Author
Tara Ghafari
University of OxfordORCID 0000-0002-5178-8702Unclaimed ORCID profile · follow to get notified of new preprints
Biology Disciplines
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2023-2026 · peak 4/yrCo-Author Collaboration Network
10 top collaborators · 18 mutual co-author links6 papers
Recent·Most cited
- NeuroscienceFrontiers in PsychologyPeer-reviewed
STEMorph: A Set of Morphed Emotional Face Stimuli from Angry to Happy Derived from NimStim
Mohammad Ebrahim Katebi, Mohammad Hossein Ghafari, Tara Ghafari
STEMorph creates a validated set of morphed emotional faces from angry to happy to study emotion recognition biases, showing strong correlation between morph levels and perceived emotion intensity.
DOI 10.3389/fpsyg.2026.1751625 - NeurosciencebioRxivPreprint
OPM-FLUX: A Pipeline for OPM MEG Data Analysis
Arnab Rakshit, Tara Ghafari, Anna Kowalczyk, Ole B. Jensen
OPM-FLUX provides a standardized analysis pipeline for OPM-MEG data, improving reproducibility and comparability across platforms and promoting consistent methodological practices.
2 citations - NeurosciencebioRxiv
Hemispheric laterality of the putamen predicts pseudoneglect
Tara Ghafari, Mohammad Ebrahim Katebi, Mohammad Hossein Ghafari…·Ole B. Jensen
The paper finds that leftward putamen asymmetry predicts pseudoneglect, a leftward attentional bias in healthy individuals, linking subcortical structure to spatial attention.
- NeuroscienceeLifePeer-reviewed
Modulation of alpha oscillations by attention is predicted by hemispheric asymmetries of subcortical regions.
Tara Ghafari, Cecilia Mazzetti, Kelly Garner…·Ole B. Jensen
This study investigates how subcortical structures' hemispheric asymmetries predict alpha oscillation modulations during spatial attention tasks with varying perceptual loads.
7 citations - NeurosciencebioRxivPreprint
An adversarial collaboration to critically evaluate theories of consciousness
Oscar Ferrante, Urszula Górska, Simon E. Henin…·Lucía Melloni
A preregistered multimodal neuroimaging study across 256 human participants directly tests Integrated Information Theory against Global Neuronal Workspace Theory, confirming some predictions while revealing major empirical challenges for both.