Gordon, J. et al. The road towards understanding embodied decisions. Neurosci. Biobehav. Rev. 131, 722–736 (2021).Thank you for reading this post, don’t forget to subscribe! Article PubMed PubMed Central Google Scholar Maselli, A. et al. Beyond simple laboratory studies: developing sophisticated models to study rich behavior. Phys. Life Rev. 46, 220–244 (2023). Article ADS PubMed
Gordon, J. et al. The road towards understanding embodied decisions. Neurosci. Biobehav. Rev. 131, 722–736 (2021).
Thank you for reading this post, don't forget to subscribe!Google Scholar
Maselli, A. et al. Beyond simple laboratory studies: developing sophisticated models to study rich behavior. Phys. Life Rev. 46, 220–244 (2023).
Google Scholar
Yoo, S. B. M., Hayden, B. Y. & Pearson, J. M. Continuous decisions. Philos. Trans. R. Soc. B 376, 20190664 (2021).
Google Scholar
Merel, J., Botvinick, M. & Wayne, G. Hierarchical motor control in mammals and machines. Nat. Commun. 10, 5489 (2019).
Google Scholar
Cisek, P. Making decisions through a distributed consensus. Curr. Opin. Neurobiol. 22, 927–936 (2012).
Google Scholar
Gallivan, J. P., Chapman, C. S., Wolpert, D. M. & Flanagan, J. R. Decision-making in sensorimotor control. Nat. Rev. Neurosci. 19, 519–534 (2018).
Google Scholar
Yoo, S. B. M., Tu, J. C., Piantadosi, S. T. & Hayden, B. Y. The neural basis of predictive pursuit. Nat. Neurosci. 23, 252–259 (2020).
Google Scholar
Fabian, S. T., Sumner, M. E., Wardill, T. J., Rossoni, S. & Gonzalez-Bellido, P. T. Interception by two predatory fly species is explained by a proportional navigation feedback controller. J. R. Soc. Interface. 15, 20180466 (2018).
Google Scholar
Sridhar, V. H. et al. The geometry of decision-making in individuals and collectives. Proc. Natl Acad. Sci. USA 118, e2102157118 (2021).
Google Scholar
Yang, Q. et al. Monkey plays Pac-Man with compositional strategies and hierarchical decision-making. eLife 11, e74500 (2022).
Google Scholar
Bertsekas, D. P. in Encyclopedia of Optimization (eds Pardalos, P. M. & Prokopyev, O. A.) 1–6 (Springer, 2025).
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, 1998).
Theodorou, E. A., Buchli, J. & Schaal, S. A generalized path integral control approach to reinforcement learning. J. Mach. Learn. Res. 11, 3137–3181 (2010).
Google Scholar
Dvijotham, K. & Todorov, E. in Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (eds Lewis. F. L. & Liu, D.) 119–141 (Wiley, 2012).
Wolpert, D. M. & Kawato, M. Multiple paired forward and inverse models for motor control. Neural Netw. 11, 1317–1329 (1998).
Google Scholar
Lake, B. & Baroni, M. Generalization without systematicity: on the compositional skills of sequence-to-sequence recurrent networks. In Proc. 35th International Conference on Machine Learning (eds. Dy, J. & Krause, A.) 2873–2882 (PMLR, 2018).
Todorov, E. Compositionality of optimal control laws. In Advances in Neural Information Processing Systems 22 (eds Bengio, Y. et al.) (NeurIPS, 2009).
Kurth-Nelson, Z. et al. Replay and compositional computation. Neuron 111, 454–469 (2023).
Google Scholar
Whittington, J. C. R., McCaffary, D., Bakermans, J. J. W. & Behrens, T. E. J. How to build a cognitive map. Nat. Neurosci. 25, 1257–1272 (2022).
Google Scholar
Eichenbaum, H. & Cohen, N. J. Can we reconcile the declarative memory and spatial navigation views on hippocampal function? Neuron 83, 764–770 (2014).
Google Scholar
Behrens, T. E. J. et al. What is a cognitive map? Organizing knowledge for flexible behavior. Neuron 100, 490–509 (2018).
Google Scholar
Kay, K. et al. Constant sub-second cycling between representations of possible futures in the hippocampus. Cell 180, 552–567.e25 (2020).
Google Scholar
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.e8 (2020).
Google Scholar
Sanders, H., Wilson, M. A. & Gershman, S. J. Hippocampal remapping as hidden state inference. eLife 9, e51140 (2020).
Google Scholar
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).
Google Scholar
Wikenheiser, A. M. & Schoenbaum, G. Over the river, through the woods: cognitive maps in the hippocampus and orbitofrontal cortex. Nat. Rev. Neurosci. 17, 513–523 (2016).
Google Scholar
Yeung, N. & Summerfield, C. Metacognition in human decision-making: confidence and error monitoring. Philos. Trans. R. Soc. B 367, 1310–1321 (2012).
Google Scholar
Eppinger, B., Goschke, T. & Musslick, S. Meta-control: From psychology to computational neuroscience. Cogn. Affect. Behav. Neurosci. 21, 447–452 (2021).
Google Scholar
Musslick, S., Cohen, J. D. & Goschke, T. in Encyclopedia of the Human Brain (ed. Grafman, J. H.) 269–285 (Elsevier, 2025).
Alexander, W. H. & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nat. Neurosci. 14, 1338–1344 (2011).
Google Scholar
Kennerley, S. W., Walton, M. E., Behrens, T. E. J., Buckley, M. J. & Rushworth, M. F. S. Optimal decision making and the anterior cingulate cortex. Nat. Neurosci. 9, 940–947 (2006).
Google Scholar
Kolling, N., Behrens, T., Wittmann, M. & Rushworth, M. Multiple signals in anterior cingulate cortex. Curr. Opin. Neurobiol. 37, 36–43 (2016).
Google Scholar
Shenhav, A., Botvinick, M. M. & Cohen, J. D. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron 79, 217–240 (2013).
Google Scholar
Akam, T. et al. The anterior cingulate cortex predicts future states to mediate model-based action selection. Neuron 109, 149–163.e7 (2021).
Google Scholar
Heilbronner, S. R. & Hayden, B. Y. Dorsal anterior cingulate cortex: a bottom-up view. Annu. Rev. Neurosci. 39, 149–170 (2016).
Google Scholar
Sarafyazd, M. & Jazayeri, M. Hierarchical reasoning by neural circuits in the frontal cortex. Science 364, eaav8911 (2019).
Google Scholar
Padoa-Schioppa, C. Neurobiology of economic choice: a good-based model. Annu. Rev. Neurosci. 34, 333–359 (2011).
Google Scholar
Hunt, L. T. et al. Triple dissociation of attention and decision computations across prefrontal cortex. Nat. Neurosci. 21, 1471–1481 (2018).
Google Scholar
Wilson, R. C., Takahashi, Y. K., Schoenbaum, G. & Niv, Y. Orbitofrontal cortex as a cognitive map of task space. Neuron 81, 267–279 (2014).
Google Scholar
Elston, T. W. & Wallis, J. D. Context-dependent decision-making in the primate hippocampal–prefrontal circuit. Nat. Neurosci. 28, 374–382 (2025).
Google Scholar
Yoo, S. B. M., Tu, J. C. & Hayden, B. Y. Multicentric tracking of multiple agents by anterior cingulate cortex during pursuit and evasion. Nat. Commun. 12, 1985 (2021).
Google Scholar
Wilson, R. C. & Collins, A. G. E. Ten simple rules for the computational modeling of behavioral data. eLife 8, e49547 (2019).
Google Scholar
Khona, M. & Fiete, I. R. Attractor and integrator networks in the brain. Nat. Rev. Neurosci. 23, 744–766 (2022).
Google Scholar
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).
Google Scholar
Anderson, D. J. & Perona, P. Toward a science of computational ethology. Neuron 84, 18–31 (2014).
Google Scholar
Brown, A. E. X. & de Bivort, B. Ethology as a physical science. Nat. Phys. 14, 653–657 (2018).
Google Scholar
Milner, D. & Goodale, M. The Visual Brain in Action (Oxford Univ. Press, 2006).
Gershman, S. J. & Niv, Y. Learning latent structure: carving nature at its joints. Curr. Opin. Neurobiol. 20, 251–256 (2010).
Google Scholar
Zutshi, I. et al. Hippocampal neuronal activity is aligned with action plans. Nature 639, 153–161 (2025).
Google Scholar
Shadmehr, R. & Krakauer, J. W. A computational neuroanatomy for motor control. Exp. Brain Res. 185, 359–381 (2008).
Google Scholar
Bakkour, A. et al. The hippocampus supports deliberation during value-based decisions. eLife 8, e46080 (2019).
Google Scholar
Stachenfeld, K. L., Botvinick, M. M. & Gershman, S. J. The hippocampus as a predictive map. Nat. Neurosci. 20, 1643–1653 (2017).
Google Scholar
Vikbladh, O. M. et al. Hippocampal contributions to model-based planning and spatial memory. Neuron 102, 683–693.e4 (2019).
Google Scholar
Edelson, M. G. & Hare, T. A. Goal-dependent hippocampal representations facilitate self-control. J. Neurosci. 43, 7822–7830 (2023).
Google Scholar
Cohen, J. D., Botvinick, M. & Carter, C. S. Anterior cingulate and prefrontal cortex: who’s in control?. Nat. Neurosci. 3, 421–423 (2000).
Google Scholar
Verguts, T. Binding by random bursts: a computational model of cognitive control. J. Cogn. Neurosci. 29, 1103–1118 (2017).
Google Scholar
Silvetti, M., Vassena, E., Abrahamse, E. & Verguts, T. Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner. PLoS Comput. Biol. 14, e1006370 (2018).
Google Scholar
Friedman, A. et al. A corticostriatal path targeting striosomes controls decision-making under conflict. Cell 161, 1320–1333 (2015).
Google Scholar
Kane, G. A. et al. Rat anterior cingulate cortex continuously signals decision variables in a patch foraging task. J. Neurosci. 42, 5730–5744 (2022).
Google Scholar
Botvinick, M. M., Niv, Y. & Barto, A. G. Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective. Cognition 113, 262–280 (2009).
Google Scholar
Franch, M. et al. A population code for semantics in human hippocampus. Preprint at bioRxiv https://doi.org/10.1101/2025.02.21.639601 (2025).
Chaure, F. J., Rey, H. G. & Quian Quiroga, R. A novel and fully automatic spike-sorting implementation with variable number of features. J. Neurophysiol. 120, 1859–1871 (2018).
Google Scholar
Groppe, D. M. et al. iELVis: An open source MATLAB toolbox for localizing and visualizing human intracranial electrode data. J. Neurosci. Methods 281, 40–48 (2017).
Google Scholar
Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001).
Google Scholar
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
Google Scholar
Joshi, A. et al. Unified framework for development, deployment and robust testing of neuroimaging algorithms. Neuroinformatics 9, 69–84 (2011).
Google Scholar
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. Neuroimage 9, 179–194 (1999).
Google Scholar
Yang, A. I. et al. Localization of dense intracranial electrode arrays using magnetic resonance imaging. Neuroimage 63, 157–165 (2012).
Google Scholar
Magnotti, J. F., Wang, Z. & Beauchamp, M. S. RAVE: Comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data. Neuroimage 223, 117341 (2020).
Google Scholar
Wang, Z., Magnotti, J. F., Zhang, X. & Beauchamp, M. S. YAEL: your advanced electrode localizer. eNeuro 10, ENEURO.0328-23.2023 (2023).
Google Scholar
Chericoni, A. et al. Neural geometry in the human hippocampus enables generalization across spatial position and gaze. Preprint at arXiv https://doi.org/10.48550/arXiv.2603.04747 (2026).
Gómez, V., Kappen, H. J., Peters, J. & Neumann, G. in Machine Learning and Knowledge Discovery in Databases (eds Calders, T. et al.) 482–497 (2014).
Peng, X. Bin, Chang, M., Zhang, G., Abbeel, P. & Levine, S. MCP: learning composable hierarchical control with multiplicative compositional policies. In Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) (NeurIPS, 2019).
Matsuo, Y. et al. Deep learning, reinforcement learning, and world models. Neural Netw. 152, 267–275 (2022).
Google Scholar
Murphy, K. P. Probabilistic Machine Learning: An Introduction (The MIT Press, 2022).
Wood, S. N. Generalized additive models. Annu. Rev. Stat. Appl. 12, 497–526 (2025).
Google Scholar
Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. In Proc. 9th Python in Science Conference (SciPy 2010) https://doi.org/10.25080/Majora-92bf1922-011 (SciPy, 2010).
Balzani, E., Lakshminarasimhan, K., Angelaki, D. & Savin, C. Efficient estimation of neural tuning during naturalistic behavior. In Advances in Neural Information Processing Systems 33 (eds. Larochelle, H. et al.) (NeurIPS, 2020).
Wood, S. N. Generalized Additive Models: An Introduction with R (CRC Press/Taylor & Francis Group, 2017).
Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).
Google Scholar
Kriegeskorte, N. & Wei, X.-X. Neural tuning and representational geometry. Nat. Rev. Neurosci. 22, 703–718 (2021).
Google Scholar
Kobak, D. et al. Demixed principal component analysis of neural population data. eLife 5, e10989 (2016).
Google Scholar
Chericoni, A. Dataset for: Neural basis of compositional control. Figshare https://doi.org/10.6084/m9.figshare.32572764.v3 (2026).
Check back often for more exciting news!
















