Gemma Roig

How the eyes move, not where they land, predicts what we remember across the lifespan

The study shows that how eyes move, not where they land, predicts memory across the lifespan in a cohort of 179 participants aged 5-79.

Investigating the temporal dynamics and modelling of mid-level feature representations in humans

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

One Hundred Neural Networks and Brains Watching Videos: Lessons from Alignment

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.

BrainACTIV: Identifying visuo-semantic properties driving cortical selectivity using diffusion-based image manipulation

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

BOLD Moments: modeling short visual events through a video fMRI dataset and metadata

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.

Visual features are processed before navigational affordances in the human brain

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

A large and rich EEG dataset for modeling human visual object recognition

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

Unveiling functions of the visual cortex using task-specific deep neural networks

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

Unravelling Representations in Scene-selective Brain Regions Using Scene Parsing Deep Neural Networks

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.

Task-specific vision models explain task-specific areas ofvisual cortex

The paper shows that DNNs trained on tasks related to a brain region's function better explain its responses than those trained on unrelated tasks.