{"id":"W2043187735","doi":"10.3819/ccbr.2014.90001","title":"Forgetting from Short-Term Memory in Delayed Matching to Sample: A Reinforcement Context Model","year":2014,"lang":"en","type":"article","venue":"Comparative Cognition & Behavior Reviews","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Forgetting; Reinforcement; Psychology; Context (archaeology); Term (time); Reading (process); Reinforcement learning; Cognitive psychology; Matching (statistics); Cognition; Developmental psychology; Social psychology; Artificial intelligence; Linguistics; Computer science; Neuroscience; History; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004936479,0.00185144,0.003073781,0.00203034,0.0008266683,0.002835947,0.00611299,0.002494535,0.01138533],"category_scores_gemma":[0.01644058,0.001336923,0.004101058,0.00115691,0.002546637,0.006186906,0.002619188,0.004380226,0.001748889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002091552,"about_ca_system_score_gemma":0.001792301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00865541,"about_ca_topic_score_gemma":0.006820033,"domain_scores_codex":[0.9987558,0.0003445731,0.00009199985,0.0003562357,0.000176878,0.0002744613],"domain_scores_gemma":[0.9841339,0.008815982,0.002564054,0.001652441,0.001292738,0.001540839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004933441,0.001920104,0.02742354,0.001007875,0.001194824,0.002658535,0.001251018,0.3761212,0.01059119,0.4502599,0.007896988,0.1147414],"study_design_scores_gemma":[0.0003286665,0.0005788399,0.006821061,0.0000897188,0.0004247523,0.000701211,0.00008568452,0.7502016,0.0007944838,0.2388104,0.001012601,0.000150849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5183187,0.004601631,0.446802,0.006199971,0.000793013,0.0004316869,0.001753686,0.0009057542,0.02019371],"genre_scores_gemma":[0.9552001,0.002057472,0.02261752,0.0004971945,0.0004163679,0.0003089882,0.0007562991,0.0001715112,0.01797453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01138533,"threshold_uncertainty_score":0.03808767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1463125152487954,"score_gpt":0.3950135759685805,"score_spread":0.2487010607197851,"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."}}