{"id":"W2033565928","doi":"10.1007/s00221-010-2419-8","title":"Using eye tracking to study numerical cognition: the case of the ratio effect","year":2010,"lang":"en","type":"article","venue":"Experimental Brain Research","topic":"Cognitive and developmental aspects of mathematical skills","field":"Mathematics","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Numerical cognition; Arabic numerals; Magnitude (astronomy); Eye movement; Numerosity adaptation effect; Cognition; Eye tracking; Psychology; Mathematics; Statistics; Computer science; Artificial intelligence; Arithmetic; Neuroscience; Physics","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":[0.003719365,0.0005430068,0.0004824242,0.001066079,0.000463149,0.001220566,0.0007253692,0.00175519,0.002017956],"category_scores_gemma":[0.0238854,0.0003725619,0.0003658597,0.0007803411,0.002311141,0.002804163,0.001001129,0.001497917,0.0002712852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003584964,"about_ca_system_score_gemma":0.0004652124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003503583,"about_ca_topic_score_gemma":0.002217796,"domain_scores_codex":[0.9989605,0.0004645989,0.00005217409,0.0002343051,0.0002289894,0.00005940704],"domain_scores_gemma":[0.9868931,0.009627812,0.0008676168,0.002095193,0.000364772,0.0001514573],"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":[0.002419303,0.001362811,0.08533147,0.001065565,0.0006370341,0.004384884,0.007647643,0.006313233,0.4395691,0.1083161,0.002845877,0.340107],"study_design_scores_gemma":[0.0007874259,0.003379226,0.4960764,0.0003694679,0.0005800973,0.01408997,0.003013378,0.03608857,0.1776489,0.2416017,0.02582093,0.0005437632],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9047163,0.003210998,0.05596191,0.001420254,0.0002347973,0.0001750935,0.000207206,0.0001605117,0.03391299],"genre_scores_gemma":[0.9687558,0.001438217,0.02764936,0.0003927691,0.00008060962,0.0000800471,0.00005694445,0.00004948665,0.001496802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003719365,"threshold_uncertainty_score":0.01967007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1384607757774382,"score_gpt":0.4952688982914253,"score_spread":0.3568081225139871,"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."}}