HI and HD signals in the real world

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Table of contents

Introduction
Expected utility hypothesis
Bayesian probabilities
Bayesian models of human inference
Online inference
Questions
Outline
1 Human online inference in the presence of temporal structure
Results
Behavioral task, and history-independent vs. history-dependent stimuli .
Learning rates adapt to the temporal statistics in the stimulus
The variability in subjects’ responses varies over the course of inference
A simple, approximate Bayesian model
Human repetition propensity
Discussion
HI and HD signals in the real world
Behavioral and neural responses in the presence of different temporal
statistics
Inference with HI and HD change points
Methods
Details of the behavioral task
Subjects
Details of the signal
Training runs
Empirical run-length
Details of approximate model
Model self-consistency
Statistical tests
Supplementary tables: statistical tests
Supplementary figure: data analysis excluding all occurrences of repetitions .
2 Cognitive models of human online inference
Results
Behavioral inference task
Optimal estimation: Bayesian update and maximization of expected reward
The optimal model captures qualitative trends in learning rate and repetition propensity
Impact of an erroneous belief on the temporal statistics of the signal
Impact of limited run-length memory
Does behavioral variability depend on the inference process?
Models with limited memory and variability in the inference step or the
response-selection step
Stochastic model with sampling in time and in state space: the particle
filter
Fitting models to experimental data favors sample-based inference
Discussion
Online Bayesian inference
Holding an incorrect belief on temporal statistics
Sampling versus noisy maximization
Stochastic inference and particle filters
Robustness of model fitting
Sample-based representations of probability
Methods
Bayesian update equation
Nodes model
Particle Filter
Model Fit
3 Sequential effects in the online inference of a Bernoulli parameter
Sequential effects
A framework of Bayesian inference under constraint
Predictability-cost models
The Bernoulli case
Inferring conditional probabilities
Representation-cost models
The Bernoulli observer
The conditional-probabilities observer
Discussion
Preliminary experimental results
Leaky integration
Variational approach to approximate Bayesian inference
Representation cost and the theory of rational inattention
Bibliography