Michael Woodford

The paper shows that perceptual imprecision in number sense depends on task context and prior distribution, suggesting endogenous determination of perceptual noise.

The paper shows that error distributions in behavioral tasks are non-Gaussian due to the geometry of the encoding manifold, and applies this to visual short-term memory tasks with two parameters.

The paper shows that imprecise counting of observations in averaging tasks leads to primacy and recency effects, modeled by a Bayesian central tendency mechanism.

This paper introduces a framework combining encoding and decoding in neural populations using variational autoencoders, showing that models with broad tuning curves achieve accurate stimulus reconstruction and generalization.

The paper shows that individual risk aversion in financial decisions stems from noisy perception of numerical magnitudes rather than subjective valuation.

This study develops a framework for understanding decision-making by modeling how humans efficiently sample information and make decisions under noise and limited data.

This paper introduces a variational auto-encoder model of neural coding that adapts to environmental statistics and explains perceptual discrimination and neural tuning curves.

The paper proposes an information-theoretic model of subjective value coding that explains variability in value reports and preference choices, and predicts a new preference illusion validated with empirical data.