{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007157321,0.000943208,0.0008416112,0.001369395,0.002817117,0.006034941,0.002154564,0.005419456,0.01273298],"category_scores_gemma":[0.04366528,0.0005045237,0.0007419229,0.001205972,0.02232069,0.0139759,0.006348846,0.008256665,0.007253814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002629095,"about_ca_system_score_gemma":0.003039233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001669971,"about_ca_topic_score_gemma":0.001512633,"domain_scores_codex":[0.9887673,0.003658114,0.0007382737,0.001975105,0.004065593,0.0007956716],"domain_scores_gemma":[0.9725659,0.01088552,0.00302638,0.006208663,0.005846772,0.001466712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001774064,0.00006452084,0.00266329,0.0006286909,0.00007268698,0.0004307943,0.02111392,0.0002211259,0.005307735,0.7188513,0.1243335,0.1261352],"study_design_scores_gemma":[0.00002908713,0.00008119803,0.002374325,0.0004829317,0.00003620418,0.00107609,0.005035406,0.0005589586,0.002850106,0.495596,0.4917879,0.00009176144],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.04894301,0.02018292,0.195857,0.2451486,0.0159919,0.0002334843,0.0004039886,0.00314417,0.470095],"genre_scores_gemma":[0.7222892,0.007591074,0.05289,0.1220177,0.006528849,0.0004761338,0.0004204491,0.002241426,0.0855452],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.01273298,"threshold_uncertainty_score":0.04259604,"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."}}