{"id":"W4386471771","doi":"10.1101/2023.09.06.556533","title":"Alignment of auditory artificial networks with massive individual fMRI brain data leads to generalizable improvements in brain encoding and downstream tasks","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Courtois Foundation; Région Bretagne","keywords":"Downstream (manufacturing); Encoding (memory); Computer science; Brain activity and meditation; Psychology; Speech recognition; Neuroscience; Cognitive psychology; Artificial intelligence; Electroencephalography; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001755092,0.0005546728,0.000674273,0.0004891971,0.0001562447,0.0005628239,0.002191334,0.0003567862,0.00001035568],"category_scores_gemma":[0.0001854634,0.0005589296,0.00004680455,0.001019545,0.000139358,0.0005215568,0.004469182,0.0006027932,0.00001096368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001946308,"about_ca_system_score_gemma":0.0007485462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001807172,"about_ca_topic_score_gemma":0.00003755168,"domain_scores_codex":[0.9957138,0.0001741804,0.0007701435,0.00183262,0.000729878,0.000779413],"domain_scores_gemma":[0.9966707,0.0001562753,0.0006413502,0.002057922,0.0001818639,0.0002919133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001801389,0.000950532,0.0338412,0.002569041,0.0009558199,0.000772571,0.0009328276,0.09678484,0.801844,0.003495106,0.05611569,0.001558252],"study_design_scores_gemma":[0.005882434,0.001388136,0.1783736,0.01279075,0.0005165602,3.414982e-7,0.0001199699,0.3359596,0.4388607,0.0001183685,0.01697446,0.009015171],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6725498,0.0004343262,0.3187045,0.003965675,0.001997384,0.001494135,0.0004382837,0.0004045886,0.00001121445],"genre_scores_gemma":[0.9560912,0.0000363022,0.04168206,0.00118826,0.0007229411,0.0001639571,0.000004098507,0.0000921105,0.00001904534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3629833,"threshold_uncertainty_score":0.9996862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03492999307056813,"score_gpt":0.2497903552277553,"score_spread":0.2148603621571872,"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."}}