline, J.-B. Region of interest analysis using the MarsBar toolbox for SPM 99. Neuroimage 16, S497 (2002).


    Google Scholar
     

  • 81.

    Nili, H. et al. A toolbox for representational similarity analysis. PLoS Comput. Biol. 10, e1003553 (2014).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     


  • Source link

    Related Articles

    Leave a Reply

    Your email address will not be published. Required fields are marked *

    Back to top button