Radoslaw Martin Cichy

The paper investigates how the brain processes natural images and line drawings, finding shared neural mechanisms across different levels of visual abstraction.

This study examines visual category representations in infants and adults using EEG data, revealing developmental changes in temporal dynamics, representational format, and spectral properties.

The paper introduces a large EEG dataset for visual object recognition and demonstrates its utility through multiple computational modeling approaches.

The paper investigates five animacy-related object dimensions in brain and behavior using fMRI, EEG, and similarity judgments, finding they explain variance in both brain representations and behavior.

The paper compares EEG, fMRI, and ECoG signals using visual object representations to understand their relationship in human neural activity.

The paper uses computational models to predict fMRI patterns during abstract concept processing and shows how concept similarities shape brain representations.

This study benchmarks three MRI sequences to differentiate feedforward and feedback signals in human V1 cortex, finding SE-BOLD provides the best depth-dependent separation.

The paper investigates whether N300/400 EEG differences from scene-object consistency reflect changes in object representations, finding they instead indicate semantic violations and are task-dependent.

The paper shows that perceived and mentally rotated visual contents are represented in different cortical depth regions of V1, suggesting functional segregation through anatomical compartmentalization.

This study maps brain regions to deep neural network tasks using fMRI data, showing structured correlations between tasks and visual cortex streams.

This study shows that coherent scene structure improves object information extraction in visual cortex during object categorization tasks but not during scene categorization.

The paper investigates how temporal uncertainty affects neural responses to predictable visual stimuli, finding that uncertainty increases expectation suppression and reduces sustained decoding accuracy for early visual responses.

The paper provides code and a pipeline for time-resolved multivariate pattern analysis of infant EEG data using linear SVM classification, showing reliable above-chance accuracy in both infants and adults.

The paper identifies three dissociable components of oscillatory activity in MEG data that underlie information encoding in human perception.

This study decodes visual representations from EEG data of infants and adults, finding that infants' neural representations are not linearly separable across visual domains and show developmental reorganization.

This study uses deep neural networks to analyze fMRI data and shows that scene-selective brain regions like OPA and PPA respond differently to specific scene components such as floor, wall, and ceiling.

This study investigates which MEG oscillatory components correlate with low- or high-level visual object representations using fMRI and DNN layer representations as templates.

The paper shows that the human brain rapidly sorts visual fragments according to their typical vertical location in the environment, using EEG and representational similarity analysis.

The paper shows that real-world scene structure enables faster extraction of category information from visual brain signals compared to jumbled scenes.

The paper shows that cortical processing is more sensitive to spatial than categorical scene structure, as demonstrated through fMRI and EEG experiments with multivariate decoding.

This study evaluates the reliability and generalizability of fMRI-MEG data fusion using representational similarity analysis in human visual streams.

This study uses EEG to show that face identity representations emerge gradually, becoming invariant to sex and image variations by 400ms after stimulus onset.

The paper shows that objects in typical visual-field locations overcome suppression more efficiently than atypical ones, suggesting environmental statistics shape visual processing.

This paper introduces a scale-specific analysis method for fMRI data on the cortical surface, showing improved detection of visual object information and scale-related encoding patterns in the ventral visual pathway.

This study uses ultra-rapid serial visual presentation with MEG to distinguish feedforward and feedback processing stages in the ventral visual pathway.

The paper demonstrates a method to link decodable neural information to behaviour by combining multivariate decoding with machine learning in object perception studies.

The paper shows that objects in typical retinotopic locations are processed more efficiently, as evidenced by earlier and more accurate EEG classification.

This study investigates how task context influences object processing in humans using MEG and fMRI, revealing distinct temporal dynamics and interactions between task and object representations.

This study compares multivariate pattern analysis of MEG and EEG data to assess their representational structures in time and space, finding convergent and unique insights into visual processing.

This study uses multivariate pattern analysis on MEG data to show that gamma oscillations encode orientation information, with stronger decoding for cardinal than oblique orientations.