Joshua Caleb Peterson

Joshua Peterson

Email: jpeterson "at" berkeley "dot" edu

I'm a postdoc in the Computer Science department at Princeton University. My PhD was completed at UC Berkeley, where I focused on repurposing representations learned by deep neural networks as proxies to study humans and bootstrap cognitive models.

Selected Publications

Peterson, J., Abbott, J., & Griffiths, T. (accepted). Evaluating (and improving) the correspondence between deep neural networks and human representations.
Cognitive Science.
PDF

Janata, P., Peterson, J., Ngan, C., Keum, B., Whiteside, H., & Ran, S. (forthcoming). 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

Battleday, R., Peterson, J., & Griffiths, T. (2017). Modeling human categorization of natural images using deep feature representations.
Presented at the 39th Annual Conference of the Cognitive Science Society.

Dawn, C., Peterson, J., & Griffiths, T. (2017). Evaluating vector-space models of analogy.
Presented at the 39th Annual Conference of the Cognitive Science Society.
PDF DATASET

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. (25% acceptance rate)
PDF WEBSITE ABSTRACT   * co-first author

Complete Publication List

Peterson, J., Abbott, J., & Griffiths, T. (accepted). Evaluating (and improving) the correspondence between deep neural networks and human representations.
Cognitive Science.
PDF

Janata, P., Peterson, J., Ngan, C., Keum, B., Whiteside, H., & Ran, S. (forthcoming). 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

Battleday, R., Peterson, J., & Griffiths, T. (2017). Modeling human categorization of natural images using deep feature representations.
Presented at the 39th Annual Conference of the Cognitive Science Society.

Dawn, C., Peterson, J., & Griffiths, T. (2017). Evaluating vector-space models of analogy.
Presented at the 39th Annual Conference of the Cognitive Science Society.
PDF DATASET

Tang, S., Peterson, J., & Pardos, Z. (2017). Predictive Modelling of Student Behaviour Using Granular Large-Scale Action Data.
In G. Siemens & C. Lang (Eds.), Handbook of learning analytics. Beaumont, AB: Society for Learning Analytics Research.

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]

Palmer, S., & Peterson, J. (2016). Hierarchical Structure of Musical and Visual Meter in Cross-modal "Fit" Judgments. 14th International Conference on Music Perception and Cognition.
PDF ABSTRACT

Tang, S., Peterson, J., & Pardos, Z. (2016). Deep Neural Networks and How They Apply to Sequential Education Data. In the Proceedings of the 3rd Annual ACM Conference on Learning at Scale.
ABSTRACT

*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. (25% acceptance rate)
PDF WEBSITE ABSTRACT   * co-first author

Peterson, J., Pardos, Z., Rau, M., Swigart, A., Colin, G., McKinsey, J. (2015). Understanding Student Success in Chemistry using Gaze Tracking & Pupillometry. In the Proceedings of the Artificial Intelligence in Education Conference, Madrid, Spain, June 2015.(28% acceptance rate)
PDF ABSTRACT

Peterson, J., & Palmer, S. (2015). Emotionally mediated crossmodal correspondences affect classification performance. Presented at the 37th Annual Meeting of the Cognitive Science Society, Pasadena, CA, July 2015.
PDF ABSTRACT

Dubey, R., Peterson, J., Ghanem, B., Yang, M., & Hsieh, P. (2015). Exploring the visual components that make an image memorable. Presented at the 14th Annual Meeting of the Vision Sciences Society, St. Pete Beach, FL, May 2015.
ABSTRACT