{"id":"W4407168549","doi":"10.1109/lra.2025.3539103","title":"Quantifying Human Mental State in Interactive pHRI: Maintaining Balancing","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Council","keywords":"State (computer science); Mental state; Computer science; Human–computer interaction; Psychology; Process management; Business; Applied psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001496538,0.0001171372,0.000136269,0.000221284,0.0001536469,0.0001790316,0.0001116494,0.00002360267,0.00000379808],"category_scores_gemma":[0.00003097996,0.0001158824,0.00003240045,0.0001809803,0.00005103165,0.0002742082,0.00005209151,0.0001616442,0.000004657634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008009389,"about_ca_system_score_gemma":0.00001031246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003497243,"about_ca_topic_score_gemma":0.00002660565,"domain_scores_codex":[0.9990764,0.00008627188,0.0002506281,0.000272347,0.0001107739,0.0002035768],"domain_scores_gemma":[0.9996095,0.0001783856,0.00008828646,0.00008815232,0.000009241147,0.00002641047],"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":[0.000007728522,0.00003051798,0.003545115,0.00003431772,0.00000951982,0.00001834826,0.002922199,0.04384814,0.9452047,0.001350374,0.0004514948,0.002577514],"study_design_scores_gemma":[0.001154611,0.00005940321,0.01934531,0.0007901517,0.00001178815,0.00001922805,0.0005492425,0.5627707,0.4137755,0.0009306427,0.0002123417,0.0003810739],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9615551,0.000008839548,0.03477181,0.002660144,0.000593568,0.0001311817,0.000002930946,0.0000809505,0.0001954965],"genre_scores_gemma":[0.9953482,0.000004784894,0.0009288936,0.003641753,0.00002282378,0.000004405897,0.000001989748,0.000007841084,0.00003933239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5314292,"threshold_uncertainty_score":0.4725548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02923871463433123,"score_gpt":0.3111385279413175,"score_spread":0.2818998133069863,"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."}}