{"id":"W4409761624","doi":"10.1109/vrw66409.2025.00384","title":"Attention’s Substates: Unlocking Adaptive VR through EEG Insights","year":2025,"lang":"en","type":"article","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Electroencephalography; Computer science; Psychology; 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":[0.000194789,0.0002219387,0.0001059017,0.000326735,0.00006312449,0.0003856582,0.0001444084,0.0001980586,0.0006571314],"category_scores_gemma":[0.001781718,0.00009006903,0.0001434284,0.0001685794,0.0001790293,0.0004452337,0.0003020773,0.0002045738,0.0000967646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007333242,"about_ca_system_score_gemma":0.00008719348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009059932,"about_ca_topic_score_gemma":0.001382612,"domain_scores_codex":[0.9999024,0.0000260497,0.000005539451,0.00002575461,0.00002416323,0.00001610026],"domain_scores_gemma":[0.9997254,0.0001649131,0.00004317046,0.00001557845,0.0000398446,0.00001113757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001042095,0.0001487733,0.02270648,0.0004972374,0.0001004156,0.0004983554,0.002394859,0.006304555,0.5228888,0.002365746,0.0008351743,0.4402175],"study_design_scores_gemma":[0.000132809,0.001804587,0.6190548,0.0002693251,0.0003726139,0.002260238,0.00265451,0.201052,0.1501531,0.01300064,0.009114418,0.0001309482],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.886829,0.0009024439,0.1071387,0.0001824395,0.00004212198,0.00009967919,0.0002322124,0.0002578434,0.004315609],"genre_scores_gemma":[0.9892845,0.0001834489,0.01010083,0.00003438218,0.00001525502,0.00001331397,0.00004599371,0.000009842192,0.0003125225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009059932,"threshold_uncertainty_score":0.002198339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02233868393916225,"score_gpt":0.2784838071503705,"score_spread":0.2561451232112082,"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."}}