{"id":"W3081572948","doi":"10.1017/s0142716420000181","title":"Reading minds in motion: Mouse tracking reveals transposed-character effects in Chinese compound word recognition","year":2020,"lang":"en","type":"article","venue":"Applied Psycholinguistics","topic":"Reading and Literacy Development","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Psychology; Character (mathematics); Vocabulary; Reading (process); Lexical decision task; Word (group theory); Word recognition; Compound; Chinese characters; Communication; Linguistics; Cognitive psychology; Cognition; 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":[0.000349297,0.0002572685,0.0002137534,0.0005222661,0.0001245264,0.0002483004,0.0001666396,0.0002492221,0.002067239],"category_scores_gemma":[0.002109473,0.0001599496,0.0001392539,0.0001761912,0.0003168441,0.0003683715,0.0003116719,0.0003484657,0.0002227973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001168543,"about_ca_system_score_gemma":0.0001726102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00176152,"about_ca_topic_score_gemma":0.001891317,"domain_scores_codex":[0.9997968,0.00003036184,0.00001686857,0.0000743153,0.00005383549,0.00002779289],"domain_scores_gemma":[0.9988709,0.0003513228,0.000383787,0.0001324492,0.0001594034,0.0001021147],"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":[0.0003792716,0.00010595,0.08394229,0.00008620656,0.00002338228,0.0005677519,0.001532891,0.000124829,0.8940622,0.000126321,0.000130557,0.01891837],"study_design_scores_gemma":[0.00001051984,0.0004048532,0.9430147,0.000007789877,0.00002435058,0.0006441858,0.0004695844,0.0009287389,0.05408696,0.0001020874,0.0002900141,0.00001623471],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989157,0.00002905125,0.0007140333,0.000009183568,0.000002457743,0.000007361351,0.00003630091,0.0000155547,0.0002703175],"genre_scores_gemma":[0.9982855,0.00002731983,0.00101448,0.00001541001,0.00000202449,0.00002012047,0.00005612142,0.00001019235,0.0005688522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002067239,"threshold_uncertainty_score":0.006915629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03177403552994341,"score_gpt":0.3092677188927591,"score_spread":0.2774936833628157,"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."}}