{"id":"W2341761158","doi":"10.1038/srep24673","title":"Perceptual learning shapes multisensory causal inference via two distinct mechanisms","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Multisensory perception and integration","field":"Psychology","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Association for Canadian Studies; Université de Montréal","funders":"Irish Research Council; European Commission; Wellcome Trust","keywords":"Crossmodal; Multisensory integration; Computer science; Inference; Bayesian inference; Causal inference; Perception; Bayesian probability; Task (project management); Artificial intelligence; Machine learning; Visual perception; Psychology; Neuroscience; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009928503,0.0002294718,0.000219861,0.0002063056,0.0004765113,0.0001712293,0.0001422428,0.000126924,0.04102631],"category_scores_gemma":[0.0004668286,0.0001580686,0.0001236518,0.0002106316,0.0004085712,0.0002668186,0.00006183577,0.0002093444,0.004081797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009298074,"about_ca_system_score_gemma":0.00005272757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001671191,"about_ca_topic_score_gemma":0.0002413398,"domain_scores_codex":[0.9971123,0.0002649946,0.0005978587,0.001069899,0.0004957998,0.000459194],"domain_scores_gemma":[0.9985288,0.0001214173,0.0003077548,0.0005945527,0.0002485998,0.0001988851],"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":[0.00002918491,0.0001271989,0.008353434,0.000004965769,0.00002217356,0.0003024543,0.003674774,0.00001981495,0.8897688,0.002015844,0.007464205,0.08821713],"study_design_scores_gemma":[0.004376954,0.0008662802,0.5497385,0.0004663707,0.0001927507,0.002939521,0.0104846,0.008673386,0.0385908,0.03240111,0.3476849,0.003584727],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8896418,0.00001516501,0.08023663,0.0001595679,0.01325679,0.000312113,0.00000593201,0.0003437127,0.01602826],"genre_scores_gemma":[0.9036835,0.000001264885,0.000736922,0.00004515518,0.0001594813,0.00003766938,0.00003467973,0.00002605745,0.09527521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8511781,"threshold_uncertainty_score":0.9966936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04932046038774392,"score_gpt":0.3480401047426209,"score_spread":0.298719644354877,"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."}}