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Research

Probabilistic models for integrating theory and experiment

Probabilistic models for integrating theory and experiment

The mission of the Computational Cognitive Neuroscience lab is to formulate quantitative theories of human brain function and to empirically validate these using non-invasive functional neuroimaging methods (EEG and fMRI). Our current work clusters in four project areas that are fundamentally related by the notions of probabilistic modelling and inference.

The neural dynamics of decision making under uncertainty

Human decision making often requires planning processes that involve the anticipation, evaluation, and comparison of uncertainty imbued state-action-reward contingencies. Such planning processes are fundamental to decision problems which are referred to as “goal-directed”. In this project cluster, we combine sequential decision paradigms with artificial intelligence agent models and neuroimaging to study the neural mechanisms of goal-directed choice behavior in humans.

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Neurcomputational mechanisms of sequential decison making under uncertainty

Computational mechanisms of state-action-reward contingency learning under perceptual uncertainty

A perceptual decision making EEG/fMRI data set

A normative inference approach for optimal sample sizes in decisions from experience

EEG-fMRI based information theoretic characterization of the human perceptual decision system

Perceptual decisions formed by accumulation of audiovisual evidence in prefrontal cortex

Flexible coding for categorical decisions in the human brain

The neural dynamics of statistical learning

The ability to infer the dynamic statistical properties of the environment is a hallmark of cortical function and a necessary precondition for allostatic behaviour. In this project cluster, we investigate the degree to which these neural mechanisms can be described by probabilistic inference schemes. To this end, we combine behavioural experimentation with non-invasive brain imaging and computational probabilistic modelling.

  • Related Publications

A normative inference approach for optimal sample sizes in decisions from experience

Evidence for neural encoding of Bayesian surprise in human somatosensation

Probabilistic EEG-fMRI analysis and integration

We have previously used a set of information-theoretic functionals defined on a “model-free” probabilistic model to study the correlative structure of simultaneous EEG-fMRI and behavioural data features to arbitrary order. In this work, we could show that combining EEG and fMRI time-domain features by quantifying the information in their joint distribution is more informative about external stimuli than treating each modality in isolation. In more recent work, we aim for a “model-based” approach that embeds delay differential equation systems describing the interaction between cortical units in fully probabilistic forward models for EEG-fMRI.

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Power, positive predictive value, and sample size calculations for random field theory-based fMRI inference

Random field theory-based p-values: a review of the SPM implementation

Computing integrated information

Reliability of information-based integration of EEG and fMRI data: a simulation study

Information theoretic approaches to functional neuroimaging

Voxel-wise information theoretic EEG-fMRI feature integration

An information theoretic approach to EEG-fMRI integration of visually evoked responses

Variational Bayesian methods for stochastic time-series analysis.

Variational Bayes is a deterministic-approximate inference framework for probabilitistic models. In the brain imaging field, variational Bayes enjoys widespread popularity as a general theory of brain function and as an inversion scheme for models of functional neural connectivity under the labels "Free Energy Principle" and "Dynamic Causal Modelling", respectively. In this project cluster, we explore the analytical underpinnings and qualitative properties of variational Bayesian methods.

  • Related Publications

Variational Bayesian parameter estimation techniques for the general linear model

Probabilistic delay differential equation modelling of event-related potentials

A tutorial on variational Bayes for latent linear stochastic time-series models