A novel deep-learning-based computational method using object recognition to quantify visual context in naturalistic, multimodal stimuli demonstrates that a concept's perceived abstractness or concreteness dynamically depends on its visual context.

When a person listens to music, their brain continually predicts upcoming notes, based on that person's likely lifelong musical experience.

A web-based analysis platform for public fMRI data using naturalistic stimuli, leveraging state-of-the-art feature extraction models to enable more generalizable and reproducible findings.

A match between neuronal variability and tuning enables optimized coding of natural self-motion in early vestibular pathways.

A novel computational pipeline uses time-frequency analysis to capture the dynamics of human facial expressions, and demonstrates abnormal facial dynamics in melancholic depression.

The response from discrete stages of the early auditory pathway can be measured by subtle manipulations to long-form natural speech stimuli paired with deconvolution analysis of electroencephalography data.

Naturalistic animal behavior exhibits a complex organization in the temporal domain, whose variability stems from hierarchical, contextual, and stochastic sources and can be naturally explained in terms of metastable attractor models.

A novel naturalistic environment with a touchscreen enables high-quality eye tracking for studying visual cognition in monkeys, and allows naive monkeys to learn complex tasks through a combination of social observation of trained monkeys and trial-and-error learning.

Changes in the interpretation of specific scenes in a narrative trigger corresponding updates in the neural patterns evoked by those scenes in the default mode network.

New computational models provide insights into how the insect brain estimates the speed and direction of movement.