Miami University Deception Detection Database
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In the present work, we introduce the Miami University Deception Detection Database (MU3D), a free resource containing 320 videos of target individuals telling truths and lies. Eighty (20 Black female, 20 Black male, 20 White female, and 20 White male) different targets were recorded speaking honestly and dishonestly about their social relationships. Each target generated four different videos (i.e., positive truth, negative truth, positive lie, negative lie), yielding 320 videos fully crossing target race, target gender, statement valence, and statement veracity. These videos were transcribed by trained research assistants and evaluated by naïve raters. Descriptive analyses of the video characteristics (e.g., length) and subjective ratings (e.g., target attractiveness) are provided. The stimuli and an information codebook can be accessed free of charge for academic research purposes from http://hdl.handle.net/2374.MIA/6067 . The MU3D offers scholars the ability to conduct research using standardized stimuli that can aid in building more comprehensive theories of interpersonal sensitivity, enhance replication among labs, facilitate the use of signal detection analyses, and promote consideration of race, gender, and their interactive effects in deception detection research.
Multimodal machine learning for deception detection using behavioral and physiological data.
Joshi G, Tasgaonkar V, Deshpande A, Desai A, Shah B, Kushawaha A Sci Rep. 2025; 15(1):8943.
PMID: 40089524 DOI: 10.1038/s41598-025-92399-6.
Experimental economics for machine learning-a methodological contribution on lie detection.
Bershadskyy D, Dinges L, Fiedler M, Al-Hamadi A, Ostermaier N, Weimann J PLoS One. 2024; 19(12):e0314806.
PMID: 39739665 PMC: 11687750. DOI: 10.1371/journal.pone.0314806.
Development of the RIKEN database for dynamic facial expressions with multiple angles.
Namba S, Sato W, Namba S, Nomiya H, Shimokawa K, Osumi M Sci Rep. 2023; 13(1):21785.
PMID: 38066065 PMC: 10709572. DOI: 10.1038/s41598-023-49209-8.
Krumpholz C, Quigley C, Fusani L, Leder H Behav Res Methods. 2023; 56(4):2923-2940.
PMID: 37950115 PMC: 11133183. DOI: 10.3758/s13428-023-02264-5.
Bhatt P, Sethi A, Tasgaonkar V, Shroff J, Pendharkar I, Desai A Brain Inform. 2023; 10(1):18.
PMID: 37524933 PMC: 10390406. DOI: 10.1186/s40708-023-00196-6.