{"id":"W2805028526","doi":"10.5334/joc.25","title":"Experiential History as a Tuning Parameter for Attention","year":2018,"lang":"en","type":"article","venue":"Journal of Cognition","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; University of British Columbia","funders":"","keywords":"Salience (neuroscience); Selection (genetic algorithm); Conceptualization; Terminology; Experiential learning; Computer science; Context (archaeology); Cognitive psychology; Process (computing); Task (project management); Cognitive science; Artificial intelligence; Psychology; History","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":[0.003318683,0.0003817464,0.0004350983,0.0004678658,0.001153737,0.003751298,0.002387146,0.004466667,0.01643854],"category_scores_gemma":[0.02013694,0.0003290852,0.0004842274,0.0004068974,0.007796585,0.006142376,0.003198747,0.00655285,0.002983703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001543119,"about_ca_system_score_gemma":0.0007158636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00180885,"about_ca_topic_score_gemma":0.001331405,"domain_scores_codex":[0.9986371,0.0003703817,0.00008804673,0.0004740364,0.0003343068,0.00009608579],"domain_scores_gemma":[0.9899856,0.007022154,0.000616917,0.0008426311,0.001160656,0.0003721344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002713809,0.00003696017,0.001781028,0.0003991876,0.0000592086,0.0002749678,0.001767753,0.001733664,0.00820948,0.7341709,0.1504849,0.1008107],"study_design_scores_gemma":[0.00005871259,0.00009190691,0.008693124,0.0003623918,0.00005945665,0.0004497276,0.000535552,0.01407592,0.005742729,0.7594209,0.2103363,0.0001733646],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03996118,0.01586938,0.2427445,0.4861398,0.02510609,0.000141955,0.0007163647,0.001664806,0.1876561],"genre_scores_gemma":[0.8112263,0.007507776,0.03706839,0.09758177,0.01989501,0.0003488772,0.0002641407,0.000966497,0.02514131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01643854,"threshold_uncertainty_score":0.05499238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2304715912181669,"score_gpt":0.4043261887053304,"score_spread":0.1738545974871635,"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."}}