What counts as understanding? (Lupyan)

Gary Lupyan, University of Wisconsin-Madison, Feb 22, 2024

ABSTRACT: The question of what it means to understand has taken on added urgency with the recent leaps in capabilities of generative AI such as large language models (LLMs). Can we really tell from observing the behavior of LLMs whether underlying the behavior is some notion of understanding? What kinds of successes are most indicative of understanding and what kinds of failures are most indicative of a failure to understand? If we applied the same standards to our own behavior, what might we conclude about the relationship between between understanding, knowing and doing?

Gary Lupyan is Professor of Psychology at the University of Wisconsin-Madison. His work has focused on how natural language scaffolds and augments human cognition, and attempts to answer the question of what the human mind would be like without language. He also studies the evolution of language, and the ways that language adapts to the needs of its learners and users.

References

Liu, E., & Lupyan, G. (2023). Cross-domain semantic alignment: Concrete concepts are more abstract than you think. Philosophical Transactions of the Royal Society B. DOI: 10.1098/rstb.2021-0372

Duan, Y., & Lupyan, G. (2023). Divergence in Word Meanings and its Consequence for Communication. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 45, No. 45)

van Dijk, B. M. A., Kouwenhoven, T., Spruit, M. R., & van Duijn, M. J. (2023). Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding (arXiv:2310.19671). arXiv.

Aguera y Arcas, B. (2022). Do large language models understand us? Medium.

Titus, L. M. (2024). Does ChatGPT have semantic understanding? A problem with the statistics-of-occurrence strategy. Cognitive Systems Research, 83.

Pezzulo, G., Parr, T., Cisek, P., Clark, A., & Friston, K. (2024). Generating meaning: Active inference and the scope and limits of passive AI. Trends in Cognitive Sciences, 28(2), 97–112.

Learning Categories by Creating New Descriptions (Goldstone)

Robert Goldstone, Indiana University, February 1, 2024

ABSTRACT: In Bongard problems, problem-solvers must come up with a rule for distinguishing visual scenes that fall into two categories. Only a handful of examples of each category are presented. This requires the open-ended creation of new descriptions. Physical Bongard Problems (PBPs) require perceiving and predicting the spatial dynamics of the scenes. We compare the performance of a new computational model (PATHS) to human performance. During continual perception of new scene descriptions over the course of category learning, hypotheses are constructed by combining descriptions into rules for distinguishing the categories. Spatially or temporally juxtaposing similar scenes promotes category learning when the scenes belong to different categories but hinders learning when the similar scenes belong to the same category.

Robert Goldstone is a Distinguished Professor in the Department of Psychological and Brain Sciences and Program in Cognitive Science at Indiana University. His research interests include concept learning and representation, perceptual learning, educational applications of cognitive science, and collective behavior.

Goldstone, R. L., Dubova, M., Aiyappa, R., & Edinger, A. (2023). The spread of beliefs in partially modularized communities. Perspectives on Psychological Science, 0(0). https://doi.org/10.1177/17456916231198238

Goldstone, R. L., Andrade-Lotero, E., Hawkins, R. D., & Roberts, M. E. (2023). The emergence of specialized roles within groups. Topics in Cognitive Science, DOI: 10.1111/tops.12644.

Weitnauer, E., Goldstone, R. L., & Ritter, H. (2023). Perception and simulation during concept learning. Psychological Review, https://doi.org/10.1037/rev0000433.

Enactivist Symbol Grounding: From Attentional Anchors to Mathematical Discourse (Abrahamson)

Dor Abrahamson , Faculty of Education, UC-Berkeley, 26-Oct 2023

VIDEO

ABSTRACT: According to the embodiment hypothesis knowledge is the capacity for perceptuomotor enactment, situated in the world as much as in the body: a way of engaging the environment in anticipation of accomplishing interactions. What does this mean for educational practice? What is the embodiment or enactment of abstract ideas, like justice, photosynthesis, or algebra? What is the teacher’s role in embodied designs for learning? I will describe my lab’s educational design-based collaborative research on mathematical learning, and how we came to view in the analysis and promotion of content learning. I will describe how students spontaneously generate perceptual solutions to motor-control problems. These then become verbal through adopting symbolic artifacts provided by the teacher. This approach can also help students with diverse sensorimotor capacities.

Dor Abrahamson is Professor in the Graduate School of Education at the University of California Berkeley, where he established the Embodied Design Research Laboratory devoted to pedagogical technologies for teaching and learning mathematics. He is particularly interested in relations between learning to move in new ways and learning mathematicaal concepts. His research draws on embodied cognition, dynamic systems theory, and sociocultural theory.

Abrahamson, D., & Sánchez-García, R. (2016). Learning is moving in new ways: The ecological dynamics of mathematics education. Journal of the Learning Sciences, 25(2), 203-239.

Abrahamson, D. (2021). Grasp actually: An evolutionist argument for enactivist mathematics education. Human Development, 65(2), 1–17. https://doi.org/10.1159/000515680

Shvarts, A., & Abrahamson, D. (2023). Coordination dynamics of semiotic mediation: A functional dynamic systems perspective on mathematics teaching/learning. In T. Veloz, R. Videla, & A. Riegler (Eds.), Education in the 21st century [Special issue]. Constructivist Foundations, 18(2), 220–234. https://constructivist.info/18/2