{"id":"W2494117141","doi":"10.1098/rstb.2016.0278","title":"Inferring brain-computational mechanisms with models of activity measurements","year":2016,"lang":"en","type":"article","venue":"Philosophical Transactions of the Royal Society B Biological Sciences","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"European Research Council; Medical Research Council","keywords":"Computer science; Artificial intelligence; Voxel; Brain activity and meditation; Functional magnetic resonance imaging; Pattern recognition (psychology); Leverage (statistics); Probabilistic logic; Inference; Machine learning; Generative model; Computational model; Set (abstract data type); Convolutional neural network; Electroencephalography; Neuroscience; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004209986,0.0001275989,0.0001808505,0.00002020166,0.0004080708,0.00001297631,0.0004441403,0.00008995325,0.00005331207],"category_scores_gemma":[0.0001237153,0.00005251327,0.0002538362,0.000415964,0.001644791,0.0001706641,0.00003732171,0.0001338061,0.000001232654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003483438,"about_ca_system_score_gemma":0.00004653962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001822886,"about_ca_topic_score_gemma":0.000001413148,"domain_scores_codex":[0.9984454,0.0001546809,0.000206273,0.0003735426,0.0006050775,0.000215008],"domain_scores_gemma":[0.9989461,0.0006449383,0.0001533811,0.000132721,0.00006183451,0.00006104048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001361565,0.0004627685,0.0008823958,0.0000187695,0.00003457632,2.792086e-7,0.00006634827,0.09395434,0.8175889,0.0815188,0.000006413879,0.005330296],"study_design_scores_gemma":[0.0005504219,0.000889187,0.003634634,0.00006851854,0.00001917773,0.000004923998,0.00001611834,0.07841786,0.1684985,0.7476958,0.000004531695,0.0002002816],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5643058,0.000003310136,0.4229965,0.01190457,0.0001004272,0.0001748751,0.00002951508,0.00003121777,0.000453806],"genre_scores_gemma":[0.9975613,0.000006298391,0.002037643,0.0003364209,0.00001813623,0.00001036143,1.231671e-7,0.000003953875,0.00002577535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.666177,"threshold_uncertainty_score":0.60603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1368311443561623,"score_gpt":0.2802349556162428,"score_spread":0.1434038112600805,"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."}}