Joshua Caleb Peterson

Joshua Peterson

joshuacp "at" princeton "dot" edu

I'm a postdoc in the computer science department at Princeton University. My PhD was completed at UC Berkeley, advised by Tom Griffiths. My research employs machine learning and large datasets as tools to understand human cognition and predict behavior.

Publications

*Battleday, R., *Peterson, J., & Griffiths, T. (2020). Capturing human categorization of natural images by combining deep networks and cognitive models
Nature Communications.
PDF WEB ARTICLE DATASET  * equal contribution

Agrawal, M., Peterson, J., & Griffiths, T. (2020). Scaling up psychology via Scientific Regret Minimization
Proceedings of the National Academy of Sciences (PNAS).
PDF ABSTRACT

Peterson, J., Chen, D., & Griffiths, T. (2020). Parallelograms revisited: Exploring the limitations of vector space models for simple analogies.
Cognition.
PDF ABSTRACT DATASET

Peterson, J.*, Battleday, R.*, Griffiths, T., & Russakovsky, O. (2019). Human uncertainty makes classification more robust.
In Proceedings of the IEEE International Conference on Computer Vision (ICCV).
PDF ABSTRACT DATASET  * equal contribution

*Bourgin, D., *Peterson, J., Reichman, D., Griffiths, T., & Russell, S. (2019). Cognitive model priors for predicting human decisions.
In the Proceedings of the International Conference on Machine Learning (ICML).
PDF ABSTRACT  * equal contribution

Grant, E., Peterson, J., & Griffiths, T. (2019). Learning deep taxonomic priors for concept learning from few positive examples.
In the Proceedings of the 41st Annual Conference of the Cognitive Science Society.

Agrawal, M., Peterson, J., & Griffiths, T. (2019). Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning.
In the Proceedings of the 41st Annual Conference of the Cognitive Science Society.

Peterson, J., Abbott, J., & Griffiths, T. (2018). Evaluating (and improving) the correspondence between deep neural networks and human representations.
Cognitive Science, 42(8), 2648-2669.
PDF

Janata, P., Peterson, J., Ngan, C., Keum, B., Whiteside, H., & Ran, S. (2018). Psychological and musical factors underlying engagement with unfamiliar music.
Music Perception.

Peterson, J., Suchow, J., Aghi, K., Ku, A., & Griffiths, T. (2018). Capturing human category representations by sampling in deep feature spaces.
Presented at the 40th Annual Conference of the Cognitive Science Society.
PDF

Peterson, J., Soulos, P., Nematzadeh, A., & Griffiths, T. (2018).
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels.
Presented at the 40th Annual Conference of the Cognitive Science Society.
PDF POSTER

Peterson, J., & Griffiths, T. (2017). Evidence for the size principle in semantic and perceptual domains.
Presented at the 39th Annual Conference of the Cognitive Science Society.
PDF

Peterson, J., Abbott, J., & Griffiths, T. (2016). Adapting Deep Network Features to Capture Psychological Representations.
Presented at the 38th Annual Conference of the Cognitive Science Society. (34% acceptance rate)
PDF ABSTRACT
[Winner of the Computational Modeling Prize in Perception/Action]
[Selected for IJCAI-17 Sister Conference Best Paper Track]

*Dubey, R., *Peterson, J., Khosla, A., Yang, M., & Ghanem, B. (2015). What makes an object memorable?
In the Proceedings of the International Conference on Computer Vision (ICCV). (25% acceptance rate)
PDF WEBSITE ABSTRACT   * equal contribution