Joshua C. Peterson

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Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton UniversityVerified email at princeton.edu
Ruairidh McLennan BattledayPostdoctoral Researcher, Harvard UniversityVerified email at g.harvard.edu
Mayank AgrawalPrinceton UniversityVerified email at princeton.edu
Daniel ReichmanWorcester Polytechnic InstituteVerified email at wpi.edu
Jordan William SuchowStevens Institute of TechnologyVerified email at stevens.edu
Joshua T. AbbottAdobeVerified email at adobe.com
David D BourginAdobeVerified email at adobe.com
Zachary A. PardosAssociate Professor at UC BerkeleyVerified email at berkeley.edu
Stefan UddenbergAsst. Prof. of Psychology, University of Illinois Urbana-ChampaignVerified email at illinois.edu
Stuart RussellProfessor of Computer Science, University of California, BerkeleyVerified email at cs.berkeley.edu
Rachit DubeyIncoming Asst Prof, UCLAVerified email at princeton.edu
Bernard GhanemProfessor, King Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Ming-Hsuan YangUniversity of California at Merced; Google DeepMindVerified email at ucmerced.edu
Olga RussakovskyAssociate Professor, Princeton UniversityVerified email at cs.princeton.edu
Dawn ChenUniversity of California, BerkeleyVerified email at berkeley.edu
Aditya KhoslaCTO, Iterative HealthVerified email at iterative.health
Ori PlonskyAssistant Professor, Technion - Israel Institute of TechnologyVerified email at technion.ac.il
Ido ErevProfessor of Behavioral Science, TechnionVerified email at tx.technion.ac.il
Evan C CarterU.S. Army Research LaboratoryVerified email at mail.mil
Moshe TennenholtzVerified email at ie.technion.ac.il

Joshua C. Peterson

Boston University

Verified email at bu.edu


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	329	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	262	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	182	2018
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	147	2015
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	128	2020
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	119*	2019
Adapting deep network features to capture psychological representations JC Peterson, JT Abbott, TL Griffiths arXiv preprint arXiv:1608.02164, 2016	97	2016
Evaluating vector-space models of analogy D Chen, JC Peterson, TL Griffiths arXiv preprint arXiv:1705.04416, 2017	84	2017
Predicting human decisions with behavioral theories and machine learning O Plonsky, R Apel, E Ert, M Tennenholtz, D Bourgin, JC Peterson, ... arXiv preprint arXiv:1904.06866, 2019	83	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	78	2022
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	72	2016
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	68	2020
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	42	2021
Parallelograms revisited: Exploring the limitations of vector space models for simple analogies JC Peterson, D Chen, TL Griffiths Cognition 205, 104440, 2020	39	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
Extracting low‐dimensional psychological representations from convolutional neural networks A Jha, JC Peterson, TL Griffiths Cognitive science 47 (1), e13226, 2023	30	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	27	2020
Psychological and musical factors underlying engagement with unfamiliar music P Janata, J Peterson, C Ngan, B Keum, H Whiteside, S Ran Music Perception: An Interdisciplinary Journal 36 (2), 175-200, 2018	24	2018
Modeling human categorization of natural images using deep feature representations RM Battleday, JC Peterson, TL Griffiths arXiv preprint arXiv:1711.04855, 2017	24	2017
Understanding Student Success in Chemistry using Gaze Tracking & Pupillometry J Peterson, Z Pardos, M Rau, A Swigart, C Gerber, J McKinsey Artificial Intelligence in Education, 2015	23	2015

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