{"id":"W4376225590","doi":"10.1111/ejn.16045","title":"A machine learning approach towards the differentiation between interoceptive and exteroceptive attention","year":2023,"lang":"en","type":"article","venue":"European Journal of Neuroscience","topic":"Psychosomatic Disorders and Their Treatments","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"National Center for Advancing Translational Sciences; National Institutes of Health; University of Washington","keywords":"Interoception; Psychology; Functional magnetic resonance imaging; Cognitive psychology; Sensibility; Neuroimaging; Neuroscience; Perception","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":[0.004093428,0.0009444382,0.0008779537,0.001570271,0.0003204779,0.001550906,0.0009778944,0.001154253,0.0009266739],"category_scores_gemma":[0.01004878,0.0002661131,0.0008332792,0.0009451151,0.000666284,0.0007719844,0.0009079289,0.001986201,0.0002785255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007290318,"about_ca_system_score_gemma":0.0007121789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001969637,"about_ca_topic_score_gemma":0.001370765,"domain_scores_codex":[0.9983347,0.0009445954,0.0001129088,0.0003683157,0.0001512482,0.00008815566],"domain_scores_gemma":[0.9945704,0.004297156,0.0003135399,0.0003678242,0.000357753,0.00009321869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006056709,0.0008756425,0.03577684,0.0005045426,0.0008729178,0.0002218312,0.0004824269,0.2413264,0.01422441,0.02134455,0.003502006,0.6802627],"study_design_scores_gemma":[0.00002774343,0.000270243,0.006418338,0.0000526652,0.0000472227,0.00005526867,0.00005582031,0.9639847,0.001874975,0.02613704,0.001049813,0.00002626585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08329243,0.002056045,0.9099206,0.001181697,0.00014006,0.0002352985,0.0003183849,0.0004703755,0.002385049],"genre_scores_gemma":[0.7669643,0.0006736054,0.2285724,0.0004490756,0.0002817264,0.0005111812,0.000546904,0.00004014485,0.0019608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004093428,"threshold_uncertainty_score":0.02164835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0538187608116354,"score_gpt":0.2845381722851257,"score_spread":0.2307194114734903,"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."}}