Ulrik Beierholm

The paper argues that behavioral measures of multisensory integration are composite and misinterpreted, proposing a model-based inference pipeline to disentangle underlying factors.

This study extends Bayesian Causal Inference (BCI) to multidimensional sensory processing, showing it accurately predicts multisensory perception outcomes across varying temporal and numerical disparities.

This paper introduces a Bayesian inference model for perceptual clustering in auditory streaming, demonstrating its ability to explain experimental data and make novel predictions.

The paper introduces the BCI Toolbox, an open-source Python package for Bayesian causal inference modeling, and demonstrates its stability and reliability through parameter recovery tests.

The paper introduces a method to accurately estimate sensory precision and cue weightings in perceptual tasks with continuous responses, accounting for central tendency biases.

The paper examines how reward and dopamine affect vigor in younger and older humans, finding similar reward effects across age groups with weak dopamine receptor involvement.

This study shows that humans integrate past and current sensory signals to estimate uncertainty, challenging models that assume uncertainty is based only on current stimuli.

The paper tests whether Bayesian inference is used in a spatial localization task by examining if prior knowledge is transferred to a new likelihood, finding limited evidence for Bayesian transfer.

This study shows older adults maintain multisensory integration performance by slowing their responses despite noisier sensory processing.

The paper develops a Bayesian model for auditory streaming that infers the number of signal sources from sequential auditory cues without restricting the number of sources.