{"id":"W4240690026","doi":"10.31234/osf.io/ygr4c","title":"Artificial Cognition: How Experimental Psychology Can Help Generate Explainable Artificial Intelligence","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Vector Institute","funders":"Vector Institute; Defense Advanced Research Projects Agency; Government of Canada; Canadian Institute for Advanced Research; U.S. Department of Defense","keywords":"Interpretability; Cognition; Artificial intelligence; Field (mathematics); Computer science; Transparency (behavior); Black box; Artificial neural network; Artificial psychology; Cognitive science; Psychology; Management science; Artificial Intelligence System; Engineering","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","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009936428,0.001092327,0.001066628,0.0005988497,0.0007627649,0.003452426,0.003492026,0.0009472066,0.001092199],"category_scores_gemma":[0.0002358638,0.001209976,0.0005216029,0.001239572,0.0004600666,0.001019173,0.003819664,0.001533712,0.0005140488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000393309,"about_ca_system_score_gemma":0.00085678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004934801,"about_ca_topic_score_gemma":0.001020999,"domain_scores_codex":[0.991918,0.0006385722,0.001429337,0.003247055,0.001078974,0.001688042],"domain_scores_gemma":[0.9948116,0.0001979203,0.0005726201,0.002924006,0.0009406286,0.0005532144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001640187,0.003023906,0.0000263694,0.000277937,0.0004262165,0.003259832,0.01075287,0.00820102,0.2006395,0.6380138,0.00651218,0.1287023],"study_design_scores_gemma":[0.00003483857,0.0002392137,0.000004742246,0.0001256603,0.00003323403,0.0001146919,0.005432754,0.04091245,0.8522869,0.09877731,0.0007451509,0.001293014],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04170872,0.001132608,0.9238595,0.01538965,0.007940743,0.001239241,0.00003334435,0.0009686192,0.007727573],"genre_scores_gemma":[0.9356047,0.0001714649,0.05790306,0.002766152,0.001564066,0.0006690573,0.0002422055,0.00009630742,0.0009830166],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8938959,"threshold_uncertainty_score":0.9998209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1136292644454268,"score_gpt":0.342895723843789,"score_spread":0.2292664593983622,"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."}}