{ "generated": "2026-10-19T01:28:37Z", "count": 54, "schema": "https://research.breacher.ai/guides/schema.md", "studies": [ { "id": "nightingale-farid-2022", "title": "AI-synthesized faces are indistinguishable from real faces and more trustworthy", "authors": [ "Sophie J. Nightingale", "Hany Farid" ], "year": 2022, "venue": "Proceedings of the National Academy of Sciences (PNAS)", "volume": "119(8)", "pages": "e2120481119", "doi": "10.1073/pnas.2120481119", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "3 online experiments: real-vs-synthetic classification (Exp1), same with training + trial-by-trial feedback (Exp2), trustworthiness ratings 1-7 (Exp3)", "modality": [ "image-face" ], "stimuli": "400 StyleGAN2 faces + 400 real faces matched 1:1 from a 70,000-face database (FFHQ-style); balanced 200 women/200 men; 100 each Black, White, East Asian, South Asian", "n_participants": 757, "n_note": "Exp1 n=315; Exp2 n=219; Exp3 n=223; each judged 128 faces", "topics": [ "human-detection", "hyperrealism", "trust", "training" ], "summary": "StyleGAN2 faces were classified at chance (48.2%); training+feedback raised accuracy only to 59.0%; synthetic faces were rated 7.7% more trustworthy than real faces.", "results": [ { "metric": "Exp1 accuracy (no training)", "value": "48.2% [95% CI 47.1, 49.2]", "note": "d'=-0.09, beta=0.99 (no response bias)", "source_location": null }, { "metric": "Exp2 accuracy (training + feedback)", "value": "59.0% [95% CI 57.7, 60.4]", "note": "d'=0.46; no improvement across session (first half 59.3%, second half 58.8%)", "source_location": null }, { "metric": "Exp3 trustworthiness (1-7)", "value": "synthetic 4.82 vs real 4.48", "note": "+7.7% for synthetic; t(222)=14.6, p<0.001, d=0.49", "source_location": null }, { "metric": "Gender effect on trust", "value": "women 4.94 vs men 4.36", "note": "d=0.82", "source_location": null }, { "metric": "Smiling confound check", "value": "65.5% real vs 58.8% synthetic faces smiling", "note": "smiling does not explain higher synthetic trust", "source_location": null } ], "moderators": [ "White synthetic faces were classified least accurately; male White faces worse than female White faces. Authors attribute this to over-representation of White faces in StyleGAN2 training data.", "Feedback produced a one-off gain but no within-session learning, implying some synthetic faces carry no detectable artifacts." ], "limitations": [ "Single generator (StyleGAN2)", "Curated, artifact-screened stimuli", "Context-free still faces", "Online samples" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-07", "via": "Europe PMC full text PMC8872790" }, "cited_in": [ "MR-0001", "MR-0002" ], "doi_url": "https://doi.org/10.1073/pnas.2120481119", "record_url": "https://research.breacher.ai/sources/nightingale-farid-2022.md" }, { "id": "miller-2023-ai-hyperrealism", "title": "AI hyperrealism: Why AI faces are perceived as more real than human ones", "authors": [ "Elizabeth J. Miller", "Ben A. Steward", "Zak Witkower", "Clare A. M. Sutherland", "Eva G. Krumhuber", "Amy Dawel" ], "year": 2023, "venue": "Psychological Science", "volume": "34(12)", "pages": "1390-1403", "doi": "10.1177/09567976231207095", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Reanalysis of Nightingale & Farid (2022) data; Exp1 real-vs-AI judgments with confidence (White faces, White US participants); Exp2 ratings of 14 face attributes + lens-model analysis and random-forest classifier", "modality": [ "image-face" ], "stimuli": "White and non-White StyleGAN2 faces and human faces from the Nightingale & Farid (2022) set", "n_participants": 734, "n_note": "Exp1 n=124 (White US adults); Exp2 n=610; reanalysis ≈315 (Nightingale & Farid Exp1)", "topics": [ "human-detection", "hyperrealism", "overconfidence", "uncanny-valley" ], "summary": "White AI faces were judged human 65.9% of the time vs 51.1% for real White faces ('AI hyperrealism'); the worst detectors were the most confident; a classifier on human-rated attributes reached 94%.", "results": [ { "metric": "Exp1: White AI faces judged human", "value": "65.9%", "note": "", "source_location": null }, { "metric": "Exp1: White human faces judged human", "value": "51.1%", "note": "", "source_location": null }, { "metric": "Reanalysis of Nightingale & Farid: White AI vs human judged human", "value": "69.5% vs 52.2%", "note": "non-White faces ≈ chance, no significant AI-human difference", "source_location": null }, { "metric": "Confidence vs errors (AI faces)", "value": "r = .385", "note": "more errors ↔ higher confidence (Dunning-Kruger-like)", "source_location": null }, { "metric": "Negative meta-d'", "value": "59% of participants", "note": "poor insight into own errors", "source_location": null }, { "metric": "Random forest on 14 human-rated attributes", "value": "94% [95% CI 91, 97]", "note": "cross-validated; cues exist but humans misuse them", "source_location": null } ], "moderators": [ "Participants misread facial proportions, familiarity and memorability as signs of humanness; attractiveness and symmetry were used correctly.", "Authors attribute White-only hyperrealism to StyleGAN2 training data being ≈69% White, making White AI faces more average." ], "limitations": [ "White participants only in Exp1", "StyleGAN2 only", "Stimuli reused from Nightingale & Farid" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-07", "via": "journals.sagepub.com article page" }, "cited_in": [ "MR-0001", "MR-0002" ], "doi_url": "https://doi.org/10.1177/09567976231207095", "record_url": "https://research.breacher.ai/sources/miller-2023-ai-hyperrealism.md" }, { "id": "diel-2024-deepfake-detection-meta", "title": "Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers", "authors": [ "Alexander Diel", "Tania Lalgi", "Isabel Carolin Schröter", "Karl F. MacDorman", "Martin Teufel", "Alexander Bäuerle" ], "year": 2024, "venue": "Computers in Human Behavior Reports", "volume": "16", "pages": "100538", "doi": "10.1016/j.chbr.2024.100538", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "A", "design": "Systematic review and meta-analysis of human deepfake-detection studies (k = 137 effect sizes)", "modality": [ "image-face", "video", "audio", "text" ], "stimuli": "Heterogeneous across included studies", "n_participants": 86155, "n_note": "56 papers; 137 effect sizes", "topics": [ "human-detection", "meta-analysis", "modality", "training" ], "summary": "Pooled human deepfake-detection accuracy was 55.54% (95% CI 48.87-62.10), not significantly above chance; detection-improvement strategies raised it to 65.14%.", "results": [ { "metric": "Overall accuracy", "value": "55.54% [48.87, 62.10]", "note": "k=67; CI includes 50%", "source_location": "abstract" }, { "metric": "Audio", "value": "62.08% [38.23, 83.18]", "note": "k=8", "source_location": "abstract" }, { "metric": "Video", "value": "57.31% [47.80, 66.57]", "note": "k=26", "source_location": "abstract" }, { "metric": "Image", "value": "53.16% [42.12, 64.64]", "note": "k=18", "source_location": "abstract" }, { "metric": "Text", "value": "52.00% [37.42, 65.88]", "note": "k=15", "source_location": "abstract" }, { "metric": "Odds-ratio analysis", "value": "OR 0.64 [0.52, 0.79], ≈39% detection", "note": "k=62", "source_location": "abstract" }, { "metric": "With improvement strategies (feedback training, AI support, caricaturization)", "value": "65.14% [55.21, 74.46]", "note": "k=15; strongest for video", "source_location": "abstract" }, { "metric": "Sensitivity (d')", "value": "not significantly different from chance", "note": "", "source_location": "abstract" } ], "moderators": [ "Detection-improvement strategies raised performance above chance, with strongest effects for video." ], "limitations": [ "Heterogeneous paradigms and generators", "Many included studies use pre-2022 generators", "Wide CIs, especially audio" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "Semantic Scholar API abstract; DOI resolves to Elsevier PII S2451958824001714" }, "cited_in": [ "MR-0001", "MR-0003" ], "doi_url": "https://doi.org/10.1016/j.chbr.2024.100538", "record_url": "https://research.breacher.ai/sources/diel-2024-deepfake-detection-meta.md" }, { "id": "kobis-2021-fooled-twice", "title": "Fooled twice: People cannot detect deepfakes but think they can", "authors": [ "Nils C. Köbis", "Barbora Doležalová", "Ivan Soraperra" ], "year": 2021, "venue": "iScience", "volume": "24(11)", "pages": "103364", "doi": "10.1016/j.isci.2021.103364", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Pre-registered online behavioural experiment; real vs deepfake video classification; manipulations of financial incentives and awareness; confidence ratings", "modality": [ "video" ], "stimuli": "Face-swap deepfake videos and authentic videos", "n_participants": 210, "n_note": "", "topics": [ "human-detection", "overconfidence", "response-bias", "incentives" ], "summary": "People could not reliably detect deepfake videos, were biased toward judging them authentic, overestimated their ability, and neither incentives nor awareness raising helped.", "results": [ { "metric": "Detection", "value": "not reliable", "note": "exact accuracy not yet checked", "source_location": "abstract" }, { "metric": "Response bias", "value": "deepfakes mistaken for authentic more than the reverse", "note": "'seeing-is-believing' heuristic", "source_location": "abstract" }, { "metric": "Financial incentives", "value": "no improvement", "note": "", "source_location": "abstract" }, { "metric": "Awareness raising", "value": "no improvement", "note": "", "source_location": "abstract" }, { "metric": "Calibration", "value": "overconfident", "note": "", "source_location": "abstract" } ], "moderators": [], "limitations": [ "Single generator family (2020-era face swaps)" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1016/j.isci.2021.103364", "record_url": "https://research.breacher.ai/sources/kobis-2021-fooled-twice.md" }, { "id": "groh-2022-crowds-machines", "title": "Deepfake detection by human crowds, machines, and machine-informed crowds", "authors": [ "Matthew Groh", "Ziv Epstein", "Chaz Firestone", "Rosalind Picard" ], "year": 2022, "venue": "Proceedings of the National Academy of Sciences (PNAS)", "volume": "119(1)", "pages": "e2110013119", "doi": "10.1073/pnas.2110013119", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Two online studies comparing human crowds with the leading DFDC model; machine-informed condition; face-processing disruption manipulations", "modality": [ "video" ], "stimuli": "Deepfake Detection Challenge (DFDC) videos", "n_participants": 15016, "n_note": "total across two studies", "topics": [ "human-detection", "human-machine-teaming", "face-processing" ], "summary": "Ordinary human crowds and the leading DFDC model were similarly accurate but made different mistakes; model-informed participants beat either alone on average, but inaccurate model predictions often lowered their accuracy.", "results": [ { "metric": "Crowd vs leading model", "value": "similarly accurate", "note": "different kinds of mistakes", "source_location": "abstract" }, { "metric": "Machine-informed crowd", "value": "more accurate than either alone", "note": "inaccurate model predictions often decreased accuracy", "source_location": "abstract" }, { "metric": "Face-processing disruptions", "value": "hurt humans, mostly not the model", "note": "implicates specialised face processing", "source_location": "abstract" } ], "moderators": [], "limitations": [ "Self-selected online samples", "2019-2020 face-swap deepfakes" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "arXiv 2105.06496 abstract (journal ref PNAS 119(1))" }, "cited_in": [ "MR-0001", "MR-0002" ], "doi_url": "https://doi.org/10.1073/pnas.2110013119", "record_url": "https://research.breacher.ai/sources/groh-2022-crowds-machines.md" }, { "id": "mai-2023-speech-deepfakes", "title": "Warning: Humans cannot reliably detect speech deepfakes", "authors": [ "Kimberly T. Mai", "Sergi Bray", "Toby Davies", "Lewis D. Griffin" ], "year": 2023, "venue": "PLOS ONE", "volume": "18(8)", "pages": "e0285333", "doi": "10.1371/journal.pone.0285333", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Online listening experiment (Prolific); unary (single clip) and binary (pick the fake from a pair) tasks; with vs without familiarisation (5 example deepfakes); English and Mandarin", "modality": [ "audio" ], "stimuli": "VITS text-to-speech deepfakes vs genuine speech (LJSpeech; Chinese Standard Mandarin Speech Corpus); 50 sentences per language, 100 clips, 2-11 s", "n_participants": 529, "n_note": "", "topics": [ "human-detection", "voice", "training", "human-vs-machine" ], "summary": "Listeners were 70.35% accurate on single clips (73% hit rate on fakes); familiarisation added only ≈3.8 points; crowd-aggregated judgments rivalled in-domain detectors, while out-of-domain detectors failed.", "results": [ { "metric": "Unary accuracy (single clip)", "value": "70.35%", "note": "deepfakes identified 73%; genuine 67.78%", "source_location": null }, { "metric": "Binary accuracy (pick fake from pair)", "value": "85.59%", "note": "", "source_location": null }, { "metric": "Familiarisation effect", "value": "+3.84 points (p=0.001)", "note": "unary +3.76, binary +3.85", "source_location": null }, { "metric": "Confidence-weighted AUROC", "value": "73.83% → 75.54% with familiarisation", "note": "", "source_location": null }, { "metric": "Crowd-aggregated AUROC", "value": "95.51% (94.04% with familiarisation)", "note": "comparable to in-domain detectors", "source_location": null }, { "metric": "Automated detectors AUROC", "value": "in-domain 100%; out-of-domain 25.31%", "note": "out-of-domain flagged genuine speech as fake", "source_location": null }, { "metric": "Mandarin vs English listeners", "value": "+1.79 points, n.s. (p=0.202)", "note": "", "source_location": null } ], "moderators": [ "Reference audio of the same sentence helped significantly (p<0.001) but is rarely available in practice.", "Extra time and repeated listening did little to improve performance." ], "limitations": [ "Older TTS (VITS), not current voice cloning", "Short clips", "Participants knew fakes were present" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-07", "via": "journals.plos.org article page" }, "cited_in": [ "MR-0001", "MR-0003" ], "doi_url": "https://doi.org/10.1371/journal.pone.0285333", "record_url": "https://research.breacher.ai/sources/mai-2023-speech-deepfakes.md" }, { "id": "bray-2023-deepfake-faces", "title": "Testing human ability to detect 'deepfake' images of human faces", "authors": [ "Sergi D. Bray", "Shane D. Johnson", "Bennett Kleinberg" ], "year": 2023, "venue": "Journal of Cybersecurity", "volume": "9(1)", "pages": "tyad011", "doi": "10.1093/cybsec/tyad011", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Online experiment (Prolific); 20 images per participant drawn from 50 fake + 50 real; four conditions: control, familiarisation (20 example deepfakes), one-time advice (10 tell-tale features), advice + reminders", "modality": [ "image-face" ], "stimuli": "Uncurated StyleGAN2 (FFHQ-trained) faces vs real FFHQ faces", "n_participants": 280, "n_note": "", "topics": [ "human-detection", "training", "overconfidence" ], "summary": "Accuracy was 62.18%, barely above chance; advice and familiarisation did not significantly help; confidence was high (6.83/10) and unrelated to accuracy.", "results": [ { "metric": "Overall accuracy", "value": "62.18%", "note": "'only just above chance'", "source_location": null }, { "metric": "By condition", "value": "control 59.65%, familiarisation 60.54%, one-time advice 64.15%, advice+reminders 64.26%", "note": "differences n.s. (ANOVA p>0.1)", "source_location": null }, { "metric": "Per-image accuracy", "value": "≈30% to ≈85%", "note": "≈1 in 5 images correct <50% of the time", "source_location": null }, { "metric": "Mean confidence", "value": "6.83 / 10", "note": "within-person gamma ≈0.06-0.21; between-person n.s.", "source_location": null } ], "moderators": [ "Interventions raised accuracy on fakes but lowered it on real images (more real faces called fake) — a criterion shift, not better discrimination." ], "limitations": [ "20 images per participant", "Online sample", "StyleGAN2 only" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-07", "via": "academic.oup.com article page" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1093/cybsec/tyad011", "record_url": "https://research.breacher.ai/sources/bray-2023-deepfake-faces.md" }, { "id": "frank-2024-representative-study", "title": "A Representative Study on Human Detection of Artificially Generated Media Across Countries", "authors": [ "Joel Frank", "Franziska Herbert", "Jonas Ricker", "Lea Schönherr", "Thorsten Eisenhofer", "Asja Fischer", "Markus Dürmuth", "Thorsten Holz" ], "year": 2024, "venue": "IEEE Symposium on Security and Privacy (S&P 2024)", "volume": null, "pages": null, "doi": null, "url": "https://arxiv.org/abs/2312.05976", "peer_review": "peer-reviewed-conference", "evidence_tier": "B", "design": "Online survey with representative samples in USA, Germany, China; audio, image and text; regression on individual predictors", "modality": [ "image-face", "audio", "text" ], "stimuli": "State-of-the-art (2023) generated audio, images and text vs human-made counterparts", "n_participants": 3002, "n_note": "", "topics": [ "human-detection", "cross-cultural", "hyperrealism" ], "summary": "Most participants were effectively guessing; AI-generated media was rated more human-like than real media across all media types and countries.", "results": [ { "metric": "Overall", "value": "near guessing", "note": "abstract gives no percentages", "source_location": "abstract" }, { "metric": "AI vs real 'human-like' ratings", "value": "AI rated more human-like", "note": "all media types, all three countries", "source_location": "abstract" }, { "metric": "Predictors", "value": "generalised trust, cognitive reflection, self-reported deepfake familiarity significantly influenced decisions", "note": "across all media", "source_location": "abstract" } ], "moderators": [], "limitations": [ "No video", "Fixed stimulus set" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "arXiv 2312.05976 abstract; S&P 2024 venue and DOI not confirmed" }, "cited_in": [ "MR-0001" ], "doi_url": null, "record_url": "https://research.breacher.ai/sources/frank-2024-representative-study.md" }, { "id": "cooke-2024-coin-toss", "title": "As good as a coin toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli", "authors": [ "Di Cooke", "Abigail Edwards", "Sophia Barkoff", "Kathryn Kelly" ], "year": 2025, "venue": "Communications of the ACM", "volume": "68", "pages": "100-109", "doi": "10.1145/3729417", "url": "https://arxiv.org/abs/2403.16760", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Online US survey; mixed-modality stimuli (images, audio, video, audiovisual)", "modality": [ "image-face", "video", "audio", "audiovisual" ], "stimuli": "Mixed 2023-era generators", "n_participants": 1276, "n_note": "preprint first posted March 2024 (arXiv 2403.16760)", "topics": [ "human-detection", "modality" ], "summary": "Mean detection was close to 50% chance; accuracy dropped for any synthetic content, foreign-language content, single-modality stimuli and synthetic images of faces; prior knowledge of synthetic media did not help; older participants did worse.", "results": [ { "metric": "Mean detection", "value": "close to chance (50%)", "note": "abstract gives no exact figure", "source_location": "abstract" }, { "metric": "Lower accuracy when", "value": "stimulus contains synthetic content, foreign language, single modality, or a synthetic human face", "note": "", "source_location": "abstract" }, { "metric": "Mixed-authenticity audiovisual", "value": "harder", "note": "", "source_location": "abstract" }, { "metric": "Prior knowledge of synthetic media", "value": "no significant effect", "note": "", "source_location": "abstract" }, { "metric": "Age", "value": "older participants performed worse", "note": "", "source_location": "abstract" } ], "moderators": [], "limitations": [ "US sample only" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "arXiv 2403.16760 abstract page (journal ref CACM 68, 2025)" }, "cited_in": [ "MR-0001", "MR-0003" ], "doi_url": "https://doi.org/10.1145/3729417", "record_url": "https://research.breacher.ai/sources/cooke-2024-coin-toss.md" }, { "id": "groh-2024-political-speech", "title": "Human detection of political speech deepfakes across transcripts, audio, and video", "authors": [ "Matthew Groh", "Aruna Sankaranarayanan", "Nikhil Singh", "Dong Young Kim", "Andrew Lippman", "Rosalind Picard" ], "year": 2024, "venue": "Nature Communications", "volume": "15", "pages": "7629", "doi": "10.1038/s41467-024-51998-z", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "5 pre-registered experiments; real vs fabricated political speeches shown as transcript, silent video (± subtitles), audio (± subtitles), or video+audio (± subtitles)", "modality": [ "text", "audio", "video", "audiovisual" ], "stimuli": "Real and fabricated political speeches; fabricated audio from state-of-the-art TTS or voice actors", "n_participants": 2215, "n_note": "2,215 recruited participants across 5 experiments, plus 41,313 non-recruited participants in Exp 1b (Exp 1a recruited n=501)", "topics": [ "human-detection", "modality", "voice" ], "summary": "Accuracy rose with each added modality (transcript 57% → silent video 64% → audio 81% → video+audio 85-86% in Exp 1a); people relied more on how something was said than on what was said, and TTS fakes were harder to spot than voice-actor fakes.", "results": [ { "metric": "Exp 1a (recruited, n=501): transcript", "value": "57%", "note": "content alone ≈ slightly above chance", "source_location": null }, { "metric": "Exp 1a: silent video", "value": "64%", "note": "", "source_location": null }, { "metric": "Exp 1a: audio", "value": "81%", "note": "with or without subtitles; Discussion reports 80.5%", "source_location": null }, { "metric": "Exp 1a: video + audio", "value": "85% (with subtitles), 86% (without)", "note": "", "source_location": null }, { "metric": "Exp 1b (non-recruited, n=41,313): transcript / silent video / audio / video+audio", "value": "43% / 65% / 74% / 82-83%", "note": "", "source_location": null }, { "metric": "Exp 3: transcript / subtitled video / audio / video+audio", "value": "58% / 62% / 65% / 74%", "note": "same ordering at lower levels", "source_location": null }, { "metric": "Exp 2: TTS vs voice-actor deepfakes identified", "value": "72% vs 83%", "note": "state-of-the-art TTS harder to detect", "source_location": null }, { "metric": "Cue reliance", "value": "delivery (audio-visual) > content", "note": "authors' conclusion", "source_location": null } ], "moderators": [ "Participants struggled to distinguish a speaker's real audio from TTS trained on that speaker." ], "limitations": [ "Political figures only (familiar speakers may aid detection)", "Participants knew fakes were present" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-07", "via": "nature.com article page (open access); author list on page truncated, middle authors from compiler knowledge" }, "cited_in": [ "MR-0001", "MR-0003" ], "doi_url": "https://doi.org/10.1038/s41467-024-51998-z", "record_url": "https://research.breacher.ai/sources/groh-2024-political-speech.md" }, { "id": "barrington-2025-voice-clones", "title": "People are poorly equipped to detect AI-powered voice clones", "authors": [ "Sarah Barrington", "Emily A. Cooper", "Hany Farid" ], "year": 2025, "venue": "Scientific Reports", "volume": "15", "pages": "11004", "doi": "10.1038/s41598-025-94170-3", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Online perceptual studies: identity study (same speaker? for real-real, real-clone and different-speaker pairs) and naturalness study (real vs AI classification of single clips); small exploratory follow-up", "modality": [ "audio" ], "stimuli": "ElevenLabs Instant Voice Cloning clones of 220 real speakers from the DeepSpeak dataset (32 prompts per speaker); identity batches of 44 stimuli", "n_participants": 634, "n_note": "Identity study n=304; naturalness study n=300 recruited (294 analysed); exploratory follow-up n=30", "topics": [ "human-detection", "voice", "voice-clone", "identity", "vishing" ], "summary": "Listeners judged a commercial voice clone to be the same person as the real speaker about 80% of the time (median 83.3% of real-clone pairs) and identified clones as AI only 60.8% of the time.", "results": [ { "metric": "Identity: real-real same-speaker pairs judged same", "value": "median 100.0% (IQR 85.7-100.0)", "note": "", "source_location": null }, { "metric": "Identity: real-clone pairs judged same speaker", "value": "median 83.3% (IQR 69.2-100.0)", "note": "abstract: about 80% of the time; 26.6% of listeners said 'same' on every real-clone trial", "source_location": null }, { "metric": "Identity: different real speakers judged same", "value": "median 11.1%", "note": "", "source_location": null }, { "metric": "Naturalness: real clips correctly classified", "value": "67.4% (SD 14.8)", "note": "", "source_location": null }, { "metric": "Naturalness: AI clones correctly classified", "value": "60.8% (SD 16.7)", "note": "21.0% of listeners at or below chance on clones", "source_location": null } ], "moderators": [ "Exploratory follow-up: unscripted clips easier than scripted (median 76.7% vs 56.7%)" ], "limitations": [ "Unfamiliar speakers", "Single commercial cloning service", "Short clips, online listening" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-12", "via": "nature.com full text, doi:10.1038/s41598-025-94170-3" }, "cited_in": [ "MR-0001", "MR-0003" ], "doi_url": "https://doi.org/10.1038/s41598-025-94170-3", "record_url": "https://research.breacher.ai/sources/barrington-2025-voice-clones.md" }, { "id": "gray-2025-super-recognizers", "title": "Training human super-recognizers' detection and discrimination of AI-generated faces", "authors": [ "Katie L. H. Gray", "Josh P. Davis", "Carl Bunce", "Eilidh Noyes", "Kay L. Ritchie" ], "year": 2025, "venue": "Royal Society Open Science", "volume": "12(11)", "pages": "250921", "doi": "10.1098/rsos.250921", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Two online experiments (Exp1 single-face detection; Exp2 two-face 2AFC discrimination); super-recognisers vs Prolific and database controls; with vs without ≈5-minute artefact training with 10 feedback trials", "modality": [ "image-face" ], "stimuli": "80 StyleGAN3 faces and 80 FFHQ real faces, demographically matched", "n_participants": 664, "n_note": "Super-recognisers n=283, controls n=381 across conditions after catch-trial exclusions", "topics": [ "human-detection", "training", "individual-differences", "hyperrealism" ], "summary": "Untrained controls labelled only 31% of StyleGAN3 faces as AI (below chance) and super-recognisers 41%; ≈5 minutes of artefact training raised these to 51% and 64%, with no retention test.", "results": [ { "metric": "Exp1 AI faces detected, untrained", "value": "super-recognisers 41%; Prolific controls 31%; database controls 30%", "note": "d' 0.024 / -0.489 / -0.466: controls below chance", "source_location": null }, { "metric": "Exp1 AI faces detected, trained", "value": "super-recognisers 64%; Prolific controls 51%", "note": "d' 0.738 / -0.066: only super-recognisers above chance", "source_location": null }, { "metric": "Exp2 2AFC discrimination, untrained", "value": "super-recognisers 54%; controls 42% / 42%", "note": "", "source_location": null }, { "metric": "Exp2 2AFC discrimination, trained", "value": "super-recognisers 64%; Prolific controls 48%", "note": "", "source_location": null } ], "moderators": [ "Super-recogniser advantage persisted after training; training effect similar in both groups" ], "limitations": [ "No retention test", "Single generator (StyleGAN3)", "Training relies on visible rendering artefacts", "Online testing" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-12", "via": "White Rose eprint 241331 (typeset published PDF)" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1098/rsos.250921", "record_url": "https://research.breacher.ai/sources/gray-2025-super-recognizers.md" }, { "id": "lu-2023-fake2m", "title": "Seeing is not always believing: Benchmarking human and model perception of AI-generated images", "authors": [ "Zeyu Lu", "Di Huang", "Lei Bai", "Jingjing Qu", "Chengyue Wu", "Xihui Liu", "Wanli Ouyang" ], "year": 2023, "venue": "NeurIPS 2023 Datasets and Benchmarks Track", "volume": null, "pages": null, "doi": null, "url": "https://arxiv.org/abs/2304.13023", "peer_review": "peer-reviewed-conference", "evidence_tier": "B", "design": "Human perception benchmark (HPBench) on AI-generated vs real images; model benchmark (MPBench) on the Fake2M dataset", "modality": [ "image-general" ], "stimuli": "Text-to-image diffusion outputs vs real photos", "n_participants": null, "n_note": "not stated in abstract", "topics": [ "human-detection", "text-to-image", "human-vs-machine" ], "summary": "Humans misclassified 38.7% of images; the best model failed on 13% under the same conditions.", "results": [ { "metric": "Human misclassification rate (HPBench)", "value": "38.7%", "note": "≈61.3% accuracy", "source_location": "abstract" }, { "metric": "Best model failure rate (MPBench)", "value": "13%", "note": "≈87% accuracy", "source_location": "abstract" } ], "moderators": [], "limitations": [ "Not face-specific", "Venue (NeurIPS D&B) from compiler knowledge" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "arXiv 2304.13023 abstract" }, "cited_in": [ "MR-0001" ], "doi_url": null, "record_url": "https://research.breacher.ai/sources/lu-2023-fake2m.md" }, { "id": "lago-2022-more-real-than-real", "title": "More real than real: A study on human visual perception of synthetic faces", "authors": [ "Federica Lago", "Cecilia Pasquini", "Rainer Böhme", "Hélène Dumont", "Valérie Goasdoué", "Giulia Boato" ], "year": 2022, "venue": "IEEE Signal Processing Magazine", "volume": "39(1)", "pages": "109-116", "doi": "10.1109/MSP.2021.3120982", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Perceptual experiment rating realism of real vs synthetic faces", "modality": [ "image-face" ], "stimuli": "StyleGAN2 faces vs real faces", "n_participants": null, "n_note": "not recorded", "topics": [ "human-detection", "hyperrealism" ], "summary": "An early report that StyleGAN2 faces were rated as more real than real faces.", "results": [ { "metric": "Realism ratings", "value": "synthetic ≥ real", "note": "", "source_location": null } ], "moderators": [], "limitations": [ "Single generator" ], "mechanism_supported": null, "verification": { "status": "unverified", "confidence": "medium", "checked": null, "via": "compiler knowledge; DOI not resolved" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1109/MSP.2021.3120982", "record_url": "https://research.breacher.ai/sources/lago-2022-more-real-than-real.md" }, { "id": "tucciarelli-2022-realness", "title": "On the realness of people who do not exist: The social processing of artificial faces", "authors": [ "Raffaele Tucciarelli", "Neza Vehar", "Shamil Chandaria", "Manos Tsakiris" ], "year": 2022, "venue": "iScience", "volume": "25", "pages": null, "doi": null, "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Real-vs-artificial classification plus social trait ratings (incl. trustworthiness)", "modality": [ "image-face" ], "stimuli": "StyleGAN-family faces vs real faces", "n_participants": null, "n_note": "not recorded", "topics": [ "human-detection", "hyperrealism", "trust" ], "summary": "GAN faces were judged as real as, or more real than, real faces; faces rated more trustworthy were more often judged real.", "results": [ { "metric": "Realness classification", "value": "near chance; artificial often judged real", "note": "", "source_location": null }, { "metric": "Trust-realness link", "value": "positive", "note": "more trustworthy → more likely judged real", "source_location": null } ], "moderators": [], "limitations": [ "Details pending verification" ], "mechanism_supported": null, "verification": { "status": "unverified", "confidence": "medium", "checked": null, "via": "compiler knowledge; DOI unknown" }, "cited_in": [ "MR-0001" ], "doi_url": null, "record_url": "https://research.breacher.ai/sources/tucciarelli-2022-realness.md" }, { "id": "iproov-2025-consumer-study", "title": "iProov deepfake blindspot consumer study (industry report)", "authors": [ "iProov" ], "year": 2025, "venue": "Industry report / press release", "volume": null, "pages": null, "doi": null, "url": null, "peer_review": "industry", "evidence_tier": "E", "design": "Online consumer test with mixed real and deepfake images and videos", "modality": [ "image-face", "video" ], "stimuli": "Vendor-selected", "n_participants": 2000, "n_note": "UK and US consumers (approximate)", "topics": [ "human-detection", "overconfidence" ], "summary": "Vendor-reported: only ≈0.1% of participants classified every item correctly; older adults performed worse; confidence was high regardless of accuracy.", "results": [ { "metric": "All items correct", "value": "≈0.1% of participants", "note": "all-correct is a strict criterion, not mean accuracy", "source_location": null } ], "moderators": [ "Older adults performed worse" ], "limitations": [ "Not peer reviewed", "Vendor has commercial interest", "Methods not fully disclosed" ], "mechanism_supported": null, "verification": { "status": "unverified", "confidence": "medium", "checked": null, "via": "compiler knowledge" }, "cited_in": [ "MR-0001" ], "doi_url": null, "record_url": "https://research.breacher.ai/sources/iproov-2025-consumer-study.md" }, { "id": "ivory-2026-faces-voices-domain", "title": "Human detection of AI-generated faces and voices is not domain-general", "authors": [ "Matthew R. Ivory", "Carolyn McGettigan", "Helen E. Nuttall", "Sophie J. Nightingale" ], "year": 2026, "venue": "Scientific Reports", "volume": "16", "pages": "28639", "doi": "10.1038/s41598-026-59364-3", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Preregistered within-participant study: real-vs-synthetic classification of faces and of voices, each followed by confidence ratings; signal detection (d', c) and type-II AUROC for metacognition", "modality": [ "image-face", "audio" ], "stimuli": "20 FFHQ real faces + 20 Adobe Firefly (diffusion) faces; 40 VCTK real speakers and ElevenLabs Instant Voice Cloning clones (each listener heard 20 real + 20 clones of other speakers)", "n_participants": 172, "n_note": "200 recruited on Prolific, 172 analysed; ages 18-30", "topics": [ "human-detection", "voice", "voice-clone", "confidence", "individual-differences" ], "summary": "Faces were detected above chance (65.48%) but cloned voices barely (53.95%); face and voice skill were not significantly related, and confidence tracked accuracy for faces but not voices.", "results": [ { "metric": "Face accuracy (diffusion faces)", "value": "65.48% [95% CI 63.75, 67.21]", "note": "d' = .85", "source_location": null }, { "metric": "Voice-clone accuracy", "value": "53.95% [95% CI 52.64, 55.27]", "note": "d' = .20; 35.47% of participants at or below chance (faces 12.80%)", "source_location": null }, { "metric": "Face d' predicting voice d'", "value": "beta = .21, p = .053, R² = .02", "note": "not significant; no evidence of a domain-general ability", "source_location": null }, { "metric": "Response criterion c", "value": "faces -.24; voices -.07", "note": "both biased toward 'real'", "source_location": null }, { "metric": "Metacognitive AUROC", "value": "faces .59 (above chance); voices .48 (below chance)", "note": "confidence informative for faces only", "source_location": null } ], "moderators": [ "Skin smoothness was used as an AI cue for faces and predicted accuracy; breathing predicted voice accuracy" ], "limitations": [ "Task difficulty not matched across modalities", "Static faces and short clips only, single play", "Participants warned that stimuli could be synthetic", "Young sample (18-30)" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-12", "via": "nature.com full text, doi:10.1038/s41598-026-59364-3" }, "cited_in": [ "MR-0001", "MR-0003" ], "doi_url": "https://doi.org/10.1038/s41598-026-59364-3", "record_url": "https://research.breacher.ai/sources/ivory-2026-faces-voices-domain.md" }, { "id": "lavan-2025-voice-clones-hyperrealism", "title": "Voice clones sound realistic but not (yet) hyperrealistic", "authors": [ "Nadine Lavan", "Mairi Irvine", "Victor Rosi", "Carolyn McGettigan" ], "year": 2025, "venue": "PLOS ONE", "volume": "20(9)", "pages": "e0332692", "doi": "10.1371/journal.pone.0332692", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Three online experiments: generic AI voices vs human (Exp1a), voice clones vs the same humans (Exp1b), both together (Exp2); realness, human-vs-AI classification, trustworthiness and dominance ratings", "modality": [ "audio" ], "stimuli": "40 VCTK speakers; 40 ElevenLabs Instant Voice Cloning clones (≈4 min of training audio each); 40 ElevenLabs Voice Design generic voices; one Rainbow Passage sentence each, artefact-screened", "n_participants": 150, "n_note": "n=50 per experiment; UK native English speakers via Prolific; not preregistered", "topics": [ "human-detection", "voice", "voice-clone", "hyperrealism", "trust" ], "summary": "Listeners could not tell voice clones from the real speakers (d' 0.10-0.13) and rated them as real as human voices, but clones were not judged more human than humans (no hyperrealism); AI voices were rated more dominant.", "results": [ { "metric": "Exp1b proportion labelled human: clones vs real", "value": "0.58 vs 0.62", "note": "OR = 0.84, p = .239; d' = 0.10, n.s.", "source_location": null }, { "metric": "Exp2 proportion labelled human: clones vs real", "value": "0.70 vs 0.72", "note": "d' clones 0.13; generic AI voices 0.39 vs 0.72, d' 2.93", "source_location": null }, { "metric": "Exp2 realness ratings: clones vs real", "value": "63.0 vs 63.8", "note": "p = .682", "source_location": null }, { "metric": "Dominance: clones vs real", "value": "49.4 vs 44.0", "note": "p = .015; generic AI 58.1 vs 40.4", "source_location": null }, { "metric": "Trustworthiness: clones vs real", "value": "58.9 vs 57.1", "note": "n.s.; generic AI voices 57.9 vs 53.8, p = .002", "source_location": null } ], "moderators": [ "Generic (non-cloned) AI voices were easy to spot; clones were not", "Criterion biased toward 'human' for clones (c = -0.65 in Exp2)" ], "limitations": [ "Unfamiliar voices only", "Artefact-screened, idealised stimuli", "One sentence per voice", "Not preregistered", "Context effects across experiments" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-12", "via": "journals.plos.org full text" }, "cited_in": [ "MR-0001", "MR-0002", "MR-0003" ], "doi_url": "https://doi.org/10.1371/journal.pone.0332692", "record_url": "https://research.breacher.ai/sources/lavan-2025-voice-clones-hyperrealism.md" }, { "id": "pehlivanoglu-2026-machines-humans", "title": "Is this real? Susceptibility to deepfakes in machines and humans", "authors": [ "Didem Pehlivanoglu", "Mengdi Zhu", "Jialong Zhen", "Aude A. Gagnon-Roberge", "Rebecca K. Kern", "Damon Woodard", "Brian S. Cahill", "Natalie C. Ebner" ], "year": 2026, "venue": "Cognitive Research: Principles and Implications", "volume": "11", "pages": "3", "doi": "10.1186/s41235-025-00700-y", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Study 1: humans rated 200 faces 1 (fake) to 10 (real) plus individual-difference measures; compared with ResNet-50 CNN and frequency-domain SVM. Study 2: same comparison for deepfake videos", "modality": [ "image-face", "video" ], "stimuli": "Study 1: 300 FFHQ real faces and 300 artefact-screened StyleGAN2 faces (sets of 200 per participant). Study 2: deepfake videos (details not read)", "n_participants": 2203, "n_note": "Study 1 N=2,203 undergraduates (age 18-58, M 19.64); Study 2 N not read", "topics": [ "human-detection", "human-vs-machine", "truth-bias", "confidence", "individual-differences" ], "summary": "On StyleGAN2 faces humans were at chance (49%, AUC 0.53) with a strong truth bias while a CNN scored 97%; on videos the pattern reversed: machines near chance, humans better.", "results": [ { "metric": "Study 1 human accuracy (faces)", "value": "49%; mean AUC 0.53", "note": "N=2,203", "source_location": null }, { "metric": "Study 1 deepfakes judged real", "value": "69%", "note": "real judged real 67%: truth bias", "source_location": null }, { "metric": "Study 1 confidence", "value": "real 6.77 vs deepfake 4.11 (1-10 realness scale)", "note": "", "source_location": null }, { "metric": "Study 1 machine accuracy", "value": "CNN 97%; frequency-domain SVM 79%", "note": "", "source_location": null }, { "metric": "Study 2 (videos)", "value": "machines near chance; humans outperformed machines", "note": "abstract only; values not read", "source_location": null } ], "moderators": [ "Study 1: no individual-difference measure predicted face discrimination", "Study 2 (abstract): higher analytical thinking, lower positive affect and greater internet skills predicted better video detection" ], "limitations": [ "Undergraduate sample, 75% female", "Single GAN for faces", "Online, uncontrolled viewing", "Study 2 values not checked for this record" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "medium", "checked": "2026-10-12", "via": "link.springer.com full text (Study 1 methods and results); Study 2 from abstract only" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1186/s41235-025-00700-y", "record_url": "https://research.breacher.ai/sources/pehlivanoglu-2026-machines-humans.md" }, { "id": "dunn-2026-super-recognizers-average", "title": "Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it", "authors": [ "James D. Dunn", "David White", "Clare A. M. Sutherland", "Elizabeth J. Miller", "Ben A. Steward", "Amy Dawel" ], "year": 2026, "venue": "British Journal of Psychology", "volume": null, "pages": null, "doi": "10.1111/bjop.70063", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Super-recognisers vs motivated controls classifying real vs AI faces; correlation with face-recognition tests (CFMT+, UNSW Face Test); face-space centrality from two identity DNNs (VGG2, ArcFace)", "modality": [ "image-face" ], "stimuli": "100 StyleGAN2 and 100 FFHQ White faces; each participant saw one gender (50 real, 50 AI)", "n_participants": 125, "n_note": "Super-recognisers n=36, controls n=89 after exclusions", "topics": [ "human-detection", "individual-differences", "hyperrealism", "mechanism" ], "summary": "Super-recognisers detected AI faces modestly above chance (57.3%, d' 0.41) while controls were at chance (50.7%); AI faces sit closer to the centre of face space, and super-recognisers use this 'hyper-averageness' as a cue.", "results": [ { "metric": "Accuracy: super-recognisers vs controls", "value": "57.3% vs 50.7%", "note": "d' 0.41 vs 0.04; t(66.0) = 2.58, p = .012", "source_location": null }, { "metric": "Face recognition ability vs AI-face sensitivity (controls)", "value": "r(87) = .35 [95% CI .15, .52]", "note": "", "source_location": null }, { "metric": "DNN centrality: AI vs real faces", "value": "AI faces closer to the face-space centroid", "note": "b = -0.210, p < .001", "source_location": null }, { "metric": "Use of centrality as AI cue", "value": "super-recognisers yes; controls n.s.", "note": "", "source_location": null } ], "moderators": [ "Face-recognition ability" ], "limitations": [ "White faces and StyleGAN2 only", "Effect size reported as d = 0.55 in abstract and 0.50 in results" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-12", "via": "UNSWorks repository submitted version (hdl 1959.4/107078)" }, "cited_in": [ "MR-0001", "MR-0002" ], "doi_url": "https://doi.org/10.1111/bjop.70063", "record_url": "https://research.breacher.ai/sources/dunn-2026-super-recognizers-average.md" }, { "id": "dawel-2026-training-ai-faces", "title": "Training humans to detect AI-generated faces", "authors": [ "Amy Dawel", "Tanya George", "Eric Y. Mah", "James D. Dunn", "Clare A. M. Sutherland", "Nick Argument", "James W. Tanaka" ], "year": 2026, "venue": "Proceedings of the National Academy of Sciences (PNAS)", "volume": "123(27)", "pages": "e2602122123", "doi": "10.1073/pnas.2602122123", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Pre-post training study on untrained test faces; test-retest control study; online replication", "modality": [ "image-face" ], "stimuli": "AI-generated and real faces (details not in abstract)", "n_participants": 45, "n_note": "Main pre-post study N=45; control and online replication Ns not in abstract", "topics": [ "human-detection", "training", "confidence", "hyperrealism" ], "summary": "Training people to notice the global impressions AI faces systematically evoke (rather than artefacts) improved every participant, roughly doubled mean accuracy, and made confidence well calibrated; a test-retest control ruled out practice effects.", "results": [ { "metric": "Pre-post accuracy", "value": "all participants improved; mean accuracy nearly doubled", "note": "exact values not in abstract", "source_location": null }, { "metric": "High performers", "value": "near-perfect detection after training", "note": "", "source_location": "abstract" }, { "metric": "Confidence", "value": "well calibrated only after training", "note": "", "source_location": "abstract" }, { "metric": "Controls", "value": "test-retest control ruled out practice effects; online replication supported scalability", "note": "", "source_location": "abstract" } ], "moderators": [], "limitations": [ "Small main sample", "Retention not reported in abstract", "Values not checked against full text" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "medium", "checked": "2026-10-12", "via": "OpenAlex abstract record for doi:10.1073/pnas.2602122123 (publisher page not reachable)" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1073/pnas.2602122123", "record_url": "https://research.breacher.ai/sources/dawel-2026-training-ai-faces.md" }, { "id": "rehman-2025-training-synthetic-faces", "title": "Training humans for synthetic face image detection", "authors": [ "Ramlah Sara Rehman", "Ewald Meier", "Mathias Ibsen", "Christian Rathgeb", "Robert Nichols", "Christoph Busch" ], "year": 2025, "venue": "Frontiers in Artificial Intelligence", "volume": "8", "pages": "1568267", "doi": "10.3389/frai.2025.1568267", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Online randomised experiment: 16 trials, then artefact/semantic-region training (experimental) or a 5-min break (control), then 16 trials", "modality": [ "image-face" ], "stimuli": "StyleGAN2 and diffusion (dezgo.com) synthetic faces plus FFHQ and online real faces; 8 GAN and 8 diffusion images in total", "n_participants": 184, "n_note": "Experimental n=95, control n=89 (254 started)", "topics": [ "human-detection", "training", "criterion-shift" ], "summary": "One-shot artefact training raised trained participants' accuracy on synthetic faces (79.08% vs 58.29% for controls) but lowered accuracy on real faces, so overall accuracy did not differ significantly between groups.", "results": [ { "metric": "Overall accuracy, experimental pre vs post", "value": "67.43% vs 71.12%", "note": "p = 0.01", "source_location": null }, { "metric": "Overall accuracy, control pre vs post", "value": "68.12% vs 68.33%", "note": "p = 0.88", "source_location": null }, { "metric": "Post-intervention, experimental vs control", "value": "difference 2.79 points", "note": "p = 0.08, n.s.", "source_location": null }, { "metric": "Post-intervention synthetic faces correct", "value": "experimental 79.08% vs control 58.29%", "note": "p < 0.001", "source_location": null }, { "metric": "Post-intervention real faces correct", "value": "experimental lower than control (control 78.37%)", "note": "t(182) = -5.37, p < 0.001: consistent with a shift toward 'synthetic'", "source_location": null }, { "metric": "Pre-intervention accuracy on GAN vs diffusion fakes", "value": "GAN 25.26% / 24.72% vs diffusion 88.68% / 90.45% (experimental / control)", "note": "the diffusion fakes used were easy; GAN fakes were mostly judged real", "source_location": null } ], "moderators": [ "Generator: GAN fakes far harder than the diffusion fakes used" ], "limitations": [ "No d' or criterion reported", "8 images per generator", "Single session, no retention", "Online, uncontrolled" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-12", "via": "frontiersin.org full text" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.3389/frai.2025.1568267", "record_url": "https://research.breacher.ai/sources/rehman-2025-training-synthetic-faces.md" }, { "id": "kramer-2024-crowds-synthetic-faces", "title": "Crowds Improve Human Detection of AI-Synthesised Faces", "authors": [ "Robin S. S. Kramer", "Charlotte Cartledge" ], "year": 2024, "venue": "Applied Cognitive Psychology", "volume": "38(5)", "pages": null, "doi": "10.1002/acp.4245", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Exp1: real-vs-synthetic ratings on a 1-7 scale, completed twice (inner and outer crowds). Exp2: single binary judgement with confidence and estimate of others' agreement (majority vote, confidence-weighted and 'surprisingly popular' aggregation)", "modality": [ "image-face" ], "stimuli": "AI-synthesised and real faces (details not in abstract)", "n_participants": null, "n_note": "not in abstract", "topics": [ "human-detection", "crowds", "aggregation" ], "summary": "Aggregating different people's judgements (outer crowds) improved synthetic-face detection as crowd size grew, with gains comparable to training; averaging one person's two attempts (inner crowd) added little.", "results": [ { "metric": "Inner crowd", "value": "little benefit over individuals", "note": "", "source_location": "abstract" }, { "metric": "Outer crowd", "value": "performance improved with crowd size", "note": "", "source_location": "abstract" }, { "metric": "Aggregation rule", "value": "majority vote best for small crowds; 'surprisingly popular' best for larger crowds", "note": "", "source_location": "abstract" }, { "metric": "Personality", "value": "no association with performance", "note": "", "source_location": "abstract" } ], "moderators": [ "Crowd size", "Aggregation rule" ], "limitations": [ "Values not checked against full text" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "medium", "checked": "2026-10-12", "via": "OpenAlex abstract record for doi:10.1002/acp.4245" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1002/acp.4245", "record_url": "https://research.breacher.ai/sources/kramer-2024-crowds-synthetic-faces.md" }, { "id": "kramer-2026-aggregation-human-ai", "title": "Improving human performance in deepfake detection: limits of aggregation and human–AI collaboration", "authors": [ "Robin S. S. Kramer", "Victoria P. M. Lister" ], "year": 2026, "venue": "Cognitive Research: Principles and Implications", "volume": null, "pages": null, "doi": "10.1186/s41235-026-00758-2", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Four experiments, real-vs-deepfake video judgements. Exp1: response aggregation across the sample. Exp2: confidence ratings, metacognitive sensitivity, confidence-weighted fusion of participant pairs. Exp3-4: decisions before and after simulated algorithmic guidance, varying guidance accuracy and deepfake prevalence", "modality": [ "video" ], "stimuli": "YouTube clips and their corresponding deepfakes (generator not in abstract)", "n_participants": null, "n_note": "not in abstract", "topics": [ "human-detection", "crowds", "aggregation", "metacognition", "human-ai-teaming" ], "summary": "Individuals detected deepfake videos above chance but modestly; whole-sample aggregation helped, but confidence did not discriminate correct from incorrect trials, confidence-weighted pairing reduced accuracy, and gains from accurate AI guidance were offset by following incorrect advice.", "results": [ { "metric": "Individual accuracy", "value": "above chance but modest and error-prone", "note": "no values in abstract", "source_location": "abstract" }, { "metric": "Aggregation across full sample (Exp1)", "value": "reliable accuracy gains", "note": "wisdom of the crowd", "source_location": "abstract" }, { "metric": "Metacognitive sensitivity (Exp2)", "value": "no evidence", "note": "confidence modestly higher for correct decisions but did not reliably discriminate at trial level", "source_location": "abstract" }, { "metric": "Confidence-weighted fusion of pairs (Exp2)", "value": "reduced accuracy", "note": "", "source_location": "abstract" }, { "metric": "Simulated AI guidance (Exp3-4)", "value": "helped when highly accurate; offset by following incorrect advice", "note": "reliance unaffected by deepfake prevalence", "source_location": "abstract" } ], "moderators": [ "Guidance accuracy", "Aggregation method" ], "limitations": [ "Values not checked against full text", "Simulated, not real, algorithmic guidance" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "medium", "checked": "2026-10-18", "via": "OpenAlex abstract record for doi:10.1186/s41235-026-00758-2" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1186/s41235-026-00758-2", "record_url": "https://research.breacher.ai/sources/kramer-2026-aggregation-human-ai.md" }, { "id": "plohl-2026-political-deepfakes-plausibility", "title": "How deepfake plausibility, media literacy, and personal attitudes shape detection, liking, and intentions to share political deepfakes on social media", "authors": [ "Nejc Plohl", "Izidor Mlakar", "Letizia Aquilino", "Michele Brienza", "Piercosma Bisconti", "Urška Smrke" ], "year": 2026, "venue": "Humanities and Social Sciences Communications", "volume": null, "pages": null, "doi": "10.1057/s41599-026-09122-7", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Online experiment in three countries; participants watched manipulated political videos (climate change, immigration) varying in plausibility (technical realism and fit with the speaker's known stance); outcomes: detection, liking, sharing intention", "modality": [ "video", "audiovisual" ], "stimuli": "Political deepfake videos on climate change and immigration, high vs low plausibility", "n_participants": 1124, "n_note": "UK, Slovenia and Italy", "topics": [ "human-detection", "political-deepfakes", "media-literacy", "sharing" ], "summary": "High-plausibility political deepfakes were detected less often and liked more; higher media literacy predicted better detection, less liking and lower sharing intention; favourable attitudes to the depicted person raised liking and sharing.", "results": [ { "metric": "Plausibility → detection", "value": "high-plausibility deepfakes less likely to be detected", "note": "no values in abstract", "source_location": "abstract" }, { "metric": "Plausibility → liking / sharing", "value": "more liking; no significant effect on sharing intention", "note": "", "source_location": "abstract" }, { "metric": "Media literacy", "value": "better detection, less liking, lower sharing intention", "note": "strong predictor across outcomes", "source_location": "abstract" }, { "metric": "Attitude toward depicted person", "value": "more positive → more liking and sharing intention", "note": "", "source_location": "abstract" }, { "metric": "Mediation", "value": "liking partially mediated detection → sharing", "note": "", "source_location": "abstract" } ], "moderators": [ "Plausibility (technical realism, stance congruence)", "Media literacy", "Attitude toward the speaker", "Topic" ], "limitations": [ "Values not checked against full text", "Detection rates not reported in abstract" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "medium", "checked": "2026-10-18", "via": "OpenAlex abstract record for doi:10.1057/s41599-026-09122-7" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1057/s41599-026-09122-7", "record_url": "https://research.breacher.ai/sources/plohl-2026-political-deepfakes-plausibility.md" }, { "id": "san-segundo-2026-audio-language-style", "title": "Human perception of audio deepfakes: the role of language and speaking style", "authors": [ "Eugenia San Segundo", "Aurora López-Jareño", "Xin Wang", "Junichi Yamagishi" ], "year": 2026, "venue": "Speech Communication", "volume": "185", "pages": "103487", "doi": "10.1016/j.specom.2026.103487", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Perception experiment: listeners classified voices as natural or synthetic and justified each choice; stimuli varied by language (Spanish, Japanese), speaking style (audiobook, interview) and voice familiarity (familiar, unfamiliar)", "modality": [ "audio" ], "stimuli": "80 stimuli, 50% artificial (synthesis system not in abstract)", "n_participants": 54, "n_note": "28 native Spanish speakers, 26 Japanese speakers", "topics": [ "human-detection", "audio", "cross-linguistic", "cues" ], "summary": "Spanish and Japanese listeners averaged 59.11% when classifying natural vs synthetic speech, doing better on real voices, and relied mainly on prosodic and extralinguistic cues (intonation, rhythm, fluency, pauses, breathing, laughter).", "results": [ { "metric": "Average accuracy", "value": "59.11%", "note": "better on authentic samples", "source_location": "abstract" }, { "metric": "Cues used", "value": "mainly suprasegmental / extralinguistic", "note": "intonation, rhythm, fluency, pauses, speed, breathing, laughter rather than segmental features", "source_location": "abstract" }, { "metric": "Cross-linguistic", "value": "shared cues plus notable differences in how 'humanness' is conceptualised", "note": "Japanese vs Spanish listeners", "source_location": "abstract" } ], "moderators": [ "Listener language", "Speaking style", "Voice familiarity" ], "limitations": [ "Values not checked against full text", "Small N", "Condition-level accuracies not in abstract" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "medium", "checked": "2026-10-18", "via": "OpenAlex abstract record for doi:10.1016/j.specom.2026.103487" }, "cited_in": [ "MR-0001", "MR-0003" ], "doi_url": "https://doi.org/10.1016/j.specom.2026.103487", "record_url": "https://research.breacher.ai/sources/san-segundo-2026-audio-language-style.md" }, { "id": "manhede-2026-online-markets-training", "title": "Human discernment of artificial intelligence in online markets can be shaped by experience and training", "authors": [ "Tanaka Manhede", "Yuliana Fartachuk", "Steven A. Martinez", "Allison Langer", "Jason M. Chein" ], "year": 2026, "venue": "AI & Society", "volume": null, "pages": null, "doi": "10.1007/s00146-026-03373-3", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Correlational baseline (AI attitudes, AI engagement vs discernment) plus a two-group training study: experimental group trained on labelled human/AI examples, then immediate feedback and per-correct monetary reward; control group did similar exercises with unlabelled examples and delayed, performance-based reward", "modality": [ "text", "image-general" ], "stimuli": "Vacation-rental listings: human-written (Airbnb) or AI-generated (ChatGPT or Claude) descriptions; half text-only, half paired with property-exterior and host-face images", "n_participants": 117, "n_note": "120 recruited, 3 excluded; experimental n=60, control n=57", "topics": [ "human-detection", "ai-text", "training", "individual-differences" ], "summary": "Baseline discernment of AI-generated vs human rental listings was 57.4%; heavier AI engagement predicted worse discernment (r = -0.29) while AI attitudes did not; labelled training with feedback added 9.4 points, control training none.", "results": [ { "metric": "Baseline accuracy (all)", "value": "57.4%", "note": "t(116)=6.87, p<0.001; experimental 57.6%, control 57.3%", "source_location": null }, { "metric": "AI engagement (SCAIES) vs discernment", "value": "r(115) = -0.29, p = 0.002", "note": "socioaffective engagement r = -0.26", "source_location": null }, { "metric": "Attitudes toward AI vs discernment", "value": "r = -0.14, p = 0.14", "note": "n.s.", "source_location": null }, { "metric": "Training gain, experimental", "value": "+9.4 points baseline → final", "note": "≈6.7 after stage 1; further 2.7 n.s. (p = 0.085)", "source_location": null }, { "metric": "Change, control", "value": "-0.6 points (p = 0.74)", "note": "", "source_location": null } ], "moderators": [ "AI-platform engagement", "Labelled examples + immediate feedback" ], "limitations": [ "Single listing genre", "Short-term; no retention test", "Logged ChatGPT use did not significantly correlate with discernment (r = -0.11)" ], "mechanism_supported": null, "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-18", "via": "link.springer.com article page (open access, CC BY)" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1007/s00146-026-03373-3", "record_url": "https://research.breacher.ai/sources/manhede-2026-online-markets-training.md" }, { "id": "sulimani-2026-recognition-ai-faces", "title": "Recognition of real and artificial intelligence-generated faces", "authors": [ "N. Sulimani", "A. van Hunenstijn", "A. Albonico" ], "year": 2026, "venue": "Perception", "volume": "55(10)", "pages": "924-937", "doi": "10.1177/03010066261442089", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Face-memory (recognition) task with real and AI-generated faces, plus a human-vs-AI classification task; correlation between recognition and classification accuracy", "modality": [ "image-face" ], "stimuli": "Real and AI-generated faces (generator not in abstract)", "n_participants": null, "n_note": "not in abstract", "topics": [ "face-recognition", "memory", "human-detection", "covert-recognition" ], "summary": "AI faces were recognised (remembered) more accurately than real faces, both face types were classified above chance with no difference, and recognition and classification accuracy were not correlated.", "results": [ { "metric": "Recognition memory", "value": "significantly higher for AI faces than real faces", "note": "no values in abstract", "source_location": "abstract" }, { "metric": "Classification", "value": "above chance for both face types; no difference between them", "note": "", "source_location": "abstract" }, { "metric": "Recognition vs classification", "value": "not significantly correlated", "note": "authors suggest partial unawareness of the AI-face memory advantage", "source_location": "abstract" } ], "moderators": [], "limitations": [ "Values not checked against full text", "Generator and N not recorded" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "medium", "checked": "2026-10-18", "via": "Europe PMC abstract record (PMID 42053250)" }, "cited_in": [ "MR-0001", "MR-0002" ], "doi_url": "https://doi.org/10.1177/03010066261442089", "record_url": "https://research.breacher.ai/sources/sulimani-2026-recognition-ai-faces.md" }, { "id": "yeung-2026-isthisai-strategies", "title": "Is This AI? Longitudinal Analysis of Strategies Used for AI Detection on Two Subreddits", "authors": [ "Christina Yeung", "Galen Weld", "Jaron Mink", "Franziska Roesner" ], "year": 2026, "venue": "Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES)", "volume": "9(4)", "pages": "2800-2812", "doi": "10.1609/aies.v9i4.43351", "url": null, "peer_review": "peer-reviewed-conference", "evidence_tier": "B", "design": "Mixed-methods longitudinal content analysis of crowd AI-detection discussions on r/isthisAI and r/RealOrAI over 2 years 8 months", "modality": [ "image-general", "video", "text" ], "stimuli": "13,098 posts and 222,060 comments", "n_participants": null, "n_note": "observational; posts/comments rather than participants", "topics": [ "detection-strategies", "folk-heuristics", "crowds" ], "summary": "Catalogued 12 strategies people use in the wild to judge whether content is AI-generated (e.g., close inspection of physical details, recognising AI content trends, assumptions about model capabilities) and showed that strategies shift over time with AI capabilities and online trends.", "results": [ { "metric": "Corpus", "value": "13,098 posts; 222,060 comments", "note": "2 years 8 months", "source_location": "abstract" }, { "metric": "Strategies catalogued", "value": "12", "note": "prevalence analysed; no percentages in abstract", "source_location": "abstract" }, { "metric": "Change over time", "value": "strategies and mental models shift with AI capabilities and social trends", "note": "", "source_location": "abstract" } ], "moderators": [ "Time / generator generation" ], "limitations": [ "Does not measure accuracy of strategies", "Self-selected Reddit communities", "Values not checked against full text" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "medium", "checked": "2026-10-18", "via": "ojs.aaai.org/index.php/AIES/article/view/43351 (abstract page)" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1609/aies.v9i4.43351", "record_url": "https://research.breacher.ai/sources/yeung-2026-isthisai-strategies.md" }, { "id": "mori-2012-uncanny-valley", "title": "The uncanny valley [From the Field]", "authors": [ "Masahiro Mori", "Karl F. MacDorman", "Norri Kageki" ], "year": 2012, "venue": "IEEE Robotics & Automation Magazine", "volume": "19(2)", "pages": "98-100", "doi": "10.1109/MRA.2012.2192811", "url": null, "peer_review": "essay", "evidence_tier": "C", "design": "Authorised English translation of Mori's 1970 essay 'Bukimi no tani' (Energy 7(4):33-35). Theoretical, non-empirical.", "modality": [ "robot", "prosthetic" ], "stimuli": "Thought experiment (industrial robot, toy robot, prosthetic hand, bunraku puppet, corpse, zombie)", "n_participants": null, "n_note": "non-empirical", "topics": [ "uncanny-valley", "theory" ], "summary": "Proposes that affinity rises with human likeness, drops sharply just short of full likeness, then recovers; movement amplifies the curve; designers should aim for the first peak.", "results": [ { "metric": "Hypothesised curve", "value": "non-monotonic (valley near high human likeness)", "note": "y-axis 'shinwakan' variously translated as affinity/familiarity", "source_location": null } ], "moderators": [ "Movement deepens the valley (hypothesised)" ], "limitations": [ "Not empirically derived" ], "mechanism_supported": [ "human-likeness" ], "verification": { "status": "unverified", "confidence": "high", "checked": null, "via": "compiler knowledge" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1109/MRA.2012.2192811", "record_url": "https://research.breacher.ai/sources/mori-2012-uncanny-valley.md" }, { "id": "mathur-reichling-2016-cartography", "title": "Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley", "authors": [ "Maya B. Mathur", "David B. Reichling" ], "year": 2016, "venue": "Cognition", "volume": "146", "pages": "22-32", "doi": "10.1016/j.cognition.2015.09.008", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Ratings of 80 objectively sampled real-world robot faces plus a controlled set of edited faces; explicit likability; implicit trust via an investment game", "modality": [ "robot-face" ], "stimuli": "80 real robot faces; digitally edited face series", "n_participants": null, "n_note": "not in abstract", "topics": [ "uncanny-valley", "trust", "valley-shape" ], "summary": "Demonstrated an uncanny valley in explicit likability of real robot faces that extended to implicit trust decisions; category confusion may occur but did not mediate the effect.", "results": [ { "metric": "Explicit likability", "value": "uncanny valley present", "note": "", "source_location": "abstract" }, { "metric": "Implicit trust (investment game)", "value": "valley extends to trust decisions", "note": "driven by subtle facial-expression cues also used for humans", "source_location": "abstract" }, { "metric": "Category confusion", "value": "may occur; does not mediate likability effect", "note": "preliminary", "source_location": "abstract" } ], "moderators": [], "limitations": [ "Static images", "Robot faces, not synthetic human faces" ], "mechanism_supported": [ "against-categorization-ambiguity" ], "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1016/j.cognition.2015.09.008", "record_url": "https://research.breacher.ai/sources/mathur-reichling-2016-cartography.md" }, { "id": "mathur-2020-uncanny-not-confusing", "title": "Uncanny but not confusing: Multisite study of perceptual category confusion in the Uncanny Valley", "authors": [ "Maya B. Mathur", "David B. Reichling", "Francesca Lunardini", "et al." ], "year": 2020, "venue": "Computers in Human Behavior", "volume": "103", "pages": "21-30", "doi": "10.1016/j.chb.2019.08.029", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Multisite replication and extension of Mathur & Reichling (2016)", "modality": [ "robot-face" ], "stimuli": "Robot faces", "n_participants": null, "n_note": "multisite (not recorded)", "topics": [ "uncanny-valley", "replication", "valley-shape" ], "summary": "The likability valley replicated across sites; perceptual category confusion again did not mediate it.", "results": [ { "metric": "Valley replication", "value": "replicated", "note": "", "source_location": null }, { "metric": "Category confusion mediation", "value": "not supported", "note": "", "source_location": null } ], "moderators": [], "limitations": [ "Robot faces, not synthetic human faces" ], "mechanism_supported": [ "against-categorization-ambiguity" ], "verification": { "status": "unverified", "confidence": "medium", "checked": null, "via": "compiler knowledge; DOI not resolved" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1016/j.chb.2019.08.029", "record_url": "https://research.breacher.ai/sources/mathur-2020-uncanny-not-confusing.md" }, { "id": "katsyri-2015-uv-review", "title": "A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness", "authors": [ "Jari Kätsyri", "Klaus Förger", "Meeri Mäkäräinen", "Tapio Takala" ], "year": 2015, "venue": "Frontiers in Psychology", "volume": "6", "pages": "390", "doi": "10.3389/fpsyg.2015.00390", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "C", "design": "Narrative review of empirical tests of uncanny valley hypotheses", "modality": [ "review" ], "stimuli": "n/a", "n_participants": null, "n_note": "review", "topics": [ "uncanny-valley", "mechanisms", "review" ], "summary": "The simplest valley hypothesis and the movement claim lacked consistent support; categorization ambiguity had too little research to judge; perceptual mismatch received good support; the valley appears only under specific conditions.", "results": [ { "metric": "Simple human-likeness valley", "value": "no consistent support", "note": "", "source_location": "abstract" }, { "metric": "Movement modulates valley", "value": "no consistent support", "note": "", "source_location": "abstract" }, { "metric": "Categorization ambiguity", "value": "insufficient research", "note": "", "source_location": "abstract" }, { "metric": "Perceptual mismatch", "value": "good support", "note": "", "source_location": "abstract" } ], "moderators": [], "limitations": [ "Narrative, not quantitative" ], "mechanism_supported": [ "perceptual-mismatch" ], "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.3389/fpsyg.2015.00390", "record_url": "https://research.breacher.ai/sources/katsyri-2015-uv-review.md" }, { "id": "diel-2022-uv-meta", "title": "A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables", "authors": [ "Alexander Diel", "Sarah Weigelt", "Karl F. MacDorman" ], "year": 2022, "venue": "ACM Transactions on Human-Robot Interaction", "volume": "11(1)", "pages": "Article 1", "doi": "10.1145/3470742", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "A", "design": "Meta-analysis: 72 of 468 screened studies; 247 effect sizes; moderator analysis by stimulus-creation technique and measure", "modality": [ "robot", "cg-face", "android" ], "stimuli": "10 stimulus-creation techniques; 39 affect measures; 14 indirect measures", "n_participants": null, "n_note": "72 studies, 247 effect sizes; published online 2021", "topics": [ "uncanny-valley", "meta-analysis", "mechanisms", "measurement" ], "summary": "Across 72 studies the uncanny valley effect was large (Hedges' g = 1.01); face distortion produced the largest effect (g = 1.46), followed by distinct entities, realism rendering and morphing.", "results": [ { "metric": "Overall effect", "value": "Hedges' g = 1.01 [0.80, 1.22]", "note": "247 effect sizes", "source_location": "abstract" }, { "metric": "Face distortion", "value": "g = 1.46 [0.69, 2.24]", "note": "largest", "source_location": "abstract" }, { "metric": "Distinct entities", "value": "g = 1.20 [1.02, 1.38]", "note": "", "source_location": "abstract" }, { "metric": "Realism render", "value": "g = 0.99 [0.62, 1.36]", "note": "", "source_location": "abstract" }, { "metric": "Morphing", "value": "g = 0.94 [0.64, 1.24]", "note": "", "source_location": "abstract" }, { "metric": "Largest-effect affect measures", "value": "threatening, likable, aesthetics, familiarity, eeriness", "note": "", "source_location": "abstract" }, { "metric": "Largest-effect indirect measures", "value": "dislike frequency, categorization RT, like frequency, avoidance, viewing duration", "note": "", "source_location": "abstract" } ], "moderators": [ "Stimulus-creation technique; outcome measure" ], "limitations": [ "No consensus on theoretical basis or methodology (authors)", "Heterogeneous stimuli" ], "mechanism_supported": [ "atypicality", "perceptual-mismatch" ], "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1145/3470742", "record_url": "https://research.breacher.ai/sources/diel-2022-uv-meta.md" }, { "id": "macdorman-chattopadhyay-2016", "title": "Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not", "authors": [ "Karl F. MacDorman", "Debaleena Chattopadhyay" ], "year": 2016, "venue": "Cognition", "volume": "146", "pages": "190-205", "doi": "10.1016/j.cognition.2015.09.019", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Three rounds of experiments; categorisation and ratings of humans, animals and objects from computer-animated to real; realism consistency reduced by manipulating feature sets (for humans: eyes-eyelashes-mouth vs skin-nose-eyebrows)", "modality": [ "cg-face" ], "stimuli": "Human, animal and object continua from computer-animated to real", "n_participants": 548, "n_note": "across three rounds", "topics": [ "uncanny-valley", "mechanisms" ], "summary": "Reducing realism consistency made humans and animals (not objects) eerier and colder; the most category-ambiguous stimuli were neither the eeriest nor the coldest, contradicting the category-uncertainty account.", "results": [ { "metric": "Realism inconsistency → eeriness/coldness", "value": "increase for humans and animals; not objects", "note": "", "source_location": "abstract" }, { "metric": "Max category uncertainty → eeriness", "value": "no peak", "note": "Jentsch (1906) predictions not supported", "source_location": "abstract" } ], "moderators": [], "limitations": [ "CG stimuli", "Online samples" ], "mechanism_supported": [ "perceptual-mismatch", "against-categorization-ambiguity" ], "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1016/j.cognition.2015.09.019", "record_url": "https://research.breacher.ai/sources/macdorman-chattopadhyay-2016.md" }, { "id": "seyama-nagayama-2007", "title": "The uncanny valley: Effect of realism on the impression of artificial human faces", "authors": [ "Jun'ichiro Seyama", "Ruth S. Nagayama" ], "year": 2007, "venue": "Presence: Teleoperators and Virtual Environments", "volume": "16(4)", "pages": "337-351", "doi": "10.1162/pres.16.4.337", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Morphs between real and artificial (doll/mask) faces with eye-size manipulation; pleasantness ratings", "modality": [ "image-face" ], "stimuli": "Real-to-artificial morph continua", "n_participants": null, "n_note": "small (not recorded)", "topics": [ "uncanny-valley", "mechanisms", "atypicality" ], "summary": "Realism alone did not produce unpleasantness; strong unpleasantness appeared only when highly realistic faces had abnormal eye size.", "results": [ { "metric": "Realism alone", "value": "no valley", "note": "", "source_location": null }, { "metric": "Abnormal features × high realism", "value": "strong unpleasantness", "note": "realism amplifies sensitivity to deviations", "source_location": null } ], "moderators": [], "limitations": [ "Small samples", "Static images" ], "mechanism_supported": [ "atypicality", "perceptual-mismatch" ], "verification": { "status": "unverified", "confidence": "high", "checked": null, "via": "compiler knowledge" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1162/pres.16.4.337", "record_url": "https://research.breacher.ai/sources/seyama-nagayama-2007.md" }, { "id": "cheetham-2014-happy-valley", "title": "Perceptual discrimination difficulty and familiarity in the Uncanny Valley: more like a 'Happy Valley'", "authors": [ "Marcus Cheetham", "Pascal Suter", "Lutz Jäncke" ], "year": 2014, "venue": "Frontiers in Psychology", "volume": "5", "pages": "1219", "doi": "10.3389/fpsyg.2014.01219", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Three experiments on avatar-to-human morph continua: discrimination difficulty, affect, processing-bias control", "modality": [ "cg-face" ], "stimuli": "Avatar-human morph continua (static faces)", "n_participants": null, "n_note": "not in abstract", "topics": [ "uncanny-valley", "categorical-perception" ], "summary": "Contrary to the uncanny valley hypothesis, category-ambiguous faces were easier (not harder) to discriminate, and greater discrimination difficulty went with more positive affect — a 'Happy Valley'.", "results": [ { "metric": "Discrimination difficulty", "value": "lower for ambiguous faces, higher for human faces", "note": "opposite of the profile UVH assumes", "source_location": "abstract" }, { "metric": "Difficulty ↔ affect", "value": "more difficulty → more positive affect", "note": "strongest for ambiguous faces", "source_location": "abstract" }, { "metric": "Processing level", "value": "avatars at category level, humans at individual level", "note": "", "source_location": "abstract" } ], "moderators": [], "limitations": [ "Static morph stimuli" ], "mechanism_supported": [ "against-categorization-ambiguity" ], "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.3389/fpsyg.2014.01219", "record_url": "https://research.breacher.ai/sources/cheetham-2014-happy-valley.md" }, { "id": "burleigh-2013-does-uv-exist", "title": "Does the uncanny valley exist? An empirical test of the relationship between eeriness and the human likeness of digitally created faces", "authors": [ "Tyler J. Burleigh", "Jordan R. Schoenherr", "Guy L. Lacroix" ], "year": 2013, "venue": "Computers in Human Behavior", "volume": "29(3)", "pages": "759-771", "doi": "10.1016/j.chb.2012.11.021", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Two studies varying human likeness of computer-generated faces; eeriness ratings", "modality": [ "cg-face" ], "stimuli": "Digitally created faces", "n_participants": null, "n_note": "modest (not recorded)", "topics": [ "uncanny-valley", "valley-shape" ], "summary": "Eeriness was mostly a linear function of human likeness; non-linear effects appeared only when stimuli combined conflicting or atypical features.", "results": [ { "metric": "Eeriness vs human likeness", "value": "mostly linear", "note": "no valley along a single axis", "source_location": null }, { "metric": "Conflicting/atypical features", "value": "non-linear effects", "note": "", "source_location": null } ], "moderators": [], "limitations": [ "CG stimuli only" ], "mechanism_supported": [ "atypicality", "perceptual-mismatch" ], "verification": { "status": "verified-doi", "confidence": "medium", "checked": "2026-10-07", "via": "Semantic Scholar metadata (title, authors, venue, DOI); abstract not available" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1016/j.chb.2012.11.021", "record_url": "https://research.breacher.ai/sources/burleigh-2013-does-uv-exist.md" }, { "id": "steckenfinger-ghazanfar-2009", "title": "Monkey visual behavior falls into the uncanny valley", "authors": [ "Shawn A. Steckenfinger", "Asif A. Ghazanfar" ], "year": 2009, "venue": "Proceedings of the National Academy of Sciences (PNAS)", "volume": "106(43)", "pages": "18362-18366", "doi": "10.1073/pnas.0910063106", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Looking-time preference in macaque monkeys for real, realistic-synthetic, and unrealistic-synthetic monkey faces", "modality": [ "image-face", "non-human-primate" ], "stimuli": "Real monkey faces; realistic and unrealistic synthetic monkey faces", "n_participants": 5, "n_note": "macaques (approximate)", "topics": [ "uncanny-valley", "evolution" ], "summary": "Macaques looked less at realistic synthetic faces than at real or unrealistic ones—an uncanny-valley analogue in another species.", "results": [ { "metric": "Looking time", "value": "realistic-synthetic < real and < unrealistic", "note": "", "source_location": null } ], "moderators": [], "limitations": [ "Very small N", "Looking time is an indirect aversion proxy" ], "mechanism_supported": [ "evolved-perceptual-basis" ], "verification": { "status": "unverified", "confidence": "high", "checked": null, "via": "compiler knowledge" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1073/pnas.0910063106", "record_url": "https://research.breacher.ai/sources/steckenfinger-ghazanfar-2009.md" }, { "id": "wang-2015-uv-existence-explanations", "title": "The uncanny valley: Existence and explanations", "authors": [ "Shensheng Wang", "Scott O. Lilienfeld", "Philippe Rochat" ], "year": 2015, "venue": "Review of General Psychology", "volume": "19(4)", "pages": "393-407", "doi": "10.1037/gpr0000056", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "C", "design": "Theoretical review; proposes the dehumanization hypothesis", "modality": [ "review" ], "stimuli": "n/a", "n_participants": null, "n_note": "review", "topics": [ "uncanny-valley", "mechanisms", "theory" ], "summary": "Proposes that humanlike agents are first processed as human; detecting non-human cues then triggers dehumanization, which produces eeriness.", "results": [ { "metric": "Dehumanization hypothesis", "value": "proposed", "note": "few direct tests", "source_location": null } ], "moderators": [], "limitations": [ "Theory, not data" ], "mechanism_supported": [ "dehumanization" ], "verification": { "status": "unverified", "confidence": "high", "checked": null, "via": "compiler knowledge" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1037/gpr0000056", "record_url": "https://research.breacher.ai/sources/wang-2015-uv-existence-explanations.md" }, { "id": "gray-wegner-2012-mind-perception", "title": "Feeling robots and human zombies: Mind perception and the uncanny valley", "authors": [ "Kurt Gray", "Daniel M. Wegner" ], "year": 2012, "venue": "Cognition", "volume": "125(1)", "pages": "125-130", "doi": "10.1016/j.cognition.2012.06.007", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "3 experiments: humanlike vs mechanical view of the same robot; computer described as able to feel vs act; human described as lacking experience", "modality": [ "robot", "vignette" ], "stimuli": "KASPAR robot video (front vs back); vignettes", "n_participants": null, "n_note": "3 modest samples (not recorded)", "topics": [ "uncanny-valley", "mind-perception" ], "summary": "Unease was driven by attributing the capacity to feel (experience) to machines—or denying it to humans—not by appearance alone.", "results": [ { "metric": "Humanlike robot unease", "value": "higher; mediated by perceived experience", "note": "", "source_location": null }, { "metric": "Machine that can feel vs act", "value": "feeling machine more unnerving", "note": "", "source_location": null }, { "metric": "Human lacking experience", "value": "unnerving", "note": "", "source_location": null } ], "moderators": [], "limitations": [ "Small samples", "Vignette paradigms" ], "mechanism_supported": [ "mind-perception" ], "verification": { "status": "verified-doi", "confidence": "high", "checked": "2026-10-07", "via": "OpenAlex metadata (title, authors, venue, volume, pages, DOI); abstract not available; findings from compiler knowledge" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1016/j.cognition.2012.06.007", "record_url": "https://research.breacher.ai/sources/gray-wegner-2012-mind-perception.md" }, { "id": "stein-ohler-2017-uncanny-mind", "title": "Venturing into the uncanny valley of mind—The influence of mind attribution on the acceptance of human-like characters in a virtual reality setting", "authors": [ "Jan-Philipp Stein", "Peter Ohler" ], "year": 2017, "venue": "Cognition", "volume": "160", "pages": "43-50", "doi": "10.1016/j.cognition.2016.12.010", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "2×2 between-subjects VR study: identical characters having an empathic conversation, framed as human- vs AI-controlled and scripted vs autonomous", "modality": [ "vr-avatar" ], "stimuli": "Identical VR characters; framing manipulated", "n_participants": 92, "n_note": "", "topics": [ "uncanny-valley", "mind-perception" ], "summary": "With visuals held constant, eeriness was highest when empathic behaviour was attributed to an autonomous AI—an 'uncanny valley of mind'.", "results": [ { "metric": "Eeriness peak condition", "value": "autonomous AI + empathic behaviour", "note": "appearance held constant", "source_location": null } ], "moderators": [], "limitations": [ "Single scenario", "Moderate N" ], "mechanism_supported": [ "mind-perception" ], "verification": { "status": "unverified", "confidence": "high", "checked": null, "via": "compiler knowledge" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1016/j.cognition.2016.12.010", "record_url": "https://research.breacher.ai/sources/stein-ohler-2017-uncanny-mind.md" }, { "id": "saygin-2012-predictive-coding", "title": "The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions", "authors": [ "Ayse Pinar Saygin", "Thierry Chaminade", "Hiroshi Ishiguro", "Jon Driver", "Chris Frith" ], "year": 2012, "venue": "Social Cognitive and Affective Neuroscience", "volume": "7(4)", "pages": "413-422", "doi": "10.1093/scan/nsr025", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Event-related fMRI repetition suppression (3T); 2-s grayscale clips of 8 actions by a human, the Repliee Q2 android (human appearance, mechanical motion) and the same robot with skin removed (mechanical appearance and motion)", "modality": [ "video", "robot" ], "stimuli": "Action videos: Human, Android, Robot", "n_participants": 19, "n_note": "20 recruited; 1 excluded for head motion", "topics": [ "uncanny-valley", "neural", "predictive-coding" ], "summary": "Repetition suppression in bilateral anterior intraparietal sulcus was stronger for the android (appearance–motion mismatch) than for the human or robot, interpreted as prediction error; authors state this cannot establish a causal link to the uncanny valley.", "results": [ { "metric": "aIPS repetition suppression", "value": "Android > Human and Robot (bilateral)", "note": "key action-perception node", "source_location": null }, { "metric": "Left extrastriate body area", "value": "Human and Android > Robot", "note": "sensitive to humanlike form", "source_location": null }, { "metric": "Selectivity for appearance or motion alone", "value": "little (outside EBA)", "note": "network responded to the match between them", "source_location": null } ], "moderators": [], "limitations": [ "N=19", "Single android", "Authors: no conclusive or causal link to the uncanny valley" ], "mechanism_supported": [ "perceptual-mismatch", "predictive-coding" ], "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-07", "via": "academic.oup.com article page" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1093/scan/nsr025", "record_url": "https://research.breacher.ai/sources/saygin-2012-predictive-coding.md" }, { "id": "rosenthal-von-der-putten-2019-vmpfc", "title": "Neural mechanisms for accepting and rejecting artificial social partners in the uncanny valley", "authors": [ "Astrid M. Rosenthal-von der Pütten", "Nicole C. Krämer", "Stefan Maderwald", "Matthias Brand", "Fabian Grabenhorst" ], "year": 2019, "venue": "Journal of Neuroscience", "volume": "39(33)", "pages": "6555-6570", "doi": "10.1523/JNEUROSCI.2956-18.2019", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "7T fMRI over six sessions; rating task (72 trials: likability, familiarity, humanlikeness) and choice task (108 trials: choose which of two agents receives a personal gift)", "modality": [ "image", "robot", "android" ], "stimuli": "Six categories: humans, humans with impairments, artificial humans, androids, humanoid robots, mechanoid robots", "n_participants": 21, "n_note": "26 recruited; 4 excluded", "topics": [ "uncanny-valley", "neural", "decision-making" ], "summary": "vmPFC encoded likability as a non-linear function of humanlikeness with selectively low likability for highly humanlike agents; behavioural valley in 18 of 21 participants; DMPFC encoded choices about artificial agents and an amygdala signal predicted their rejection.", "results": [ { "metric": "Behavioural valley (artificial humans below linear trend)", "value": "significant in 18 of 21 individuals", "note": "", "source_location": null }, { "metric": "vmPFC", "value": "non-linear likability (valley) signal", "note": "approximated by multiplicative combination of TPJ, DMPFC, fusiform signals", "source_location": null }, { "metric": "TPJ", "value": "linear humanlikeness code", "note": "", "source_location": null }, { "metric": "DMPFC, fusiform", "value": "non-linear human/non-human distinction", "note": "", "source_location": null }, { "metric": "Choice decision variable", "value": "vmPFC for human choices; DMPFC for artificial-agent choices", "note": "", "source_location": null }, { "metric": "Amygdala", "value": "predicted rejection of artificial agents", "note": "", "source_location": null } ], "moderators": [], "limitations": [ "N=21", "Static images" ], "mechanism_supported": [ "valuation" ], "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-07", "via": "jneurosci.org article page" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1523/JNEUROSCI.2956-18.2019", "record_url": "https://research.breacher.ai/sources/rosenthal-von-der-putten-2019-vmpfc.md" }, { "id": "schindler-2017-erp-realism", "title": "Differential effects of face-realism and emotion on event-related brain potentials and their implications for the uncanny valley theory", "authors": [ "Sebastian Schindler", "Eduard Zell", "Mario Botsch", "Johanna Kissler" ], "year": 2017, "venue": "Scientific Reports", "volume": "7", "pages": "45003", "doi": "10.1038/srep45003", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "EEG/ERP; the same faces rendered at 6 realism levels (1 = abstract to 6 = real photograph) × happy/angry/neutral expressions; no eeriness or likability ratings", "modality": [ "cg-face" ], "stimuli": "Stylisation continuum from abstract to photographic", "n_participants": 33, "n_note": "recruited (22 female, mean age 23.3); analysed N not confirmed", "topics": [ "uncanny-valley", "neural" ], "summary": "N170 was U-shaped across realism (strongest for most abstract and most realistic faces), LPP increased linearly with realism, and EPN was unaffected by realism; no uncanny 'dip' was directly observed.", "results": [ { "metric": "N170", "value": "U-shaped (quadratic) across realism", "note": "stronger for most abstract and most realistic; realism × emotion interaction", "source_location": null }, { "metric": "EPN", "value": "no realism effect", "note": "modulated by emotion only (largest for angry)", "source_location": null }, { "metric": "LPP", "value": "linear increase with realism", "note": "no emotion effect", "source_location": null }, { "metric": "Uncanny valley in ERPs", "value": "not directly observed", "note": "authors suggest a perceptual basis for the hypothesis", "source_location": null } ], "moderators": [ "Happy expressions elicited weaker N170 as realism increased; angry expressions stronger" ], "limitations": [ "No eeriness/likability measure", "Stylised CG continuum, not synthetic photoreal faces" ], "mechanism_supported": [ "perceptual-basis", "against-affective-valley" ], "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-07", "via": "nature.com article page (open access); author list on page may be truncated" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1038/srep45003", "record_url": "https://research.breacher.ai/sources/schindler-2017-erp-realism.md" }, { "id": "moshel-2022-decoding-ai-faces", "title": "Are you for real? Decoding realistic AI-generated faces from neural activity", "authors": [ "Michoel L. Moshel", "Amanda K. Robinson", "Thomas A. Carlson", "Tijl Grootswagers" ], "year": 2022, "venue": "Vision Research", "volume": "199", "pages": "108079", "doi": "10.1016/j.visres.2022.108079", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Behavioural real-vs-AI classification plus EEG multivariate decoding of real vs AI faces", "modality": [ "image-face" ], "stimuli": "Realistic GAN faces vs real faces", "n_participants": null, "n_note": "not in abstract", "topics": [ "uncanny-valley", "neural", "implicit-detection", "human-detection" ], "summary": "Group-level behavioural judgments were near chance (people often called real faces fake and vice versa), yet neural activity reliably distinguished AI-generated from real faces.", "results": [ { "metric": "Behavioural real-vs-AI judgments", "value": "near chance at group level", "note": "frequent label swaps; ≈37% figure in press coverage not checked", "source_location": null }, { "metric": "EEG decoding of real vs AI", "value": "reliable (above chance)", "note": "≈54% figure in press coverage not checked", "source_location": null } ], "moderators": [], "limitations": [ "Decoding ≠ conscious or affective response", "Sample sizes not checked" ], "mechanism_supported": [ "implicit-sensitivity" ], "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_in": [ "MR-0001", "MR-0002" ], "doi_url": "https://doi.org/10.1016/j.visres.2022.108079", "record_url": "https://research.breacher.ai/sources/moshel-2022-decoding-ai-faces.md" }, { "id": "eiserbeck-2023-deepfake-smiles", "title": "Deepfake smiles matter less—the psychological and neural impact of presumed AI-generated faces", "authors": [ "Anna Eiserbeck", "Martin Maier", "Julia Baum", "Rasha Abdel Rahman" ], "year": 2023, "venue": "Scientific Reports", "volume": "13", "pages": "16111", "doi": "10.1038/s41598-023-42802-x", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "EEG (64 ch) belief-manipulation study: real face photographs labelled on screen as 'REAL' or 'FAKE' (cover story), valence rating on 100-point scale; label and emotion counterbalanced", "modality": [ "image-face" ], "stimuli": "540 real photographs: 180 identities × angry/neutral/happy (RaFD, KDEF, FACES databases); no image was actually synthetic", "n_participants": 30, "n_note": "21 female, 9 male; mean age 25.9; 7 datasets excluded and replaced", "topics": [ "uncanny-valley", "neural", "labelling", "emotion" ], "summary": "Simply labelling a real smiling face as 'fake' made it rated less positive and removed its early neural smile effects (P1/N170/EPN), while increasing late, effortful processing (LPP); angry faces were unaffected by the label.", "results": [ { "metric": "Valence rating, happy faces", "value": "fake 73.12 vs real 74.97 (0-100)", "note": "label effect only for happy faces", "source_location": null }, { "metric": "Rating RT, happy faces", "value": "≈1,366 ms fake vs ≈1,290 ms real", "note": "slower for presumed fakes", "source_location": null }, { "metric": "Overall valence", "value": "fake 49.80 vs real 50.78", "note": "small", "source_location": null }, { "metric": "P1 / N170 / EPN smile effect (happy vs neutral)", "value": "present for real-labelled only", "note": "EPN for fake p=.066", "source_location": null }, { "metric": "LPP smile effect", "value": "present for fake-labelled only", "note": "more positive LPP → more effortful evaluation", "source_location": null }, { "metric": "Angry vs neutral", "value": "N170, EPN, LPP effects for both labels", "note": "negative expressions unaffected by label", "source_location": null } ], "moderators": [], "limitations": [ "Label × emotion interaction significant behaviourally but not in ERPs (authors' caveat)", "Small N", "Belief manipulation only; no actual synthetic faces" ], "mechanism_supported": [ "top-down-belief", "dehumanization" ], "verification": { "status": "verified-fulltext", "confidence": "high", "checked": "2026-10-07", "via": "nature.com article page (open access); author list on page truncated" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1038/s41598-023-42802-x", "record_url": "https://research.breacher.ai/sources/eiserbeck-2023-deepfake-smiles.md" }, { "id": "weisman-pena-2021-doppelganger", "title": "Face the uncanny: The effects of doppelganger talking head avatars on affect-based trust toward artificial intelligence technology are mediated by uncanny valley perceptions", "authors": [ "William D. Weisman", "Jorge F. Peña" ], "year": 2021, "venue": "Cyberpsychology, Behavior, and Social Networking", "volume": "24(3)", "pages": "182-187", "doi": null, "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Experiment with self-resembling deepfake talking-head avatars; uncanniness and affect-based trust measures", "modality": [ "video" ], "stimuli": "Participant-doppelganger deepfake talking heads", "n_participants": null, "n_note": "not recorded", "topics": [ "uncanny-valley", "deepfake-video", "trust" ], "summary": "Self-resembling deepfake avatars increased uncanniness, which in turn reduced affect-based trust.", "results": [ { "metric": "Doppelganger → uncanniness", "value": "increase", "note": "", "source_location": null }, { "metric": "Uncanniness → affect-based trust", "value": "decrease (mediation)", "note": "", "source_location": null } ], "moderators": [], "limitations": [ "Details pending verification", "2020-era deepfake quality" ], "mechanism_supported": [ "uncanny-to-trust" ], "verification": { "status": "unverified", "confidence": "medium", "checked": null, "via": "compiler knowledge; DOI unknown" }, "cited_in": [ "MR-0002" ], "doi_url": null, "record_url": "https://research.breacher.ai/sources/weisman-pena-2021-doppelganger.md" }, { "id": "lee-2026-ai-chest-radiographs", "title": "Complementary Error Profiles of Human Readers and Large Language Models in Detecting AI-Generated Chest Radiographs: A Comparative Reader Study", "authors": [ "Suji Lee", "Jei Youn Park", "Yongsik Sim", "Youho Myong" ], "year": 2026, "venue": "Journal of Imaging Informatics in Medicine", "volume": null, "pages": null, "doi": "10.1007/s10278-026-02339-7", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Reader study: 2 radiologists, 2 nonradiologist physicians and 3 frontier LLMs (GPT-5.5, Gemini 3.5 Flash, Claude Opus 4.8) classified chest radiographs as real or AI-generated; LLMs re-run 3 months later and with laterality markers/borders masked; frequency-domain baseline classifier for comparison", "modality": [ "image-general" ], "stimuli": "160 posteroanterior chest radiographs: 40 real (NIH ChestX-ray14) and 120 synthetic (diffusion and text/LLM-based generation pipelines); 75% synthetic prevalence", "n_participants": 4, "n_note": "4 human readers (2 radiologists, 2 nonradiologist physicians) plus 3 LLM evaluators", "topics": [ "human-detection", "human-vs-machine", "expertise" ], "summary": "Expert and non-expert physicians and frontier LLMs could not reliably tell AI-generated chest X-rays from real ones: the best human (radiologist, 75.8% balanced accuracy) and best LLM (77.9%) were similar but made different errors, radiologists called 38-45% of real images fake, and a strong frequency-domain detector collapsed on a held-out generator (12% sensitivity).", "results": [ { "metric": "Top evaluator (Gemini 3.5 Flash) balanced accuracy", "value": "77.9%", "note": "specificity 100%, sensitivity 55.8%", "source_location": "abstract" }, { "metric": "Best human reader (radiologist RAD1) balanced accuracy", "value": "75.8%", "note": "sensitivity 55.0%, specificity 96.7%; paired difference −2.1 points [95% CI −5.8, 1.2]", "source_location": "abstract" }, { "metric": "Human readers: diffusion-generated images detected", "value": "82-100%", "note": "all four human readers", "source_location": "abstract" }, { "metric": "Radiologists: real images misclassified as synthetic", "value": "38-45%", "note": "", "source_location": "abstract" }, { "metric": "Nonradiologist physicians", "value": "near chance", "note": "", "source_location": "abstract" }, { "metric": "LLMs: LLM-generated images detected", "value": "32-72%", "note": "", "source_location": "abstract" }, { "metric": "LLM specificity with markers/borders masked", "value": "Gemini 62.5%; GPT-5.5 85.0%", "note": "vs 90-100% in same-day unmasked runs", "source_location": "abstract" }, { "metric": "Agreement among LLMs (Fleiss' kappa)", "value": "0.213", "note": "low interevaluator agreement", "source_location": "abstract" }, { "metric": "Frequency-domain baseline", "value": "balanced accuracy 98.3% in-distribution; 12% sensitivity on a held-out generator", "note": "", "source_location": "abstract" } ], "moderators": [ "Generator type: diffusion images were caught by humans far more often than LLM/text-pipeline images were caught by LLMs.", "Expertise: radiologists outperformed nonradiologists but at the cost of many false alarms on real images." ], "limitations": [ "Four human readers", "Medical images only", "Values from abstract; full text not read", "Fixed 75% synthetic prevalence" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-19", "via": "OpenAlex abstract record W7221727670 for doi:10.1007/s10278-026-02339-7 (publisher page could not be fetched)" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1007/s10278-026-02339-7", "record_url": "https://research.breacher.ai/sources/lee-2026-ai-chest-radiographs.md" }, { "id": "chen-2026-humans-ai-deepfake-teaming", "title": "How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming", "authors": [ "Xinyi Chen", "et al." ], "year": 2026, "venue": "Proceedings of the Human Factors and Ergonomics Society Annual Meeting", "volume": null, "pages": null, "doi": "10.1177/10711813261487968", "url": null, "peer_review": "peer-reviewed-conference", "evidence_tier": "B", "design": "Eye-tracking and EEG comparison of human and AI attention allocation in deepfake detection, across levels of AI literacy", "modality": [ "video" ], "stimuli": "Deepfake media manipulating lip movements, facial expressions and voices (details not in abstract)", "n_participants": null, "n_note": "N not reported in the abstract; OpenAlex lists Jing Yang (University at Buffalo) as last author; full author list not confirmed", "topics": [ "human-detection", "human-ai-teaming", "neural" ], "summary": "Humans and AI detectors relied on different cues; eye-tracking and EEG suggested holistic, flexible human viewing that catches some inconsistencies AI misses, and people with more AI exposure matched or exceeded AI detectors in some contexts (abstract; no numbers reported).", "results": [ { "metric": "Cue use", "value": "humans and AI rely on different cues and reasoning strategies", "note": "qualitative", "source_location": "abstract" }, { "metric": "Human vs AI detector", "value": "people with more AI exposure may match or exceed AI detectors in some contexts", "note": "no figures in abstract", "source_location": "abstract" }, { "metric": "Processing style", "value": "holistic, flexible visual processing", "note": "eye-tracking and EEG evidence", "source_location": "abstract" } ], "moderators": [ "AI literacy / AI exposure" ], "limitations": [ "Abstract reports no N or effect sizes", "Short proceedings paper", "Full text not read (closed access)" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "medium", "checked": "2026-10-19", "via": "OpenAlex abstract record W7221715535 for doi:10.1177/10711813261487968" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1177/10711813261487968", "record_url": "https://research.breacher.ai/sources/chen-2026-humans-ai-deepfake-teaming.md" }, { "id": "naseem-2026-in-the-wild-detection-review", "title": "From curated datasets to social-media reality: In-the-wild, multimodal, and generalizable deepfake detection", "authors": [ "Muhammad Tahir Naseem", "et al." ], "year": 2026, "venue": "Computer Science Review", "volume": "63", "pages": "101081", "doi": "10.1016/j.cosrev.2026.101081", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "C", "design": "Narrative review of deepfake detection from the perspective of in-the-wild robustness, multimodal reasoning and cross-domain generalization (no systematic protocol described in abstract)", "modality": [ "review" ], "stimuli": "n/a (review)", "n_participants": null, "n_note": "Review; OpenAlex lists Wajid Zaman (Yeungnam University) as last author; full author list not confirmed", "topics": [ "detector-generalization", "review" ], "summary": "Review concluding that strong benchmark performance of deepfake detectors does not necessarily carry over to real-world media, because of platform compression, reposting, post-processing, modality mismatch, temporal drift and new generators; calls for deployment-oriented, continually re-benchmarked detection.", "results": [ { "metric": "Benchmark-to-wild transfer", "value": "curated-benchmark performance does not necessarily translate into reliable real-world performance", "note": "qualitative", "source_location": "abstract" }, { "metric": "Causes of detector failure", "value": "generator shift, platform-induced degradation, modality mismatch, language and speaker variation, contextual diversity", "note": "", "source_location": "abstract" }, { "metric": "Recommendations", "value": "continually updated benchmarks, missing-modality robustness, uncertainty-aware detection, provenance integration", "note": "", "source_location": "abstract" } ], "moderators": [], "limitations": [ "Narrative review", "No quantitative synthesis in abstract" ], "mechanism_supported": null, "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-19", "via": "OpenAlex abstract record W7222019517 for doi:10.1016/j.cosrev.2026.101081" }, "cited_in": [ "MR-0001" ], "doi_url": "https://doi.org/10.1016/j.cosrev.2026.101081", "record_url": "https://research.breacher.ai/sources/naseem-2026-in-the-wild-detection-review.md" }, { "id": "mamsheli-2026-eeg-ai-faces", "title": "Processing of Real and AI-Generated Human Faces: An Exploratory EEG Study", "authors": [ "Saryz Taha Mamsheli", "Amanda Tunes Paulsen", "Enrico Cipriani", "Simone Grassini" ], "year": 2026, "venue": "Proceedings of the European Conference on Cognitive Ergonomics (ECCE 2026)", "volume": null, "pages": "375-379", "doi": "10.1145/3822301.3822351", "url": null, "peer_review": "peer-reviewed-conference", "evidence_tier": "B", "design": "Exploratory EEG (ERP) study with post-recording questionnaire; poster analyses restricted to real vs AI-generated human faces", "modality": [ "image-face" ], "stimuli": "Images of humans, AI-generated humans, robots and dolls with happy, fearful and neutral expressions (generator not stated in abstract)", "n_participants": 28, "n_note": "", "topics": [ "uncanny-valley", "neural", "implicit-detection" ], "summary": "Real and AI-generated faces evoked different early brain responses (N170, N400, EPN) but not a different late LPP, and were rated differently for expressiveness and 'sense of self', while perceived intelligence and appeal did not differ.", "results": [ { "metric": "ERP differences, real vs AI-generated faces", "value": "significant for N170, N400 and EPN; not for LPP", "note": "early stages differentiate the faces", "source_location": "abstract" }, { "metric": "Questionnaire: expressiveness; sense of self and inner consciousness", "value": "significantly different between real and AI faces", "note": "", "source_location": "abstract" }, { "metric": "Questionnaire: perceived intelligence and appeal", "value": "n.s.", "note": "", "source_location": "abstract" } ], "moderators": [], "limitations": [ "Exploratory poster analyses", "Small N", "Generator not stated in abstract", "Values from abstract only" ], "mechanism_supported": [ "implicit-sensitivity" ], "verification": { "status": "verified-abstract", "confidence": "medium", "checked": "2026-10-19", "via": "OpenAlex abstract record W7212435617 for doi:10.1145/3822301.3822351" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1145/3822301.3822351", "record_url": "https://research.breacher.ai/sources/mamsheli-2026-eeg-ai-faces.md" }, { "id": "hsieh-2026-robot-gaze-uncanny", "title": "Robot gaze-timing combinations and the uncanny valley", "authors": [ "Te-Yi Hsieh", "Pai-He Hsiao", "Sin-Cheng Ciou", "Chia-Hui Pan", "Tzu-Chun Chen", "Chia-Chun Tsai", "Yu Fang", "Su-Ling Yeh" ], "year": 2026, "venue": "Computers in Human Behavior Reports", "volume": "24", "pages": "101378", "doi": "10.1016/j.chbr.2026.101378", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Within-subject ratings of a virtual robot in nine gaze conditions (direct vs averted gaze ratios and durations) during storytelling, with eye tracking (EMHMM) and personality/attitude measures", "modality": [ "robot-face" ], "stimuli": "Virtual anthropomorphic robot, nine gaze-timing combinations", "n_participants": 61, "n_note": "", "topics": [ "uncanny-valley", "valley-shape", "individual-differences" ], "summary": "Across nine robot gaze-timing conditions, likability followed an uncanny-valley (cubic) curve over perceived anthropomorphism while perceived empathy rose steadily; extraversion, negative attitudes and neuroticism moderated these relations.", "results": [ { "metric": "Anthropomorphism-likability relation", "value": "cubic, uncanny-valley pattern", "note": "", "source_location": "abstract" }, { "metric": "Anthropomorphism-empathy relation", "value": "rose steadily (no valley)", "note": "", "source_location": "abstract" }, { "metric": "Moderators (exploratory)", "value": "extraversion and negative attitudes toward robots (likability); neuroticism (empathy)", "note": "", "source_location": "abstract" }, { "metric": "Eye-movement patterns (EMHMM)", "value": "two: analytic and holistic", "note": "higher openness → more analytic viewing of the highest-rated gaze conditions", "source_location": "abstract" } ], "moderators": [ "Personality (extraversion, neuroticism, openness)", "Attitudes toward robots" ], "limitations": [ "Virtual robot, not synthetic human media", "Values from abstract only", "Exploratory moderator analyses" ], "mechanism_supported": [ "human-likeness" ], "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-19", "via": "OpenAlex abstract record W7221007893 for doi:10.1016/j.chbr.2026.101378" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.1016/j.chbr.2026.101378", "record_url": "https://research.breacher.ai/sources/hsieh-2026-robot-gaze-uncanny.md" }, { "id": "jang-2026-uncanny-conversation", "title": "Uncanny Valley in Conversation? Effects of Internal and External Human-Likeness on Intimacy and Trust", "authors": [ "Jungwoo Jang", "Jae-gil Lee", "Hayeon Song" ], "year": 2026, "venue": "Behavioral Sciences", "volume": "16(10)", "pages": "1794", "doi": "10.3390/bs16101794", "url": null, "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "2 (appearance: machine-like vs human-like) × 3 (self-disclosure depth: none, low, high) between-subjects experiment; one scripted counselling session with an LLM-style AI counsellor prototype; six outcome measures, controlling for prior mind perception", "modality": [ "text" ], "stimuli": "AI counsellor prototype varying appearance and depth of its own self-disclosure", "n_participants": 193, "n_note": "University students", "topics": [ "uncanny-valley", "mind-perception", "trust" ], "summary": "A machine-like AI counsellor was liked and trusted most at low self-disclosure and less at high disclosure (an uncanny-valley-like curve of mind), whereas a human-like counsellor gained with deeper disclosure; internal and external human-likeness interact.", "results": [ { "metric": "Machine-like counsellor: intimacy, social presence, trust, authenticity", "value": "peaked at low disclosure, declined at high", "note": "uncanny-valley-like pattern", "source_location": "abstract" }, { "metric": "Human-like counsellor", "value": "evaluations generally rose with disclosure depth", "note": "", "source_location": "abstract" }, { "metric": "Perceived inappropriateness", "value": "not higher for deeper disclosure", "note": "", "source_location": "abstract" }, { "metric": "Mediation (machine-like only)", "value": "deeper disclosure → lower anthropomorphism → lower intimacy and trust", "note": "", "source_location": "abstract" } ], "moderators": [ "Agent appearance" ], "limitations": [ "Student sample", "Single scripted session", "Values from abstract only" ], "mechanism_supported": [ "mind-perception" ], "verification": { "status": "verified-abstract", "confidence": "high", "checked": "2026-10-19", "via": "OpenAlex abstract record W7215112744 for doi:10.3390/bs16101794" }, "cited_in": [ "MR-0002" ], "doi_url": "https://doi.org/10.3390/bs16101794", "record_url": "https://research.breacher.ai/sources/jang-2026-uncanny-conversation.md" } ] }