{"id":"W2739143307","doi":"10.1101/165399","title":"Serial dependence transfers between perceptual objects","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Perception; Object (grammar); Orientation (vector space); Context (archaeology); Visual perception; Communication; Psychology; Cognitive psychology; Smoothing; Computer science; Artificial intelligence; Computer vision; Mathematics; Geography; Geometry; 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":[0.001230069,0.0004906824,0.0005311268,0.0008140628,0.0003313876,0.001565865,0.0007451557,0.0005463252,0.006602395],"category_scores_gemma":[0.008829134,0.0006511562,0.0004059666,0.0004609564,0.0008977537,0.002416909,0.001599624,0.001357596,0.0005746363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007093101,"about_ca_system_score_gemma":0.0003223126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007811394,"about_ca_topic_score_gemma":0.0003200919,"domain_scores_codex":[0.9989471,0.00008723329,0.00006853212,0.0003262315,0.0004835938,0.00008737533],"domain_scores_gemma":[0.9919735,0.002434162,0.001483485,0.002697334,0.0009813946,0.0004301582],"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":[0.001200609,0.0005674922,0.01753576,0.0002257094,0.000136966,0.0003620254,0.0006481134,0.004276549,0.8700182,0.01121315,0.0008380406,0.09297729],"study_design_scores_gemma":[0.0001306824,0.001188453,0.5733624,0.00008570721,0.0002001211,0.0008659457,0.0004236459,0.05193254,0.3029789,0.06575242,0.002947082,0.0001321533],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9680023,0.0001863623,0.02323129,0.0001063014,0.00004945599,0.0000381065,0.0001053443,0.0003106011,0.007970222],"genre_scores_gemma":[0.993744,0.00007560602,0.004619025,0.00007236745,0.00003264856,0.0000179236,0.0001482628,0.0001017778,0.001188354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006602395,"threshold_uncertainty_score":0.02208728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06085872284422409,"score_gpt":0.2902460524059663,"score_spread":0.2293873295617423,"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."}}