{"id":"W4416346197","doi":"10.1016/j.visres.2026.108865","title":"Connecting the dots - Recognition of artificial and natural shapes relies on representing points of high information","year":2025,"lang":"en","type":"article","venue":"Vision Research","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Curvature; Maxima and minima; Maxima; Pattern recognition (psychology); Mean curvature flow; ENCODE","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":[0.0006649462,0.0002413932,0.000409353,0.0003281119,0.0002386454,0.00123068,0.0003654321,0.0004655803,0.002447947],"category_scores_gemma":[0.004633987,0.0003415165,0.0005264588,0.000210717,0.0006296518,0.00131573,0.001209399,0.0004757718,0.0006276617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003962662,"about_ca_system_score_gemma":0.0002948652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006357024,"about_ca_topic_score_gemma":0.000717327,"domain_scores_codex":[0.9993049,0.0001175917,0.00006059737,0.0002753885,0.0001874458,0.00005396899],"domain_scores_gemma":[0.9979341,0.0007410009,0.0004629426,0.000562656,0.0001320399,0.0001673512],"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":[0.001102329,0.0000755586,0.01844786,0.0001314271,0.00005113872,0.0001428881,0.0003362884,0.003875849,0.9302745,0.002666086,0.000137408,0.04275869],"study_design_scores_gemma":[0.00006572223,0.001676079,0.2141568,0.00004766816,0.0001110581,0.00145214,0.0004906716,0.112542,0.6587178,0.007571054,0.003094047,0.00007492649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857668,0.00005892015,0.01301393,0.00003223645,0.000007657828,0.00001906261,0.0000344055,0.00009746296,0.0009695526],"genre_scores_gemma":[0.9912714,0.00004124906,0.00808691,0.00001842502,0.000002435077,0.000009632045,0.00009862396,0.00001984294,0.0004515704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002447947,"threshold_uncertainty_score":0.008189142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1062114666240055,"score_gpt":0.4103007643950201,"score_spread":0.3040892977710146,"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."}}