{"id":"W2755805722","doi":"10.1038/s41598-017-11754-4","title":"Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Deception detection and forensic psychology","field":"Psychology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Zhejiang Province; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National University of Singapore; National Institutes of Health; Social Sciences and Humanities Research Council of Canada; National Natural Science Foundation of China","keywords":"Honesty; Deception; Lying; Dishonesty; Volition (linguistics); Psychology; Graph; Computer science; Artificial neural network; Graph theory; Social psychology; Artificial intelligence; Medicine; Theoretical computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007121375,0.0001906784,0.0001916298,0.0006441395,0.0001395063,0.0004849489,0.0002111048,0.0002850855,0.00109671],"category_scores_gemma":[0.005892334,0.0002430815,0.0002251377,0.0003282286,0.0007440998,0.001026547,0.000389709,0.0004333734,0.00009750709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002968897,"about_ca_system_score_gemma":0.0001472414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002167769,"about_ca_topic_score_gemma":0.003309545,"domain_scores_codex":[0.9996352,0.0001267964,0.00001694683,0.00009842694,0.00007660419,0.0000460383],"domain_scores_gemma":[0.9969511,0.001654333,0.0008920793,0.0002122719,0.0001527257,0.0001375223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008840462,0.0001075692,0.8068967,0.0002397418,0.0003269016,0.001624385,0.008128619,0.0103085,0.09751634,0.006661867,0.0006866991,0.06661872],"study_design_scores_gemma":[0.000009076208,0.0000832822,0.9826015,0.00001314461,0.00003914915,0.0007582927,0.0008569665,0.00819552,0.004014685,0.003009626,0.0004010432,0.00001770634],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963325,0.00009939863,0.00295589,0.00005366932,0.000002294097,0.000004347498,0.00007110511,0.00001205544,0.0004688276],"genre_scores_gemma":[0.9984642,0.00007670214,0.001272997,0.000006573167,0.000001647626,0.000004712635,0.00007016729,0.000005505837,0.00009752658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002167769,"threshold_uncertainty_score":0.00431031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02703628740354858,"score_gpt":0.3046671762270868,"score_spread":0.2776308888235383,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}