{"id":"W4388002190","doi":"10.1162/imag.a.1256","title":"Gamer in the scanner : Event-related analysis of fMRI activity during retro videogame play guided by automated annotations of game content","year":2023,"lang":"en","type":"preprint","venue":"Imaging Neuroscience","topic":"Educational Games and Gamification","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Institut Universitaire de Gériatrie de Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Courtois Foundation; Canada Research Chairs","keywords":"Computer science; Functional magnetic resonance imaging; Annotation; Artificial intelligence; Event (particle physics); Cognition; Human–computer interaction; Psychology","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":[0.0003106164,0.0003793784,0.0002472652,0.0004303749,0.0001445522,0.0003703117,0.0004689367,0.0003551619,0.002076572],"category_scores_gemma":[0.0007598736,0.0002706208,0.0001838112,0.0001697003,0.0002342544,0.0001977467,0.0004762693,0.0003358589,0.0005906735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001199703,"about_ca_system_score_gemma":0.0001838813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001399111,"about_ca_topic_score_gemma":0.005537832,"domain_scores_codex":[0.9998997,0.00002477135,0.000003372679,0.0000410556,0.00001563456,0.00001542345],"domain_scores_gemma":[0.9998605,0.00004279942,0.00002265142,0.00001962365,0.00002290615,0.00003146077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002539724,0.0005421984,0.02892218,0.0004025865,0.0002639084,0.0008130855,0.0008804492,0.005358501,0.8371136,0.0007424867,0.005586672,0.1168346],"study_design_scores_gemma":[0.0002476192,0.001612867,0.715025,0.0001249537,0.0003001426,0.003188336,0.0005489443,0.1367725,0.1305846,0.004143752,0.007248014,0.0002033626],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9049018,0.0002428414,0.08783919,0.0001608267,0.00005713847,0.0002534205,0.001957502,0.001689692,0.002897601],"genre_scores_gemma":[0.9500653,0.000103448,0.04691406,0.0001530108,0.0000343593,0.000257737,0.000993843,0.000233944,0.001244345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002076572,"threshold_uncertainty_score":0.006946802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07164519167252459,"score_gpt":0.3855951779510922,"score_spread":0.3139499862785676,"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."}}