I am a postdoctoral scholar with Steven Piantadosi and Alison Gopnik at UC Berkeley. I completed my PhD with Josh Tenenbaum at MIT (thesis · precis).

My research uses behavioral experiments and computational methods to study how people develop conceptual systems and leverage them to accomplish their goals. My work revolves around an idea called the child as hacker—that symbolic programs provide the most compelling account of sophisticated mental representations and that learning is analogous to a particular style of programming called hacking. I want to understand how human programmers write code–the goals, activities, and tools they use to make code better–and apply these insights to better describe learning in children and adults.

Data and code for published and ongoing work is available upon request.

Publications

Under Review

  1. [15]    Rule, J. S., Piantadosi, S. T., & Tenenbaum, J. B. (under review). Learning as programming: Efficient search in models of human concept learning.
    @inproceedings{rule2022learning,
      title = {Learning as programming: {{Efficient}} search in models of human concept learning},
      author = {Rule, Joshua S and Piantadosi, Steven T and Tenenbaum, Joshua B},
      status = {under review}
    }
    

2022

  1. [14]    Goddu, M. K., Rule, J. S., Bonawitz, E., Gonik, A., & Ullman, T. (2022). Fun isn’t easy: Children optimize for difficulty when “playing for fun” vs. “playing to win” in a game design task [Poster and abstract]. Society for Research in Child Development’s Learning through Play and Imagination: Expanding Perspectives. Abstract
    @inproceedings{goddu2022learning3,
      title = {Fun isn’t easy: {{C}}hildren optimize for difficulty when ``playing for fun'' vs. ``playing to win'' in a game design task},
      author = {Goddu, Mariel K and Rule, Joshua S and Bonawitz, Elizabeth and Gonik, Alison and Ullman, Tomer},
      year = {2022},
      booktitle = {{{Society for Research in Child Development’s Learning through Play and Imagination: Expanding Perspectives}}},
      note = {Poster and abstract}
    }
    
  2. [13]    Goddu, M. K., Rule, J. S., Bonawitz, E., Gonik, A., & Ullman, T. (2022). Fun isn’t easy: Children optimize for difficulty when “playing for fun” vs. “playing to win” in a game design task [Poster and abstract]. Cognitive Development Society Abstract Book.
    @inproceedings{goddu2022learning2,
      title = {Fun isn’t easy: {{C}}hildren optimize for difficulty when ``playing for fun'' vs. ``playing to win'' in a game design task},
      author = {Goddu, Mariel K and Rule, Joshua S and Bonawitz, Elizabeth and Gonik, Alison and Ullman, Tomer},
      year = {2022},
      booktitle = {{{Cognitive Development Society}} Abstract Book},
      note = {Poster and abstract}
    }
    
  3. [12]    Goddu, M. K., Rule, J. S., Bonawitz, E., Gonik, A., & Ullman, T. (2022). Fun isn’t easy: Children optimize for difficulty when “playing for fun” vs. “playing to win” in a game design task [Talk and abstract]. Budapest CEU Conference on Cognitive Development Programs and Abstracts.
    @inproceedings{goddu2022learning,
      title = {Fun isn’t easy: {{C}}hildren optimize for difficulty when ``playing for fun'' vs. ``playing to win'' in a game design task},
      author = {Goddu, Mariel K and Rule, Joshua S and Bonawitz, Elizabeth and Gonik, Alison and Ullman, Tomer},
      year = {2022},
      booktitle = {{{Budapest CEU Conference on Cognitive Development}} Programs and Abstracts},
      note = {Talk and abstract}
    }
    

2021

  1. [11]    Rule, J. S., & Riesenhuber, M. (2021). Leveraging prior concept learning improves ability to generalize from few examples in computational models of human object recognition. Frontiers in Computational Neuroscience. Paper
    @article{rule2021leveraging,
      title = {Leveraging prior concept learning improves ability to generalize from few examples in computational models of human object recognition},
      author = {Rule, Joshua S. and Riesenhuber, Maximilian},
      year = {2021},
      journaltitle = {Frontiers in Computational Neuroscience}
    }
    

2020

  1. [10]    Rule, J. S., Piantadosi, S. T., & Tenenbaum, J. B. (2020). The child as hacker. Trends in Cognitive Sciences. Paper
    @article{rule2020child2,
      title = {The child as hacker},
      author = {Rule, Joshua S. and Piantadosi, Steven T. and Tenenbaum, Joshua B.},
      year = {2020},
      journaltitle = {Trends in Cognitive Sciences}
    }
    
  2. [9]    Rule, J. S. (2020). The child as hacker: Building more human-like models of learning [PhD thesis, MIT]. Paper Precis
    @phdthesis{rule2020child,
      title = {The child as hacker: {{Building}} more human-like models of learning},
      author = {Rule, Joshua S},
      year = {2020},
      institution = {MIT}
    }
    

2019

  1. [8]    Rule, J. S., Piantadosi, S. T., & Tenenbaum, J. B. (2019). Learning a novel rule-based conceptual system [Poster and abstract]. Proceedings of the Cognitive Science Society. Abstract Paper
    @inproceedings{rule2019learning,
      title = {Learning a novel rule-based conceptual system},
      author = {Rule, Joshua S and Piantadosi, Steven T and Tenenbaum, Joshua B},
      booktitle = {Proceedings of the {{Cognitive Science Society}}},
      year = {2019},
      note = {Poster and abstract}
    }
    

2018

  1. [7]    Rule, J., Schulz, E., Piantadosi, S. T., & Tenenbaum, J. B. (2018). Learning list concepts through program induction. Proceedings of the Cognitive Science Society. Paper
    @inproceedings{rule2018learning,
      title = {Learning list concepts through program induction},
      booktitle = {Proceedings of the {{Cognitive Science Society}}},
      author = {Rule, Joshua and Schulz, Eric and Piantadosi, Steven T. and Tenenbaum, Joshua B.},
      year = {2018}
    }
    

2015

  1. [6]    Rule, J., Dechter, E., & Tenenbaum, J. B. (2015). Representing and learning a large system of number concepts with Latent Predicate Networks. Proceedings of the Cognitive Science Society. Paper
    @inproceedings{rule2015representing,
      title = {Representing and learning a large system of number concepts with {{Latent Predicate Networks}}},
      booktitle = {Proceedings of the {{Cognitive Science Society}}},
      author = {Rule, Joshua and Dechter, Eyal and Tenenbaum, Joshua B},
      year = {2015}
    }
    
  2. [5]    Glezer, L. S., Kim, J., Rule, J., Jiang, X., & Riesenhuber, M. (2015). Adding words to the brain’s visual dictionary: Novel word learning selectively sharpens orthographic representations in the VWFA. Journal of Neuroscience, 35(12). Paper
    @article{glezer2015adding,
      title = {Adding words to the brain's visual dictionary: {{Novel}} word learning selectively sharpens orthographic representations in the {{VWFA}}},
      author = {Glezer, L. S. and Kim, J. and Rule, Joshua and Jiang, X. and Riesenhuber, M.},
      year = {2015},
      journaltitle = {Journal of Neuroscience},
      volume = {35},
      number = {12}
    }
    
  3. [4]    Dechter, E., Rule, J., & Tenenbaum, J. B. (2015). Latent Predicate Networks: Concept learning with probabilistic context-sensitive grammars [Poster and abstract]. Proceedings of the AAAI Spring Symposium Series. Abstract
    @inproceedings{dechter2015latent,
      title = {{{Latent Predicate Networks}}: {{Concept}} learning with probabilistic context-sensitive grammars},
      author = {Dechter, Eyal and Rule, Joshua and Tenenbaum, Joshua B},
      year = {2015},
      booktitle = {Proceedings of the  AAAI Spring Symposium Series},
      note = {Poster and abstract}
    }
    

2014

  1. [3]    Dechter, E., Rule, J., & Tenenbaum, J. B. (2014). Unsupervised learning of probabilistic programs with Latent Predicate Networks [Poster and abstract]. Proceedings of the NIPS Workshop on Probabilistic Programming. Abstract
    @inproceedings{dechter2014unsupervised,
      title = {Unsupervised learning of probabilistic programs with {{Latent Predicate Networks}}},
      author = {Dechter, Eyal and Rule, Joshua and Tenenbaum, Joshua B.},
      year = {2014},
      booktitle = {Proceedings of the NIPS Workshop on Probabilistic Programming},
      note = {Poster and abstract}
    }
    

2013

  1. [2]    Glezer, L. S., Kim, J. S., Rule, J., Jiang, X., & Riesenhuber, M. (2013). Novel word learning selectively sharpens orthographic representations in the VWFA [Poster and abstract]. Neuroscience 2013 Abstracts. Abstract
    @inproceedings{glezer2013novel,
      title = {Novel word learning selectively sharpens orthographic representations in the {{VWFA}}},
      author = {Glezer, Laurie S. and Kim, Judy S. and Rule, Joshua and Jiang, Xiong and Riesenhuber, Maximilian},
      year = {2013},
      booktitle = {Neuroscience 2013 Abstracts},
      note = {Poster and abstract}
    }
    

2009

  1. [1]    Sammons, M., Vydiswaran, V. G. V., Vieira, T., Johri, N., Chang, M.-W., Goldwasser, D., Srikumar, V., Kundu, G., Tu, Y., Small, K., Rule, J., Do, Q., & Roth, D. (2009). Relation alignment for textual entailment recognition. Proceedings of the Textual Alignment Conference. Paper
    @inproceedings{sammons2009relation,
      title = {Relation alignment for textual entailment recognition},
      author = {Sammons, Mark and Vydiswaran, V G Vinod and Vieira, Tim and Johri, Nikhil and Chang, Ming-Wei and Goldwasser, Dan and Srikumar, Vivek and Kundu, Gourab and Tu, Yuancheng and Small, Kevin and Rule, Joshua and Do, Quang and Roth, Dan},
      booktitle = {Proceedings of the Textual Alignment Conference},
      year = {2009}
    }