Mitchell, M. (2026). On evaluating abstraction and analogy in humans and machines. To appear in Current Directions in Psychological Science.

Johnson, S.G B., Karimi, A. H., Bengio, Y., Chater, N., Gerstenberg, T., Larson, K., Levine, S., Mitchell, M., Rahwan, I, Schölkopf, B., Grossmann, I. (2026). Imagining and building wise machines: The centrality of AI metacognition. Trends in Cognitive Science.

Stevenson, C. E., Pafford, A., van der Maas, H. L. J., and Mitchell, M. (2025). Can large language models generalize analogy solving like children can? To appear in Transactions of the Association for Computational Linguistics.

Krakauer, D. C., Krakauer, J. W., and Mitchell, M. (2025). Large Language Models and Emergence: A Complex Systems Perspective. To appear in Philosophical Transactions of the Royal Society A.

Lewis, M. and Mitchell, M. (2025). Evaluating the Robustness of Analogical Reasoning in GPT Models. Transactions on Machine Learning Research.

Mitchell, M. (2024). Large language models. In M. C. Frank & A. Majid (Eds.), Open Encyclopedia of Cognitive Science. MIT Press.

Lewis, M. and Mitchell, M. (2024). Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models. To appear in Proceedings of the Cognitive Science Society Conference, 2024.

Mitchell, M., Palmarini, A. B., and Moskvichev, A. (2023). Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks. In Proceedings of the LLM-CP Workshop, AAAI-24.

Moskvichev, A., Odouard, V. V., and Mitchell, M. (2023). The ConceptARC benchmark: Evaluating understanding and generalization in the ARC domain. Transactions on Machine Learning Research.

Burnell, R., Schellaert, W., Burden, J., Ullman, T. D., Martinez-Plumed, F., Tenenbaum, J. B., Rutar, D., Cheke, L. G., Sohl-Dickstein, J., Mitchell, M., Kiela, D., Shanahan, M. Voorhees, E. M., Cohn, A. G., Leibo, J. Z., and Hernandez-Orallo, J. (2023). Rethink reporting of evaluation results in AI. Science, 380 (6641), 136-138. Non-paywalled preprint.

Mitchell, M. and Krakauer, D. C. (2023). The debate over understanding in AI's large language models. Proceedings of the National Academy of Sciences, 120 (13).

Shiffren, R. and Mitchell, M. (2023) Probing the psychology of AI models. Proceedings of the National Academy of Sciences, 120 (10).

Odouard, V. V. and Mitchell, M. (2022). Evaluating understanding on conceptual abstraction benchmarks. In Proceedings of the AI Evaluation Beyond Metrics Workshop (IJCAI-2022).

Shanahan, M. and Mitchell, M. (2022). Abstraction for Deep Reinforcement Learning. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-2022).

Springer, J. M., Mitchell, M., and Kenyon, G. T. (2021). A little robustness goes a long way: Leveraging robust features for targeted transfer attacks. In Proceedings of 35th Conference on Neural Information Processing Systems (NeurIPS 2021).

Springer, J. M., Mitchell, M., and Kenyon, G. T. (2021). Uncovering universal features: How adversarial training improves adversarial transferability. Workshop on Adversarial Machine Learning, International Conference on Machine Learning (ICML).

Mitchell, M. (2021). Why AI is Harder Than We Think, arXiv: 2104.12871. To appear in Mind Design 3.

Mitchell, M. (2021). Abstraction and Analogy-Making in Artificial Intelligence. Annals of the New York Academy of Sciences, 1505 (1), 79–101.

Springer, J. M., Mitchell, M., and Kenyon, G. T. (2021). Adversarial perturbations are not so weird: Entanglement of robust and non-robust features in neural network classifiers, arXiv:2102.05110.

Jenkins, O. C., Lopresti, D., and Mitchell, M. (2020). Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable. CCC Quadrennial Paper. November 2020.

Mitchell, M. (2020). On crashing the barrier of meaning in AI. AI Magazine, 41(2), 86-92.

Conser, E., Hahn, K., Watson, C. M., and Mitchell, M. (2019). Revisiting visual grounding. To appear in Proceedings of the Workshop on Shortcomings in Vision and Language, NAACL-2019, ACL.

Quinn, M. H., Conser, E., Witte, J. M., and Mitchell, M. (2018). Semantic image retrieval via active grounding of visual situations . In Proceedings of the 12th International Conference on Semantic Computing. IEEE.

Lundquist, S. Y., Mitchell, M., and Kenyon, G. T. (2017). Sparse coding on stereo video for object detection. In Proceedings of the NIPS Workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond.

Rhodes, A. D., Witte, J., Mitchell, M., and Jedynak, B. (2017). Bayesian optimization for refining object proposals. In Proceedings of the Seventh International Conference on Image Processing Theory, Tools, and Applications (IPTA 2017). IEEE.

Rhodes, A. D., Quinn, M. H., and Mitchell, M. (2016). Fast on-line kernel density estimation for active object localization. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2017.

Quinn, M. H., Rhodes, A. D., and Mitchell, M. (2016). Active object localization in visual situations. arXiv 1607.00548, 2017.

Ghosh, P., Mitchell, M., Tanyi, J. A., and Hung, A. Y. (2015). Incorporating priors for medical image segmentation using a genetic algorithm. To appear in Neurocomputing Journal.

Thomure, M. D., Mitchell, M., and Kenyon, G. T. (2013). On the role of shape prototypes in hierarchical models of vision. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2013.

Landecker, W., Thomure, M. D., Bettencourt, L. M. A., Mitchell, M., Kenyon, G. T., and Brumby, S. P. (2013). Interpreting individual classifications of hierarchical networks. In Proceedings of the 2013 Conference on Computational Intelligence and Data Mining (CIDM 2013).

Ghosh, P., Mitchell, M., and Gold, J. (2010). LSGA: Combining level-sets and genetic algorithms for segmentation. Evolutionary Intelligence, 3, 1-11.

Ghosh, P., Mitchell, M., and Gold, J. (2010). Segmentation of thermographic images of hands using a genetic algortithm. In Proceedings of SPIE, Vol. 7538, 75380D (2010).

Ghosh, P., Mitchell, M., Tanyi, J. A., and Hung, A. (2009). A genetic algorithm-based level-set curve evolution for prostate segmentation on pelvic CT and MRI images. E. Romero and F. Gonzalez (editors), Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques, ICI Global.

Ghosh, P. and Mitchell, M. (2008). Prostate segmentation on pelvic CT images using a genetic algorithm . In Proceedings of the International Society for Optical Engineering (SPIE), Conference on Medical Imaging, February, 2008. SPIE Press.

Marques-Pita, M., Mitchell, M., and Rocha, L. (2008). The role of conceptual structure in designing cellular automata to perform collective computation. In Proceedings of the Conference on Unconventional Computation, UC 2008, Springer (Lecture Notes in Computer Science).

Juengling, R. and Mitchell, M. (2007). Combinatorial shape decomposition. In Proceedings of the Third International Symposium on Visual Computing (ISVC07). Springer (Lecture Notes in Computer Science).

Mitchell, M. (2006) Complex systems: Network thinking. Artificial Intelligence, 170(18), 1194-1212.

Ghosh, P. and Mitchell, M. (2006). Segmentation of medical images using a genetic algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2006, pp. 1171-1178.

Mitchell, M (2006). Coevolutionary learning with spatially distributed populations. In G. Y. Yen and D. B. Fogel (editors), Computational Intelligence: Principles and Practice . New York: IEEE Computational Intelligence Society.

Mitchell, M., Thomure, M. D., and Williams, N. L. (2006). The role of space in the success of coevolutionary learning. In Proceedings of Artificial Life X: Tenth Annual Conference on the Simulation and Synthesis of Living Systems . Cambridge, MA: MIT Press.

Williams, N. L. and Mitchell, M. (2005) Investigating the success of spatial coevolutionary learning. In H. G. Beyer et al. (editors), Proceedings of the 2005 Genetic and Evolutionary Computation Conference, GECCO-2005 , New York: ACM Press, 523-530.

Mitchell, M. (2005). Self-awareness and control in decentralized systems. In Working Papers of the AAAI 2005 Spring Symposium on Metacognition in Computation. Menlo Park, CA: AAAI Press.

Crutchfield, J. P., Mitchell, M., and Das, R. (2003). The evolutionary design of collective computation in cellular automata. In J. P. Crutchfield and P. K. Schuster (editors), Evolutionary Dynamics---Exploring the Interplay of Selection, Neutrality, Accident, and Function, pp. 361--411. New York: Oxford University Press.

Mitchell, M. and Newman, M. (2002). Complex systems theory and evolution. In M. Pagel (editor), Encyclopedia of Evolution , New York: Oxford University Press.

Jimenez-Morales, F., Mitchell, M., and Crutchfield, J. P. (2002). Evolving one dimensional cellular automata to perform a non-trivial collective behavior task: One case study. In P. M. A. Sloot, C. J. K. Tan, J. J. Dongarra and A. G. Hoekstra (editors), Computational Science-ICCS 2002, Part I, Proceedings 2329, 793-802. Berlin: Springer-Verlag.

Pagie, L. and Mitchell, M. (2002). A comparison of evolutionary and coevolutionary search. International Journal of Computational Intelligence and Applications, 2(1), 53--69.

Jimenez-Morales, F., Crutchfield, J. P., and Mitchell, M. (2001). Evolving two-dimensional cellular automata to perform density classification: A report on work in progress. Parallel Computing, 27 (5), 571--585.

Mitchell, M. (2001). Life and evolution in computers. History and Philosophy of the Life Sciences, 23, 361-383.

Mitchell, M. (2001). Analogy-making as a complex adaptive system. In L. Segel and I. Cohen (editors), Design Principles for the Immune System and Other Distributed Autonomous Systems . New York: Oxford University Press.

Werfel, J., Mitchell, M., and Crutchfield, J. P. (2000). Resource sharing and coevolution in evolving cellular automata. IEEE Transactions on Evolutionary Computation, 4(4), 388--393.

Brumby, S. P., Perkins, S. J., Theiler, J., Szymanski, J. J., Bloch, J. J., and Mitchell, M. (1999). Investigation of image feature extraction by a genetic algorithm. In Proceedings of the International Society for Optical Engineering, Proceedings of SPIE 3812, 24-31. Bellingham, WA: SPIE Press.

van Nimwegen, E., Crutchfield, J. P., and Mitchell, M. (1999). Statistical dynamics of the Royal Road genetic algorithm. Theoretical Computer Science, 229 (1), 41-102.

Mitchell, M. (1999). Evolutionary computation. In R. Wilson and F. Keil (editors), The MIT Encyclopedia of the Cognitive Sciences . Cambridge, MA: MIT Press.

Mitchell, M. and Taylor, C. E. (1999) Evolutionary computation: An overview. Annual Review of Ecology and Systematics, 30, 593-616

Mitchell, M. and Forrest, S. (1998). Royal Road functions. In T. Back, D. Fogel, and Z. Michalewicz (editors), Handbook of Evolutionary Computation . Oxford: Oxford University Press.

Hordijk, W., Crutchfield, J. P., and Mitchell, M. (1998). Mechanisms of emergent computation in cellular automata. In A. E. Eiben (ed.), Proceedings of the Fifth International Conference on Parallel Problem Solving From Nature---PPSN V. New York: Springer.

Mitchell, M. (1998). A complex-systems perspective on the ``computation vs. dynamics'' debate in cognitive science. In M. A. Gernsbacher and S. J. Derry (eds.), Proceedings of the 20th Annual Conference of the Cognitive Science Society---Cogsci98, 710-715.

Mitchell, M. (1998). Theories of structure versus theories of change. (Commentary on ``The dynamical hypothesis in cognitive science'', by T. van Gelder.) Behavioral and Brain Sciences, 21, 645-646 .

Mitchell, M. (1998). Computation in cellular automata: A selected review. In T. Gramss, S. Bornholdt, M. Gross, M. Mitchell, and T. Pellizzari, Nonstandard Computation , pp. 95--140. Weinheim: VCH Verlagsgesellschaft.

Mitchell, M., Crutchfield, J. P., and Das, R. (1998). Evolving cellular automata to perform computations. In T. Back, D. Fogel, and Z. Michalewicz (editors), Handbook of Evolutionary Computation. Oxford: Oxford University Press.

van Nimwegen, E., Crutchfield, J. P., and Mitchell, M. (1997). Finite populations induce metastability in evolutionary search. Physics Letters A, 229 (2), 144-150.

Belew, R. K., Mitchell, M., and Ackley, D. H. (1996). Computation and the natural sciences. In R. K. Belew and M. Mitchell (editors), Adaptive Individuals in Evolving Populations: Models and Algorithms. Reading, MA: Addison-Wesley.

Mitchell, M., Crutchfield, J. P., and Das, R. (1996). Evolving cellular automata to perform computations: A review of recent work. In Proceedings of the First International Conference on Evolutionary Computation and its Applications (EvCA '96). Moscow, Russia: Russian Academy of Sciences.

Hordijk, W., Crutchfield, J. P., and Mitchell, M. (1996). Embedded particle computation in evolved cellular automata. In Proceedings of the Conference on Physics and Computation---PhysComp96, Boston, MA.

Crutchfield, J. P. and Mitchell, M. (1995). The evolution of emergent computation. Proceedings of the National Academy of Sciences, USA, 92 (23): 10742.

Das, R., Crutchfield, J. P., Mitchell, M., and Hanson, J. E. (1995). Evolving globally synchronized cellular automata. In L. J. Eshelman (editor), Proceedings of the Sixth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann.

Das, R., Mitchell, M., and Crutchfield, J. P. (1994). A genetic algorithm discovers particle-based computation in cellular automata. In Y. Davidor, H.-P. Schwefel, and R. Manner (editors), Parallel Problem Solving from Nature---PPSN III. Berlin: Springer-Verlag.

Mitchell, M., Crutchfield, J. P., and Hraber, P. (1994). Evolving cellular automata to perform computations: Mechanisms and impediments. Physica D, 75, 361--391.

Mitchell, M., Crutchfield, J. P., and Hraber, P. T. (1994). Dynamics, computation, and the ``edge of chaos'': A re-examination. In G. Cowan, D. Pines, and D. Melzner (editors), Complexity: Metaphors, Models, and Reality. Reading, MA: Addison-Wesley.

Mitchell, M., Holland, J. H., and Forrest, S. (1994). When will a genetic algorithm outperform hill climbing? In J. D. Cowan, G. Tesauro, and J. Alspector (editors), Advances in Neural Information Processing Systems 6, 51-58, San Mateo, CA: Morgan Kaufmann.

Mitchell, M. (1995). Genetic algorithms: An overview. Complexity, 1 (1) 31--39.

Hofstadter, D. R. and Mitchell, M. (1995). The The Copycat project: A model of mental fluidity and analogy-making Chapter 5 in D. R. Hofstadter, Fluid Concepts and Creative Analogies. Basic Books.

Mitchell M., and Forrest, S. (1994). Genetic algorithms and artificial life. Artificial Life, 1 (3), 267-289.

Mitchell, M., Hraber, P. T., and Crutchfield, J. P. (1993). Revisiting the edge of chaos: Evolving cellular automata to perform computations. Complex Systems, 7, 89-130.

Forrest, S. and Mitchell, M. (1993). What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation. Machine Learning, 13, 285-319.

Forrest, S. and Mitchell, M. (1993). Relative building-block fitness and the building-block hypothesis. In D. Whitley (editor), Foundations of Genetic Algorithms 2, San Mateo, CA: Morgan Kaufmann.

Mitchell, M. (1993). Genetic algorithms. In L. Nadel and D. L. Stein (editors), 1992 Lectures in Complex Systems. Reading, MA: Addison-Wesley.

Mitchell, M., Forrest, S., and Holland, J. H. (1992). The royal road for genetic algorithms: Fitness landscapes and GA performance. In F. J. Varela and P. Bourgine (editors), Proceedings of the First European Conference on Artificial Life. Cambridge, MA: MIT Press.

Forrest, S. and Mitchell, M. (1991). The performance of genetic algorithms on Walsh polynomials: Some anomalous results and their explanation. In R. Belew and L. Booker (editors), Proceedings of the Fourth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann.

Mitchell, M. and Hofstadter, D. R. (1990). The emergence of understanding in a computer model of concepts and analogy-making. Physica D, 42, 322-334.

Mitchell, M. and Hofstadter, D. R. (1990). The right concept at the right time: How concepts emerge as relevant in response to context-dependent pressures. In Proceedings of the Twelfth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum Associates.

Mitchell, M. and Hofstadter, D. R. (1989). The role of computational temperature in a computer model of concepts and analogy-making. In Proceedings of the Eleventh Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum Associates.

Hofstadter, D. R. and Mitchell, M. (1988). Conceptual slippage and analogy-making: A report on the Copycat project. In Proceedings of the Tenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum Associates.

Hofstadter, D. R. and Mitchell, M. (1988). Concepts, analogies, and creativity. In Proceedings of the Canadian Society for Computational Studies of Intelligence. Edmonton: University of Alberta.

Seward, F. D. and Mitchell, M. (1981). An X-ray survey of the Small Magellanic Cloud. Astrophysical Journal, 243, 736.

Mitchell, M. (1979). Period changes in two W Virginis variables. Journal of the American Association of Variable Star Observers, 8 (2).