{"id":"W3008156119","doi":"10.12688/f1000research.21946.1","title":"Under construction: ventral and lateral frontal lobe contributions to value-based decision-making and learning","year":2020,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health; Brown University","keywords":"Orbitofrontal cortex; Value (mathematics); Ventromedial prefrontal cortex; Frontal lobe; Cognitive psychology; Prefrontal cortex; Psychology; Functional magnetic resonance imaging; Neuroscience; Neuroeconomics; Predictive value; Computer science; Medicine; Cognition; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002673512,0.0002531265,0.000349,0.0001707616,0.0007172963,0.000258026,0.0002723338,0.0001862826,0.0001132595],"category_scores_gemma":[0.0009445085,0.0002259049,0.00007501297,0.0002248973,0.0006654339,0.00007655589,0.001371665,0.001643232,0.00006786103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009325502,"about_ca_system_score_gemma":0.0001037628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004625415,"about_ca_topic_score_gemma":0.00001233265,"domain_scores_codex":[0.9973857,0.0003995839,0.0002624111,0.0009606142,0.0004795456,0.0005121229],"domain_scores_gemma":[0.998619,0.0007387332,0.00006525105,0.000202055,0.00009957272,0.0002753577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004673528,0.0006776326,0.2269035,0.0005394511,0.0001880097,0.002975402,0.002451807,0.005570877,0.5973673,0.01055203,0.01554141,0.132559],"study_design_scores_gemma":[0.008786258,0.004098718,0.7015362,0.002996247,0.0004170162,0.001213978,0.001743825,0.01552873,0.09190623,0.1465653,0.02105927,0.004148149],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985002,0.0002210289,0.001562723,0.01136812,0.0006257246,0.0006408063,0.0002199895,0.0001267093,0.0002329333],"genre_scores_gemma":[0.9973658,0.00006364097,0.001124985,0.001048458,0.0001822885,0.00005709166,0.000008931362,0.00002403847,0.0001247757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5054611,"threshold_uncertainty_score":0.9212134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1446225340607116,"score_gpt":0.45540402481449,"score_spread":0.3107814907537784,"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."}}