{"id":"W3127448647","doi":"10.1101/2021.02.02.429272","title":"Active Inference as a Framework for Brain-Computer Interfaces","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Inference; Computer science; Brain–computer interface; Context (archaeology); Artificial intelligence; Bayesian inference; Machine learning; Human–computer interaction; Bayesian probability; Electroencephalography; Psychology","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":[0.004816618,0.001120673,0.001179808,0.001345798,0.0006464602,0.00300421,0.002579525,0.002313612,0.005380958],"category_scores_gemma":[0.006658493,0.0007120696,0.001596894,0.001075857,0.003814844,0.002826532,0.002622762,0.003940304,0.001059944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001817901,"about_ca_system_score_gemma":0.001209303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004007184,"about_ca_topic_score_gemma":0.002792732,"domain_scores_codex":[0.9972991,0.001406506,0.0001136005,0.0003710439,0.0007181511,0.00009143145],"domain_scores_gemma":[0.9977406,0.001622537,0.000105308,0.0001355281,0.0003090326,0.00008701368],"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":[0.0000277433,0.00002428619,0.00008500669,0.0001086989,0.00005894163,0.0000474978,0.00008754998,0.1162514,0.000930657,0.8591282,0.001968632,0.02128132],"study_design_scores_gemma":[0.00001384151,0.00001715194,0.00004058222,0.00003371444,0.00000969622,0.00001864264,0.000009940786,0.4953002,0.0003199895,0.498513,0.00571137,0.00001194279],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007017449,0.001189537,0.992595,0.000868139,0.00008767492,0.00002081651,0.00003509695,0.00008582421,0.00441614],"genre_scores_gemma":[0.2637745,0.003008191,0.7197109,0.0007820104,0.0007509327,0.0004144806,0.0001967277,0.0002390937,0.01112312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005380958,"threshold_uncertainty_score":0.02547306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02874302322609473,"score_gpt":0.2821798187751687,"score_spread":0.2534367955490739,"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."}}