Jakob Heinzle

This paper extends HGF models to jointly model prediction and confidence ratings in a predictive inference task, showing good fit and uncovering metacognitive alterations in psychopathology.

The paper tests a computational model of perceived control over breathing by analyzing data from a novel behavioral task, finding that perceived control depends on interoceptive outcomes and prediction errors.

The paper investigates layer-specific activation in the insula during interoceptive and exteroceptive attention tasks using fMRI and finds increased activity in upper cortical layers during interoceptive attention.

The paper applies Bayesian workflow to improve parameter and model identifiability in hierarchical Bayesian models using multivariate behavioural data from a reward learning task.

This study evaluates the test-retest reliability and validity of auditory MMN measured with OPM-MEG, finding good agreement with EEG/MEG results and reliable amplitude measures.

This study uses fMRI and a Bayesian model to show that the anterior insula is involved in thermoceptive predictions and prediction errors in humans.

The paper uses a computational model to show that GABAergic enhancement improves conflict adaptation but impairs response inhibition in cognitive tasks.

This paper demonstrates high-resolution single-shot spiral fMRI at 7T with sub-millimeter resolution, achieving excellent image quality and BOLD sensitivity with minimal blurring artifacts.

This paper introduces a new integration scheme for delay differential equations in DCMs, offering both speed and accuracy for electrophysiological data analysis.

This paper introduces thermodynamic integration for Bayesian model selection and averaging in dynamic causal models, demonstrating its feasibility with efficient implementation and parallel processing.

TAPAS provides open-source tools for computational assays in psychiatry to support differential diagnosis and treatment prediction through end-to-end pipelines.

This paper uses dynamic causal models to infer muscarinic receptor function from EEG data in rats, showing graded effects and predictive validity across pharmacological conditions.

This paper introduces regression dynamic causal modeling (rDCM) for resting-state fMRI, providing directional connectivity estimates and scalability to whole-brain networks.

This paper introduces a Hilbert-based method to estimate respiratory volume per unit time from bellows recordings, improving fMRI noise correction by better capturing atypical breathing events.

This paper presents a dynamic causal model of a two-layered cortical microcircuit using magnetoencephalography to study non-invasive layered computations.

The paper tests predictive coding theory by showing that different visual features elicit distinct prediction errors in specific brain regions during a visual mismatch task.

This paper introduces thermodynamic integration for dynamic causal modeling, showing it provides more robust log evidence estimates than variational Bayes in complex models.

This study investigates how task design affects antisaccade errors, finding that both inhibition failures and inaccurate voluntary action initiation contribute to errors in both asynchronous and synchronous cue conditions.

The paper introduces the SERIA model to explain antisaccade task errors and reaction times, showing it outperforms existing models by incorporating a late decision process.