Nosek, B. A. et al. Promoting an open research culture. Science 348, 1422–1425 (2015).
Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. NeuroImage 80, 62–79 (2013).
Pavlov, Y. G. et al. #EEGManyLabs: investigating the replicability of influential EEG experiments. Cortex, https://doi.org/10.1016/j.cortex.2021.03.013 (2021).
Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage 166, 400–424 (2018).
Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) Study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).
Thompson, P. M. et al. ENIGMA and global neuroscience: a decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl. Psychiatry 10, 100 (2020).
Markiewicz, C. J. et al. The OpenNeuro resource for sharing of neuroscience data. eLife 10, e71774 (2021).
Urai, A. E., Doiron, B., Leifer, A. M. & Churchland, A. K. Large-scale neural recordings call for new insights to link brain and behavior. Nat. Neurosci. 25, 11–19 (2022).
Beste, C. et al. Moving intentions from brains to machines. Trends Cogn. Sci. https://doi.org/10.1016/j.tics.2025.12.003 (2026).
Donders, F. C. On the speed of mental processes. Acta Psychol. 30, 412–431 (1969).
Henson, R. What can functional neuroimaging tell the experimental psychologist? Q. J. Exp. Psychol. Sect. A 58, 193–233 (2005).
Kriegeskorte, N. & Diedrichsen, J. Peeling the onion of brain representations. Annu. Rev. Neurosci. 42, 407–432 (2019).
Poldrack, R. Can cognitive processes be inferred from neuroimaging data? Trends Cogn. Sci. 10, 59–63 (2006).
Anderson, M. L. Neural reuse: a fundamental organizational principle of the brain. Behav. Brain Sci. 33, 245–266 (2010) .
Fedorenko, E., Duncan, J. & Kanwisher, N. Broad domain generality in focal regions of frontal and parietal cortex. Proc. Natl. Acad. Sci. USA 110, 16616–16621 (2013).
Friston, K. Beyond phrenology: what can neuroimaging tell us about distributed circuitry? Annu. Rev. Neurosci. 25, 221–250 (2002).
Kira, S., Safaai, H., Morcos, A. S., Panzeri, S. & Harvey, C. D. A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions. Nat. Commun. 14, 2121 (2023).
Cole, M. W. Cognitive flexibility as the shifting of brain network flows by flexible neural representations. Curr. Opin. Behav. Sci. 57, 101384 (2024).
Hauptman, M., Liu, Y.-F. & Bedny, M. Built to adapt: mechanisms of cognitive flexibility in the human brain. Annu. Rev. Dev. Psychol. 6, 133–162 (2024).
Zühlsdorff, K., Dalley, J. W., Robbins, T. W. & Morein-Zamir, S. Cognitive flexibility: neurobehavioral correlates of changing one’s mind. Cereb. Cortex 33, 5436–5446 (2023).
Tye, K. M. et al. Mixed selectivity: cellular computations for complexity. Neuron 112, 2289–2303 (2024).
Ostojic, S. & Fusi, S. Computational role of structure in neural activity and connectivity. Trends Cogn. Sci. 28, 677–690 (2024).
Vyas, S., Golub, M. D., Sussillo, D. & Shenoy, K. V. Computation through neural population dynamics. Annu. Rev. Neurosci. 43, 249–275 (2020).
Cisek, P. Resynthesizing behavior through phylogenetic refinement. Atten. Percept. Psychophys. 81, 2265–2287 (2019).
Griffiths, T. L., Lieder, F. & Goodman, N. D. Rational use of cognitive resources: levels of analysis between the computational and the algorithmic. Top. Cogn. Sci. 7, 217–229 (2015).
Koechlin, E. & Summerfield, C. An information theoretical approach to prefrontal executive function. Trends Cogn. Sci. 11, 229–235 (2007).
International Brain Laboratory et al. A brain-wide map of neural activity during complex behaviour. Nature 645, 191–177 (2025).
Beste, C., Münchau, A. & Frings, C. Towards a systematization of brain oscillatory activity in actions. Commun. Biol. 6, 137 (2023).
Cai, W., Taghia, J. & Menon, V. A multi-demand operating system underlying diverse cognitive tasks. Nat. Commun. 15, 2185 (2024).
Courellis, H. S. et al. Abstract representations emerge in human hippocampal neurons during inference. Nature 632, 841–849 (2024).
Duncan, J. Building cognitive functions from distributed brain activity. Neuron 112, 692–693 (2024).
Duncan, J., Assem, M. & Shashidhara, S. Integrated intelligence from distributed brain activity. Trends Cogn. Sci. 24, 838–852 (2020).
Frings, C. et al. Binding and retrieval in action control (BRAC). Trends Cogn. Sci. 24, 375–387 (2020).
MacDowell, C. J. et al. Multiplexed subspaces route neural activity across brain-wide networks. Nat. Commun. 16, 3359 (2025).
Ritz, H. & Shenhav, A. Orthogonal neural encoding of targets and distractors supports multivariate cognitive control. Nat. Hum. Behav. 8, 945–961 (2024).
Frings, C. et al. Consensus definitions of perception-action-integration in action control. Commun. Psychol. 2, 7 (2024).
The Oxford Handbook of Developmental Cognitive Neuroscience, https://doi.org/10.1093/oxfordhb/9780198827474.001.0001 (Oxford University Press, 2020).
Khona, M. & Fiete, I. R. Attractor and integrator networks in the brain. Nat. Rev. Neurosci. 23, 744–766 (2022).
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).
Cohen, J. D. Cognitive control: core constructs and current considerations. in (ed. Egner, T.) The Wiley Handbook of Cognitive Control, 1–28 (Wiley, 2017).
Panichello, M. F. & Buschman, T. J. Shared mechanisms underlie the control of working memory and attention. Nature 592, 601–605 (2021).
Nieuwenhuis, S., Forstmann, B. U. & Wagenmakers, E.-J. Erroneous analyses of interactions in neuroscience: a problem of significance. Nat. Neurosci. 14, 1105–1107 (2011).
Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl. Acad. Sci. USA 113, 12574–12579 (2016).
Huntenburg, J. M., Bazin, P.-L. & Margulies, D. S. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21–31 (2018).
Vos de Wael, R. et al. BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun. Biol. 3, 103 (2020).
Kriegeskorte, N. & Kievit, R. A. Representational geometry: integrating cognition, computation, and the brain. Trends Cogn. Sci. 17, 401–412 (2013).
Gallego, J. A., Perich, M. G., Miller, L. E. & Solla, S. A. Neural manifolds for the control of movement. Neuron 94, 978–984 (2017).
Perich, M. G., Narain, D. & Gallego, J. A. A neural manifold view of the brain. Nat. Neurosci. 28, 1582–1597 (2025).
Ebitz, R. B. & Hayden, B. Y. The population doctrine in cognitive neuroscience. Neuron 109, 3055–3068 (2021).
Nieh, E. H. et al. Geometry of abstract learned knowledge in the hippocampus. Nature 595, 80–84 (2021).
Richards, B. A. et al. A deep learning framework for neuroscience. Nat. Neurosci. 22, 1761–1770 (2019).
Wang, J. X. et al. Prefrontal cortex as a meta-reinforcement learning system. Nat. Neurosci. 21, 860–868 (2018).
Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J. & Kurth-Nelson, Z. Deep reinforcement learning and its neuroscientific implications. Neuron 107, 603–616 (2020).
Srinivasan, A. et al. Hippocampal and medial prefrontal ensemble spiking represents episodes and rules in similar task spaces. Cell Rep. 42, 113296 (2023).
Mouille, A. et al. The prefrontal cortex encodes task-identity information and flexibly adjusts its sensory processes as a function of the specific ongoing task. PLoS Biol. 23, e3003353 (2025).
Stokes, M. G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364–375 (2013).
Do, J., James, O. & Kim, Y.-J. Choice-dependent delta-band neural trajectory during semantic category decision making in the human brain. iScience 27, 110173 (2024).
Freund, M. C., Etzel, J. A. & Braver, T. S. Neural coding of cognitive control: the representational similarity analysis approach. Trends Cogn. Sci. 25, 622–638 (2021).
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79, 2554–2558 (1982).
Woodward, J. What is a mechanism? A counterfactual account. Philos. Sci. 69, S366–S377 (2002).
Ross, L. N. & Bassett, D. S. Causation in neuroscience: keeping mechanism meaningful. Nat. Rev. Neurosci. 25, 81–90 (2024).
Chen, M., Mei, S., Fan, J. & Wang, M. Opportunities and challenges of diffusion models for generative AI. Natl. Sci. Rev. 11, nwae348 (2024).
Sengar, S. S., Hasan, A. B., Kumar, S. & Carroll, F. Generative artificial intelligence: a systematic review and applications. Multimed. Tools Appl. 84, 23661–23700 (2024).
Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. High-resolution image synthesis with latent diffusion models. In Proc.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 10674–10685, https://doi.org/10.1109/CVPR52688.2022.01042 (IEEE, 2022).
Ferrante, M., Boccato, T., Bargione, S. & Toschi, N. Decoding visual brain representations from electroencephalography through knowledge distillation and latent diffusion models. Comput. Biol. Med. 178, 108701 (2024).
Ororbia, A. & Kifer, D. The neural coding framework for learning generative models. Nat. Commun. 13, 2064 (2022).
Vetter, J., Macke, J. H. & Gao, R. Generating realistic neurophysiological time series with denoising diffusion probabilistic models. Patterns 5, 101047 (2024).
Binz, M. et al. A foundation model to predict and capture human cognition. Nature 644, 1002–1009 (2025).
Cheng, F. L. et al. Reconstructing visual illusory experiences from human brain activity. Sci. Adv. 9, eadj3906 (2023).
Ji-An, L., Benna, M. K. & Mattar, M. G. Discovering cognitive strategies with tiny recurrent neural networks. Nature 644, 993–1001 (2025).
Lin, X.-X., Nieder, A. & Jacob, S. N. The neuronal implementation of representational geometry in primate prefrontal cortex. Sci. Adv. 9, eadh8685 (2023).
Vahid, A., Mückschel, M., Stober, S., Stock, A.-K. & Beste, C. Conditional generative adversarial networks applied to EEG data can inform about the inter-relation of antagonistic behaviors on a neural level. Commun. Biol. 5, 148 (2022).
Durstewitz, D., Koppe, G. & Thurm, M. I. Reconstructing computational system dynamics from neural data with recurrent neural networks. Nat. Rev. Neurosci. 24, 693–710 (2023).
Hommel, B., Colzato, L. & Beste, C. No convincing evidence for the independence of persistence and flexibility. Nat. Rev. Psychol. 3, 638–638 (2024).
Beste, C., Moll, C. K. E., Pötter-Nerger, M. & Münchau, A. Striatal microstructure and its relevance for cognitive control. Trends Cogn. Sci. 22, 747–751 (2018).
Colzato, L. S., Hommel, B., Zhang, W., Roessner, V. & Beste, C. The metacontrol hypothesis as diagnostic framework of OCD and ADHD: a dimensional approach based on shared neurobiological vulnerability. Neurosci. Biobehav. Rev. 137, 104677 (2022).
Schneider, S., Lee, J. H. & Mathis, M. W. Learnable latent embeddings for joint behavioural and neural analysis. Nature 617, 360–368 (2023).
Wang, R. & Chen, Z. S. Large-scale foundation models and generative AI for BigData neuroscience. Neurosci. Res. 215, 3–14 (2025).
Gao, S., Mishne, G. & Scheinost, D. Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics. Hum. Brain Mapp. 42, 4510–4524 (2021).
Langdon, C., Genkin, M. & Engel, T. A. A unifying perspective on neural manifolds and circuits for cognition. Nat. Rev. Neurosci. 24, 363–377 (2023).
Lin, B. & Kriegeskorte, N. The topology and geometry of neural representations. Proc. Natl. Acad. Sci. USA 121, e2317881121 (2024).
Yamins, D. L. K. et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc. Natl. Acad. Sci. USA 111, 8619–8624 (2014).
Schyns, P. G., Snoek, L. & Daube, C. Degrees of algorithmic equivalence between the brain and its DNN models. Trends Cogn. Sci. 26, 1090–1102 (2022).
Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A. & Konkle, T. A large-scale examination of inductive biases shaping high-level visual representation in brains and machines. Nat. Commun. 15, 9383 (2024).
Bowers, J. S. et al. Deep problems with neural network models of human vision. Behav. Brain Sci. 46, e385 (2023).
Chen, X. et al. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. Preprint at https://doi.org/10.48550/arXiv.1606.03657 (2016).
Shon, K., Sung, K. R., Kwak, J., Shin, J. W. & Lee, J. Y. Development of a β-variational autoencoder for disentangled latent space representation of anterior segment optical coherence tomography images. Transl. Vis. Sci. Technol. 11, 11 (2022).
Gomez, C. et al. Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness. Sci. Rep. 14, 31606 (2024).
Vázquez-García, C., Martínez-Murcia, F. J., Román, F. S. & Górriz, J. M. A review of latent representation models in neuroimaging. Preprint at https://doi.org/10.48550/ARXIV.2412.19844 (2024).
Shenhav, A. et al. Integrative psychological, computational, and mechanistic approaches to frontal lobe function. in (eds Banich, M. T., Haber, S. N. & Robbins, T. W.) The Frontal Cortex, 225–262 (The MIT Press, 2024).
Van Schependom, J., Baetens, K., Nagels, G., Olmi, S. & Beste, C. Neurophysiological avenues to better conceptualizing adaptive cognition. Commun. Biol. 7, 626 (2024).
Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).
Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M. & Harris, K. D. High-dimensional geometry of population responses in visual cortex. Nature 571, 361–365 (2019).
Koide-Majima, N., Nishimoto, S. & Majima, K. Mental image reconstruction from human brain activity: neural decoding of mental imagery via deep neural network-based Bayesian estimation. Neural Netw. 170, 349–363 (2024).
Diedrichsen, J. & Kriegeskorte, N. Representational models: a common framework for understanding encoding, pattern-component, and representational-similarity analysis. PLOS Comput. Biol. 13, e1005508 (2017).
Pohl, S. et al. Clarifying the conceptual dimensions of representation in neuroscience. Preprint at https://doi.org/10.48550/ARXIV.2403.14046 (2024).
Masson, M. E. J. A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behav. Res. Methods 43, 679–690 (2011).
Williams, A. H., Kunz, E., Kornblith, S. & Linderman, S. W. Generalized shape metrics on neural representations. Adv. Neural Inf. Process. Syst. 34, 4738–4750 (2021).
Schyns, P. G., Zhan, J., Jack, R. E. & Ince, R. A. A. Revealing the information contents of memory within the stimulus information representation framework. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190705 (2020).
Avberšek, L. K. & Repovš, G. Deep learning in neuroimaging data analysis: applications, challenges, and solutions. Front. Neuroimaging 1, 981642 (2022).
Livezey, J. A. & Glaser, J. I. Deep learning approaches for neural decoding across architectures and recording modalities. Brief. Bioinform. 22, 1577–1591 (2021).
Schrimpf, M. et al. Integrative benchmarking to advance neurally mechanistic models of human intelligence. Neuron 108, 413–423 (2020).
Siegle, J. H. et al. Survey of spiking in the mouse visual system reveals functional hierarchy. Nature 592, 86–92 (2021).
Vogelstein, J. T. et al. A community-developed open-source computational ecosystem for big neuro data. Nat. Methods 15, 846–847 (2018).
Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).
Van Dijk, H. et al. The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database. Sci. Data 9, 333 (2022).
Babayan, A. et al. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci. Data 6, 180308 (2019).
Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).
Kiar, G. et al. Why experimental variation in neuroimaging should be embraced. Nat. Commun. 15, 9411 (2024).
Gunning, D. et al. XAI—explainable artificial intelligence. Sci. Robot. 4, eaay7120 (2019).
Bowers, J. S., Puebla, G., Thorat, S., Tsetsos, K. & Ludwig, C. J. H. On the misuse of LLMs as models of mind: a case study of Centaur. Preprint at https://doi.org/10.31234/osf.io/v9w37_v2 (2025).
Beste, C. Disconnected psychology and neuroscience-implications for scientific progress, replicability and the role of publishing. Commun. Biol. 4, 1099 (2021).