{"id":"W2583017765","doi":"10.1101/100800","title":"Associative Learning from Replayed Experience","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Associative property; Associative learning; Computer science; Content-addressable memory; Extinction (optical mineralogy); Simple (philosophy); Spontaneous recovery; Artificial intelligence; Machine learning; Cognitive psychology; Psychology; Mathematics; Epistemology; Artificial neural network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005429897,0.0007209013,0.0007841392,0.0001591735,0.001051926,0.000687643,0.001785049,0.0007000598,0.0001939921],"category_scores_gemma":[0.006454972,0.0007647524,0.0002328416,0.0002208584,0.0002369056,0.0004473252,0.001304687,0.002003508,0.0003689443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002543427,"about_ca_system_score_gemma":0.0003174403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005390015,"about_ca_topic_score_gemma":0.000001808127,"domain_scores_codex":[0.9950182,0.000651469,0.0005731465,0.002190609,0.0007722108,0.0007943538],"domain_scores_gemma":[0.9955347,0.0004925656,0.001335504,0.002056993,0.0002107085,0.0003695714],"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":[0.00004367781,0.00006309683,0.001438872,0.00003562619,0.00002406756,0.0002510192,0.00009094504,0.00002191083,0.9974356,0.0005149145,0.00007831693,0.000002007316],"study_design_scores_gemma":[0.0003505133,0.00006307516,0.00855184,0.0002925206,0.00004459727,1.264061e-8,0.000007932804,0.0007345406,0.9879615,0.00003063756,0.001118283,0.0008445622],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936376,0.000148935,0.0008583154,0.000176532,0.00351497,0.000622875,0.0002085524,0.000739402,0.00009281122],"genre_scores_gemma":[0.9973246,0.0002180086,0.0008336152,0.0005207743,0.0006565329,0.0002406948,2.022394e-7,0.0001368666,0.00006868233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00947406,"threshold_uncertainty_score":0.9994804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07617786316276004,"score_gpt":0.2898408722331242,"score_spread":0.2136630090703641,"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."}}