Computational cognitive modeling - Spring 2022

NYU PSYCH-GA 3405.004 / DS-GA 1016.003

This project is maintained by brendenlake

Final Project Ideas

Here is a list of final project ideas organized by topic. For many of these ideas, a first-rate project would be a novel contribution to research in computational cognitive modeling. In the list below, each bullet point is a separate project idea.

Neural networks - Memory

References:
McClelland, J. L. (1981). Retrieving general and specific information from stored knowledge of specifics. In Proceedings of the third annual meeting of the cognitive science society.

Probabilistic graphical models - Memory

References:
McClelland, J. L. (1981). Retrieving general and specific information from stored knowledge of specifics. In Proceedings of the third annual meeting of the cognitive science society. McClelland, J. L. (2013). Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review. Frontiers in psychology, 4, 503.

Neural networks - Semantic Cognition

References
McClelland, J. L., & Rogers, T. T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4(4), 310.

Neural networks - Language

References
Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. Radford et al., (2019). Language Models are Unsupervised Multitask Learners. arXiv preprint.

Neural networks - visual representations

References
Lake, B. M., Zaremba, W., Fergus, R. and Gureckis, T. M. (2015). Deep Neural Networks Predict Category Typicality Ratings for Images. In Proceedings of the 37th Annual Conference of the Cognitive Science Society. 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.

Bayesian modeling / Probabilistic programming - Number game

References
Gerstenberg, T., & Goodman, N. (2012). Ping Pong in Church: Productive use of concepts in human probabilistic inference. In Proceedings of the Annual Meeting of the Cognitive Science Society.

Bayesian modeling – Categorical perception

References
Feldman, N. H., & Griffiths, T. L. (2007). A rational account of the perceptual magnet effect. In Proceedings of the Annual Meeting of the Cognitive Science Society. (http://ling.umd.edu/~nhf/papers/PerceptualMagnet.pdf)

Reinforcement learning and Deep Q-Learning

References

The OpenAI Gym: https://gym.openai.com/envs/#atari

Dubey, R., Agrawal, P., Pathak, D., Griffiths, T. L., & Efros, A. A. (preprint). Investigating human priors for playing video games. In Proceedings of the 35th International Conference on Machine Learning (ICML 2018). https://rach0012.github.io/humanRL_website/ (paper and project website)

Decision Making

References
The Choice Prediction Challenge website: https://cpc-18.com

Plonsky, Ori and Erev, Ido and Hazan, Tamir and Tennenholtz, Moshe, Psychological Forest: Predicting Human Behavior (May 19, 2016). Available at SSRN: https://ssrn.com/abstract=2816450 or http://dx.doi.org/10.2139/ssrn.2816450

Categorization and Category Learning

Contribute to open science! As mentioned in the lecture on categorization there are a variety of different theories of how people learn categories and concepts from examples, and many of these models draw from similar approaches to machine classification. Currently there is a community-led effort to implement all existing category learning models in R so they can be simultaneously compared to the same sets of human data patterns. While the project is well developed and documented there are many example models which have not yet been implement (some of which are actually somewhat easy). One nice final project would be to read one of the papers listed below which describe a famous formal model of human categorization and implement it for submission to the catlearn R package. This is best for a group with some expertise in R (as opposed to python). If your group makes a report showing how this model does on the existing data set in catlearn it would make a nice final paper and by making a pull request against the catlearn package your work for class might live forever to help advance science! You could also choose to implement these models in python in which case you could still make an impact by helping to verify the previous results (see below).

References:

Catlearn R pacakage: https://ajwills72.github.io/catlearn/

Examples:

Replicate and verify!

Psychology is largely an empirical science and thus findings need to be independently replicated before they should be widely accepted. This is true for computational cognitive modeling as well. A line of interesting final projects would be to pick a recent computational modeling paper and to re-implement and verify the results reported by the authors. In doing this exercise you might come up with your own ideas about a feature to alter or change in their simulations that might be interesting. A couple good sources for papers would be to look at the titles from the most recent Proceedings of the Cognitive Science society or a new journal called Computational Brain and Behavior.

References