{"id":"W2735041065","doi":"10.1145/3099023.3099059","title":"Leveraging Pupil Dilation Measures for Understanding Users' Cognitive Load During Visualization Processing","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Visualization; Pupillary response; Computer science; Bar chart; Human–computer interaction; Pupil; Data visualization; Cognitive load; Graph; Cognition; Artificial intelligence; Psychology; Theoretical computer science","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":[0.002258191,0.0009220123,0.0004813573,0.001412026,0.0003853951,0.00175498,0.0005477912,0.0007181037,0.004579091],"category_scores_gemma":[0.0237579,0.000330653,0.0003618569,0.0005513663,0.0003311624,0.002454776,0.000888468,0.0008658164,0.0006091531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002294945,"about_ca_system_score_gemma":0.000269675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006879521,"about_ca_topic_score_gemma":0.0009205478,"domain_scores_codex":[0.9988921,0.0003711031,0.00009784885,0.0002463418,0.000305343,0.00008721826],"domain_scores_gemma":[0.9841882,0.01104357,0.001688731,0.001117408,0.001553561,0.0004084918],"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":[0.003543454,0.0009124609,0.08636343,0.002689062,0.0003891859,0.0003566455,0.008723494,0.004338554,0.5044493,0.002047134,0.003934275,0.382253],"study_design_scores_gemma":[0.0002403058,0.005330523,0.7227233,0.0003106736,0.0006231258,0.001033072,0.003653021,0.05112315,0.197241,0.007662879,0.009588585,0.0004705238],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8016216,0.001257858,0.1849954,0.0004709706,0.0001537855,0.0006247927,0.001024129,0.002766995,0.007084607],"genre_scores_gemma":[0.9345268,0.0004980832,0.06256959,0.0001228121,0.00007833196,0.0004088832,0.0003239434,0.0003239849,0.001147532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004579091,"threshold_uncertainty_score":0.01531863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1246196722729714,"score_gpt":0.3614669554580493,"score_spread":0.2368472831850779,"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."}}