{"id":"W2607432382","doi":"10.1371/journal.pone.0172500","title":"Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Canadian Institutes of Health Research; National Institutes of Health; National Science Foundation","keywords":"Decoding methods; Neuroimaging; Computer science; Multivariate statistics; Artificial intelligence; Neural decoding; Population; Psychology; Cognitive psychology; Machine learning; Neuroscience; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000769939,0.0006339106,0.0003134783,0.0006627909,0.000191173,0.0005255653,0.0004108589,0.0003323612,0.001358422],"category_scores_gemma":[0.004701944,0.0001975028,0.0005032778,0.0008376154,0.0003406771,0.0006841447,0.0005998611,0.0006378155,0.000338826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000142971,"about_ca_system_score_gemma":0.000403007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001949224,"about_ca_topic_score_gemma":0.002411086,"domain_scores_codex":[0.9996418,0.0001298064,0.00002205813,0.00009588491,0.00008446696,0.00002596985],"domain_scores_gemma":[0.9993399,0.0003324341,0.0000976056,0.0001183715,0.00008584068,0.00002585783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000280161,0.0000659711,0.004903452,0.0001860223,0.000167803,0.0001741215,0.0004712594,0.02504875,0.169411,0.01403178,0.002563505,0.7826961],"study_design_scores_gemma":[0.00004559839,0.0002485892,0.03067934,0.00005243755,0.0001169387,0.00100073,0.0001564201,0.7956082,0.1119996,0.05167892,0.008296469,0.0001168825],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0255392,0.00006884342,0.9727646,0.0000523147,0.00001903648,0.00003254421,0.0001387127,0.0006353658,0.0007494004],"genre_scores_gemma":[0.2593503,0.0002111088,0.7388276,0.00004336803,0.00004214387,0.0001139546,0.0002239296,0.0002479195,0.0009396963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001949224,"threshold_uncertainty_score":0.004544377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1181339301922476,"score_gpt":0.316367186902926,"score_spread":0.1982332567106784,"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."}}