{ "schema_version": "1.0", "generated": "2026-10-19T01:28:37Z", "site_version": "0.4.0", "license": "CC BY 4.0", "count": 54, "canonical_url": "https://research.breacher.ai/data/citations.json", "how_to_verify": "1. Pick a source in `sources` (the URL fragment # names the entry whose id is ). 2. Resolve it through doi_url or url; if that is paywalled, use oa_url or one of the resolvers (DOI, OpenAlex, Crossref, PubMed, arXiv); if it has no identifier, run search_query in a scholarly search engine. 3. For each item in claims, compare value with the paper at source_location (abstract, full text, or a named section/table); verification says what the compiler read (status, checked date, via). 4. Each entry in findings lists its source_ids, and each source's cited_by lists the findings it supports, so every synthesis claim can be rebuilt from the primary papers. 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Cooper", "Hany Farid" ], "year": 2025, "venue": "Scientific Reports", "volume": "15", "pages": "11004", "doi": "10.1038/s41598-025-94170-3", "doi_url": "https://doi.org/10.1038/s41598-025-94170-3", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1038/s41598-025-94170-3", "https://api.openalex.org/works/doi:10.1038/s41598-025-94170-3", "https://api.crossref.org/works/10.1038/s41598-025-94170-3" ], "search_query": "\"People are poorly equipped to detect AI-powered voice clones\" Barrington 2025", "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", "n_participants": 634, "claims": [ { "metric": "Identity: real-real same-speaker pairs judged same", "value": "median 100.0% (IQR 85.7-100.0)", "note": null, "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": null, "source_location": null }, { "metric": "Naturalness: real clips correctly classified", "value": "67.4% (SD 14.8)", "note": null, "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 } ], "verification": { "status": "verified-fulltext", "checked": "2026-10-12", "via": "nature.com full text, doi:10.1038/s41598-025-94170-3" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F12" ] }, { "metareport_id": "MR-0003", "metareport_url": "/metareports/voice-cloning-human-detection.md", "finding_ids": [ "MR-0003.F1", "MR-0003.F2" ] } ], "record_url": "/sources/barrington-2025-voice-clones.md" }, { "id": "bray-2023-deepfake-faces", "citation": "Bray, S. D., Johnson, S. D., & Kleinberg, B. (2023). Testing human ability to detect 'deepfake' images of human faces. Journal of Cybersecurity, 9(1), tyad011.", "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", "doi_url": "https://doi.org/10.1093/cybsec/tyad011", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1093/cybsec/tyad011", "https://api.openalex.org/works/doi:10.1093/cybsec/tyad011", "https://api.crossref.org/works/10.1093/cybsec/tyad011" ], "search_query": "\"Testing human ability to detect 'deepfake' images of human faces\" Bray 2023", "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", "n_participants": 280, "claims": [ { "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 } ], "verification": { "status": "verified-fulltext", "checked": "2026-10-07", "via": "academic.oup.com article page" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F5", "MR-0001.F7" ] } ], "record_url": "/sources/bray-2023-deepfake-faces.md" }, { "id": "burleigh-2013-does-uv-exist", "citation": "Burleigh, T. J., Schoenherr, J. R., & Lacroix, G. L. (2013). Does the uncanny valley exist? An empirical test of the relationship between eeriness and the human likeness of digitally created faces. Computers in Human Behavior, 29(3), 759-771.", "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", "doi_url": "https://doi.org/10.1016/j.chb.2012.11.021", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1016/j.chb.2012.11.021", "https://api.openalex.org/works/doi:10.1016/j.chb.2012.11.021", "https://api.crossref.org/works/10.1016/j.chb.2012.11.021" ], "search_query": "\"Does the uncanny valley exist? An empirical test of the relationship between eeriness and the human likeness of digitally created faces\" Burleigh 2013", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Two studies varying human likeness of computer-generated faces; eeriness ratings", "n_participants": null, "claims": [ { "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": null, "source_location": null } ], "verification": { "status": "verified-doi", "checked": "2026-10-07", "via": "Semantic Scholar metadata (title, authors, venue, DOI); abstract not available" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F2" ] } ], "record_url": "/sources/burleigh-2013-does-uv-exist.md" }, { "id": "cheetham-2014-happy-valley", "citation": "Cheetham, M., Suter, P., & Jäncke, L. (2014). Perceptual discrimination difficulty and familiarity in the Uncanny Valley: more like a 'Happy Valley'. Frontiers in Psychology, 5, 1219.", "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", "doi_url": "https://doi.org/10.3389/fpsyg.2014.01219", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.3389/fpsyg.2014.01219", "https://api.openalex.org/works/doi:10.3389/fpsyg.2014.01219", "https://api.crossref.org/works/10.3389/fpsyg.2014.01219" ], "search_query": "\"Perceptual discrimination difficulty and familiarity in the Uncanny Valley: more like a 'Happy Valley'\" Cheetham 2014", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Three experiments on avatar-to-human morph continua: discrimination difficulty, affect, processing-bias control", "n_participants": null, "claims": [ { "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": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F4" ] } ], "record_url": "/sources/cheetham-2014-happy-valley.md" }, { "id": "chen-2026-humans-ai-deepfake-teaming", "citation": "Chen, X., et al. (2026). How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming. Proceedings of the Human Factors and Ergonomics Society Annual Meeting.", "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", "doi_url": "https://doi.org/10.1177/10711813261487968", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": "W7221715535", "oa_url": null, "resolvers": [ "https://doi.org/10.1177/10711813261487968", "https://api.openalex.org/works/doi:10.1177/10711813261487968", "https://api.crossref.org/works/10.1177/10711813261487968", "https://api.openalex.org/works/W7221715535" ], "search_query": "\"How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming\" Chen 2026", "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", "n_participants": null, "claims": [ { "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" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-19", "via": "OpenAlex abstract record W7221715535 for doi:10.1177/10711813261487968" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F9" ] } ], "record_url": "/sources/chen-2026-humans-ai-deepfake-teaming.md" }, { "id": "cooke-2024-coin-toss", "citation": "Cooke, D., Edwards, A., Barkoff, S., & Kelly, K. (2025). As good as a coin toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli. Communications of the ACM, 68, 100-109.", "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", "doi_url": "https://doi.org/10.1145/3729417", "url": "https://arxiv.org/abs/2403.16760", "pmid": null, "pmcid": null, "arxiv_id": "2403.16760", "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1145/3729417", "https://api.openalex.org/works/doi:10.1145/3729417", "https://api.crossref.org/works/10.1145/3729417", "https://arxiv.org/abs/2403.16760" ], "search_query": "\"As good as a coin toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli\" Cooke 2025", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Online US survey; mixed-modality stimuli (images, audio, video, audiovisual)", "n_participants": 1276, "claims": [ { "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": null, "source_location": "abstract" }, { "metric": "Mixed-authenticity audiovisual", "value": "harder", "note": null, "source_location": "abstract" }, { "metric": "Prior knowledge of synthetic media", "value": "no significant effect", "note": null, "source_location": "abstract" }, { "metric": "Age", "value": "older participants performed worse", "note": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-07", "via": "arXiv 2403.16760 abstract page (journal ref CACM 68, 2025)" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F1", "MR-0001.F8", "MR-0001.F11" ] }, { "metareport_id": "MR-0003", "metareport_url": "/metareports/voice-cloning-human-detection.md", "finding_ids": [] } ], "record_url": "/sources/cooke-2024-coin-toss.md" }, { "id": "dawel-2026-training-ai-faces", "citation": "Dawel, A., George, T., Mah, E. Y., Dunn, J. D., Sutherland, C. A. M., Argument, N., & Tanaka, J. W. (2026). Training humans to detect AI-generated faces. Proceedings of the National Academy of Sciences (PNAS), 123(27), e2602122123.", "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", "doi_url": "https://doi.org/10.1073/pnas.2602122123", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1073/pnas.2602122123", "https://api.openalex.org/works/doi:10.1073/pnas.2602122123", "https://api.crossref.org/works/10.1073/pnas.2602122123" ], "search_query": "\"Training humans to detect AI-generated faces\" Dawel 2026", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Pre-post training study on untrained test faces; test-retest control study; online replication", "n_participants": 45, "claims": [ { "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": null, "source_location": "abstract" }, { "metric": "Confidence", "value": "well calibrated only after training", "note": null, "source_location": "abstract" }, { "metric": "Controls", "value": "test-retest control ruled out practice effects; online replication supported scalability", "note": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-12", "via": "OpenAlex abstract record for doi:10.1073/pnas.2602122123 (publisher page not reachable)" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F7" ] } ], "record_url": "/sources/dawel-2026-training-ai-faces.md" }, { "id": "diel-2022-uv-meta", "citation": "Diel, A., Weigelt, S., & MacDorman, K. F. (2022). A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables. ACM Transactions on Human-Robot Interaction, 11(1), Article 1.", "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", "doi_url": "https://doi.org/10.1145/3470742", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1145/3470742", "https://api.openalex.org/works/doi:10.1145/3470742", "https://api.crossref.org/works/10.1145/3470742" ], "search_query": "\"A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables\" Diel 2022", "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", "n_participants": null, "claims": [ { "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": null, "source_location": "abstract" }, { "metric": "Realism render", "value": "g = 0.99 [0.62, 1.36]", "note": null, "source_location": "abstract" }, { "metric": "Morphing", "value": "g = 0.94 [0.64, 1.24]", "note": null, "source_location": "abstract" }, { "metric": "Largest-effect affect measures", "value": "threatening, likable, aesthetics, familiarity, eeriness", "note": null, "source_location": "abstract" }, { "metric": "Largest-effect indirect measures", "value": "dislike frequency, categorization RT, like frequency, avoidance, viewing duration", "note": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F1", "MR-0002.F3" ] } ], "record_url": "/sources/diel-2022-uv-meta.md" }, { "id": "diel-2024-deepfake-detection-meta", "citation": "Diel, A., Lalgi, T., Schröter, I. C., MacDorman, K. F., Teufel, M., & Bäuerle, A. (2024). Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers. Computers in Human Behavior Reports, 16, 100538.", "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", "doi_url": "https://doi.org/10.1016/j.chbr.2024.100538", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1016/j.chbr.2024.100538", "https://api.openalex.org/works/doi:10.1016/j.chbr.2024.100538", "https://api.crossref.org/works/10.1016/j.chbr.2024.100538" ], "search_query": "\"Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers\" Diel 2024", "peer_review": "peer-reviewed-journal", "evidence_tier": "A", "design": "Systematic review and meta-analysis of human deepfake-detection studies (k = 137 effect sizes)", "n_participants": 86155, "claims": [ { "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": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-07", "via": "Semantic Scholar API abstract; DOI resolves to Elsevier PII S2451958824001714" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F1", "MR-0001.F7" ] }, { "metareport_id": "MR-0003", "metareport_url": "/metareports/voice-cloning-human-detection.md", "finding_ids": [ "MR-0003.F5" ] } ], "record_url": "/sources/diel-2024-deepfake-detection-meta.md" }, { "id": "dunn-2026-super-recognizers-average", "citation": "Dunn, J. D., White, D., Sutherland, C. A. M., Miller, E. J., Steward, B. A., & Dawel, A. (2026). Too good to be true: Synthetic AI faces are more average than real faces and super-recognizers know it. British Journal of Psychology.", "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. 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Nightingale" ], "year": 2026, "venue": "Scientific Reports", "volume": "16", "pages": "28639", "doi": "10.1038/s41598-026-59364-3", "doi_url": "https://doi.org/10.1038/s41598-026-59364-3", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1038/s41598-026-59364-3", "https://api.openalex.org/works/doi:10.1038/s41598-026-59364-3", "https://api.crossref.org/works/10.1038/s41598-026-59364-3" ], "search_query": "\"Human detection of AI-generated faces and voices is not domain-general\" Ivory 2026", "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", "n_participants": 172, "claims": [ { "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 } ], "verification": { "status": "verified-fulltext", "checked": "2026-10-12", "via": "nature.com full text, doi:10.1038/s41598-026-59364-3" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F5", "MR-0001.F6", "MR-0001.F12", "MR-0001.F13" ] }, { "metareport_id": "MR-0003", "metareport_url": "/metareports/voice-cloning-human-detection.md", "finding_ids": [ "MR-0003.F2", "MR-0003.F3", "MR-0003.F7", "MR-0003.F8" ] } ], "record_url": "/sources/ivory-2026-faces-voices-domain.md" }, { "id": "jang-2026-uncanny-conversation", "citation": "Jang, J., Lee, J.-g., & Song, H. (2026). Uncanny Valley in Conversation? Effects of Internal and External Human-Likeness on Intimacy and Trust. Behavioral Sciences, 16(10), 1794.", "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", "doi_url": "https://doi.org/10.3390/bs16101794", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": "W7215112744", "oa_url": "https://doi.org/10.3390/bs16101794", "resolvers": [ "https://doi.org/10.3390/bs16101794", "https://api.openalex.org/works/doi:10.3390/bs16101794", "https://api.crossref.org/works/10.3390/bs16101794", "https://api.openalex.org/works/W7215112744" ], "search_query": "\"Uncanny Valley in Conversation? Effects of Internal and External Human-Likeness on Intimacy and Trust\" Jang 2026", "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", "n_participants": 193, "claims": [ { "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": null, "source_location": "abstract" }, { "metric": "Perceived inappropriateness", "value": "not higher for deeper disclosure", "note": null, "source_location": "abstract" }, { "metric": "Mediation (machine-like only)", "value": "deeper disclosure → lower anthropomorphism → lower intimacy and trust", "note": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-19", "via": "OpenAlex abstract record W7215112744 for doi:10.3390/bs16101794" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F5" ] } ], "record_url": "/sources/jang-2026-uncanny-conversation.md" }, { "id": "katsyri-2015-uv-review", "citation": "Kätsyri, J., Förger, K., Mäkäräinen, M., & Takala, T. (2015). A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness. Frontiers in Psychology, 6, 390.", "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", "doi_url": "https://doi.org/10.3389/fpsyg.2015.00390", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.3389/fpsyg.2015.00390", "https://api.openalex.org/works/doi:10.3389/fpsyg.2015.00390", "https://api.crossref.org/works/10.3389/fpsyg.2015.00390" ], "search_query": "\"A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness\" Kätsyri 2015", "peer_review": "peer-reviewed-journal", "evidence_tier": "C", "design": "Narrative review of empirical tests of uncanny valley hypotheses", "n_participants": null, "claims": [ { "metric": "Simple human-likeness valley", "value": "no consistent support", "note": null, "source_location": "abstract" }, { "metric": "Movement modulates valley", "value": "no consistent support", "note": null, "source_location": "abstract" }, { "metric": "Categorization ambiguity", "value": "insufficient research", "note": null, "source_location": "abstract" }, { "metric": "Perceptual mismatch", "value": "good support", "note": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F2", "MR-0002.F3" ] } ], "record_url": "/sources/katsyri-2015-uv-review.md" }, { "id": "kobis-2021-fooled-twice", "citation": "Köbis, N. C., Doležalová, B., & Soraperra, I. (2021). Fooled twice: People cannot detect deepfakes but think they can. iScience, 24(11), 103364.", "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", "doi_url": "https://doi.org/10.1016/j.isci.2021.103364", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1016/j.isci.2021.103364", "https://api.openalex.org/works/doi:10.1016/j.isci.2021.103364", "https://api.crossref.org/works/10.1016/j.isci.2021.103364" ], "search_query": "\"Fooled twice: People cannot detect deepfakes but think they can\" Köbis 2021", "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", "n_participants": 210, "claims": [ { "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": null, "source_location": "abstract" }, { "metric": "Awareness raising", "value": "no improvement", "note": null, "source_location": "abstract" }, { "metric": "Calibration", "value": "overconfident", "note": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F5", "MR-0001.F6", "MR-0001.F7" ] } ], "record_url": "/sources/kobis-2021-fooled-twice.md" }, { "id": "kramer-2024-crowds-synthetic-faces", "citation": "Kramer, R. 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Exp2: single binary judgement with confidence and estimate of others' agreement (majority vote, confidence-weighted and 'surprisingly popular' aggregation)", "n_participants": null, "claims": [ { "metric": "Inner crowd", "value": "little benefit over individuals", "note": null, "source_location": "abstract" }, { "metric": "Outer crowd", "value": "performance improved with crowd size", "note": null, "source_location": "abstract" }, { "metric": "Aggregation rule", "value": "majority vote best for small crowds; 'surprisingly popular' best for larger crowds", "note": null, "source_location": "abstract" }, { "metric": "Personality", "value": "no association with performance", "note": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-12", "via": "OpenAlex abstract record for doi:10.1002/acp.4245" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F10" ] } ], "record_url": "/sources/kramer-2024-crowds-synthetic-faces.md" }, { "id": "kramer-2026-aggregation-human-ai", "citation": "Kramer, R. S. S., & Lister, V. P. M. (2026). Improving human performance in deepfake detection: limits of aggregation and human–AI collaboration. Cognitive Research: Principles and Implications.", "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", "doi_url": "https://doi.org/10.1186/s41235-026-00758-2", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1186/s41235-026-00758-2", "https://api.openalex.org/works/doi:10.1186/s41235-026-00758-2", "https://api.crossref.org/works/10.1186/s41235-026-00758-2" ], "search_query": "\"Improving human performance in deepfake detection: limits of aggregation and human–AI collaboration\" Kramer 2026", "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", "n_participants": null, "claims": [ { "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": null, "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" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-18", "via": "OpenAlex abstract record for doi:10.1186/s41235-026-00758-2" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F5", "MR-0001.F9" ] } ], "record_url": "/sources/kramer-2026-aggregation-human-ai.md" }, { "id": "lago-2022-more-real-than-real", "citation": "Lago, F., Pasquini, C., Böhme, R., Dumont, H., Goasdoué, V., & Boato, G. (2022). More real than real: A study on human visual perception of synthetic faces. IEEE Signal Processing Magazine, 39(1), 109-116.", "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", "doi_url": "https://doi.org/10.1109/MSP.2021.3120982", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1109/MSP.2021.3120982", "https://api.openalex.org/works/doi:10.1109/MSP.2021.3120982", "https://api.crossref.org/works/10.1109/MSP.2021.3120982" ], "search_query": "\"More real than real: A study on human visual perception of synthetic faces\" Lago 2022", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Perceptual experiment rating realism of real vs synthetic faces", "n_participants": null, "claims": [ { "metric": "Realism ratings", "value": "synthetic ≥ real", "note": null, "source_location": null } ], "verification": { "status": "unverified", "checked": null, "via": "compiler knowledge; DOI not resolved" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F2" ] } ], "record_url": "/sources/lago-2022-more-real-than-real.md" }, { "id": "lavan-2025-voice-clones-hyperrealism", "citation": "Lavan, N., Irvine, M., Rosi, V., & McGettigan, C. (2025). Voice clones sound realistic but not (yet) hyperrealistic. PLOS ONE, 20(9), e0332692.", "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", "doi_url": "https://doi.org/10.1371/journal.pone.0332692", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1371/journal.pone.0332692", "https://api.openalex.org/works/doi:10.1371/journal.pone.0332692", "https://api.crossref.org/works/10.1371/journal.pone.0332692" ], "search_query": "\"Voice clones sound realistic but not (yet) hyperrealistic\" Lavan 2025", "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", "n_participants": 150, "claims": [ { "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 } ], "verification": { "status": "verified-fulltext", "checked": "2026-10-12", "via": "journals.plos.org full text" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F6", "MR-0001.F12" ] }, { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F13" ] }, { "metareport_id": "MR-0003", "metareport_url": "/metareports/voice-cloning-human-detection.md", "finding_ids": [ "MR-0003.F2", "MR-0003.F3", "MR-0003.F4" ] } ], "record_url": "/sources/lavan-2025-voice-clones-hyperrealism.md" }, { "id": "lee-2026-ai-chest-radiographs", "citation": "Lee, S., Park, J. Y., Sim, Y., & Myong, Y. (2026). Complementary Error Profiles of Human Readers and Large Language Models in Detecting AI-Generated Chest Radiographs: A Comparative Reader Study. Journal of Imaging Informatics in Medicine.", "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", "doi_url": "https://doi.org/10.1007/s10278-026-02339-7", "url": null, "pmid": "42867583", "pmcid": null, "arxiv_id": null, "openalex_id": "W7221727670", "oa_url": "https://link.springer.com/content/pdf/10.1007/s10278-026-02339-7.pdf", "resolvers": [ "https://doi.org/10.1007/s10278-026-02339-7", "https://api.openalex.org/works/doi:10.1007/s10278-026-02339-7", "https://api.crossref.org/works/10.1007/s10278-026-02339-7", "https://api.openalex.org/works/W7221727670", "https://pubmed.ncbi.nlm.nih.gov/42867583/" ], "search_query": "\"Complementary Error Profiles of Human Readers and Large Language Models in Detecting AI-Generated Chest Radiographs: A Comparative Reader Study\" Lee 2026", "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", "n_participants": 4, "claims": [ { "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": null, "source_location": "abstract" }, { "metric": "Nonradiologist physicians", "value": "near chance", "note": null, "source_location": "abstract" }, { "metric": "LLMs: LLM-generated images detected", "value": "32-72%", "note": null, "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": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-19", "via": "OpenAlex abstract record W7221727670 for doi:10.1007/s10278-026-02339-7 (publisher page could not be fetched)" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F10" ] } ], "record_url": "/sources/lee-2026-ai-chest-radiographs.md" }, { "id": "lu-2023-fake2m", "citation": "Lu, Z., Huang, D., Bai, L., Qu, J., Wu, C., Liu, X., & Ouyang, W. (2023). Seeing is not always believing: Benchmarking human and model perception of AI-generated images. NeurIPS 2023 Datasets and Benchmarks Track.", "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, "doi_url": null, "url": "https://arxiv.org/abs/2304.13023", "pmid": null, "pmcid": null, "arxiv_id": "2304.13023", "openalex_id": null, "oa_url": null, "resolvers": [ "https://arxiv.org/abs/2304.13023" ], "search_query": "\"Seeing is not always believing: Benchmarking human and model perception of AI-generated images\" Lu 2023", "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", "n_participants": null, "claims": [ { "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" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-07", "via": "arXiv 2304.13023 abstract" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F10" ] } ], "record_url": "/sources/lu-2023-fake2m.md" }, { "id": "macdorman-chattopadhyay-2016", "citation": "MacDorman, K. F., & Chattopadhyay, D. (2016). Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not. Cognition, 146, 190-205.", "title": "Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not", "authors": [ "Karl F. 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A., Langer, A., & Chein, J. M. (2026). Human discernment of artificial intelligence in online markets can be shaped by experience and training. AI & Society.", "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", "doi_url": "https://doi.org/10.1007/s00146-026-03373-3", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1007/s00146-026-03373-3", "https://api.openalex.org/works/doi:10.1007/s00146-026-03373-3", "https://api.crossref.org/works/10.1007/s00146-026-03373-3" ], "search_query": "\"Human discernment of artificial intelligence in online markets can be shaped by experience and training\" Manhede 2026", "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", "n_participants": 117, "claims": [ { "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. 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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", "doi_url": "https://doi.org/10.1016/j.chb.2019.08.029", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1016/j.chb.2019.08.029", "https://api.openalex.org/works/doi:10.1016/j.chb.2019.08.029", "https://api.crossref.org/works/10.1016/j.chb.2019.08.029" ], "search_query": "\"Uncanny but not confusing: Multisite study of perceptual category confusion in the Uncanny Valley\" Mathur 2020", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Multisite replication and extension of Mathur & Reichling (2016)", "n_participants": null, "claims": [ { "metric": "Valley replication", "value": "replicated", "note": null, "source_location": null }, { "metric": "Category confusion mediation", "value": "not supported", "note": null, "source_location": null } ], "verification": { "status": "unverified", "checked": null, "via": "compiler knowledge; DOI not resolved" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F1", "MR-0002.F4" ] } ], "record_url": "/sources/mathur-2020-uncanny-not-confusing.md" }, { "id": "mathur-reichling-2016-cartography", "citation": "Mathur, M. B., & Reichling, D. B. (2016). Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley. Cognition, 146, 22-32.", "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", "doi_url": "https://doi.org/10.1016/j.cognition.2015.09.008", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1016/j.cognition.2015.09.008", "https://api.openalex.org/works/doi:10.1016/j.cognition.2015.09.008", "https://api.crossref.org/works/10.1016/j.cognition.2015.09.008" ], "search_query": "\"Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley\" Mathur 2016", "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", "n_participants": null, "claims": [ { "metric": "Explicit likability", "value": "uncanny valley present", "note": null, "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" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-07", "via": "Semantic Scholar API abstract" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F1", "MR-0002.F4" ] } ], "record_url": "/sources/mathur-reichling-2016-cartography.md" }, { "id": "miller-2023-ai-hyperrealism", "citation": "Miller, E. 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Krumhuber", "Amy Dawel" ], "year": 2023, "venue": "Psychological Science", "volume": "34(12)", "pages": "1390-1403", "doi": "10.1177/09567976231207095", "doi_url": "https://doi.org/10.1177/09567976231207095", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1177/09567976231207095", "https://api.openalex.org/works/doi:10.1177/09567976231207095", "https://api.crossref.org/works/10.1177/09567976231207095" ], "search_query": "\"AI hyperrealism: Why AI faces are perceived as more real than human ones\" Miller 2023", "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", "n_participants": 734, "claims": [ { "metric": "Exp1: White AI faces judged human", "value": "65.9%", "note": null, "source_location": null }, { "metric": "Exp1: White human faces judged human", "value": "51.1%", "note": null, "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 } ], "verification": { "status": "verified-fulltext", "checked": "2026-10-07", "via": "journals.sagepub.com article page" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F2", "MR-0001.F3", "MR-0001.F5", "MR-0001.F6", "MR-0001.F10" ] }, { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F9" ] } ], "record_url": "/sources/miller-2023-ai-hyperrealism.md" }, { "id": "mori-2012-uncanny-valley", "citation": "Mori, M., MacDorman, K. 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(2026). How deepfake plausibility, media literacy, and personal attitudes shape detection, liking, and intentions to share political deepfakes on social media. 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S., Meier, E., Ibsen, M., Rathgeb, C., Nichols, R., & Busch, C. (2025). Training humans for synthetic face image detection. Frontiers in Artificial Intelligence, 8, 1568267.", "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", "doi_url": "https://doi.org/10.3389/frai.2025.1568267", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.3389/frai.2025.1568267", "https://api.openalex.org/works/doi:10.3389/frai.2025.1568267", "https://api.crossref.org/works/10.3389/frai.2025.1568267" ], "search_query": "\"Training humans for synthetic face image detection\" Rehman 2025", "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", "n_participants": 184, "claims": [ { "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 } ], "verification": { "status": "verified-fulltext", "checked": "2026-10-12", "via": "frontiersin.org full text" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F7" ] } ], "record_url": "/sources/rehman-2025-training-synthetic-faces.md" }, { "id": "rosenthal-von-der-putten-2019-vmpfc", "citation": "Rosenthal-von der Pütten, A. M., Krämer, N. C., Maderwald, S., Brand, M., & Grabenhorst, F. (2019). Neural mechanisms for accepting and rejecting artificial social partners in the uncanny valley. Journal of Neuroscience, 39(33), 6555-6570.", "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", "doi_url": "https://doi.org/10.1523/JNEUROSCI.2956-18.2019", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1523/JNEUROSCI.2956-18.2019", "https://api.openalex.org/works/doi:10.1523/JNEUROSCI.2956-18.2019", "https://api.crossref.org/works/10.1523/JNEUROSCI.2956-18.2019" ], "search_query": "\"Neural mechanisms for accepting and rejecting artificial social partners in the uncanny valley\" Rosenthal-von der Pütten 2019", "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)", "n_participants": 21, "claims": [ { "metric": "Behavioural valley (artificial humans below linear trend)", "value": "significant in 18 of 21 individuals", "note": null, "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": null, "source_location": null }, { "metric": "DMPFC, fusiform", "value": "non-linear human/non-human distinction", "note": null, "source_location": null }, { "metric": "Choice decision variable", "value": "vmPFC for human choices; DMPFC for artificial-agent choices", "note": null, "source_location": null }, { "metric": "Amygdala", "value": "predicted rejection of artificial agents", "note": null, "source_location": null } ], "verification": { "status": "verified-fulltext", "checked": "2026-10-07", "via": "jneurosci.org article page" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F1", "MR-0002.F6" ] } ], "record_url": "/sources/rosenthal-von-der-putten-2019-vmpfc.md" }, { "id": "san-segundo-2026-audio-language-style", "citation": "San Segundo, E., López-Jareño, A., Wang, X., & Yamagishi, J. (2026). Human perception of audio deepfakes: the role of language and speaking style. Speech Communication, 185, 103487.", "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", "doi_url": "https://doi.org/10.1016/j.specom.2026.103487", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1016/j.specom.2026.103487", "https://api.openalex.org/works/doi:10.1016/j.specom.2026.103487", "https://api.crossref.org/works/10.1016/j.specom.2026.103487" ], "search_query": "\"Human perception of audio deepfakes: the role of language and speaking style\" San Segundo 2026", "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)", "n_participants": 54, "claims": [ { "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" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-18", "via": "OpenAlex abstract record for doi:10.1016/j.specom.2026.103487" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F15" ] }, { "metareport_id": "MR-0003", "metareport_url": "/metareports/voice-cloning-human-detection.md", "finding_ids": [ "MR-0003.F5", "MR-0003.F8" ] } ], "record_url": "/sources/san-segundo-2026-audio-language-style.md" }, { "id": "saygin-2012-predictive-coding", "citation": "Saygin, A. P., Chaminade, T., Ishiguro, H., Driver, J., & Frith, C. (2012). The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions. Social Cognitive and Affective Neuroscience, 7(4), 413-422.", "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", "doi_url": "https://doi.org/10.1093/scan/nsr025", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1093/scan/nsr025", "https://api.openalex.org/works/doi:10.1093/scan/nsr025", "https://api.crossref.org/works/10.1093/scan/nsr025" ], "search_query": "\"The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions\" Saygin 2012", "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)", "n_participants": 19, "claims": [ { "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 } ], "verification": { "status": "verified-fulltext", "checked": "2026-10-07", "via": "academic.oup.com article page" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F3", "MR-0002.F6" ] } ], "record_url": "/sources/saygin-2012-predictive-coding.md" }, { "id": "schindler-2017-erp-realism", "citation": "Schindler, S., Zell, E., Botsch, M., & Kissler, J. 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Scientific Reports, 7, 45003.", "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", "doi_url": "https://doi.org/10.1038/srep45003", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1038/srep45003", "https://api.openalex.org/works/doi:10.1038/srep45003", "https://api.crossref.org/works/10.1038/srep45003" ], "search_query": "\"Differential effects of face-realism and emotion on event-related brain potentials and their implications for the uncanny valley theory\" Schindler 2017", "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", "n_participants": 33, "claims": [ { "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 } ], "verification": { "status": "verified-fulltext", "checked": "2026-10-07", "via": "nature.com article page (open access); author list on page may be truncated" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F7" ] } ], "record_url": "/sources/schindler-2017-erp-realism.md" }, { "id": "seyama-nagayama-2007", "citation": "Seyama, J., & Nagayama, R. S. (2007). The uncanny valley: Effect of realism on the impression of artificial human faces. Presence: Teleoperators and Virtual Environments, 16(4), 337-351.", "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", "doi_url": "https://doi.org/10.1162/pres.16.4.337", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1162/pres.16.4.337", "https://api.openalex.org/works/doi:10.1162/pres.16.4.337", "https://api.crossref.org/works/10.1162/pres.16.4.337" ], "search_query": "\"The uncanny valley: Effect of realism on the impression of artificial human faces\" Seyama 2007", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Morphs between real and artificial (doll/mask) faces with eye-size manipulation; pleasantness ratings", "n_participants": null, "claims": [ { "metric": "Realism alone", "value": "no valley", "note": null, "source_location": null }, { "metric": "Abnormal features × high realism", "value": "strong unpleasantness", "note": "realism amplifies sensitivity to deviations", "source_location": null } ], "verification": { "status": "unverified", "checked": null, "via": "compiler knowledge" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F2", "MR-0002.F3" ] } ], "record_url": "/sources/seyama-nagayama-2007.md" }, { "id": "steckenfinger-ghazanfar-2009", "citation": "Steckenfinger, S. A., & Ghazanfar, A. A. (2009). Monkey visual behavior falls into the uncanny valley. Proceedings of the National Academy of Sciences (PNAS), 106(43), 18362-18366.", "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", "doi_url": "https://doi.org/10.1073/pnas.0910063106", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1073/pnas.0910063106", "https://api.openalex.org/works/doi:10.1073/pnas.0910063106", "https://api.crossref.org/works/10.1073/pnas.0910063106" ], "search_query": "\"Monkey visual behavior falls into the uncanny valley\" Steckenfinger 2009", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Looking-time preference in macaque monkeys for real, realistic-synthetic, and unrealistic-synthetic monkey faces", "n_participants": 5, "claims": [ { "metric": "Looking time", "value": "realistic-synthetic < real and < unrealistic", "note": null, "source_location": null } ], "verification": { "status": "unverified", "checked": null, "via": "compiler knowledge" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F8" ] } ], "record_url": "/sources/steckenfinger-ghazanfar-2009.md" }, { "id": "stein-ohler-2017-uncanny-mind", "citation": "Stein, J.-P., & Ohler, P. (2017). Venturing into the uncanny valley of mind—The influence of mind attribution on the acceptance of human-like characters in a virtual reality setting. Cognition, 160, 43-50.", "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", "doi_url": "https://doi.org/10.1016/j.cognition.2016.12.010", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1016/j.cognition.2016.12.010", "https://api.openalex.org/works/doi:10.1016/j.cognition.2016.12.010", "https://api.crossref.org/works/10.1016/j.cognition.2016.12.010" ], "search_query": "\"Venturing into the uncanny valley of mind—The influence of mind attribution on the acceptance of human-like characters in a virtual reality setting\" Stein 2017", "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", "n_participants": 92, "claims": [ { "metric": "Eeriness peak condition", "value": "autonomous AI + empathic behaviour", "note": "appearance held constant", "source_location": null } ], "verification": { "status": "unverified", "checked": null, "via": "compiler knowledge" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F5" ] } ], "record_url": "/sources/stein-ohler-2017-uncanny-mind.md" }, { "id": "sulimani-2026-recognition-ai-faces", "citation": "Sulimani, N., van Hunenstijn, A., & Albonico, A. (2026). Recognition of real and artificial intelligence-generated faces. Perception, 55(10), 924-937.", "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", "doi_url": "https://doi.org/10.1177/03010066261442089", "url": null, "pmid": "42053250", "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1177/03010066261442089", "https://api.openalex.org/works/doi:10.1177/03010066261442089", "https://api.crossref.org/works/10.1177/03010066261442089", "https://pubmed.ncbi.nlm.nih.gov/42053250/" ], "search_query": "\"Recognition of real and artificial intelligence-generated faces\" Sulimani 2026", "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", "n_participants": null, "claims": [ { "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": null, "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" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-18", "via": "Europe PMC abstract record (PMID 42053250)" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [] }, { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F9" ] } ], "record_url": "/sources/sulimani-2026-recognition-ai-faces.md" }, { "id": "tucciarelli-2022-realness", "citation": "Tucciarelli, R., Vehar, N., Chandaria, S., & Tsakiris, M. (2022). On the realness of people who do not exist: The social processing of artificial faces. iScience, 25.", "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, "doi_url": null, "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [], "search_query": "\"On the realness of people who do not exist: The social processing of artificial faces\" Tucciarelli 2022", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Real-vs-artificial classification plus social trait ratings (incl. trustworthiness)", "n_participants": null, "claims": [ { "metric": "Realness classification", "value": "near chance; artificial often judged real", "note": null, "source_location": null }, { "metric": "Trust-realness link", "value": "positive", "note": "more trustworthy → more likely judged real", "source_location": null } ], "verification": { "status": "unverified", "checked": null, "via": "compiler knowledge; DOI unknown" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F2", "MR-0001.F4" ] } ], "record_url": "/sources/tucciarelli-2022-realness.md" }, { "id": "wang-2015-uv-existence-explanations", "citation": "Wang, S., Lilienfeld, S. O., & Rochat, P. (2015). The uncanny valley: Existence and explanations. Review of General Psychology, 19(4), 393-407.", "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", "doi_url": "https://doi.org/10.1037/gpr0000056", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1037/gpr0000056", "https://api.openalex.org/works/doi:10.1037/gpr0000056", "https://api.crossref.org/works/10.1037/gpr0000056" ], "search_query": "\"The uncanny valley: Existence and explanations\" Wang 2015", "peer_review": "peer-reviewed-journal", "evidence_tier": "C", "design": "Theoretical review; proposes the dehumanization hypothesis", "n_participants": null, "claims": [ { "metric": "Dehumanization hypothesis", "value": "proposed", "note": "few direct tests", "source_location": null } ], "verification": { "status": "unverified", "checked": null, "via": "compiler knowledge" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F11" ] } ], "record_url": "/sources/wang-2015-uv-existence-explanations.md" }, { "id": "weisman-pena-2021-doppelganger", "citation": "Weisman, W. D., & Peña, J. F. (2021). Face the uncanny: The effects of doppelganger talking head avatars on affect-based trust toward artificial intelligence technology are mediated by uncanny valley perceptions. Cyberpsychology, Behavior, and Social Networking, 24(3), 182-187.", "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, "doi_url": null, "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [], "search_query": "\"Face the uncanny: The effects of doppelganger talking head avatars on affect-based trust toward artificial intelligence technology are mediated by uncanny valley perceptions\" Weisman 2021", "peer_review": "peer-reviewed-journal", "evidence_tier": "B", "design": "Experiment with self-resembling deepfake talking-head avatars; uncanniness and affect-based trust measures", "n_participants": null, "claims": [ { "metric": "Doppelganger → uncanniness", "value": "increase", "note": null, "source_location": null }, { "metric": "Uncanniness → affect-based trust", "value": "decrease (mediation)", "note": null, "source_location": null } ], "verification": { "status": "unverified", "checked": null, "via": "compiler knowledge; DOI unknown" }, "cited_by": [ { "metareport_id": "MR-0002", "metareport_url": "/metareports/uncanny-valley-and-synthetic-faces.md", "finding_ids": [ "MR-0002.F12" ] } ], "record_url": "/sources/weisman-pena-2021-doppelganger.md" }, { "id": "yeung-2026-isthisai-strategies", "citation": "Yeung, C., Weld, G., Mink, J., & Roesner, F. (2026). Is This AI? Longitudinal Analysis of Strategies Used for AI Detection on Two Subreddits. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES), 9(4), 2800-2812.", "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", "doi_url": "https://doi.org/10.1609/aies.v9i4.43351", "url": null, "pmid": null, "pmcid": null, "arxiv_id": null, "openalex_id": null, "oa_url": null, "resolvers": [ "https://doi.org/10.1609/aies.v9i4.43351", "https://api.openalex.org/works/doi:10.1609/aies.v9i4.43351", "https://api.crossref.org/works/10.1609/aies.v9i4.43351" ], "search_query": "\"Is This AI? Longitudinal Analysis of Strategies Used for AI Detection on Two Subreddits\" Yeung 2026", "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", "n_participants": null, "claims": [ { "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": null, "source_location": "abstract" } ], "verification": { "status": "verified-abstract", "checked": "2026-10-18", "via": "ojs.aaai.org/index.php/AIES/article/view/43351 (abstract page)" }, "cited_by": [ { "metareport_id": "MR-0001", "metareport_url": "/metareports/human-detection-accuracy.md", "finding_ids": [ "MR-0001.F15" ] } ], "record_url": "/sources/yeung-2026-isthisai-strategies.md" } ], "findings": [ { "id": "MR-0001.F1", "metareport_id": "MR-0001", "text": "Average human deepfake detection is not significantly above chance across modalities (pooled 55.54%, CI includes 50%).", "confidence": "High", "source_ids": [ "diel-2024-deepfake-detection-meta", "cooke-2024-coin-toss", "frank-2024-representative-study" ], "status": "current", "revised": [] }, { "id": "MR-0001.F2", "metareport_id": "MR-0001", "text": "High-quality GAN faces are classified at or below chance, and AI faces can be judged human *more* often than real faces (hyperrealism).", "confidence": "High", "source_ids": [ "nightingale-farid-2022", "miller-2023-ai-hyperrealism", "frank-2024-representative-study", "lago-2022-more-real-than-real", "tucciarelli-2022-realness", "moshel-2022-decoding-ai-faces" ], "status": "current", "revised": [] }, { "id": "MR-0001.F3", "metareport_id": "MR-0001", "text": "Hyperrealism has so far been shown for White faces, and is attributed to StyleGAN2's training data (≈69% White).", "confidence": "Moderate", "source_ids": [ "nightingale-farid-2022", "miller-2023-ai-hyperrealism" ], "status": "current", "revised": [] }, { "id": "MR-0001.F4", "metareport_id": "MR-0001", "text": "AI faces are rated more trustworthy than real faces (+7.7%, d = 0.49).", "confidence": "Moderate (one verified study)", "source_ids": [ "nightingale-farid-2022", "tucciarelli-2022-realness" ], "status": "current", "revised": [] }, { "id": "MR-0001.F5", "metareport_id": "MR-0001", "text": "Confidence predicts accuracy poorly. For AI faces, the least accurate people are the most confident. Where confidence carries some signal (faces), it carries none for voice clones (revised 2026-10-12). For deepfake video, confidence did not discriminate correct from incorrect trials and confidence-weighted pairing lowered accuracy (revised 2026-10-18).", "confidence": "High", "source_ids": [ "miller-2023-ai-hyperrealism", "bray-2023-deepfake-faces", "kobis-2021-fooled-twice", "iproov-2025-consumer-study", "ivory-2026-faces-voices-domain", "kramer-2026-aggregation-human-ai" ], "status": "revised", "revised": [ "2026-10-12", "2026-10-18" ] }, { "id": "MR-0001.F6", "metareport_id": "MR-0001", "text": "Default bias favours \"real/authentic\" for video, faces and voice clones (revised 2026-10-12).", "confidence": "High", "source_ids": [ "kobis-2021-fooled-twice", "miller-2023-ai-hyperrealism", "frank-2024-representative-study", "pehlivanoglu-2026-machines-humans", "ivory-2026-faces-voices-domain", "lavan-2025-voice-clones-hyperrealism" ], "status": "revised", "revised": [ "2026-10-12" ] }, { "id": "MR-0001.F7", "metareport_id": "MR-0001", "text": "Interventions mostly yield 0–20 point gains; some \"gains\" are criterion shifts. Impression-based (not artefact) training reportedly nearly doubled accuracy in one study (N = 45, abstract). No retention data (revised 2026-10-12). Labelled examples plus immediate feedback also lifted AI-text discernment (+9.4 points; control none) (revised 2026-10-18).", "confidence": "High", "source_ids": [ "nightingale-farid-2022", "bray-2023-deepfake-faces", "kobis-2021-fooled-twice", "mai-2023-speech-deepfakes", "diel-2024-deepfake-detection-meta", "gray-2025-super-recognizers", "rehman-2025-training-synthetic-faces", "dawel-2026-training-ai-faces", "manhede-2026-online-markets-training" ], "status": "revised", "revised": [ "2026-10-12", "2026-10-18" ] }, { "id": "MR-0001.F8", "metareport_id": "MR-0001", "text": "Accuracy rises with each modality added (text < silent video < audio < audio+video). Delivery matters more than content.", "confidence": "High (verified, large N)", "source_ids": [ "groh-2024-political-speech", "cooke-2024-coin-toss" ], "status": "current", "revised": [] }, { "id": "MR-0001.F9", "metareport_id": "MR-0001", "text": "Human crowds and a leading model were similarly accurate on 2020-era video with different errors; eye-tracking/EEG work suggests humans and AI use different cues (revised 2026-10-19). Model advice helps on average, but wrong advice lowers accuracy. A 2026 four-experiment study replicated this with simulated guidance: benefits from accurate advice were offset by following incorrect advice, regardless of deepfake prevalence (revised 2026-10-18).", "confidence": "Moderate", "source_ids": [ "groh-2022-crowds-machines", "kramer-2026-aggregation-human-ai", "chen-2026-humans-ai-deepfake-teaming" ], "status": "revised", "revised": [ "2026-10-18", "2026-10-19" ] }, { "id": "MR-0001.F10", "metareport_id": "MR-0001", "text": "Machines beat individuals on still images but can collapse out-of-domain (speech AUROC 25.31%) or on video. Aggregated human crowds are robust (speech AUROC 95.51%; faces improve with crowd size) (revised 2026-10-12). On AI-generated chest X-rays, the best radiologist and best LLM were similar (75.8% vs 77.9% balanced accuracy) with complementary errors, and a 98.3% frequency-domain detector fell to 12% sensitivity on a held-out generator; a review finds benchmark accuracy does not transfer reliably to in-the-wild media (revised 2026-10-19).", "confidence": "Moderate", "source_ids": [ "lu-2023-fake2m", "miller-2023-ai-hyperrealism", "mai-2023-speech-deepfakes", "pehlivanoglu-2026-machines-humans", "kramer-2024-crowds-synthetic-faces", "lee-2026-ai-chest-radiographs", "naseem-2026-in-the-wild-detection-review" ], "status": "revised", "revised": [ "2026-10-12", "2026-10-19" ] }, { "id": "MR-0001.F11", "metareport_id": "MR-0001", "text": "Individual differences are modest. Trust, cognitive reflection and familiarity influence decisions; older adults do worse; super-recognisers are better but unreliable (57–64%); face-recognition ability correlates with AI-face detection (r = .35) (revised 2026-10-12). Media literacy predicts better detection; heavier AI-platform engagement predicts *worse* AI-text discernment (r = −0.29) while attitudes to AI do not (revised 2026-10-18).", "confidence": "Moderate", "source_ids": [ "frank-2024-representative-study", "cooke-2024-coin-toss", "gray-2025-super-recognizers", "dunn-2026-super-recognizers-average", "pehlivanoglu-2026-machines-humans", "iproov-2025-consumer-study", "plohl-2026-political-deepfakes-plausibility", "manhede-2026-online-markets-training" ], "status": "revised", "revised": [ "2026-10-12", "2026-10-18" ] }, { "id": "MR-0001.F12", "metareport_id": "MR-0001", "text": "Voice clones of unfamiliar speakers are near-indistinguishable from the real speaker: accepted as the same identity in most pairs, detected near chance (d′ ≈ 0.1; 54–61% correct), and rated as real as human voices, though not more real. Detailed in MR-0003.", "confidence": "High", "source_ids": [ "barrington-2025-voice-clones", "lavan-2025-voice-clones-hyperrealism", "ivory-2026-faces-voices-domain" ], "status": "current", "revised": [] }, { "id": "MR-0001.F13", "metareport_id": "MR-0001", "text": "Detection skill is not domain-general: in the same people, face-detection ability did not significantly predict voice-clone detection. AI faces are \"hyper-average\" (central in face space), and skilled observers use that as a cue.", "confidence": "Low", "source_ids": [ "ivory-2026-faces-voices-domain", "dunn-2026-super-recognizers-average" ], "status": "current", "revised": [] }, { "id": "MR-0001.F14", "metareport_id": "MR-0001", "text": "Content plausibility matters beyond technical quality: political deepfakes consistent with the speaker's known stance are detected less often and liked more, and sharing intention is driven by liking and attitude toward the speaker rather than plausibility itself.", "confidence": "Low", "source_ids": [ "plohl-2026-political-deepfakes-plausibility" ], "status": "current", "revised": [] }, { "id": "MR-0001.F15", "metareport_id": "MR-0001", "text": "Listeners judge synthetic speech mainly on prosodic and extralinguistic cues (intonation, rhythm, fluency, pauses, breathing, laughter), with shared and language-specific cues across Spanish and Japanese listeners; accuracy 59.11%. Everyday \"is this AI?\" heuristics shift as generators change.", "confidence": "Low", "source_ids": [ "san-segundo-2026-audio-language-style", "mai-2023-speech-deepfakes", "yeung-2026-isthisai-strategies" ], "status": "current", "revised": [] }, { "id": "MR-0002.F1", "metareport_id": "MR-0002", "text": "The uncanny valley effect is large and robust across studies that set out to elicit it (g = 1.01). It also appears for real robot faces and extends to implicit trust behaviour. A cubic, valley-shaped likability curve also appeared across nine gaze-timing variants of a virtual robot (N = 61), while perceived empathy rose linearly (revised 2026-10-19).", "confidence": "High", "source_ids": [ "diel-2022-uv-meta", "mathur-reichling-2016-cartography", "mathur-2020-uncanny-not-confusing", "rosenthal-von-der-putten-2019-vmpfc", "hsieh-2026-robot-gaze-uncanny" ], "status": "revised", "revised": [ "2026-10-19" ] }, { "id": "MR-0002.F2", "metareport_id": "MR-0002", "text": "The simplest hypothesis (near-human likeness alone causes a dip) and Mori's movement claim lack consistent support. The valley is conditional.", "confidence": "Moderate–High", "source_ids": [ "katsyri-2015-uv-review", "burleigh-2013-does-uv-exist", "seyama-nagayama-2007" ], "status": "current", "revised": [] }, { "id": "MR-0002.F3", "metareport_id": "MR-0002", "text": "Perceptual mismatch and distortion are the best-supported causes. Face distortion yields the largest effects, and inconsistent realism increases eeriness.", "confidence": "High", "source_ids": [ "diel-2022-uv-meta", "macdorman-chattopadhyay-2016", "katsyri-2015-uv-review", "seyama-nagayama-2007", "saygin-2012-predictive-coding" ], "status": "current", "revised": [] }, { "id": "MR-0002.F4", "metareport_id": "MR-0002", "text": "Category ambiguity does not explain eeriness. Ambiguous stimuli are not the eeriest, are easier to discriminate, and confusion does not mediate the valley.", "confidence": "High", "source_ids": [ "macdorman-chattopadhyay-2016", "cheetham-2014-happy-valley", "mathur-reichling-2016-cartography", "mathur-2020-uncanny-not-confusing" ], "status": "current", "revised": [] }, { "id": "MR-0002.F5", "metareport_id": "MR-0002", "text": "Attributing feelings to machines causes unease independent of appearance (an \"uncanny valley of mind\"). In conversation, deep self-disclosure by a machine-like AI counsellor lowered intimacy and trust via lower anthropomorphism, while a human-like counsellor gained from it (N = 193) (revised 2026-10-19).", "confidence": "Moderate", "source_ids": [ "gray-wegner-2012-mind-perception", "stein-ohler-2017-uncanny-mind", "jang-2026-uncanny-conversation" ], "status": "revised", "revised": [ "2026-10-19" ] }, { "id": "MR-0002.F6", "metareport_id": "MR-0002", "text": "Neural correlates: anterior intraparietal prediction error for appearance–motion mismatch; a vmPFC non-linear likability signal; DMPFC choice signals for artificial agents; amygdala-predicted rejection.", "confidence": "Moderate (verified, small N)", "source_ids": [ "saygin-2012-predictive-coding", "rosenthal-von-der-putten-2019-vmpfc" ], "status": "current", "revised": [] }, { "id": "MR-0002.F7", "metareport_id": "MR-0002", "text": "ERPs: N170 is U-shaped across realism, LPP rises linearly, EPN is unaffected. No direct \"dip\" in the ERPs.", "confidence": "Moderate (verified, single study)", "source_ids": [ "schindler-2017-erp-realism" ], "status": "current", "revised": [] }, { "id": "MR-0002.F8", "metareport_id": "MR-0002", "text": "A valley-like looking-time effect appears in macaques.", "confidence": "Low", "source_ids": [ "steckenfinger-ghazanfar-2009" ], "status": "current", "revised": [] }, { "id": "MR-0002.F9", "metareport_id": "MR-0002", "text": "High-quality GAN faces are past the valley on explicit measures: chance-level detection, a trust advantage, and hyperrealism. Neural-network face spaces confirm they are more average than real faces (revised 2026-10-12). Average-looking AI faces are nonetheless remembered *better* than real faces in a recognition task, without people being aware of it (revised 2026-10-18).", "confidence": "High", "source_ids": [ "nightingale-farid-2022", "miller-2023-ai-hyperrealism", "dunn-2026-super-recognizers-average", "sulimani-2026-recognition-ai-faces" ], "status": "revised", "revised": [ "2026-10-12", "2026-10-18" ] }, { "id": "MR-0002.F13", "metareport_id": "MR-0002", "text": "Voice clones have reached realism parity but not hyperrealism: they are rated as real as the speakers they copy, not more real, while generic AI voices are still rated less real.", "confidence": "Low", "source_ids": [ "lavan-2025-voice-clones-hyperrealism" ], "status": "current", "revised": [] }, { "id": "MR-0002.F10", "metareport_id": "MR-0002", "text": "Implicit neural discrimination of AI faces persists despite near-chance behaviour. Real and AI faces also differed in early ERPs (N170, N400, EPN) but not the late LPP in an exploratory study (N = 28) (revised 2026-10-19).", "confidence": "Moderate", "source_ids": [ "moshel-2022-decoding-ai-faces", "groh-2022-crowds-machines", "mamsheli-2026-eeg-ai-faces" ], "status": "revised", "revised": [ "2026-10-19" ] }, { "id": "MR-0002.F11", "metareport_id": "MR-0002", "text": "*Believing* a face is AI blunts the positive response to smiles: lower ratings, slower responses, no early ERP smile effect, more effortful late processing (LPP). Angry faces are unaffected.", "confidence": "Moderate (verified, N = 30)", "source_ids": [ "eiserbeck-2023-deepfake-smiles", "wang-2015-uv-existence-explanations" ], "status": "current", "revised": [] }, { "id": "MR-0002.F12", "metareport_id": "MR-0002", "text": "Imperfect, self-resembling deepfake avatars can still produce uncanniness that lowers trust.", "confidence": "Low", "source_ids": [ "weisman-pena-2021-doppelganger" ], "status": "current", "revised": [] }, { "id": "MR-0003.F1", "metareport_id": "MR-0003", "text": "Commercial voice clones of unfamiliar speakers are accepted as the same identity as the real speaker in most pairs (median 83.3%; about 80% in the abstract).", "confidence": "Moderate (one verified large-N study)", "source_ids": [ "barrington-2025-voice-clones" ], "status": "current", "revised": [] }, { "id": "MR-0003.F2", "metareport_id": "MR-0003", "text": "Listener sensitivity to modern voice clones is near chance: d′ 0.10–0.20, with 53.95–60.8% of clones correctly identified across three studies.", "confidence": "High", "source_ids": [ "barrington-2025-voice-clones", "lavan-2025-voice-clones-hyperrealism", "ivory-2026-faces-voices-domain" ], "status": "current", "revised": [] }, { "id": "MR-0003.F3", "metareport_id": "MR-0003", "text": "Judgments of clones are biased toward \"human/real\" (c = −0.65; c = −.07), extending the truth default of MR-0001.F6 to voices.", "confidence": "Moderate", "source_ids": [ "lavan-2025-voice-clones-hyperrealism", "ivory-2026-faces-voices-domain" ], "status": "current", "revised": [] }, { "id": "MR-0003.F4", "metareport_id": "MR-0003", "text": "Clones reach realism parity with human voices but not hyperrealism (63.0 vs 63.8); generic, non-cloned AI voices remain easy to spot (d′ 2.93) and are rated more dominant.", "confidence": "Low", "source_ids": [ "lavan-2025-voice-clones-hyperrealism" ], "status": "current", "revised": [] }, { "id": "MR-0003.F5", "metareport_id": "MR-0003", "text": "Detectability depends on synthesis method: older TTS is caught on ≈70% of single clips, state-of-the-art TTS less often than voice actors (72% vs 83%), and pooled audio accuracy (62.08%) has a CI spanning chance.", "confidence": "Moderate", "source_ids": [ "mai-2023-speech-deepfakes", "groh-2024-political-speech", "diel-2024-deepfake-detection-meta", "san-segundo-2026-audio-language-style" ], "status": "current", "revised": [] }, { "id": "MR-0003.F6", "metareport_id": "MR-0003", "text": "Comparison beats single judgment: choosing the fake from a pair reached 85.59% vs 70.35% for single clips, and same-sentence reference audio helped significantly; familiarisation (+3.84 points), extra time and repeated listening helped little.", "confidence": "Moderate", "source_ids": [ "mai-2023-speech-deepfakes" ], "status": "current", "revised": [] }, { "id": "MR-0003.F7", "metareport_id": "MR-0003", "text": "Confidence carries no information about voice-clone accuracy (metacognitive AUROC .48), and voice-clone detection skill is not predicted by face-detection skill.", "confidence": "Low", "source_ids": [ "ivory-2026-faces-voices-domain" ], "status": "current", "revised": [] }, { "id": "MR-0003.F8", "metareport_id": "MR-0003", "text": "Listeners rely on prosodic and extralinguistic cues (intonation, rhythm, fluency, pauses, breathing, laughter) and on delivery more than content; breathing cues predicted voice accuracy.", "confidence": "Moderate", "source_ids": [ "san-segundo-2026-audio-language-style", "ivory-2026-faces-voices-domain", "groh-2024-political-speech" ], "status": "current", "revised": [] }, { "id": "MR-0003.F9", "metareport_id": "MR-0003", "text": "Aggregating many listeners is robust (AUROC 95.51%) where an out-of-domain automated detector failed (25.31%).", "confidence": "Low", "source_ids": [ "mai-2023-speech-deepfakes" ], "status": "current", "revised": [] } ], "metareports": [ { "id": "MR-0001", "title": "Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training", "url": "/metareports/human-detection-accuracy.md", "version": "0.4.0", "updated": "2026-10-19", "source_ids": [ "diel-2024-deepfake-detection-meta", "nightingale-farid-2022", "miller-2023-ai-hyperrealism", "bray-2023-deepfake-faces", "lago-2022-more-real-than-real", "tucciarelli-2022-realness", "gray-2025-super-recognizers", "moshel-2022-decoding-ai-faces", "lu-2023-fake2m", "frank-2024-representative-study", "kobis-2021-fooled-twice", "groh-2022-crowds-machines", "groh-2024-political-speech", "mai-2023-speech-deepfakes", "barrington-2025-voice-clones", "cooke-2024-coin-toss", "iproov-2025-consumer-study", "ivory-2026-faces-voices-domain", "lavan-2025-voice-clones-hyperrealism", "pehlivanoglu-2026-machines-humans", "dunn-2026-super-recognizers-average", "dawel-2026-training-ai-faces", "rehman-2025-training-synthetic-faces", "kramer-2024-crowds-synthetic-faces", "kramer-2026-aggregation-human-ai", "plohl-2026-political-deepfakes-plausibility", "san-segundo-2026-audio-language-style", "manhede-2026-online-markets-training", "sulimani-2026-recognition-ai-faces", "yeung-2026-isthisai-strategies", "lee-2026-ai-chest-radiographs", "chen-2026-humans-ai-deepfake-teaming", "naseem-2026-in-the-wild-detection-review" ] }, { "id": "MR-0002", "title": "The Uncanny Valley and Synthetic Media: Does It Exist, What Causes It, and Have Deepfakes Crossed It?", "url": "/metareports/uncanny-valley-and-synthetic-faces.md", "version": "0.4.0", "updated": "2026-10-19", "source_ids": [ "mori-2012-uncanny-valley", "mathur-reichling-2016-cartography", "mathur-2020-uncanny-not-confusing", "katsyri-2015-uv-review", "diel-2022-uv-meta", "macdorman-chattopadhyay-2016", "seyama-nagayama-2007", "cheetham-2014-happy-valley", "burleigh-2013-does-uv-exist", "steckenfinger-ghazanfar-2009", "wang-2015-uv-existence-explanations", "gray-wegner-2012-mind-perception", "stein-ohler-2017-uncanny-mind", "saygin-2012-predictive-coding", "rosenthal-von-der-putten-2019-vmpfc", "schindler-2017-erp-realism", "moshel-2022-decoding-ai-faces", "eiserbeck-2023-deepfake-smiles", "weisman-pena-2021-doppelganger", "nightingale-farid-2022", "miller-2023-ai-hyperrealism", "groh-2022-crowds-machines", "dunn-2026-super-recognizers-average", "lavan-2025-voice-clones-hyperrealism", "sulimani-2026-recognition-ai-faces", "mamsheli-2026-eeg-ai-faces", "hsieh-2026-robot-gaze-uncanny", "jang-2026-uncanny-conversation" ] }, { "id": "MR-0003", "title": "Voice Clones and Synthetic Speech: How Well Do People Detect Them?", "url": "/metareports/voice-cloning-human-detection.md", "version": "0.1.0", "updated": "2026-10-19", "source_ids": [ "barrington-2025-voice-clones", "lavan-2025-voice-clones-hyperrealism", "ivory-2026-faces-voices-domain", "mai-2023-speech-deepfakes", "san-segundo-2026-audio-language-style", "groh-2024-political-speech", "diel-2024-deepfake-detection-meta", "cooke-2024-coin-toss" ] } ] }