Joshua C. Peterson

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 44

Top Topics

Feature Representations

Cognitive Models

Top Venues

Publications

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 (2024)
Aditi Jha, Joshua C. Peterson, Thomas L. Griffiths
Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks. Cogn. Sci. 47 (1) (2023)
Raja Marjieh, Nori Jacoby, Joshua C. Peterson, Thomas L. Griffiths
The Universal Law of Generalization Holds for Naturalistic Stimuli. CoRR (2023)
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)
Joshua C. Peterson, Marina Mancoridis, Tom Griffiths
To each their own theory: Exploring the limits of individual differences in decisions under risk. CogSci (2023)
Rachit Dubey, Joshua C. Peterson
Combating the climate crisis with cognitive science. CogSci (2021)
Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Tom Griffiths
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. CogSci (2020)
Aditi Jha, Joshua C. Peterson, Tom Griffiths
Extracting low-dimensional psychological representations from convolutional neural networks. CogSci (2020)
Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior. CoRR (2020)
Joshua C. Peterson, Marco C. DeRuiter
Fluorescent Nuclei Measurements Macro (FNMM), a tool for automated cell quantification in ImageJ. Softw. Impacts 6 (2020)
Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. CoRR (2019)
Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky
Human Uncertainty Makes Classification More Robust. ICCV (2019)
David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths
Cognitive model priors for predicting human decisions. ICML (2019)
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 (2019)
Erin Grant, Joshua C. Peterson, Tom Griffiths
Learning deep taxonomic priors for concept learning from few positive examples. CogSci (2019)
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 (2019)
Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making. CoRR (2019)
Mayank Agrawal, Joshua C. Peterson, Tom Griffiths
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning. CogSci (2019)
David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell
Cognitive Model Priors for Predicting Human Decisions. CoRR (2019)
Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky
Human uncertainty makes classification more robust. CoRR (2019)
Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Tom Griffiths
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. CogSci (2018)
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)
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 (2018)
Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Thomas L. Griffiths
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels. CoRR (2018)
Jordan W. Suchow, Joshua C. Peterson, Tom Griffiths
Learning a face space for experiments on human identity. CogSci (2018)
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)
Jordan W. Suchow, Joshua C. Peterson, Thomas L. Griffiths
Learning a face space for experiments on human identity. CoRR (2018)
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) (2018)
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Leveraging deep neural networks to capture psychological representations. CoRR (2017)
Ruairidh M. Battleday, Joshua C. Peterson, Tom Griffiths
Modeling human categorization of natural images using deep feature representations. CogSci (2017)
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report. IJCAI (2017)
Dawn Chen, Joshua C. Peterson, Tom Griffiths
Evaluating vector-space models of analogy. CogSci (2017)
Joshua C. Peterson, Thomas L. Griffiths
Evidence for the size principle in semantic and perceptual domains. CoRR (2017)
Joshua C. Peterson, Thomas L. Griffiths
Evidence for the size principle in semantic and perceptual domains. CogSci (2017)
Dawn Chen, Joshua C. Peterson, Thomas L. Griffiths
Evaluating vector-space models of analogy. CoRR (2017)
Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths
Modeling Human Categorization of Natural Images Using Deep Feature Representations. CoRR (2017)
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Adapting Deep Network Features to Capture Psychological Representations. CoRR (2016)
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Adapting Deep Network Features to Capture Psychological Representations. CogSci (2016)
Steven Tang, Joshua C. Peterson, Zachary A. Pardos
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC. CoRR (2016)
Steven Tang, Joshua C. Peterson, Zachary A. Pardos
Deep Neural Networks and How They Apply to Sequential Education Data. L@S (2016)
Rachit Dubey, Joshua C. Peterson, Aditya Khosla, Ming-Hsuan Yang, Bernard Ghanem
What Makes an Object Memorable? ICCV (2015)
Joshua C. Peterson, Stephen E. Palmer
Emotionally mediated crossmodal correspondences affect classification performance. CogSci (2015)
Thomas Langlois, Joshua C. Peterson, Stephen E. Palmer
The colors and textures of musical sounds. CogSci (2015)
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)