{"id":"W3161774031","doi":"10.21203/rs.3.rs-518209/v1","title":"How Spatial Attention Affects the Decision Process: Looking through the Lens of Bayesian Hierarchical Diffusion Model &amp;amp; EEG Analysis","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bayesian probability; Bayesian inference; Computer science; Artificial intelligence; Bayesian hierarchical modeling; Cognition; Perception; Pattern recognition (psychology); Psychology; Machine learning; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001658273,0.0002895893,0.0004084883,0.0006276787,0.0002878346,0.001037102,0.0007481959,0.0006587285,0.001886768],"category_scores_gemma":[0.006782039,0.0002866889,0.0006638261,0.0003610383,0.0008342639,0.001558008,0.0006967132,0.0007420036,0.0001248485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007657723,"about_ca_system_score_gemma":0.0005971669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009319161,"about_ca_topic_score_gemma":0.003638879,"domain_scores_codex":[0.9995692,0.0001826819,0.00001462767,0.0001072945,0.00006869747,0.00005733121],"domain_scores_gemma":[0.9980687,0.001304934,0.0002690404,0.0001375923,0.0001304409,0.00008932076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003161548,0.0002276275,0.03382702,0.0001765132,0.0002929746,0.0006311222,0.001681441,0.4413958,0.02292883,0.4404991,0.001628659,0.05639468],"study_design_scores_gemma":[0.000008470243,0.00002962307,0.006571664,0.000008146677,0.00001911143,0.00005875395,0.00006403679,0.9314885,0.0005497682,0.06088886,0.0002930308,0.00002002964],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4403706,0.0004958679,0.5524548,0.001407593,0.0000315912,0.00004044575,0.0001324862,0.0001402985,0.004926255],"genre_scores_gemma":[0.9872652,0.0001206601,0.01168631,0.00003278927,0.00001446425,0.00001247282,0.00002374507,0.00001221113,0.0008320938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009319161,"threshold_uncertainty_score":0.01852983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07915721128653534,"score_gpt":0.3753068319290634,"score_spread":0.2961496206425281,"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."}}