Pith Number

pith:JRY554RW

pith:2026:JRY554RWUBTNZK3IWGY3JGPQID

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How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study

Bangde Du, Qingyao Ai, Shuqi Zhu, Yiqun Liu, Yi Zhong, Yujia Zhou, Ziyi Ye

Misjudged AI hallucinations fail to activate the brain's standard fact verification pathway, as shown by distinct EEG responses.

arxiv:2605.16953 v1 · 2026-05-16 · cs.AI · cs.CL

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\pithnumber{JRY554RWUBTNZK3IWGY3JGPQID}

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Record completeness

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3 Author claim open · sign in to claim

4 Citations 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

Notably, neural responses to hallucinations that were misjudged versus correctly judged by human participants showed significant differences. This indicates that misjudged AI-generated hallucinations failed to trigger the standard neurocognitive fact verification pathway.

C2weakest assumption

The observed ERP differences specifically reflect processing of hallucinated content rather than confounding factors such as varying task difficulty, content complexity, or individual participant differences in the verification task.

C3one line summary

EEG study of 27 participants reveals distinct neural patterns for AI-generated hallucinations, with misjudged ones failing to trigger standard fact verification pathways.

References

17 extracted · 17 resolved · 8 Pith anchors

[1] I think, therefore i hallucinate: Minds, ma- chines, and the art of being wrong.arXiv preprint arXiv:2503.05806,

[2] Generated faces in the wild: Quantitative compar- ison of stable diffusion, midjourney and dall-e 2.arXiv preprint arXiv:2210.00586,

[3] Kim, S. S., Vaughan, J. W., Liao, Q. V ., Lombrozo, T., and Russakovsky, O. Fostering appropriate reliance on large language models: The role of explanations, sources, and inconsistencies. InProceedin 2025

[4] Lin, T.-Y ., Maire, M., Belongie, S., Hays, J., Perona, P., Ra- manan, D., Doll´ar, P., and Zitnick, C. L. Microsoft coco: Common objects in context. InComputer Vision–ECCV 2014: 13th European Confere 2014

[5] SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models · arXiv:2303.08896

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:32.699460Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4c71def236a066dcab68b1b1b499f040daeebbdb8b9b87adc5e2fead7cd0a60f

Aliases

arxiv: 2605.16953 · arxiv_version: 2605.16953v1 · doi: 10.48550/arxiv.2605.16953 · pith_short_12: JRY554RWUBTN · pith_short_16: JRY554RWUBTNZK3I · pith_short_8: JRY554RW

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JRY554RWUBTNZK3IWGY3JGPQID \
  | 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: 4c71def236a066dcab68b1b1b499f040daeebbdb8b9b87adc5e2fead7cd0a60f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "55cfcc2118f0b98201fbaa79c0a8e687d7c08302585eee91540d07733727119e",
    "cross_cats_sorted": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-16T12:08:22Z",
    "title_canon_sha256": "1b53d67b8609919fb5a93647a8f0299001bc393a2f38abfd7f7a0be77c09e844"
  },
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  "source": {
    "id": "2605.16953",
    "kind": "arxiv",
    "version": 1
  }
}