{"id":"W3142032567","doi":"10.1109/thms.2021.3064815","title":"Investigating the P300 Response as a Marker of Working Memory in Virtual Training Environments","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Queen's University; Queen's University Belfast","keywords":"Event-related potential; Computer science; Latency (audio); Stimulus (psychology); Virtual reality; Context (archaeology); Electroencephalography; Psychology; Audiology; Cognitive psychology; Human–computer interaction; Medicine; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001022124,0.0002279668,0.0003196235,0.0002040617,0.0003723837,0.00009820436,0.0003592403,0.00008498809,0.00007341821],"category_scores_gemma":[0.0001144614,0.0001841519,0.0001272872,0.0004079604,0.0002023129,0.0001430373,0.000007303866,0.0005604238,0.00002293792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008287726,"about_ca_system_score_gemma":0.00007370199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001218098,"about_ca_topic_score_gemma":0.0001097662,"domain_scores_codex":[0.9965743,0.00154869,0.0006173585,0.0004920123,0.000461382,0.0003061923],"domain_scores_gemma":[0.9976116,0.001657065,0.0001955167,0.0004469209,0.00001189722,0.00007694665],"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":[0.0001222015,0.0002037996,0.00008397496,0.0000397529,0.00003124593,0.00007259228,0.01112307,0.1160046,0.8669751,0.000195265,0.00002066186,0.00512768],"study_design_scores_gemma":[0.001902466,0.0005330195,0.001086837,0.001643393,0.000047266,0.0003383904,0.007674294,0.04184711,0.9426878,0.000134377,0.001510058,0.0005949835],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811675,0.0001068833,0.0156432,0.0002806168,0.000977603,0.0003213028,0.0000245524,0.00005315258,0.001425179],"genre_scores_gemma":[0.9969602,0.00000789705,0.00004353028,0.0003057227,0.0000531035,0.00005549045,0.000001011686,0.00003327066,0.00253979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07571269,"threshold_uncertainty_score":0.7509496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08879536586935168,"score_gpt":0.3050038407637269,"score_spread":0.2162084748943752,"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."}}