1. What are the contributions in "Generalization as diffusion: human function learning on graphs" ?
The authors adapt a Bayesian framework for function learning to graph structures, and propose that people perform generalization by assuming that the observed function values diffuse across the graph.. The authors evaluate this model by asking participants to make predictions about passenger volume in a virtual subway network.. The model captures both generalization and confidence judgments, and provides a quantitatively superior account relative to several heuristic models.. Their work suggests that people exploit graph structure to make generalizations about functions in complex discrete spaces.
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2. What future works have the authors mentioned in the paper "Generalization as diffusion: human function learning on graphs" ?
Given that the GP framework can be used to compare how people learn functions over different ( i. e., spatial and conceptual ) domains ( Wu, Schulz, Garvert, Meder, & Schuck, 2018 ), comparing functional inference over conceptual and spatial graphs seems like promising extension for future studies.. While this may be a reasonable assumption in problems such as navigating a subway network where one can simply look at a map, this is not always the case.. Thus, the connection between the SR and the diffusion kernel presents a promising avenue for incorporating a plausible process model of structure learning.
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