Bayesian Accounts of the Central Tendency of Judgment

Session 3: Critical Data to Arbitrate between Models

What are the key predictions arising from theoretical models that should be tested experimentally? Can they provide, and have they already provided evidence to arbitrate between competing models, supporting some models and refuting some others?

Bayesian Accounts of the Central Tendency of Judgment

Session 3: Critical Data to Arbitrate between ModelsChair: Florent Meyniel

Documents et médias

Intervenant(s)

Pascal Mamassian

Événements

Opening

On the Adaptive Fitness of the Bayesian Brain

Circular Inference in Schizophrenia

Learning-based cross-modal suppression of ongoing activity in primary cortical areas and the descrip…

Sampling-Based Probabilistic Inference through Neural and Synaptic Dynamics

Brainstem Modulation of Cortical Inference Processes

A Computational Analysis of the Bayesian Brain

Interplay of Synaptic Plasticity and Probabilistic Theories

A Rational Sense of Confidence during Probabilistic Inference in the Human Brain

Empirical Support for Predictive Coding in Sensory Circuits?

Probabilistic Inference and Human Executive Control

Bayesian Accounts of the Central Tendency of Judgment

Hierarchical Predictive Coding and Adaptive Gain Control

Sampling-Based Representation of Uncertainty: Time is of Essence

Gaps between the Observed Stochasticity of Neural Systems and Probabilistic Inference Models

Joining the Dots in theoretical neurobiology

Voir aussi

Session 3: Critical Data to Arbitrate between Models
Chair: Florent Meyniel