Leyla Işık

Multidimensional dynamics of object representations in the human visual system

The paper examines the temporal dynamics and latent dimensional structure of natural object representations in EEG and MEG data, revealing rapid dimensionality expansion and unexplained neural variance.

Bottom-up and generative computations uniquely explain neural responses across the social brain

This study compares two computational models of social perception and inference against fMRI data, finding both explain neural activity in social brain regions.

Monkey See, Model Knew: Large Language Models Accurately Predict Visual Brain Responses in Humans AND Non-Human Primates

This paper shows that large language models can predict visual brain responses in humans and non-human primates, suggesting that language-aligned models capture general visual patterns rather than language-specific features.

Disentangled deep generative models reveal coding principles of the human face processing network

The paper introduces disentangled deep generative models to interpret human face processing brain activity and finds interpretable latent dimensions that align with fMRI data and a gradient of face feature representation.

Multidimensional neural representations of social features during movie viewing

This study examines how social features are represented in the brain during movie viewing, identifying key regions involved in social processing and mental state inference.

Rapid processing of observed touch through social perceptual brain regions: an EEG-fMRI fusion study

This study uses EEG-fMRI fusion to show that social touch is processed rapidly through a social perceptual pathway before involving somatosensory cortex.

A shared neural code for social interaction encoding and memory in the human superior temporal sulcus

The paper investigates how the superior temporal sulcus encodes and retrieves social interaction memories using fMRI and machine learning during free recall of a movie.

A data-driven investigation of human action representations

The paper identifies low-dimensional, interpretable dimensions organizing human action similarity judgments using data-driven methods.

Beyond linear regression: mapping models in cognitive neuroscience should align with research goals

The paper argues that mapping models in cognitive neuroscience should be chosen based on research goals rather than being strictly linear or nonlinear, and introduces complexity metrics for evaluation.

Social-affective features drive human representations of observed actions

The paper shows that social-affective features, not visual or action features, best predict perceived similarity of observed actions in behavioral and neural experiments.

Functional selectivity for naturalistic social interaction perception in the human superior temporal sulcus

The paper uses fMRI and machine learning to show that the superior temporal sulcus (STS) selectively processes social interactions in naturalistic movies, distinct from other social features.

The speed of human social interaction perception

This study investigates whether detecting social interactions is a rapid perceptual process or a slower post-perceptual inference using MEG decoding and computational modeling.

How face perception unfolds over time

This study investigates the time course of face perception by analyzing neural responses to faces and finds that gender and age information emerge before identity information, with familiar faces showing enhanced early processing.