{"id":"W3195282418","doi":"10.1002/wcs.1574","title":"Stop paying attention to “attention”","year":2021,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Cognitive Science","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"CLARITY; Meaning (existential); Cognition; Psychology; Cognitive psychology; Fragmentation (computing); Confusion; Term (time); Root (linguistics); Epistemology; Cognitive science; Social psychology; Computer science; Linguistics; Psychoanalysis","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","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002866298,0.0003494482,0.0004764505,0.0005268144,0.001430859,0.000845933,0.001867515,0.00005075231,0.0001195789],"category_scores_gemma":[0.001215855,0.0003106959,0.0002993274,0.005668237,0.0005750062,0.003361572,0.005524096,0.0002926585,0.002163986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001839193,"about_ca_system_score_gemma":0.0004425101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003054092,"about_ca_topic_score_gemma":0.00001540641,"domain_scores_codex":[0.995452,0.0002798698,0.0007047415,0.001821509,0.0008124565,0.0009294173],"domain_scores_gemma":[0.9968289,0.0001646758,0.0002518794,0.000821418,0.001419873,0.0005132692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001061246,0.0002292944,0.001798674,0.00007943395,0.00001591383,0.000178256,0.003128803,0.000002861284,0.1157047,0.006994151,0.002103095,0.8697541],"study_design_scores_gemma":[0.005985643,0.003827597,0.5872837,0.04681982,0.0005355753,0.004413304,0.03692668,0.03360054,0.08064398,0.07710496,0.1109157,0.0119425],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1011205,0.002487388,0.8595367,0.006177803,0.00227788,0.001210328,0.00001631997,0.000252071,0.02692096],"genre_scores_gemma":[0.9757119,0.0006366526,0.01638093,0.004504948,0.0002095891,0.0002641418,0.00001671736,0.0000190433,0.002256113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8745914,"threshold_uncertainty_score":0.9999345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04108978619568504,"score_gpt":0.3524333800690257,"score_spread":0.3113435938733407,"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."}}