Joshua C. Peterson
Joshua C. Peterson
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Cited by
Cited by
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
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
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
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
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
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
Adapting deep network features to capture psychological representations
JC Peterson, JT Abbott, TL Griffiths
arXiv preprint arXiv:1608.02164, 2016
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
Evaluating vector-space models of analogy
D Chen, JC Peterson, TL Griffiths
arXiv preprint arXiv:1705.04416, 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
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
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
Modelling student behavior using granular large scale action data from a MOOC
S Tang, JC Peterson, ZA Pardos
arXiv preprint arXiv:1608.04789, 2016
Parallelograms revisited: Exploring the limitations of vector space models for simple analogies
JC Peterson, D Chen, TL Griffiths
Cognition 205, 104440, 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
Extracting low‐dimensional psychological representations from convolutional neural networks
A Jha, JC Peterson, TL Griffiths
Cognitive science 47 (1), e13226, 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
Modeling human categorization of natural images using deep feature representations
RM Battleday, JC Peterson, TL Griffiths
arXiv preprint arXiv:1711.04855, 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
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
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