Radoslaw Martin Cichy

The paper tests whether clutter disrupts spatial tuning of object representations in human visual cortex by measuring brain signals during object viewing with and without clutter.

The paper shows that structured scene descriptions, not just object co-occurrence, predict high-level visual cortex responses better, and language-aligned models capture this structure.

This study shows that self-supervised learning in neural networks can replicate human category-selective brain regions' representational signatures.

This study investigates how scene context influences object recognition in humans and models, showing that vision-language models with language supervision better replicate human performance.

This study examines how hippocampal-cortical connectivity relates to individual differences and training gains in mnemonic discrimination.

The paper identifies auditory representations during silent reading using EEG and computational modeling, showing they emerge before word onset and are stronger for unexpected words.

The paper aligns video models with EEG data to understand dynamic visual processing, finding that mid-level temporally-integrative features best match posterior brain responses.

The paper tests the adaptive recurrence hypothesis by comparing fMRI and EEG responses to images recognized by humans but differing in their solvability by a feedforward deep neural network, finding that parieto-frontal networks are recruited when ventral stream processing is insufficient.

The paper analyzes how visual and conceptual factors influence the composition of typical scene drawings using a dataset of 1,192 drawings and shows both factors jointly predict object selection.

This study compares task-switching neural distinctiveness in children and adults using fMRI, finding similar switch-related reductions but differences in regional correlations.

The paper identifies spatiotemporal dynamics of feedforward and feedback signals in human visual cortex using fMRI and EEG, revealing their functional roles in object perception.

This study investigates the relationship between internalizing psychopathology and learning under uncertainty, finding no systematic effect on learning rates or task performance.

This study investigates how healthy aging affects object recognition by analyzing delays and dedifferentiation in high-level visual representations using EEG, fMRI, and behavioral tasks.

The paper shows that perception of visual coherence co-varies with shifts in neural activity between feedback-related alpha and feedforward-related gamma rhythms.

The paper investigates when mid-level visual features are processed in humans using EEG and compares this to CNNs.

The paper investigates how personally typical scenes are more accurately perceived and represented in the brain, showing enhanced neural responses and idiosyncratic representations.

The paper identifies and characterizes brain representations of scenes relevant for categorization behavior across different tasks using fMRI and deep neural networks.

The paper shows that attentional bias in human visual cortex decreases with increasing target-distractor similarity, explained by tuning sharpening rather than gain increase.

This study investigates the spatiotemporal evolution of braille letter representations in blind and sighted readers using MEG and pattern analysis, finding slower processing and a transformation from peripheral to high-level representations in blind individuals.

This study investigates how recurrent processing contributes to visual object recognition by analyzing EEG and fMRI data, revealing its role in shaping visual representations across the ventral visual stream.

The paper introduces BOLD Moments Dataset (BMD), a video fMRI dataset capturing whole-brain responses to naturalistic videos, and shows its ability to capture temporal dynamics and correlate with video memorability.

This study investigates how perceptual uncertainty and reward information influence decision-making through a human behavioral experiment and computational modeling.

The paper investigates whether integration-related alpha dynamics are triggered by categorical coherence in visual inputs using EEG experiments and multivariate classification.

The paper investigates how the visually deprived brain transforms braille letter representations from sensory to perceptual formats, finding a temporal sequence and behavioral relevance in perceptual representations.

This study shows that rhythmic cortical feedback in the alpha band mediates visual integration during natural vision, as revealed by EEG and fMRI experiments.

The paper shows that navigational affordances are processed later than visual features in the human brain using EEG and computational models.

A preregistered multimodal neuroimaging study across 256 human participants directly tests Integrated Information Theory against Global Neuronal Workspace Theory, confirming some predictions while revealing major empirical challenges for both.

This study shows spatial attention modulates object location representations in late visual processing stages, independent of background clutter.

The paper shows that visuo-semantic features explain more variance in higher-level ventral stream neural dynamics than deep neural networks, suggesting complementary roles in object recognition.

The paper identifies and models the neural and behavioural correlates of scene categorization in humans using EEG data and a recurrent convolutional neural network.