Iris I. A. Groen

The paper shows that divisive normalization models better capture temporal dynamics in human somatosensory cortex compared to linear models, mirroring visual cortex findings.

The paper shows that deep neural networks align more closely with human brain responses to texture-synthesized images than to natural or object-only images, suggesting shared texture statistics rather than object recognition drive this alignment.

This study shows brain alignment in deep neural networks is present early and independent of object classification, suggesting it reflects environmental structure rather than classification strategy.

The paper uses image-grounded encoding models to show that object processing in the human brain has a delayed temporal profile compared to scene processing in natural visual environments.

The paper introduces object-zoomed training for CNNs to improve shape bias, aligning CNN performance with human-like object recognition strategies.

This study improves DNN encoding models of human visual processing by aligning their spatial sampling with human retinal ganglion cell density, revealing distinct temporal profiles for central and peripheral visual information.

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 measures alpha oscillations in human visual cortex using intracranial electrodes and finds they are spatially specific, with receptive field models showing larger spatial tuning compared to broadband power.

The paper compares human behavior, fMRI data, and DNN activations to show that the human brain represents locomotive action affordances independently of other visual features, and that standard DNNs do not capture this representation well.

This study examines how vascular compartments affect pRF size estimates in early visual cortex using BOLD-fMRI and finds no significant vascular influence on pRF size.

This study evaluates how different temporal adaptation mechanisms in deep convolutional neural networks affect object recognition under various challenging viewing conditions.

This paper evaluates whether PredNet, a deep learning model based on predictive coding, captures neural signatures of short-term visual adaptation in human visual cortex, finding partial alignment with neural data.

This study compares neural representations of locomotive action affordances in human EEG and DNNs, finding distinct temporal dynamics and limited DNN alignment with affordance processing.

The paper benchmarks video models against human brain activity to understand alignment factors, finding that temporal modeling and task relevance are key for different brain regions.

This study examines how retinotopic biases and category selectivity interact in human occipitotemporal cortex using fMRI data from the same participants.

BrainACTIV uses diffusion models to manipulate images and control neural activity in specific brain regions, identifying visuo-semantic properties that drive cortical selectivity.

This study shows that temporal adaptation improves sensory representations and identifies neural computations, like divisive normalization, that mediate these benefits in human visual object recognition.

The paper examines how neural adaptation in human visual cortex varies across the visual hierarchy using iEEG data and a computational model of normalization dynamics.

This study shows that perceptual reorganisation driven by prior knowledge develops late in childhood, as demonstrated by children's inability to recognize degraded images compared to adults and neural networks.

The paper models temporal dynamics of neural responses in human visual cortex using a computational model that captures contrast and adaptation effects across different visual areas.

This study compares contralateral bias and face/scene selectivity in human occipitotemporal cortex using fMRI, finding distinct functional roles for lateral and ventral visual cortex.

The paper applies theta-burst TMS to the lateral occipital cortex and observes reduced BOLD responses across multiple visual category-selective regions in ventral temporal cortex, with effects not limited to scene-selective areas or specific stimulation sites.

The paper compares similarity judgments and fMRI responses in human visual cortex for naturalistic images, finding they reflect different visual properties and processing stages.

The paper shows that feedback activity in the brain increases during object recognition in complex natural scenes, improving performance, while feed-forward processing suffices for simpler scenes.

The paper estimates the temporal emergence of conceptual object representations by analyzing MEG data and comparing them to behavioral and semantic models.

The paper assesses how functional and deep neural network features contribute to scene representation in human brain and behavior, finding distinct roles for each in explaining similarity in behavior and neural responses.

This study examines how the depth of convolutional neural networks (CNNs) correlates with EEG responses during visual object recognition, showing that deeper CNNs improve predictions after 150 ms.