{"id":"W2979512181","doi":"10.3389/fpsyg.2019.02162","title":"Understanding Events by Eye and Ear: Agent and Verb Drive Non-anticipatory Eye Movements in Dynamic Scenes","year":2019,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Eye movement; Psychology; Verb; Cognitive psychology; Communication; Neuroscience; Artificial intelligence; Computer science","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":[0.0003521404,0.0003091393,0.0002431679,0.00028249,0.000176778,0.0008648743,0.0002717501,0.0005447748,0.001471903],"category_scores_gemma":[0.003902611,0.0003643644,0.0002497987,0.0001889749,0.0003594065,0.001280541,0.0005361938,0.0003442537,0.0001568483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002650288,"about_ca_system_score_gemma":0.0001509334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00173713,"about_ca_topic_score_gemma":0.001650324,"domain_scores_codex":[0.999744,0.00006778618,0.00001370983,0.0001010724,0.00004115339,0.00003233326],"domain_scores_gemma":[0.9992125,0.0004753333,0.0001746787,0.00005653033,0.0000466298,0.00003445632],"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":[0.0008773162,0.0001337211,0.02189282,0.0003143113,0.0000746705,0.0004456288,0.006208713,0.0009521298,0.9376882,0.002140404,0.0005496619,0.02872247],"study_design_scores_gemma":[0.0001702846,0.0008402181,0.8844534,0.0001051792,0.000200047,0.0007258498,0.003690511,0.01725111,0.07909395,0.009405641,0.003980383,0.0000833319],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887792,0.0003235638,0.007748975,0.0001092296,0.00001837642,0.00005339035,0.0001384267,0.00005792876,0.002770953],"genre_scores_gemma":[0.9947752,0.0001623282,0.00435965,0.00008143318,0.000009246814,0.00004305842,0.0001267705,0.00002907572,0.0004132396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00173713,"threshold_uncertainty_score":0.004923999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0274563239044204,"score_gpt":0.3298982299755058,"score_spread":0.3024419060710854,"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."}}