{"id":"W3191753512","doi":"10.1101/2021.07.31.452742","title":"Deliberation gated by opportunity cost adapts to context with urgency","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Deliberation; Reinforcement learning; Computer science; Context (archaeology); Task (project management); Artificial intelligence; Heuristic; Value (mathematics); Process (computing); Machine learning; Cognitive psychology; Psychology; Economics","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":[0.0008616273,0.0002494748,0.0003860301,0.0002301141,0.0002338213,0.001048844,0.0004896517,0.0004318737,0.001239821],"category_scores_gemma":[0.004635365,0.0002731924,0.000278555,0.0001516214,0.0009556776,0.0008552318,0.001073693,0.000779026,0.0001350474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005282764,"about_ca_system_score_gemma":0.0004041914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006580552,"about_ca_topic_score_gemma":0.0007810043,"domain_scores_codex":[0.9996337,0.0001198778,0.0000229127,0.0001066342,0.00005346888,0.00006341816],"domain_scores_gemma":[0.9980718,0.0008188047,0.0003953003,0.0002906448,0.0001265329,0.000297002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001260091,0.0004423874,0.07007127,0.0003498452,0.0004274709,0.0007735266,0.0007819908,0.4162675,0.2813551,0.09447066,0.001394875,0.1324053],"study_design_scores_gemma":[0.00004783733,0.0003492118,0.03568833,0.00003023339,0.00005705767,0.0001836316,0.0001321452,0.8671415,0.01693413,0.07823002,0.001127197,0.00007873658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9110041,0.0001330525,0.08509492,0.000274622,0.00003559726,0.00001806069,0.00004210759,0.000169233,0.003228242],"genre_scores_gemma":[0.9937935,0.00002396634,0.005876082,0.0000238414,0.000004336498,0.000006733155,0.0000124943,0.00001572718,0.0002433152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001239821,"threshold_uncertainty_score":0.004556775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03200909357518338,"score_gpt":0.2302162035610512,"score_spread":0.1982071099858679,"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."}}