{"id":"W3158280850","doi":"10.3758/s13423-021-01939-4","title":"Identifying the neural dynamics of category decisions with computational model-based functional magnetic resonance imaging","year":2021,"lang":"en","type":"article","venue":"Psychonomic Bulletin & Review","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; University of Toronto","funders":"Canada Foundation for Innovation; Ontario Research Foundation; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Categorization; Functional magnetic resonance imaging; Psychology; Computational model; Neural correlates of consciousness; Prefrontal cortex; Cognitive psychology; Representation (politics); Artificial neural network; Cognition; Neuroscience; Artificial intelligence; Cognitive science; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001759404,0.0004638015,0.0007295343,0.001405297,0.0001582434,0.001263896,0.001010698,0.001043677,0.0005491852],"category_scores_gemma":[0.004146767,0.0004329677,0.0006769914,0.0007176425,0.001056625,0.002155901,0.0006156628,0.00121177,0.0003109406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009653015,"about_ca_system_score_gemma":0.0006071499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001900461,"about_ca_topic_score_gemma":0.002561135,"domain_scores_codex":[0.9997907,0.00004394191,0.00001191354,0.00007385898,0.00006479149,0.00001476612],"domain_scores_gemma":[0.9983715,0.001127263,0.0001682752,0.0001337008,0.000164838,0.00003438086],"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":[0.00008803406,0.0001482494,0.01381345,0.003160527,0.0009312261,0.0002177728,0.0003818205,0.1143156,0.05865915,0.1010429,0.005676913,0.7015643],"study_design_scores_gemma":[0.00004189985,0.0002619578,0.04343036,0.0005852319,0.0005203882,0.0009979235,0.0002474019,0.5396271,0.02878441,0.3308925,0.05434697,0.0002637752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.100277,0.1385363,0.7462705,0.003497993,0.0002389573,0.0001100775,0.0003304465,0.0009787645,0.009759929],"genre_scores_gemma":[0.5510417,0.1354892,0.310078,0.0005333348,0.0003434233,0.0001822084,0.0006478347,0.000201563,0.001482676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001900461,"threshold_uncertainty_score":0.009304762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03002917800375616,"score_gpt":0.287721440946372,"score_spread":0.2576922629426158,"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."}}