{"id":"W2146845747","doi":"10.1109/icar.2005.1507451","title":"Attention shifts during action sequence recognition for social robots","year":2006,"lang":"en","type":"article","venue":"ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005.","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Imperial College of Toronto","funders":"","keywords":"Robot; Component (thermodynamics); Computer science; Action (physics); Object (grammar); Perception; Artificial intelligence; Sequence (biology); Mechanism (biology); Human–robot interaction; Human–computer interaction; Cognitive neuroscience of visual object recognition; Social robot; Mobile robot; Robot control; Psychology","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":[0.0006944991,0.000324357,0.0004670608,0.0005065707,0.0003304201,0.0003735174,0.0007449637,0.0006422165,0.001783437],"category_scores_gemma":[0.005278872,0.0003354838,0.0002453159,0.0002193013,0.0004663306,0.0007957636,0.0007490037,0.0005492881,0.0002465483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007917152,"about_ca_system_score_gemma":0.0004036439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00459963,"about_ca_topic_score_gemma":0.004469451,"domain_scores_codex":[0.9997318,0.0000674074,0.000008730277,0.00007043347,0.0000724869,0.0000491855],"domain_scores_gemma":[0.9984236,0.0009961517,0.0001701141,0.00009821767,0.0001746165,0.0001373866],"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":[0.001473377,0.0002602382,0.00786359,0.0001997797,0.0000921337,0.0005113794,0.001498776,0.03169173,0.4850922,0.002937449,0.001517138,0.4668622],"study_design_scores_gemma":[0.00007259878,0.0005852698,0.03164215,0.00001845947,0.00005521765,0.0003974685,0.0002884787,0.8639939,0.09466521,0.006794385,0.001444137,0.00004274706],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7168098,0.0004322332,0.277527,0.0002658934,0.00008013858,0.0001294708,0.0000418042,0.002342657,0.002370981],"genre_scores_gemma":[0.9762585,0.00004002275,0.02306248,0.00004442434,0.00001194422,0.00002471458,0.0000224654,0.00003244013,0.0005029741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00459963,"threshold_uncertainty_score":0.009145737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09961345915369048,"score_gpt":0.3462024112921169,"score_spread":0.2465889521384264,"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."}}