{"id":"W2808872490","doi":"10.1016/j.neuropsychologia.2018.06.013","title":"Representational differences between line drawings and photographs of natural scenes: A dissociation between multi-voxel pattern analysis and repetition suppression","year":2018,"lang":"en","type":"article","venue":"Neuropsychologia","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Voxel; Dissociation (chemistry); Retrosplenial cortex; Repetition (rhetorical device); Line drawings; Decoding methods; Communication; Cognitive psychology; Pattern recognition (psychology); Artificial intelligence; Cortex (anatomy); Computer science; Neuroscience; Chemistry; Linguistics; Algorithm","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":[0.0006660561,0.0002376575,0.0002026823,0.0005190945,0.0001495121,0.0006041054,0.00053518,0.0004209145,0.003798517],"category_scores_gemma":[0.005394362,0.0002527474,0.0002007931,0.0003026229,0.0004759632,0.001112142,0.000505398,0.0004808522,0.0003614165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001206819,"about_ca_system_score_gemma":0.0001779807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004556591,"about_ca_topic_score_gemma":0.000760667,"domain_scores_codex":[0.9997024,0.00005785882,0.00002088367,0.00007018566,0.0001150725,0.00003356636],"domain_scores_gemma":[0.9980597,0.0009579489,0.0004403758,0.0003476146,0.0001195788,0.00007485408],"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":[0.001084928,0.0001005283,0.006974704,0.0001642126,0.0000621292,0.00035702,0.000826278,0.0002364357,0.9452943,0.001146262,0.0002534053,0.04349983],"study_design_scores_gemma":[0.0001509453,0.0007236392,0.797572,0.00004380715,0.0001243808,0.007954818,0.0009669611,0.006886202,0.1756174,0.008373925,0.001542128,0.00004384121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844639,0.0001222453,0.01164031,0.0001165676,0.00001695524,0.00004607686,0.0001495303,0.0001520927,0.003292353],"genre_scores_gemma":[0.9924853,0.00008489953,0.006046479,0.00005234435,0.00001630758,0.00004382774,0.000170365,0.0000881039,0.001012319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003798517,"threshold_uncertainty_score":0.01270729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06951254880477098,"score_gpt":0.353846928477147,"score_spread":0.284334379672376,"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."}}