{"id":"W824117140","doi":"10.1167/15.5.1","title":"Modeling probability and additive summation for detection across multiple mechanisms under the assumptions of signal detection theory","year":2015,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Psychometric function; Summation; Weibull distribution; Monte Carlo method; Function (biology); Detection theory; SIGNAL (programming language); Mathematics; Computer science; Psychophysics; Statistical physics; Statistics; Physics; Perception","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":[0.004892902,0.00106109,0.00128454,0.001713617,0.0006161181,0.001683071,0.002846679,0.001480001,0.002785254],"category_scores_gemma":[0.02628627,0.0007649675,0.002089251,0.001111144,0.001495168,0.004082839,0.001471377,0.001559517,0.000657418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001745657,"about_ca_system_score_gemma":0.001617248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005176456,"about_ca_topic_score_gemma":0.002743813,"domain_scores_codex":[0.9974819,0.000604119,0.0001836838,0.0004684649,0.001011006,0.0002508322],"domain_scores_gemma":[0.9893054,0.008001341,0.0009356558,0.0007379896,0.0008442368,0.0001753352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002360831,0.0001953027,0.007289758,0.0002776358,0.0001461303,0.0006237818,0.0004659273,0.7092125,0.01967701,0.1939772,0.0009148628,0.06698379],"study_design_scores_gemma":[0.000009725983,0.00003142842,0.0006564362,0.0000100395,0.00001592236,0.0001160843,0.000008660728,0.9652022,0.001234682,0.03241008,0.0002879083,0.00001683352],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03162578,0.0001580359,0.9655071,0.0001034572,0.00001881894,0.00006575551,0.00003530972,0.0003437197,0.00214189],"genre_scores_gemma":[0.6062908,0.0004773501,0.3874223,0.0002323877,0.00007926744,0.0005282529,0.0001627359,0.0002576506,0.004549161],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005176456,"threshold_uncertainty_score":0.02587646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1088646181869008,"score_gpt":0.3689203382132337,"score_spread":0.260055720026333,"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."}}