{"id":"W4411505346","doi":"10.1016/j.visres.2025.108641","title":"CNN-extracted features generate synthetic fMRI responses to unseen images","year":2025,"lang":"en","type":"article","venue":"Vision Research","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Neuroscience; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001585126,0.0001589145,0.0001796769,0.000632262,0.0002440228,0.00018515,0.0005494314,0.0001878561,0.0001145284],"category_scores_gemma":[0.001456128,0.0001390745,0.0001127505,0.0008860885,0.0001582303,0.000005874293,0.0006002772,0.0002372828,0.0001025727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003529799,"about_ca_system_score_gemma":0.0001712367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001249373,"about_ca_topic_score_gemma":0.00004965791,"domain_scores_codex":[0.9976553,0.0005912137,0.000228951,0.0006473577,0.0004263838,0.0004507382],"domain_scores_gemma":[0.9981306,0.0001468445,0.00002925447,0.0009802037,0.0005794232,0.0001336803],"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":[0.0002327179,0.00006243997,0.0002233952,0.00001114796,0.00003150489,0.00001557434,0.000009642845,0.000004844768,0.7322723,0.00003004737,0.2513309,0.01577554],"study_design_scores_gemma":[0.0001221205,0.0002346479,0.006180687,0.00003945323,0.00001049199,0.000003092068,0.00003630359,0.00002965998,0.8305843,0.0001106529,0.1625251,0.0001234569],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9610336,0.002369322,0.004737629,0.00595741,0.00004921133,0.0007244748,0.00001739215,0.0001047626,0.02500621],"genre_scores_gemma":[0.8987122,0.0006838772,0.003434302,0.0005427994,0.0001011438,0.00008709051,0.00006219018,0.00002784832,0.09634858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09831205,"threshold_uncertainty_score":0.5671296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01910139168889587,"score_gpt":0.4127854258084662,"score_spread":0.3936840341195703,"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."}}