Bridging Levels of Analysis for Probabilistic Models of Cognition

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The Human Kernel (2015, background): “We assume the predictions y∗ are samples from the learner’s posterior distribution over possible functions, following results showing that human inferences and judgments resemble posterior samples across a wide range of perceptual and decision-making tasks [27, 28, 29].”
Amortized Inference in Probabilistic Reasoning (2014, methodology): “To show that our results are indeed expected from such algorithms , we provide here one concrete example of how a sampling algorithm (see Griffiths et al., 2012; Vul et al., in press) can give rise to the correlations observed in this 0 10 20 30 40 0 0.2 0.4 0.6 0.8 1 Number of samples Correlation…”