Publications 

• Shultz, T. R., & Nobandegani, A. S. (in press). A Computational Model of Infant Learning and Reasoning with Probabilities. Psychological Review.
  

• Nobandegani, A. S., & Shultz, T. R. (in press). Computational Approaches to Cognitive Development: Bayesian and Artificial Neural Network Models, In O. Houd and G. Borst (Eds.), The Cambridge Handbook of Cognitive Development. Cambridge University Press.

• Shultz, T. R., Nobandegani, A. S., & Fahlman, S. E. (in press). Cascade-Correlation Neural Networks, In C. Sammut and G. Webb (Eds.), The Encyclopedia of Machine Learning and Data Science. Springer.

• Shultz, T. R., & Nobandegani, A. S. (in press). Computational Models of Developmental Psychology, In R. Sun (Ed.), The Cambridge Handbook of Computational Cognitive Science. Cambridge University Press.

• Nobandegani, A. S., Shultz, T. R., & Dubé, L. (2021). A Unified, Resource-Rational Account of the Allais and Ellsberg Paradoxes. In Proc. of the 43rd Annual Conference of Cognitive Science Society (CogSci).
  

• Lizotte, M., Nobandegani, A. S., & Shultz, T. R. (2021). Emotions in Games: Toward a Unified Process-Level Account. In Proc. of the 43rd Annual Conference of Cognitive Science Society (CogSci).
  

• Nobandegani, A. S., Destais, C., & Shultz, T. R. (2020). A Resource-Rational Process Model of Fairness in the Ultimatum Game. In Proc. of the 42nd Annual Conference of Cognitive Science Society (CogSci).
  

• Nobandegani, A. S., & Shultz, T. R. (2020). A Resource-Rational Mechanistic Account of Human Coordination Strategies. In Proc. of the 42nd Annual Conference of Cognitive Science Society (CogSci).
  

• Shultz, T. R., & Nobandegani, A. S. (2020). Probability without Counting and Dividing: A Fresh Computational Perspective. In Proc. of the 42nd Annual Conference of Cognitive Science Society (CogSci).
  

• Nobandegani, A. S., & Shultz, T. R. (2020). A Resource-Rational Process-Level Account of the St. Petersburg Paradox. Topics in Cognitive Science.
  

• Nobandegani, A. S., & Shultz, T. R. (2020). The St. Petersburg Paradox: A Fresh Algorithmic Perspective. In Proc. of the 34th Conference on Artificial Intelligence (AAAI).
  

• Nobandegani, A. S., & Shultz, T. R. (2019). Toward a Formal Science of Heuristics. In Proc. of the 41st Annual Conference of Cognitive Science Society (CogSci).
  

• Forbus, K., Gentner, D., Laird, J. E., Shultz, T. R., Nobandegani, A. S., & Thagard, P. (2019). How Does Current Artificial Intelligence Stack Up Against Human Intelligence? Symposium. The 41st Annual Conference of Cognitive Science Society (CogSci).
  

• Nobandegani, A. S., da Silva Castanheira, K., Shultz, T. R., & Otto, A. R. (2019). A Resource-Rational Mechanistic Approach to One-shot Non-cooperative Games: The Case of Prisoner’s Dilemma. In Proc. of the 41st Annual Conference of Cognitive Science Society (CogSci).
  

• da Silva Castanheira, K., Nobandegani, A. S., & Otto, A. R. (2019). Sample-based Variant of Expected Utility Explains Effects of Time Pressure and Individual Differences in Processing Speed on Risk Preferences. In Proc. of the 41st Annual Conference of Cognitive Science Society (CogSci).
  

• Nobandegani, A. S., Campoli, W., & Shultz, T. R. (2019). Bringing Order to the Cognitive Fallacy Zoo. In Proc. of the 17th International Conference on Cognitive Modeling (ICCM).
  * Presented as a poster @ CogSci 2019

• Nobandegani*, A. S., da Silva Castanheira*, K., O’Donnell, T. J., & Shultz, T. R. (2019). On Robustness: An Undervalued Dimension of Human Rationality. In Proc. of the 17th International Conference on Cognitive Modeling (ICCM). *contributed equally
  * Presented as a poster @ CogSci 2019
  

• Nobandegani*, A. S., da Silva Castanheira*, K., Shultz, T. R., & Otto, A. R. (2019). Decoy Effect and Violation of Betweenness in Risky Decision Making: A Resource-Rational Mechanistic Account. In Proc. of the 17th International Conference on Cognitive Modeling (ICCM). *contributed equally
  * Presented as a poster @ CogSci 2019
  

• Yu, L., Nobandegani, A. S., & Shultz, T. R. (2019). Neural Network Modeling of Learning to Actively Learn. In Proc. of the 17th International Conference on Cognitive Modeling (ICCM).
  * Presented as a poster @ CogSci 2019
  

• Nobandegani, A. S., & Psaromiligkos, I. N. (2018). A Rational Distributed Process-level Account of Independence Judgment. In Proc. of the 40th Annual Conference of Cognitive Science Society (CogSci).
  

• Nobandegani, A. S., da Silva Castanheira, K., Otto, A. R., & Shultz, T. R. (2018). Over-representation of Extreme Events in Decision-Making: A Rational Metacognitive Account. In Proc. of the 40th Annual Conference of Cognitive Science Society (CogSci).
  

• Nobandegani, A. S., & Shultz, T. R. (2018). Example Generation under Constraints Using Cascade Correlation Neural Nets. In Proc. of the 40th Annual Conference of Cognitive Science Society (CogSci).

• Nobandegani, A. S. (2017). The Minimalist Mind: On Minimality in Learning, Reasoning, Action, & Imagination, PhD Dissertation, McGill University. (link)

• Nobandegani, A. S., & Shultz, T. R. (2017). Converting Cascade Correlation Neural Nets into Probabilistic Generative Models. In Proc. of the 39th Annual Conference of Cognitive Science Society (CogSci).

• Nobandegani, A. S., & Psaromiligkos, I. N. (2017). The Causal Frame Problem: An Algorithmic Perspective. In Proc. of the 39th Annual Conference of  Cognitive Science Society (CogSci).
  

• Nobandegani*, A. S., Kabbara*, J., & Psaromiligkos, I. N. (2017). The Relevance Effect: Exploiting Bayesian Networks to Improve Supervised Learning. In Proc. of the 30th International Joint Conference on Neural Networks (IJCNN). *contributed equally

• Nobandegani, A. S., & Psaromiligkos, I. N. (2015). Probabilistic Structural Controllability in Causal Bayesian Networks. arXiv preprint arXiv:1512.01885.

• Nobandegani, A. S., & Psaromiligkos, I. N. (2015). Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammar. In Proc. of the 31st Conference on Uncertainty in Artificial Intelligence (UAI).
  

• Nobandegani, A. S. (2011). Spectrum Sensing and Access Strategies for Markovian Primary Users, Master’s Thesis, McGill University. (link)

Peer-Reviewed Abstracts & Workshop Papers 

• da Silva Castanheira*, K., Nobandegani*, A. S., Shultz, T. R., & Otto, A. R. (2019). Contextual Effects in Value-Based Decision Making: A Resource-Rational Mechanistic Account [Abstract]. In Proc. of the 41st Annual Conference of Cognitive Science Society (CogSci). *contributed equally
  

• Destais*, C., Nobandegani*, A. S., & Shultz, T. R. (2019). An Expectation-based Model of Fairness in the Ultimatum Game. Workshop on Moral Decision Making (MoDeM). The 4th Conference on Reinforcement Learning and Decision Making (RLDM). *contributed equally

• Nobandegani, A. S., da Silva Castanheira, K., Shultz, T. R., & Otto, A. R. (2019). A Resource-Rational Model of Cooperation in One-Shot Prisoner’s Dilemma. Workshop on Moral Decision Making (MoDeM). The 4th Conference on Reinforcement Learning and Decision Making (RLDM).

• Nobandegani, A. S., da Silva Castanheira, K., Shultz, T. R., & Otto, A. R. (2019). The St. Petersburg Paradox: A Resource-Rational Process-Level Account. The 4th Conference on Reinforcement Learning and Decision Making (RLDM).

• Nobandegani, A. S., da Silva Castanheira, K., Shultz, T. R., & Otto, A. R. (2019). A Resource-Rational, Process-Level Explanation of Cooperation in One-Shot Prisoner’s Dilemma. The 4th Conference on Reinforcement Learning and Decision Making (RLDM).

• da Silva Castanheira, K., Nobandegani, A. S., & Otto, A. R. (2019). Modelling Effect of Time Pressure on Risk Preferences using Sample-Based Variant of Expected Utility. The 4th Conference on Reinforcement Learning and Decision Making (RLDM).

• Nobandegani, A. S., da Silva Castanheira, K., Shultz, T. R., & Otto, A. R. (2019). A Resource-Rational Process Model of Decoy Effect in Risky Choice. The 4th Conference on Reinforcement Learning and Decision Making (RLDM).