{"id":"W2126854951","doi":"10.1162/08989290051137639","title":"Anatomical Segregation of Component Processes in an Inductive Inference Task","year":2000,"lang":"en","type":"article","venue":"Journal of Cognitive Neuroscience","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"York University; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Wellcome Trust","keywords":"Inference; Psychology; Cognitive psychology; Cognition; Task (project management); Inductive reasoning; Prefrontal cortex; Dissociation (chemistry); Neural substrate; Selection (genetic algorithm); Set (abstract data type); Cognitive science; Neuroscience; Artificial intelligence; Computer science","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":[0.0001979047,0.0001372742,0.0002793081,0.0002243999,0.00009142572,0.00003050152,0.0003577008,0.00004320707,0.0000306952],"category_scores_gemma":[0.001418997,0.0001038825,0.00004584085,0.0009414866,0.0008383552,0.001040515,0.00003757393,0.000357185,0.000004673596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001916786,"about_ca_system_score_gemma":0.0001076895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006633356,"about_ca_topic_score_gemma":0.00001055821,"domain_scores_codex":[0.9982725,0.0002375469,0.0004884244,0.0003234934,0.0004551164,0.0002229424],"domain_scores_gemma":[0.9987378,0.000400896,0.000369706,0.00007789232,0.0003181864,0.00009548727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003063529,0.0007230957,0.03178034,0.000009266729,6.371963e-7,0.0001188759,0.0007394627,0.00002901629,0.9499988,0.00002606188,0.000005807488,0.01626227],"study_design_scores_gemma":[0.0009080408,0.001711321,0.3676486,0.0001895428,0.00001732372,0.0001836202,0.0002353224,0.00008368727,0.6278929,0.000912882,0.00005076413,0.0001660362],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990872,0.00002115986,0.00002867514,0.0002432616,0.0002140574,0.0001422423,0.0000141542,0.000008190488,0.000241099],"genre_scores_gemma":[0.9990879,0.0001425879,0.00002426569,0.000683074,0.00002684718,0.00000328608,2.132628e-7,0.00000570088,0.00002606821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3358682,"threshold_uncertainty_score":0.4236206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.163561155555609,"score_gpt":0.4143701458338839,"score_spread":0.2508089902782749,"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."}}