{"id":"W4308506790","doi":"10.2196/40119","title":"Impact of Personalized Avatars and Motion Synchrony on Embodiment and Users’ Subjective Experience: Empirical Study","year":2022,"lang":"en","type":"article","venue":"JMIR Serious Games","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Avatar; Motion (physics); Motion capture; Virtual reality; Perspective (graphical); Agency (philosophy); Psychology; sync; Human–computer interaction; Computer science; Computer vision; Artificial intelligence; Frame (networking)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002172111,0.0003147508,0.0002519776,0.0004132623,0.0003502699,0.0009010384,0.0002483558,0.0003223766,0.003019535],"category_scores_gemma":[0.01235602,0.000189621,0.0002807415,0.0002369159,0.0006102694,0.0006639587,0.0008873749,0.0003165229,0.0001990407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001955331,"about_ca_system_score_gemma":0.000172768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004549903,"about_ca_topic_score_gemma":0.0007131345,"domain_scores_codex":[0.9982748,0.0009866901,0.0001217811,0.0002276069,0.0002801886,0.0001090101],"domain_scores_gemma":[0.9911914,0.006310513,0.001142238,0.0004362084,0.0005350221,0.0003845786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003203704,0.005365625,0.689163,0.002125568,0.0003468772,0.001094064,0.1487332,0.0008191823,0.03775115,0.0007318396,0.0007108023,0.109955],"study_design_scores_gemma":[0.000131321,0.007772955,0.9340936,0.0001246948,0.000339187,0.001274154,0.04470545,0.00205076,0.006079648,0.0002319927,0.003111245,0.00008503784],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992433,0.00003646975,0.0003086327,0.000003828924,0.000001256345,0.00002410234,0.00001205973,0.000002177389,0.0003681613],"genre_scores_gemma":[0.9991719,0.0000439176,0.0004929191,0.0000073582,0.000002869599,0.00004351881,0.00001803621,0.000002501568,0.0002170459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003019535,"threshold_uncertainty_score":0.01148736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0266046685299697,"score_gpt":0.353911698665421,"score_spread":0.3273070301354514,"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."}}