{"id":"W4386864375","doi":"10.7554/elife.89369.1.sa2","title":"eLife Assessment: A cortical information bottleneck during decision-making","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; Natural Sciences and Engineering Research Council of Canada; Hellman Foundation; National Science Foundation; National Institutes of Health; Nvidia","keywords":"Computer science; Task (project management); Premotor cortex; Dorsolateral prefrontal cortex; Artificial intelligence; Representation (politics); Perception; Neuroscience; Prefrontal cortex; Bottleneck; Cognition; Machine learning; Psychology; Dorsum; Biology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002389653,0.0006408872,0.001008676,0.0005106725,0.0006593572,0.002447697,0.001722635,0.001148684,0.01023269],"category_scores_gemma":[0.01133825,0.0005760923,0.0006569605,0.0004639449,0.00106883,0.00330166,0.002472246,0.002514264,0.002690505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001460912,"about_ca_system_score_gemma":0.002214056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002729151,"about_ca_topic_score_gemma":0.002968061,"domain_scores_codex":[0.9987001,0.0001996312,0.00009334154,0.0003791478,0.0003718516,0.0002558618],"domain_scores_gemma":[0.9975254,0.0009197037,0.000353711,0.0004175011,0.0004598113,0.0003237596],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001462789,0.0002683099,0.01327078,0.0008323831,0.0003460243,0.0009867247,0.0009800418,0.04892101,0.2561349,0.07362057,0.0216345,0.5815421],"study_design_scores_gemma":[0.0002471007,0.0005675251,0.02496457,0.0002607456,0.0002230917,0.001139851,0.0005718024,0.4826479,0.1964134,0.2457845,0.04701951,0.0001600124],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.3303777,0.00280128,0.6086675,0.008419997,0.0005762063,0.0001705732,0.001090184,0.006713369,0.04118318],"genre_scores_gemma":[0.9144272,0.0008038548,0.07154578,0.001051579,0.0001034827,0.0001601139,0.0006490672,0.0006900555,0.01056888],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9976103,"threshold_uncertainty_score":0.03423172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05232870208946171,"score_gpt":0.36362473312444,"score_spread":0.3112960310349783,"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."}}