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 |