Joshua C. Peterson

According to our database, Joshua C. Peterson authored at least 31 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks.

Aditi Jha, Joshua C. Peterson, Thomas L. Griffiths

Cogn. Sci., January, 2023

The Universal Law of Generalization Holds for Naturalistic Stimuli.

Raja Marjieh, Nori Jacoby, Joshua C. Peterson, Thomas L. Griffiths

CoRR, 2023

On the informativeness of supervision signals.

Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua C. Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths

Proceedings of the Uncertainty in Artificial Intelligence, 2023

To each their own theory: Exploring the limits of individual differences in decisions under risk.

Joshua C. Peterson, Marina Mancoridis, Tom Griffiths

Proceedings of the 45th Annual Meeting of the Cognitive Science Society, 2023

2021

Combating the climate crisis with cognitive science.

Rachit Dubey, Joshua C. Peterson

Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, 2021

2020

Fluorescent Nuclei Measurements Macro (FNMM), a tool for automated cell quantification in ImageJ.

Joshua C. Peterson, Marco C. DeRuiter

Softw. Impacts, 2020

End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior.

Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Tom Griffiths

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019

Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making.

Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths

CoRR, 2019

Capturing human categorization of natural images at scale by combining deep networks and cognitive models.

Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths

CoRR, 2019

Predicting human decisions with behavioral theories and machine learning.

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

CoRR, 2019

Cognitive model priors for predicting human decisions.

David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths

Proceedings of the 36th International Conference on Machine Learning, 2019

Human Uncertainty Makes Classification More Robust.

Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Learning deep taxonomic priors for concept learning from few positive examples.

Erin Grant, Joshua C. Peterson, Tom Griffiths

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning.

Mayank Agrawal, Joshua C. Peterson, Tom Griffiths

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

2018

Leveraging deep neural networks to study human cognition.

Joshua Caleb Peterson

PhD thesis, 2018

Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations.

Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths

Cogn. Sci., 2018

Learning a face space for experiments on human identity.

Jordan W. Suchow, Joshua C. Peterson, Tom Griffiths

Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels.

Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Tom Griffiths

Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

Capturing human category representations by sampling in deep feature spaces.

Joshua C. Peterson, Jordan W. Suchow, Krisha Aghi, Alexander Y. Ku, Tom Griffiths

Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

2017

Leveraging deep neural networks to capture psychological representations.

Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths

CoRR, 2017

Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report.

Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Evidence for the size principle in semantic and perceptual domains.

Joshua C. Peterson, Thomas L. Griffiths

Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

Evaluating vector-space models of analogy.

Dawn Chen, Joshua C. Peterson, Tom Griffiths

Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

Modeling human categorization of natural images using deep feature representations.

Ruairidh M. Battleday, Joshua C. Peterson, Tom Griffiths

Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

2016

Modelling Student Behavior using Granular Large Scale Action Data from a MOOC.

Steven Tang, Joshua C. Peterson, Zachary A. Pardos

CoRR, 2016

Deep Neural Networks and How They Apply to Sequential Education Data.

Steven Tang, Joshua C. Peterson, Zachary A. Pardos

Proceedings of the Third ACM Conference on Learning @ Scale, 2016

Adapting Deep Network Features to Capture Psychological Representations.

Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths

Proceedings of the 38th Annual Meeting of the Cognitive Science Society, 2016

2015

What Makes an Object Memorable?

Rachit Dubey, Joshua C. Peterson, Aditya Khosla, Ming-Hsuan Yang, Bernard Ghanem

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Emotionally mediated crossmodal correspondences affect classification performance.

Joshua C. Peterson, Stephen E. Palmer

Proceedings of the 37th Annual Meeting of the Cognitive Science Society, 2015

The colors and textures of musical sounds.

Thomas Langlois, Joshua C. Peterson, Stephen E. Palmer

Proceedings of the 37th Annual Meeting of the Cognitive Science Society, 2015

Understanding Student Success in Chemistry Using Gaze Tracking and Pupillometry.

Joshua C. Peterson, Zachary A. Pardos, Martina A. Rau, Anna Swigart, Colin Gerber, Jonathan McKinsey

Proceedings of the Artificial Intelligence in Education - 17th International Conference, 2015