Pith Number

pith:I6U63OQ4

pith:2026:I6U63OQ4J6PLBF3X4BB6KSWTGF

not attested not anchored not stored refs resolved

EmoMind: Decoding Affective Captions from Human Brain fMRI

Bilal A. Mohammed, Lin Gu, Ruogo Fang

EmoMind decodes continuous 34-dimensional affect from fMRI to rewrite neutral scene descriptions into subject-specific affective captions.

arxiv:2605.16739 v1 · 2026-05-16 · cs.LG · cs.AI · cs.CL · q-bio.NC

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4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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.

References

35 extracted · 35 resolved · 1 Pith anchors

[1] Mind captioning: Evolving descriptive text of mental content from human brain activity.Science Advances, 2024 2024

[2] Semantic reconstruction of continuous language from non-invasive brain recordings.Nature Neuroscience, 26(5):858–866, 2023 2023

[3] Reconstructing the mind’s eye: fMRI-to-image with contrastive learning and diffusion priors.Advances in Neural Information Processing Systems (NeurIPS), 36, 2024 2024

[4] CTRL: A Conditional Transformer Language Model for Controllable Generation 1909 · arXiv:1909.05858

[5] Plug and play language models: A simple approach to controlled text generation 2020

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:02:39.183476Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

47a9edba1c4f9eb09777e043e54ad33164bc851738a0bd82f69a52881901078e

Aliases

arxiv: 2605.16739 · arxiv_version: 2605.16739v1 · doi: 10.48550/arxiv.2605.16739 · pith_short_12: I6U63OQ4J6PL · pith_short_16: I6U63OQ4J6PLBF3X · pith_short_8: I6U63OQ4

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/I6U63OQ4J6PLBF3X4BB6KSWTGF \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 47a9edba1c4f9eb09777e043e54ad33164bc851738a0bd82f69a52881901078e

Canonical record JSON

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      "q-bio.NC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-16T01:32:45Z",
    "title_canon_sha256": "46921164230298f3f5de006dea355fd3bd2c3ecdd70953d9ce67eb2f337af9d5"
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