Context tree selection for functional data
It has been repeatedly conjectured that the brain retrieves statistical regularities from stimuli. Here we present a new statistical approach allowing to address this conjecture. This approach is based on a new class of stochastic processes driven by chains with memory of variable length. It leads to a new experimental protocol in which sequences of auditory stimuli generated by a stochastic chain are presented to volunteers while electroencephalographic (EEG) data is recorded from their scalp. A new statistical model selection procedure for functional data is introduced and proved to be consistent. Applied to samples of EEG data collected using our experimental protocol it produces results supporting the conjecture that the brain effectively identifies the structure of the chain generating the sequence of stimuli.
Code (1)
Tasks
EEGElectroencephalogram (EEG)Model SelectionSimilar Papers 제목 키워드 기반
Measure Inducing Classification and Regression Trees for Functional Data
We propose a tree-based algorithm for classification and regression problems in the context of functional data analysis, which allows to leverage representation learning and multiple splitting rules at the node level, re…
ClassificationGeneral ClassificationregressionRepresentation LearningSeqROCTM: A Matlab toolbox for the analysis of Sequence of Random Objects driven by Context Tree Models
In several research problems we deal with probabilistic sequences of inputs (e.g., sequence of stimuli) from which an agent generates a corresponding sequence of responses and it is of interest to model the relation betw…
Model SelectionRelationTree Ensemble Explainability through the Hoeffding Functional Decomposition and TreeHFD Algorithm
Tree ensembles have demonstrated state-of-the-art predictive performance across a wide range of problems involving tabular data. Nevertheless, the black-box nature of tree ensembles is a strong limitation, especially for…
FVRuleLearner: Operator-Level Reasoning Tree (OP-Tree)-Based Rules Learning for Formal Verification
The remarkable reasoning and code generation capabilities of large language models (LLMs) have recently motivated increasing interest in automating formal verification (FV), a process that ensures hardware correctness th…
Code GenerationPEACH: Pretrained-embedding Explanation Across Contextual and Hierarchical Structure
In this work, we propose a novel tree-based explanation technique, PEACH (Pretrained-embedding Explanation Across Contextual and Hierarchical Structure), that can explain how text-based documents are classified by using …
Attributefeature selectiontext-classificationText Classification