{"id":"W4392096414","doi":"10.1167/jov.24.2.9","title":"Comparing eye–hand coordination between controller-mediated virtual reality, and a real-world object interaction task","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; Glenrose Rehabilitation Hospital; Women and Children’s Health Research Institute; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; TD Bank","keywords":"Headset; Eye–hand coordination; Virtual reality; Computer science; Fixation (population genetics); Gaze; Task (project management); Haptic technology; Human–computer interaction; Eye movement; Eye tracking; Avatar; Object (grammar); Computer vision; Artificial intelligence; Engineering; Medicine","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":[0.0008065638,0.0001170404,0.0002668148,0.0004291997,0.0001807552,0.0004615931,0.00009851705,0.0000614403,0.00003750639],"category_scores_gemma":[0.0003255797,0.00009141392,0.00007129287,0.0003634358,0.00005721747,0.0006983361,0.0000448645,0.0003525116,0.00002767801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000981095,"about_ca_system_score_gemma":0.00004683192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002655052,"about_ca_topic_score_gemma":0.00001672487,"domain_scores_codex":[0.998602,0.0002117483,0.0004702918,0.0001993376,0.0003852649,0.0001314107],"domain_scores_gemma":[0.9990686,0.0004118095,0.0002572584,0.00005637332,0.00009381634,0.0001121179],"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":[0.0002009779,0.00004467949,0.0004934722,0.00004017287,0.00001077544,0.00002188717,0.0006149829,0.00004350539,0.9711959,0.0003919836,0.001054395,0.02588729],"study_design_scores_gemma":[0.0109261,0.009280194,0.2463093,0.006762062,0.0005084159,0.0006270717,0.001591895,0.4394172,0.2261695,0.0109434,0.04606872,0.001396185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893662,0.00005578676,0.007732232,0.001190383,0.0007513357,0.00009666644,0.000006714234,0.00005241679,0.0007483],"genre_scores_gemma":[0.9988883,0.0001309513,0.00003928679,0.0001314905,0.0003226141,8.927839e-7,0.000003587494,0.0000146032,0.0004682569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7450264,"threshold_uncertainty_score":0.4451153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07274058472699839,"score_gpt":0.395730715503868,"score_spread":0.3229901307768696,"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."}}