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Poster A31 in Poster Session A - Tuesday, August 6, 2024, 4:15 – 6:15 pm, Johnson Ice Rink
Source Invariance and Probabilistic Transfer: A Testable Theory of Probabilistic Neural Representations
Nikolaus Kriegeskorte1 (), Samuel Lippl2, Raphael Gerraty3, John Morrison4; 1Columbia University
As animals interact with their environments, they must infer properties of their surroundings. Some animals, including humans, can represent uncertainty about those properties. But when, if ever, do they use probability distributions to represent their uncertainty? It depends on which definition we choose. In this paper, we argue that existing definitions are inadequate because they are untestable. We then propose our own definition, which defines probabilistic representations in terms of two properties: (1) invariance to the source of uncertainty and (2) consistency in how this uncertainty is taken into account by downstream computations across multiple tasks.
Keywords: probabilistic representation task transfer uncertainty representation Bayesian inference