{"id":"W2974209378","doi":"10.1167/19.10.157a","title":"Learning and visual attention across neurodevel-opmental conditions: Using Multiple Object-Tracking as a descriptor of visual attention","year":2019,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Autism; Psychology; Cognition; Cognitive psychology; Task (project management); Eye tracking; Autism spectrum disorder; Proxy (statistics); Developmental psychology; Object (grammar); Artificial intelligence; Machine learning; Computer science; 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":[0.001194476,0.0006180786,0.0004796949,0.001207272,0.000250812,0.0007883493,0.0004574014,0.0005229117,0.001068022],"category_scores_gemma":[0.006927663,0.0001769429,0.0003222656,0.0004377246,0.0006904128,0.0007925255,0.001084627,0.0007353952,0.0001130346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006988781,"about_ca_system_score_gemma":0.0003172229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005407182,"about_ca_topic_score_gemma":0.0072266,"domain_scores_codex":[0.9992639,0.00009922034,0.00006933694,0.0002712289,0.0002237691,0.00007260064],"domain_scores_gemma":[0.9967907,0.0009042658,0.001419101,0.0003695207,0.0002082995,0.0003080855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001547422,0.001788549,0.8537189,0.0001849238,0.0002870106,0.0002132308,0.002272573,0.002834101,0.05641695,0.0003145435,0.000218261,0.0802035],"study_design_scores_gemma":[0.00001275138,0.0009457364,0.9927964,0.00001211946,0.00002099016,0.0001082774,0.0001856646,0.00226548,0.003150299,0.000340325,0.0001495131,0.00001253069],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987568,0.00007604359,0.0006597743,0.00001255771,0.0000025534,0.00002615976,0.00004850174,0.00001128767,0.0004063769],"genre_scores_gemma":[0.9986137,0.00004305474,0.0008326006,0.00001421014,0.000002513492,0.00003938784,0.0001426914,0.000005739586,0.0003060397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005407182,"threshold_uncertainty_score":0.01075143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620804106820385,"score_gpt":0.3365191436782357,"score_spread":0.3203111026100319,"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."}}