Introduction

This is a collection for articles and resources relevant to AI + Psychology.

Research shows that advanced AI language models, like GPT-4, can perform better than majority of human adults on complex tasks, such as the GRE and LSAT exams, which are typically faced by highly educated individuals at te top of the pyramid. However, these AI models still struggle with simple, everyday tasks that require common sense, like understanding unspoken thoughts and feelings, which even human babies can achieve. It seems that AI and humans follow quite contradictory developmental timelines: what's difficult for one is often easy for the other. Thus, it is interesting to investigate deeper in the intersection of Artificial Intelligence and Psychology to unreveal the following questions:

  • What are the differences between mechanical minds and human minds?
  • How can we build computational modelling for human mental states and apply them to building AI models?
  • How can we probe the psychology of models?
Publication Year Author Title Publication Title Url
2020 Lieder, Falk; Griffiths, Thomas L. Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources Behavioral and Brain Sciences https://www.cambridge.org/core/product/identifier/S0140525X1900061X/type/journal_article
2015 Griffiths, Thomas L.; Lieder, Falk; Goodman, Noah D. Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic Topics in Cognitive Science https://onlinelibrary.wiley.com/doi/abs/10.1111/tops.12142
2023 Richard Shiffrina,and Melanie Mitchell Probing the psychology of AI models commentary https://www.pnas.org/doi/10.1073/pnas.2300963120
2023 Binz, Marcel; Schulz, Eric Using cognitive psychology to understand GPT-3 Proceedings of the National Academy of Sciences https://www.pnas.org/doi/10.1073/pnas.2218523120
2005 Waldmann, Michael R.; Hagmayer, York Seeing Versus Doing: Two Modes of Accessing Causal Knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition http://doi.apa.org/getdoi.cfm?doi=10.1037/0278-7393.31.2.216
2015 Lake, B. M.; Salakhutdinov, R.; Tenenbaum, J. B. Human-level concept learning through probabilistic program induction Science https://www.sciencemag.org/lookup/doi/10.1126/science.aab3050
2012 MICHAEL C. FRANK AND NOAH D. GOODMAN Predicting Pragmatic Reasoning in Language Games Science https://www.science.org/doi/10.1126/science.1218633
2007 Spelke, Elizabeth S.; Kinzler, Katherine D. Core knowledge Developmental Science https://onlinelibrary.wiley.com/doi/10.1111/j.1467-7687.2007.00569.x
2023 Chalmers, David J. Could a Large Language Model be Conscious? http://arxiv.org/abs/2303.07103
1999 Georgeff, Michael; Pell, Barney; Pollack, Martha; Tambe, Milind; Wooldridge, Michael The Belief-Desire-Intention Model of Agency Intelligent Agents V: Agents Theories, Architectures, and Languages http://link.springer.com/10.1007/3-540-49057-4_1
2016 Lake, Brenden M.; Ullman, Tomer D.; Tenenbaum, Joshua B.; Gershman, Samuel J. Building Machines That Learn and Think Like People Behavioral and brain sciences http://arxiv.org/abs/1604.00289
2011 Perfors, Amy; Tenenbaum, Joshua B.; Griffiths, Thomas L.; Xu, Fei A tutorial introduction to Bayesian models of cognitive development Cognition https://www.sciencedirect.com/science/article/pii/S001002771000291X
2023 Ziqiao Ma, Jacob Sansom, Run Peng, Joyce Chai Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models EMNLP https://arxiv.org/abs/2310.19619
2023 Grgur Kovač, Rémy Portelas, Peter Ford Dominey, Pierre-Yves Oudeyer The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents Arxiv https://arxiv.org/abs/2307.07871