Anderson, J. The Architecture of Cognition (Harvard Univ. Press, 1983).
Newell, A. Unified Theories of Cognition (Harvard Univ. Press, 1990).
Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behav. Brain Sci. 40, e253 (2017).
Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. Human-level concept learning through probabilistic program induction. Science 350, 1332–1338 (2015).
Goddu, M. K. & Gopnik, A. The development of human causal learning and reasoning. Nat. Rev. Psychol. https://doi.org/10.1038/s44159-024-00300-5 (2024).
Chu, J. & Schulz, L. E. Play, curiosity, and cognition. Annu. Rev. Dev. Psychol. 2, 317–343 (2020).
Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).
Kahneman, D. & Tversky, A. in Handbook of the Fundamentals of Financial Decision Making (eds MacLean, L. C. & Ziemba, W. T.) 99–127 (World Scientific, 2013).
Riveland, R. & Pouget, A. Natural language instructions induce compositional generalization in networks of neurons. Nat. Neurosci. 27, 988–999 (2024).
Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://arxiv.org/abs/2108.07258 (2021).
Grattafiori, A. et al. The Llama 3 herd of models. Preprint at https://arxiv.org/abs/2407.21783 (2024).
Binz, M. & Schulz, E. Using cognitive psychology to understand GPT-3. Proc. Natl Acad. Sci. USA 120, e2218523120 (2023).
Binz, M. & Schulz, E. Turning large language models into cognitive models. In Proc. 12th International Conference on Learning Representations (ICLR, 2024).
Hofman, J. M. et al. Integrating explanation and prediction in computational social science. Nature 595, 181–188 (2021).
Rocca, R. & Yarkoni, T. Putting psychology to the test: rethinking model evaluation through benchmarking and prediction. Adv. Methods Pract. Psychol. Sci. https://doi.org/10.1177/25152459211026864 (2021).
Dettmers, T., Pagnoni, A., Holtzman, A. & Zettlemoyer, L. QLORA: efficient finetuning of quantized LLMs. In Proc. Advances in Neural Information Processing Systems 36 (eds Oh, A. et al.) (NeurIPS, 2023).
Nosofsky, R. M. in Formal Approaches in Categorization (eds Pothos, E. M. & Wills, A. J.) 18–39 (Cambridge Univ. Press, 2011).
Peterson, J. C., Bourgin, D. D., Agrawal, M., Reichman, D. & Griffiths, T. L. Using large-scale experiments and machine learning to discover theories of human decision-making. Science 372, 1209–1214 (2021).
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P. & Dolan, R. J. Model-based influences on humans’ choices and striatal prediction errors. Neuron 69, 1204–1215 (2011).
Wilson, R. C., Geana, A., White, J. M., Ludvig, E. A. & Cohen, J. D. Humans use directed and random exploration to solve the explore–exploit dilemma. J. Exp. Psychol. Gen. 143, 2074–2081 (2014).
Palminteri, S., Wyart, V. & Koechlin, E. The importance of falsification in computational cognitive modeling. Trends Cogn. Sci. 21, 425–433 (2017).
van Baar, J. M., Nassar, M. R., Deng, W. & FeldmanHall, O. Latent motives guide structure learning during adaptive social choice. Nat. Hum. Behav. 6, 404–414 (2022).
Feher da Silva, C. & Hare, T. A. Humans primarily use model-based inference in the two-stage task. Nat. Hum. Behav. 4, 1053–1066 (2020).
Kool, W., Cushman, F. A. & Gershman, S. J. When does model-based control pay off? PLoS Comput. Biol. 12, e1005090 (2016).
Dubois, M. & Hauser, T. U. Value-free random exploration is linked to impulsivity. Nat. Commun. 13, 4542 (2022).
Jansen, R. A., Rafferty, A. N. & Griffiths, T. L. A rational model of the Dunning–Kruger effect supports insensitivity to evidence in low performers. Nat. Hum. Behav. 5, 756–763 (2021).
Awad, E. et al. The Moral Machine experiment. Nature 563, 59–64 (2018).
Akata, E. et al. Playing repeated games with large language models. Nat. Hum. Behav. https://doi.org/10.1038/s41562-025-02172-y (2025).
Demircan, C. et al. Evaluating alignment between humans and neural network representations in image-based learning tasks. In Proc. Advances in Neural Information Processing Systems 37 (eds Globerson, A. et al.) (NeurIPS, 2024).
Singh, M., Richie, R. & Bhatia, S. Representing and predicting everyday behavior. Comput. Brain Behav. 5, 1–21 (2022).
Xu, H. A., Modirshanechi, A., Lehmann, M. P., Gerstner, W. & Herzog, M. H. Novelty is not surprise: human exploratory and adaptive behavior in sequential decision-making. PLoS Comput. Biol. 17, e1009070 (2021).
Hick, W. E. On the rate of gain of information. Q. J. Exp. Psychol. 4, 11–26 (1952).
Coda-Forno, J., Binz, M., Wang, J. X. & Schulz, E. CogBench: a large language model walks into a psychology lab. Proc. Mach. Learn. Res. 235, 9076–9108 (2024).
Kipnis, A., Voudouris, K., Schulze Buschoff, L. M. & Schulz, E. metabench – a sparse benchmark of reasoning and knowledge in large language models. In Proc. 13th International Conference on Learning Representations (ICLR, 2025).
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).
Schrimpf, M. et al. The neural architecture of language: integrative modeling converges on predictive processing. Proc. Natl Acad. Sci. USA 118, e2105646118 (2021).
Feher da Silva, C., Lombardi, G., Edelson, M. & Hare, T. A. Rethinking model-based and model-free influences on mental effort and striatal prediction errors. Nat. Hum. Behav. 7, 956–969 (2023).
Tuckute, G. et al. Driving and suppressing the human language network using large language models. Nat. Hum. Behav. 8, 544–561 (2024).
Cochran, W. G. The combination of estimates from different experiments. Biometrics 10, 101–129 (1954).
DeepSeek-AI et al. DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning. Preprint at https://arxiv.org/abs/2501.12948 (2025).
Hilbig, B. E. & Moshagen, M. Generalized outcome-based strategy classification: comparing deterministic and probabilistic choice models. Psychon. Bull. Rev. 21, 1431–1443 (2014).
Agrawal, M., Peterson, J. C. & Griffiths, T. L. Scaling up psychology via scientific regret minimization. Proc. Natl Acad. Sci. USA 117, 8825–8835 (2020).
Rigoux, L., Stephan, K. E., Friston, K. J. & Daunizeau, J. Bayesian model selection for group studies − revisited. Neuroimage https://doi.org/10.1016/j.neuroimage.2013.08.065 (2014).
Binz, M., Gershman, S. J., Schulz, E. & Endres, D. Heuristics from bounded meta-learned inference. Psychol. Rev. 129, 1042–1077 (2022).
Musslick, S. et al. Automating the practice of science: opportunities, challenges, and implications. Proc. Natl. Acad. Sci. USA 122, e2401238121 (2025).
Rmus, M., Jagadish, A. K., Mathony, M., Ludwig, T. & Schulz, E. Generating computational cognitive models using large language models. Preprint at https://arxiv.org/abs/2502.00879 (2025).
Dillion, D., Tandon, N., Gu, Y. & Gray, K. Can AI language models replace human participants? Trends Cogn. Sci. 27, 597–600 (2023).
Huben, R., Cunningham, H., Smith, L. R., Ewart, A. & Sharkey, L. Sparse autoencoders find highly interpretable features in language models. In Proc. 12th International Conference on Learning Representations (ICLR, 2024).
Chefer, H., Gur, S. & Wolf, L. Transformer interpretability beyond attention visualization. In Proc. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 782–791 (IEEE, 2021).
Vaswani, A. et al. Attention is all you need. In Proc. Advances in Neural Information Processing Systems 30 (eds Guyon, I. et al.) (NeurIPS, 2017).
Zador, A. et al. Catalyzing next-generation artificial intelligence through NeuroAI. Nat. Commun. 14, 1597 (2023).
Ruggeri, K. et al. The globalizability of temporal discounting. Nat. Hum. Behav. 6, 1386–1397 (2022).
Wulff, D. U., Mergenthaler-Canseco, M. & Hertwig, R. A meta-analytic review of two modes of learning and the description-experience gap. Psychol. Bull. 144, 140–176 (2018).
Frey, R., Pedroni, A., Mata, R., Rieskamp, J. & Hertwig, R. Risk preference shares the psychometric structure of major psychological traits. Sci. Adv. 3, e1701381 (2017).
Enkavi, A. Z. et al. Large-scale analysis of test–retest reliabilities of self-regulation measures. Proc. Natl Acad. Sci. USA 116, 5472–5477 (2019).
Henrich, J., Heine, S. J. & Norenzayan, A. Most people are not WEIRD. Nature 466, 29 (2010).
Schrimpf, M. et al. Integrative benchmarking to advance neurally mechanistic models of human intelligence. Neuron 108, 413–423 (2020).
Poldrack, R. A. et al. The past, present, and future of the brain imaging data structure (BIDS). Imaging Neurosci. 2, 1–19 (2024).
Schulze Buschoff, L. M., Akata, E., Bethge, M. & Schulz, E. Visual cognition in multimodal large language models. Nat. Mach. Intell. https://doi.org/10.1038/s42256-024-00963-y (2025).
Vere, S. A. A cognitive process shell. Behav. Brain Sci. 15, 460–461 (1992).
Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. In Proc. 7th International Conference on Learning Representations (ICLR, 2019).
Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).
Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).
Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R. & Collins, D. L. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 47, S102 (2009).
Friston, K. J., Ashburner, J. T., Kiebel, S. J., Nichols, T. E. & Penny, W. D. (eds) Statistical Parametric Mapping: The Analysis of Functional Brain Images (Elsevier, 2011).
Gau, R. nilearn. GitHub https://github.com/nilearn/nilearn (2024).
Yax, N., Oudeyer, P.-Y. & Palminteri, S. Assessing contamination in large language models: introducing the LogProber method. Preprint at https://arxiv.org/abs/2408.14352 (2024).
Warner, B. et al. Smarter, better, faster, longer: a modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference. Preprint at https://arxiv.org/abs/2412.13663 (2024).
Wang, Z. et al. HelpSteer2-Preference: complementing ratings with preferences. In Proc. 13th International Conference on Learning Representations (ICLR, 2025).
Teknium, R., Quesnelle, J. & Guang, C. Hermes 3 technical report. Preprint at https://arxiv.org/abs/2408.11857 (2024).
Lin, S., Hilton, J. & Evans, O. TruthfulQA: measuring how models mimic human falsehoods. In Proc. 60th Annual Meeting of the Association for Computational Linguistics (eds Muresan, S. et al.) 3214–3252 (Association for Computational Linguistics, 2022).
