{"id":"W4406214298","doi":"10.1167/jov.25.1.11","title":"Target interception in virtual reality is better for natural versus unnatural trajectory shapes and orientations","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interception; Trajectory; Orientation (vector space); Computer vision; Computer science; Artificial intelligence; Visual field; Ball (mathematics); Fixation (population genetics); Perception; Geodesy; Mathematics; Simulation; Geometry; Psychology; Physics; Geology; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000656747,0.000460291,0.0003797826,0.0002808695,0.0001749095,0.001246373,0.0002887859,0.0004950301,0.002141866],"category_scores_gemma":[0.007765186,0.0002553173,0.0003198466,0.0001537048,0.0003948217,0.001094814,0.0005808123,0.0003337226,0.0002937504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001888621,"about_ca_system_score_gemma":0.0002006884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002038779,"about_ca_topic_score_gemma":0.001619573,"domain_scores_codex":[0.9993475,0.0001487926,0.00008459588,0.0001720561,0.0001678742,0.00007914298],"domain_scores_gemma":[0.9969161,0.001222408,0.001059745,0.0004819881,0.0001463412,0.0001735779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003921508,0.0004293683,0.04948853,0.0003154661,0.0001943199,0.0003871846,0.001572233,0.0184173,0.8607185,0.0008462992,0.0003214989,0.06338786],"study_design_scores_gemma":[0.0001850711,0.006345591,0.7793033,0.000076527,0.0002088582,0.001275591,0.001194848,0.05783601,0.1488819,0.002081854,0.002429535,0.0001809425],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964638,0.00004447957,0.002841996,0.00001472629,0.000004873075,0.000006573261,0.00002106485,0.00004940479,0.0005531089],"genre_scores_gemma":[0.9979796,0.00004526017,0.001639536,0.000009776259,0.000001858274,0.000005134256,0.00006437088,0.00002230449,0.0002321746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002141866,"threshold_uncertainty_score":0.007165253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02934833245673322,"score_gpt":0.3364696661301429,"score_spread":0.3071213336734097,"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."}}