Cognitive Science Colloquium

Title: Decoding Meaning and Form: Dialogues Between and About Human and Artificial Minds Across Classrooms and Disciplines

Speakers

—Renata Geld is Associate Professor and Founding Director of the Center for Cognitive Science and Director of Doctoral Program in FL education, Faculty of Humanities and Social Sciences, University of Zagreb. She does research in cognitive semantics, cognitive science and creativity & innovation, creativity and conceptual integration, visual grammar and multimodal meaning construal, cognitive strategies and conceptual integration in L1 and L2.
—Dario Bojanjac Associate Professor at the University of Zagreb, Faculty of Electrical Engineering and Computing.
—Filip Turčinović is Research Assistant at the University of Zagreb.

Abstract

Creating authentic learning environments, enhanced by real-world engagement or immersive, technology-driven simulations, means creating a space of active participation, teamwork, diverse perspectives, “learning from the future” (Pechl, 2023), “what-ifs,” and conceptual blends (Fauconnier & Turner, 2002), reflection, and the meaningful application of knowledge. These environments have the potential to prepare students for the VUCA (volatile, unpredictable, complex, ambiguous) world, where human and artificial minds are increasingly intertwined. This unavoidable entanglement calls for collaboration across disciplines, especially a meaningful conversation between the humanities and STEM. In my effort to create an authentic new learning environment, my students have produced challenging questions—many of which have emerged from their discussions on language and construction of meaning, meaning and form, intentionality and understanding, humans and machines, cognition and computation:

—Can meaning exist independent of form, or is the structure of language and thought inseparable from how we understand the world?
—Do we always want systems that replicate the human division of the world, or might AI offer new ways of interpreting meaning that we haven't yet imagined?
—If a machine can “understand” meaning, does it truly grasp human experience, or is it just decoding patterns we’ve created?
—What role does imagination play in human cognition, and could machines, in some way, enhance or even replicate this quality?
—How might we rethink our approaches to learning if we accept that both human cognition and computational models rely on abstract forms but interpret them very differently?
—Is there a limit to how well computational models can simulate human cognition, or are we getting closer to truly artificial understanding?
—How do we explain language acquisition: Is it driven by innate mechanisms, or can it be fully understood through interaction and learning from the environment, as in current AI models?
—Can computational models of language acquisition (like connectionist models) truly replicate the way humans acquire language, or must we look beyond data-driven approaches to capture the complexity of human learning?
—Are there limits to how well artificial systems can model second language acquisition (SLA)?
This talk is intended as a dialogue about the relationship between humans and machines, as well as the maturity of our university classrooms in addressing this relationship in meaningful, human-centered ways.

References

—Fauconnier, G., & Turner, M. (2002). The way we think: Conceptual blending and the mind's hidden complexities. Basic Books.
—Peschl, M.F. (2023). "Learning from the future as a novel paradigm for integrating organizational learning and innovation", The Learning Organization, Vol. 30 No. 1, pp. 6-22. https://doi.org/10.1108/TLO-01-2021-0018

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