{"id":"W2567383362","doi":"10.1167/16.12.512","title":"fMRI reveals different activation patterns for real objects vs. photographs of objects","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Fusiform gyrus; Functional magnetic resonance imaging; Intraparietal sulcus; Artificial intelligence; Visual cortex; Computer science; Pattern recognition (psychology); Computer vision; Cognitive neuroscience of visual object recognition; Psychology; Cortex (anatomy); Middle temporal gyrus; Object (grammar); Neuroscience","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":[0.0001873703,0.0001839574,0.0001512914,0.0003012039,0.00007644593,0.0002012834,0.00009262803,0.000239013,0.001630834],"category_scores_gemma":[0.0005708957,0.0001435926,0.000157348,0.0001137087,0.0002410438,0.0001889276,0.0001568443,0.0001896075,0.0001564951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009725682,"about_ca_system_score_gemma":0.0000720526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002885407,"about_ca_topic_score_gemma":0.0007606068,"domain_scores_codex":[0.9999104,0.00001722643,0.000003630049,0.00003023941,0.0000200098,0.00001856531],"domain_scores_gemma":[0.9998511,0.00006956028,0.00003118277,0.00001190924,0.000009873718,0.00002626491],"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":[0.0002261715,0.00003051192,0.001766534,0.0000384481,0.00002112635,0.00008767867,0.00007358569,0.00004351504,0.9927815,0.00006079211,0.00006556703,0.004804622],"study_design_scores_gemma":[0.00006757509,0.0007714275,0.8431588,0.00001530506,0.00007597004,0.001826035,0.0001930539,0.002201029,0.1503212,0.0006121017,0.0007368955,0.00002058299],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965311,0.0001525899,0.002171332,0.00006727231,0.000009410266,0.00001179965,0.0000682693,0.00003313039,0.0009550634],"genre_scores_gemma":[0.9958944,0.000148945,0.003029705,0.00007965983,0.00001613524,0.00002479263,0.0001304292,0.00001584752,0.0006601086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001630834,"threshold_uncertainty_score":0.005455732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03816702390317978,"score_gpt":0.3199426943402663,"score_spread":0.2817756704370866,"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."}}