Bayesian inference
Bayesian inference

Perception is not passive; the brain constantly combines prior knowledge with new sensory evidence to form beliefs about the world. Our work examines prediction errors, prior updating, and computational models of this inferential process.

6 papers • 6 posts

Posts

Curious Minds
When distraction changes rapidly, we take more care before responding
Nan Qiu and colleagues report in PLOS Computational Biology that rapidly changing distractor presence is associated with more cautious responding. A pooled increase in gaze capture is no longer …
Oct 26, 2026
Research
Domain-specific vs domain-general prediction errors
Plank and colleagues find in Imaging Neuroscience that adults with ASD, ADHD and neurotypical controls show no differences in prediction-error signals for the colors and emotions of unattended faces, …
Oct 24, 2025
Research
Atypical Prior Updating in ASD
In a duration reproduction task, individuals with ASD updated their prior expectations more strongly from each new sensory input than controls, especially in volatile settings, challenging the …
Feb 21, 2025
Research
Reference induced biases in late visual processing
Yannan Su et al. showed that an orientation reference presented after an ensemble stimulus still repels reproduced orientations, especially in a dual task, and an encoding-decoding model with …
Oct 12, 2023
Research
Perceptual biases and Individual beliefs
A two-state Bayesian model from Glasauer and Shi explains why people differ in central tendency and sequential biases: implicit beliefs about temporal continuity set how much each person leans on …
Jun 24, 2022
Research
Inter-trial effects in priming of pop-out: Comparison of computational updating models
Evidence-accumulation models with competing updating rules, fitted to priming-of-pop-out data, trace color and position priming to a shared weight resource setting accumulation rate, and response …
Sep 7, 2021