{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009570792,0.00005548262,0.00009258524,0.00007503983,0.00009205175,0.00001103156,0.00005835233,0.0000308622,0.0001167722],"category_scores_gemma":[0.0002278784,0.00004365112,0.00009535211,0.00003896667,0.0001528621,0.0002278725,0.00001055226,0.00007632976,0.00002936489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000304956,"about_ca_system_score_gemma":0.00001240535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.44014e-7,"about_ca_topic_score_gemma":5.260845e-7,"domain_scores_codex":[0.9994411,0.00004091579,0.0001921959,0.00009805321,0.0001285039,0.0000992712],"domain_scores_gemma":[0.9994746,0.00006503694,0.0002155051,0.00003661383,0.0001761323,0.00003209961],"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":[0.0002791192,0.00007403155,0.0000630329,0.000002783046,0.000003589391,0.00001228344,0.0002605265,2.026051e-8,0.9891744,0.00003226366,0.00487319,0.005224792],"study_design_scores_gemma":[0.001841539,0.003107884,0.001555557,0.00006641658,0.0001341044,0.0005149684,0.0002900606,0.00002486271,0.9393493,0.004757151,0.048196,0.0001621713],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953345,0.00003837901,0.001127003,0.0003865821,0.002093262,0.00008213061,0.000001655631,0.00000988405,0.0009265893],"genre_scores_gemma":[0.9979667,0.000009308256,0.0002252711,0.0008399808,0.000477239,0.000006691472,4.20215e-7,0.000005470943,0.0004688912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04982508,"threshold_uncertainty_score":0.1780041,"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."}}