CiNet Monthly Seminar

December 11 (Fri.), 2026
13:00-15:00 (JST)
at the Conference Room in the CiNet bldg.

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Talk title: Comparing Representational Geometries Across Tasks, Brain Regions, and AI Systems

Hakwan Lau
Center Director
Institute for Basic Science, South Korea

Abstract:
Whether two visual objects appear similar depends on the feature dimension under consideration. An apple may seem more similar to an orange than to a fire engine because both are fruits, whereas a comparison based on colour could yield a different pattern. For this reason, similarity judgments are often regarded as ill-posed unless the relevant dimension is specified. We show, however, that there is a principled and natural sense in which the similarity of two stimuli, in terms of subjective appearance, can be defined: by an observer’s own psychophysical sensitivity to discriminating them. Building on this finding, we introduce methods for recovering sensitivity-based representational geometries from neural data, capitalizing on both the phenomenon of repetition suppression and the method of multivariate pattern analysis. We show that such representations can be identified across brain regions at multiple levels of the visual-processing hierarchy. Comparing these geometries across tasks, cortical regions, and artificial-intelligence systems may provide a more precise way to assess whether their underlying computations are aligned.

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Talk title: A metacognitive architecture for credit assignment in the human brain

Aurelio Cortese
Associate Professor
Institute for Basic Science, South Korea

Abstract:
In 1846 Le Verrier saw Uranus depart from its Newtonian orbit. He assigned the error to the model, not the telescope, and found Neptune. Every agent with a world model faces this problem: an error is useful only if assigned to the right level. I will present work on how the brain uses metacognitive insight into each level, the state of the world and the percept, to decide whether the model guiding action was wrong or the evidence misread. In a dual-confidence task participants tracked uncertainty over both, and after an error the two confidences decided, in opposite directions, whether to revise the inferred state (or stay with the current strategy). We developed a noisy Bayesian observer model that apportions each error by the relative reliability of the two levels, further showing that participants with lower metacognitive ability also display noisier inference and error allocation processes. MEG, fMRI, and intracranial data show the brain divides the task space hierarchically and performs this arbitration via a prefrontal-congulate circuit, with computations at specific frequencies. Metacognition, I will argue, is how humans audit their world model and learn from failure.

Host : Rob Mok