{"id":"W3115370360","doi":"10.1145/3395035.3425203","title":"Measuring Cognitive Load: Heart-rate Variability and Pupillometry Assessment","year":2020,"lang":"en","type":"article","venue":"Companion Publication of the 2020 International Conference on Multimodal Interaction","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Pupillometry; Computer science; Cognitive load; Heart rate variability; Cognition; Field (mathematics); Artificial intelligence; Human–computer interaction; Robotics; Work (physics); Psychology; Heart rate; Pupil; Robot; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004605719,0.0001793801,0.0002001977,0.00008703081,0.0001342631,0.0002641655,0.0005394964,0.00006129511,0.0005290788],"category_scores_gemma":[0.002733496,0.0001434257,0.00009637556,0.0002800266,0.0001346371,0.0006420108,0.000278578,0.0004133209,0.00004888025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001582821,"about_ca_system_score_gemma":0.0001079324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002717712,"about_ca_topic_score_gemma":0.000001863582,"domain_scores_codex":[0.9977803,0.0004968357,0.0004530156,0.0005605183,0.0005695423,0.0001398316],"domain_scores_gemma":[0.9978064,0.0006344395,0.0004165317,0.0002130953,0.0008169676,0.0001125361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004967785,0.0005973234,0.009051675,0.00006746855,0.00008397888,9.889915e-7,0.001190807,0.0006662344,0.9152338,0.05582025,0.002454979,0.0143357],"study_design_scores_gemma":[0.0009410777,0.0002024426,0.09171126,0.0001952714,0.00001907402,0.00001866895,0.0003893219,0.6612051,0.240547,0.001106616,0.003390914,0.0002733227],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9037418,0.000003097615,0.008757111,0.06350116,0.001551574,0.0005694159,0.0001012557,0.0001187458,0.02165589],"genre_scores_gemma":[0.9964441,0.00001231999,0.0003322375,0.002877786,0.0001429946,0.00002899023,0.00001731271,0.0000114337,0.0001328068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6746868,"threshold_uncertainty_score":0.5848729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1284720286897685,"score_gpt":0.3556969571120459,"score_spread":0.2272249284222774,"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."}}