{"paper":{"title":"EmoMind: Decoding Affective Captions from Human Brain fMRI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"EmoMind decodes continuous 34-dimensional affect from fMRI to rewrite neutral scene descriptions into subject-specific affective captions.","cross_cats":["cs.AI","cs.CL","q-bio.NC"],"primary_cat":"cs.LG","authors_text":"Bilal A. Mohammed, Lin Gu, Ruogo Fang","submitted_at":"2026-05-16T01:32:45Z","abstract_excerpt":"Decoding visual experience from brain activity has advanced substantially, but cur- rent brain-to-text systems largely recover semantic content while discarding affect. Additionally, language models can generate emotional text when prompted with categorical labels, but such labels collapse rich inter-subject variability into coarse discrete bins. We present EmoMind, the first end-to-end pipeline for decoding affective captions directly from fMRI signals. EmoMind first retrieves a semanti- cally grounded neutral scene description from brain-decoded visual features, then rewrites it using a cont"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"EmoMind significantly outperforms label-prompted GPT-4 on all three axes (subject-specificity, structural geometry, causal control) across two independent emotion fMRI datasets, with largest gains on metrics requiring person-specific affective structure.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a continuous 34-dimensional emotion vector decoded from fMRI accurately captures rich inter-subject affective variability and that classifier-free guidance against an identity-preserving null branch enables controllable interpolation between semantic fidelity and affective expressivity without introducing artifacts.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"EmoMind is the first end-to-end pipeline that decodes continuous affective captions from fMRI by combining brain-decoded visual features with a 34D emotion vector and classifier-free guidance to balance semantic fidelity and affective expressivity.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"EmoMind decodes continuous 34-dimensional affect from fMRI to rewrite neutral scene descriptions into subject-specific affective 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2024","work_id":"68b3d936-f9df-4ae9-9e82-c886df6e8eaa","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Semantic reconstruction of continuous language from non-invasive brain recordings.Nature Neuroscience, 26(5):858–866, 2023","work_id":"57ce806d-bf09-46c5-8449-044b65fb20ce","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Reconstructing the mind’s eye: fMRI-to-image with contrastive learning and diffusion priors.Advances in Neural Information Processing Systems (NeurIPS), 36, 2024","work_id":"6c034156-669f-47dc-8dd7-135008f503ed","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1909,"title":"CTRL: A Conditional Transformer Language Model for Controllable Generation","work_id":"c90b18cd-644d-4503-8eca-953249853cff","ref_index":4,"cited_arxiv_id":"1909.05858","is_internal_anchor":true},{"doi":"","year":2020,"title":"Plug and play language models: A simple approach to 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