{"id":"W3042870350","doi":"10.1037/xlm0000934","title":"Working memory load dissociates contingency learning and item-specific proportion-congruent effects.","year":2020,"lang":"en","type":"article","venue":"Journal of Experimental Psychology Learning Memory and Cognition","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Contingency; Cognitive psychology; Psychology; Working memory; Contingency table; Cognitive load; Developmental psychology; Social psychology; Computer science; Cognition; Machine learning; Neuroscience; Linguistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001029068,0.0004780178,0.0004614186,0.000547311,0.0001776333,0.0006585851,0.0006710153,0.0003703612,0.003075377],"category_scores_gemma":[0.00839277,0.0004312338,0.0004005868,0.0002257426,0.0008406838,0.001065828,0.001349101,0.0008306622,0.0002949116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002311934,"about_ca_system_score_gemma":0.0002326409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003893363,"about_ca_topic_score_gemma":0.0004866428,"domain_scores_codex":[0.9990296,0.0001391142,0.0001027208,0.0003469549,0.0003237081,0.0000578377],"domain_scores_gemma":[0.9919137,0.003723557,0.00147404,0.002040494,0.0003218851,0.0005263822],"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":[0.005050496,0.001124811,0.03050522,0.0004931801,0.0002560643,0.0001861261,0.0006861672,0.0007115871,0.9062814,0.001125292,0.000242391,0.0533374],"study_design_scores_gemma":[0.0002980434,0.003213864,0.7215568,0.00005008613,0.0002836596,0.000658016,0.0001429793,0.007302656,0.259281,0.005661606,0.001482804,0.0000684198],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917796,0.0002322967,0.00516968,0.00004569698,0.00003477014,0.00006128868,0.0001240719,0.00009562066,0.002457028],"genre_scores_gemma":[0.9954447,0.00007828548,0.003285351,0.0000833251,0.00002387759,0.00009629426,0.0001989926,0.00007214432,0.0007171432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003075377,"threshold_uncertainty_score":0.01028818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0248433869899878,"score_gpt":0.2970638391860388,"score_spread":0.272220452196051,"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."}}