{"id":"W4385335562","doi":"10.1101/2023.07.25.550426","title":"Learning attentional templates for value-based decision-making","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Science Foundation","keywords":"Template; Computer science; Stimulus (psychology); Task (project management); Prefrontal cortex; Cognitive psychology; Artificial intelligence; Psychology; Cognition; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001006649,0.0002345588,0.0003296413,0.0002263914,0.0002393304,0.001378314,0.0007440594,0.000546081,0.00232259],"category_scores_gemma":[0.00489428,0.0003692688,0.0004782823,0.0001853727,0.000875597,0.001447148,0.0009468474,0.001179554,0.000396267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000741836,"about_ca_system_score_gemma":0.0005007827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008660177,"about_ca_topic_score_gemma":0.0006502161,"domain_scores_codex":[0.9995624,0.0001094283,0.00002693309,0.0001306487,0.0001003661,0.00007016773],"domain_scores_gemma":[0.9984291,0.0006101679,0.0002820225,0.0003677997,0.0001521673,0.0001588549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004993432,0.0003566121,0.009017944,0.0002459346,0.0002981123,0.0004199758,0.000710112,0.1997288,0.4357421,0.184896,0.003542899,0.1645422],"study_design_scores_gemma":[0.00005671998,0.0001553007,0.007836284,0.0000222038,0.00003704204,0.0001120395,0.0000578389,0.7632277,0.05308689,0.1735158,0.001851706,0.00004045647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6318833,0.0002822452,0.3527108,0.0007896883,0.0001274792,0.00007041802,0.0001250034,0.0006150078,0.01339605],"genre_scores_gemma":[0.9799533,0.00004698262,0.01904357,0.00005217821,0.00001367602,0.00002080178,0.00004046218,0.00003607474,0.0007930262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00232259,"threshold_uncertainty_score":0.007769823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.10354483842846,"score_gpt":0.3437503896818966,"score_spread":0.2402055512534366,"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."}}