{"id":"W1965965555","doi":"10.3758/bf03195464","title":"MouseTrace: A better mousetrap for catching decision processes","year":2002,"lang":"en","type":"article","venue":"Behavior Research Methods, Instruments, & Computers","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"University of Toronto; University of Toledo","keywords":"Variety (cybernetics); Computer science; Process (computing); Decision process; Extension (predicate logic); Artificial intelligence; Machine learning; Data mining; Management science; Engineering; Programming language","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01545573,0.0005320722,0.0009184345,0.001969523,0.001272257,0.002309587,0.00350613,0.0003496694,0.0006428729],"category_scores_gemma":[0.007883179,0.0004400749,0.0004503235,0.00252167,0.000455346,0.001246351,0.001030251,0.001045419,0.0005899157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004237517,"about_ca_system_score_gemma":0.0001933554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001009661,"about_ca_topic_score_gemma":0.00005381763,"domain_scores_codex":[0.9895173,0.001437178,0.001951338,0.002020058,0.003522193,0.001551981],"domain_scores_gemma":[0.9834776,0.01195431,0.0004844075,0.001913443,0.0016256,0.0005446562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007592011,0.0005583933,0.006081569,0.00001638816,0.00001814647,0.00002792459,0.0006365227,0.00006383388,0.0007933435,0.00006130365,0.02628039,0.9653863],"study_design_scores_gemma":[0.007258874,0.002556443,0.0113532,0.0006815183,0.0001901895,0.0004083626,0.003586902,0.04233392,0.01276739,0.1514051,0.764283,0.003175112],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8457837,0.0001943262,0.1493743,0.0008410505,0.001718156,0.001586138,0.0001161603,0.0001562532,0.0002299314],"genre_scores_gemma":[0.415109,0.00008609256,0.5829028,0.0003347424,0.0002639024,0.0004852188,0.00001672946,0.00009673727,0.0007046731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9622111,"threshold_uncertainty_score":0.9998051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5684489168392943,"score_gpt":0.5922242640286576,"score_spread":0.02377534718936325,"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."}}