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ORCIDJoshua C. Peterson
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Books and Theses
- 2018
[b1]Joshua Caleb Peterson:
Leveraging deep neural networks to study human cognition. University of California, Berkeley, USA, 2018
Journal Articles
- 2023
- [j3]Aditi Jha, Joshua C. Peterson, Thomas L. Griffiths:
Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks. Cogn. Sci. 47(1) (2023) - 2020
- [j2]Joshua C. Peterson
, Marco C. DeRuiter:
Fluorescent Nuclei Measurements Macro (FNMM), a tool for automated cell quantification in ImageJ. Softw. Impacts 6: 100030 (2020) - 2018
- [j1]Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths:
Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations. Cogn. Sci. 42(8): 2648-2669 (2018)
Conference and Workshop Papers
- 2023
- [c23]Joshua C. Peterson, Marina Mancoridis, Tom Griffiths:
To each their own theory: Exploring the limits of individual differences in decisions under risk. CogSci 2023 - [c22]Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua C. Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths:
On the informativeness of supervision signals. UAI 2023: 2036-2046 - 2021
- [c21]Rachit Dubey, Joshua C. Peterson:
Combating the climate crisis with cognitive science. CogSci 2021 - 2020
- [c20]Aditi Jha, Joshua C. Peterson, Tom Griffiths:
Extracting low-dimensional psychological representations from convolutional neural networks. CogSci 2020 - [c19]Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Tom Griffiths:
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. CogSci 2020 - 2019
- [c18]Mayank Agrawal, Joshua C. Peterson, Tom Griffiths:
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. CogSci 2019: 1318-1323 - [c17]Erin Grant, Joshua C. Peterson, Tom Griffiths:
Learning deep taxonomic priors for concept learning from few positive examples. CogSci 2019: 1865-1870 - [c16]Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky:
Human Uncertainty Makes Classification More Robust. ICCV 2019: 9616-9625 - [c15]David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths:
Cognitive model priors for predicting human decisions. ICML 2019: 5133-5141 - 2018
- [c14]Joshua C. Peterson, Jordan W. Suchow, Krisha Aghi, Alexander Y. Ku, Tom Griffiths:
Capturing human category representations by sampling in deep feature spaces. CogSci 2018 - [c13]Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Tom Griffiths:
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. CogSci 2018 - [c12]Jordan W. Suchow, Joshua C. Peterson, Tom Griffiths:
Learning a face space for experiments on human identity. CogSci 2018 - [c11]Joshua C. Peterson, Krisha Aghi, Jordan W. Suchow, Alexander Y. Ku, Tom Griffiths:
Capturing Human Category Representations by Sampling in Deep Feature Spaces. ICLR (Workshop) 2018 - 2017
- [c10]Ruairidh M. Battleday, Joshua C. Peterson, Tom Griffiths:
Modeling human categorization of natural images using deep feature representations. CogSci 2017 - [c9]Dawn Chen, Joshua C. Peterson, Tom Griffiths:
Evaluating vector-space models of analogy. CogSci 2017 - [c8]Joshua C. Peterson, Thomas L. Griffiths:
Evidence for the size principle in semantic and perceptual domains. CogSci 2017 - [c7]Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths:
Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report. IJCAI 2017: 4934-4938 - 2016
- [c6]Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths:
Adapting Deep Network Features to Capture Psychological Representations. CogSci 2016 - [c5]Steven Tang, Joshua C. Peterson, Zachary A. Pardos:
Deep Neural Networks and How They Apply to Sequential Education Data. L@S 2016: 321-324 - 2015
- [c4]Joshua C. Peterson, Zachary A. Pardos, Martina A. Rau, Anna Swigart, Colin Gerber, Jonathan McKinsey:
Understanding Student Success in Chemistry Using Gaze Tracking and Pupillometry. AIED 2015: 358-366 - [c3]Thomas Langlois, Joshua C. Peterson, Stephen E. Palmer:
The colors and textures of musical sounds. CogSci 2015 - [c2]Joshua C. Peterson, Stephen E. Palmer:
Emotionally mediated crossmodal correspondences affect classification performance. CogSci 2015 - [c1]Rachit Dubey, Joshua C. Peterson, Aditya Khosla, Ming-Hsuan Yang, Bernard Ghanem:
What Makes an Object Memorable? ICCV 2015: 1089-1097
Informal and Other Publications
- 2024
- [i20]Ryan Liu, Jiayi Geng, Joshua C. Peterson, Ilia Sucholutsky, Thomas L. Griffiths:
Large Language Models Assume People are More Rational than We Really are. CoRR abs/2406.17055 (2024) - [i19]Jian-Qiao Zhu, Joshua C. Peterson, Benjamin Enke, Thomas L. Griffiths:
Capturing the Complexity of Human Strategic Decision-Making with Machine Learning. CoRR abs/2408.07865 (2024) - 2023
- [i18]Raja Marjieh, Nori Jacoby, Joshua C. Peterson, Thomas L. Griffiths:
The Universal Law of Generalization Holds for Naturalistic Stimuli. CoRR abs/2306.08564 (2023) - 2020
- [i17]Aditi Jha, Joshua Caleb Peterson, Thomas L. Griffiths:
Extracting low-dimensional psychological representations from convolutional neural networks. CoRR abs/2005.14363 (2020) - [i16]Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths:
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. CoRR abs/2007.08723 (2020) - 2019
- [i15]Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths:
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. CoRR abs/1902.06744 (2019) - [i14]Ori Plonsky, Reut Apel, Eyal Ert, Moshe Tennenholtz, David Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell, Evan C. Carter, James F. Cavanagh, Ido Erev:
Predicting human decisions with behavioral theories and machine learning. CoRR abs/1904.06866 (2019) - [i13]Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths:
Capturing human categorization of natural images at scale by combining deep networks and cognitive models. CoRR abs/1904.12690 (2019) - [i12]David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell:
Cognitive Model Priors for Predicting Human Decisions. CoRR abs/1905.09397 (2019) - [i11]Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky:
Human uncertainty makes classification more robust. CoRR abs/1908.07086 (2019) - [i10]Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths:
Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making. CoRR abs/1910.07581 (2019) - 2018
- [i9]Joshua C. Peterson, Jordan W. Suchow, Krisha Aghi, Alexander Y. Ku, Thomas L. Griffiths:
Capturing human category representations by sampling in deep feature spaces. CoRR abs/1805.07644 (2018) - [i8]Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Thomas L. Griffiths:
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. CoRR abs/1805.07647 (2018) - [i7]Jordan W. Suchow, Joshua C. Peterson, Thomas L. Griffiths:
Learning a face space for experiments on human identity. CoRR abs/1805.07653 (2018) - 2017
- [i6]Joshua C. Peterson, Thomas L. Griffiths:
Evidence for the size principle in semantic and perceptual domains. CoRR abs/1705.03260 (2017) - [i5]Dawn Chen, Joshua C. Peterson, Thomas L. Griffiths:
Evaluating vector-space models of analogy. CoRR abs/1705.04416 (2017) - [i4]Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths:
Leveraging deep neural networks to capture psychological representations. CoRR abs/1706.02417 (2017) - [i3]Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths:
Modeling Human Categorization of Natural Images Using Deep Feature Representations. CoRR abs/1711.04855 (2017) - 2016
- [i2]Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths:
Adapting Deep Network Features to Capture Psychological Representations. CoRR abs/1608.02164 (2016) - [i1]Steven Tang, Joshua C. Peterson, Zachary A. Pardos:
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC. CoRR abs/1608.04789 (2016)
Coauthor Index
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