Marı́a Ruz

The paper analyzes EEG data from a spatial Stroop task to uncover how conflict expectations influence predictive stability through distinct spatio-temporo-spectral dynamics.

This study investigates how sex/gender stereotypes influence face perception by manipulating expectations during emotion and sex categorization tasks, revealing task-dependent effects on neural coding and behavior.

The paper investigates how the brain encodes novel task instructions using fMRI and finds that the MDN uses both abstract and high-dimensional coding to balance generalization and expressivity.

This study examines how selection and integration demands during a size judgment task influence neural coding patterns using EEG, revealing differences in categorical and exemplar encoding.

The paper uses EEG and deep neural networks to show that high-level visual predictions, not low-level features, drive neural responses to unexpected stimuli around 200ms after stimulus onset.

This study examines how metacognitive experiences like boredom and frustration evolve over time during a task and relate to cognitive processes that optimize performance.

This study uses EEG to examine how neural patterns dynamically represent and reconfigure information during novel verbal instruction tasks, revealing changes in dimensionality and abstraction over time.

This study investigates how anticipated competition levels modulate preparatory brain activity and task performance using EEG and MVPA in a sex judgment task.

This study uses fMRI and multivariate decoding to show that neural preparation for stimulus relevance and probability involves distinct brain regions with some overlap in the fusiform gyrus.

The paper uses EEG and multivariate pattern analysis to show that selective attention and perceptual expectation involve distinct neural mechanisms during preparatory processing.

This study investigates how implementation and memorization demands affect neural encoding of task-relevant variables during novel instruction processing using fMRI and multivariate analyses.

MVPAlab is a MATLAB toolbox for analyzing multidimensional EEG and MEG data using machine learning, offering preprocessing, decoding, and statistical analysis tools.

This study uses multivariate pattern analysis on EEG data from a Flanker task to investigate conflict-related neural processes and finds evidence of reinstantiation dynamics.

This study uses fMRI to investigate how the brain represents current and intended task sets during sequential face classification tasks, finding distinct neural regions for each type of information.

This study uses fMRI and representational similarity analysis to examine how brain regions organize novel task encoding based on control-related variables and reward expectations.

The paper compares atlas-based methods to Searchlight for identifying informative brain regions in fMRI data, finding they perform well for large differences but only ABLA works for small differences.

This study extends the dual model of cognitive control to novel instructed tasks, showing both fronto-parietal and cingulo-opercular networks are involved in transient and sustained control mechanisms.

This study compares pattern estimation methods and statistical tests in fMRI decoding, finding LSS and Stelzer's method most effective for handling overlapping signals in event-related designs.

The paper shows that the medial prefrontal cortex network computes value homeostasis by tracking imbalance and redress in goal-related assets during decision-making.