{"id":"W2094107698","doi":"10.1371/journal.pbio.0030204","title":"Distributed Neural Plasticity for Shape Learning in the Human Visual Cortex","year":2005,"lang":"en","type":"article","venue":"PLoS Biology","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Max-Planck-Gesellschaft; Deutsche Forschungsgemeinschaft","keywords":"Salience (neuroscience); Visual cortex; Functional magnetic resonance imaging; Artificial intelligence; Optimal distinctiveness theory; Visual processing; Neuroscience; Cognitive neuroscience of visual object recognition; Segmentation; Visual perception; Neuroplasticity; Pattern recognition (psychology); Computer science; Psychology; Cognitive psychology; Biology; Computer vision; Perception; Object (grammar)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001661878,0.0001779797,0.0001673839,0.0002017003,0.0001960755,0.000317712,0.0003780514,0.0003182628,0.001431069],"category_scores_gemma":[0.001122377,0.0001941658,0.0002913964,0.0001009565,0.0007858127,0.0005061226,0.0004765198,0.0005534722,0.0002390469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003632217,"about_ca_system_score_gemma":0.0002981668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005045715,"about_ca_topic_score_gemma":0.0009386628,"domain_scores_codex":[0.9998891,0.00001164399,0.000003855928,0.00003933196,0.00003847206,0.00001754584],"domain_scores_gemma":[0.9997378,0.00005795127,0.0000540676,0.00008553003,0.00002582991,0.00003874454],"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":[0.0001219272,0.00005703592,0.001788794,0.00005278211,0.00002720257,0.0001599389,0.00004307309,0.001829399,0.9563041,0.002112007,0.0001881967,0.03731535],"study_design_scores_gemma":[0.0001655104,0.001086167,0.5021255,0.00004714679,0.00008081985,0.004216091,0.0001606878,0.0446048,0.4096282,0.03508272,0.002746573,0.0000557267],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9427702,0.0009844428,0.04993238,0.0006266227,0.00006057701,0.0000348198,0.00006408423,0.0003145874,0.005212318],"genre_scores_gemma":[0.9939523,0.0001688997,0.00506464,0.00006476868,0.000008725645,0.00001363572,0.00002935322,0.00002305024,0.0006744021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001431069,"threshold_uncertainty_score":0.004787445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09413273950026758,"score_gpt":0.3668667381236849,"score_spread":0.2727339986234174,"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."}}