| Human uncertainty makes classification more robust JC Peterson, RM Battleday, TL Griffiths, O Russakovsky Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 425 | 2019 |
| Using large-scale experiments and machine learning to discover theories of human decision-making JC Peterson, DD Bourgin, M Agrawal, D Reichman, TL Griffiths Science 372 (6547), 1209-1214, 2021 | 360 | 2021 |
| Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations JC Peterson, JT Abbott, TL Griffiths Cognitive Science 42 (8), 2648-2669, 2018 | 209 | 2018 |
| Capturing human categorization of natural images by combining deep networks and cognitive models RM Battleday, JC Peterson, TL Griffiths Nature communications 11 (1), 5418, 2020 | 161 | 2020 |
| What makes an object memorable? R Dubey, J Peterson, A Khosla, MH Yang, B Ghanem Proceedings of the ieee international conference on computer vision, 1089-1097, 2015 | 160 | 2015 |
| Cognitive model priors for predicting human decisions J Peterson, D Bourgin, D Reichman, S Russell, T Griffiths International Conference on Machine Learning, 5133-5141, 2019 | 137* | 2019 |
| Deep models of superficial face judgments JC Peterson, S Uddenberg, TL Griffiths, A Todorov, JW Suchow Proceedings of the National Academy of Sciences 119 (17), e2115228119, 2022 | 117 | 2022 |
| Predicting human decisions with behavioural theories and machine learning O Plonsky, R Apel, E Ert, M Tennenholtz, D Bourgin, JC Peterson, ... Nature Human Behaviour, 1-14, 2025 | 108 | 2025 |
| Adapting deep network features to capture psychological representations JC Peterson, JT Abbott, TL Griffiths arXiv preprint arXiv:1608.02164, 2016 | 108 | 2016 |
| A foundation model to predict and capture human cognition M Binz, E Akata, M Bethge, F Brändle, F Callaway, J Coda-Forno, ... Nature, 1-8, 2025 | 94 | 2025 |
| Scaling up psychology via scientific regret minimization M Agrawal, JC Peterson, TL Griffiths Proceedings of the National Academy of Sciences 117 (16), 8825-8835, 2020 | 93 | 2020 |
| Evaluating vector-space models of analogy D Chen, JC Peterson, TL Griffiths arXiv preprint arXiv:1705.04416, 2017 | 87 | 2017 |
| Deep neural networks and how they apply to sequential education data S Tang, JC Peterson, ZA Pardos Proceedings of the third (2016) acm conference on learning@ scale, 321-324, 2016 | 74 | 2016 |
| From convolutional neural networks to models of higher‐level cognition (and back again) RM Battleday, JC Peterson, TL Griffiths Annals of the New York Academy of Sciences 1505 (1), 55-78, 2021 | 55 | 2021 |
| Centaur: a foundation model of human cognition M Binz, E Akata, M Bethge, F Brändle, F Callaway, J Coda-Forno, ... arXiv preprint arXiv:2410.20268, 2024 | 51 | 2024 |
| Large language models assume people are more rational than we really are R Liu, J Geng, JC Peterson, I Sucholutsky, TL Griffiths arXiv preprint arXiv:2406.17055, 2024 | 46 | 2024 |
| Parallelograms revisited: Exploring the limitations of vector space models for simple analogies JC Peterson, D Chen, TL Griffiths Cognition 205, 104440, 2020 | 46 | 2020 |
| Extracting low‐dimensional psychological representations from convolutional neural networks A Jha, JC Peterson, TL Griffiths Cognitive science 47 (1), e13226, 2023 | 38 | 2023 |
| End-to-end deep prototype and exemplar models for predicting human behavior P Singh, JC Peterson, RM Battleday, TL Griffiths arXiv preprint arXiv:2007.08723, 2020 | 33 | 2020 |
| Modelling student behavior using granular large scale action data from a MOOC S Tang, JC Peterson, ZA Pardos arXiv preprint arXiv:1608.04789, 2016 | 33 | 2016 |
Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton UniversityVerified email at princeton.edu
