{"id":"W4412459047","doi":"10.1167/jov.25.9.1959","title":"Active manipulation promotes predictive gaze strategies during virtual object exploration","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Gaze; Object (grammar); Human–computer interaction; Computer science; Computer vision; Artificial intelligence; Psychology; Cognitive psychology; Communication","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":[0.0003388374,0.0003539913,0.0003428282,0.0002811178,0.0001555154,0.0004714212,0.0003430942,0.0002736885,0.002072544],"category_scores_gemma":[0.002047216,0.0002745565,0.0002483849,0.00008505312,0.0004195869,0.0006142406,0.0009276939,0.0004188947,0.0002669526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001703754,"about_ca_system_score_gemma":0.0001964325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006656306,"about_ca_topic_score_gemma":0.0009406758,"domain_scores_codex":[0.9996691,0.00006084244,0.00001801445,0.000107835,0.00007575964,0.00006843308],"domain_scores_gemma":[0.9991099,0.0003409042,0.0002483893,0.0001399086,0.00006899016,0.00009192773],"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":[0.0004722254,0.0001387184,0.004336128,0.0001137906,0.00001449364,0.00004842394,0.0005360842,0.0001390223,0.9676228,0.0001192978,0.0001356673,0.02632333],"study_design_scores_gemma":[0.000213241,0.004583923,0.6215845,0.0001413826,0.0001374834,0.0007480397,0.000996562,0.01189297,0.3524813,0.001919416,0.005212154,0.00008899124],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956372,0.0002901552,0.003227095,0.00004203716,0.000008407327,0.00001305431,0.00002821372,0.00007338027,0.0006804119],"genre_scores_gemma":[0.9960932,0.0001948759,0.002873578,0.00002132946,0.000005030794,0.00004149887,0.00003728164,0.00003522017,0.0006980015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002072544,"threshold_uncertainty_score":0.006933331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0165299735772239,"score_gpt":0.2900472918823726,"score_spread":0.2735173183051487,"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."}}