M. Ida Gobbini

This paper introduces a method to align subcortical brain connectivity across individuals, revealing shared fine-scale functional organization that was previously obscured by anatomical variability.

Hyperface provides a naturalistic fMRI dataset for studying human face processing under ecologically valid conditions and enables benchmarking computational models against human brain responses.

This study uses response hyperalignment to show shared representational spaces in macaque fMRI data despite individual differences in functional topographies.

This study shows that using age-specific templates improves hyperalignment accuracy in brain data across different age groups.

The paper shows that behavioral models of action purpose and social content better predict neural representational geometry than other models in the action observation network.

The paper introduces a new cortical surface template, onavg, which provides uniform sampling of the cortex and improves classification accuracy and computational efficiency compared to existing templates.

The paper estimates individualized cortical functional topographies from naturalistic movies across different participants, scanners, and institutes with high fidelity.

The paper introduces the Individualized Neural Tuning (INT) model, which provides a fine-grained, reliable, and generalizable measure of brain functional organization across individuals.

This study compares representational geometries of deep convolutional neural networks, human cognition, and brain activity during naturalistic face processing and finds that late-intermediate layers better match cognitive and neural geometries than late layers.

This study finds that familiar faces lead to faster and more accurate detection of angry expressions compared to unfamiliar faces, while happy expressions are detected similarly.

This paper introduces hybrid hyperalignment, a method that aligns both neural response and functional connectivity data across brains using a single model, showing improved alignment for both types of data compared to previous methods.

The paper describes and shares an fMRI dataset from 25 subjects watching 'The Grand Budapest Hotel', focusing on social interactions and face processing.

This study investigates how familiarity affects the categorical boundaries in face recognition by mixing familiar and unfamiliar faces, finding that familiarity narrows the boundary for familiar faces.

This study investigates retinotopic bias in face identity recognition, finding that familiarity reduces spatial variability in recognition responses across visual field locations.

The paper develops a method for generalizing semantic encoding models across individuals using hyperalignment, showing it outperforms anatomical normalization and within-subject models.

This study shows that response time patterns reflect learning strategies and reward value construction during complex decision-making tasks.

This study presents fMRI data from participants viewing naturalistic animal videos in two task contexts, showing how task demands affect neural representations of complex stimuli.

This study uses fMRI and multivariate pattern analysis to show that familiarity and face identity are represented in overlapping and distinct regions of the core and extended face processing systems.

This study investigates whether feature-based face processing facilitates detection of familiar faces, finding that familiarity aids both target detection and distractor rejection, even when faces are inverted.

The paper investigates how attention affects the discriminability of distributed neural representations for behavior and taxonomy in humans.

This study uses fMRI decoding to show that identity representation becomes disentangled from head view in the right inferior frontal cortex along the human face processing pathway.