{"id":"W2114830306","doi":"10.1207/s15516709cog2501_2","title":"Comparative visual search: a difference that makes a difference","year":2001,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Visual search; Artificial intelligence; Cluster analysis; Pattern recognition (psychology); Entropy (arrow of time); Computer science; Eye tracking; Mathematics; Computer vision","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":[0.00249421,0.0002311714,0.0005339923,0.0005296163,0.0003602616,0.001542353,0.000819187,0.001394855,0.004980039],"category_scores_gemma":[0.009157016,0.0002749421,0.0003273633,0.0002908561,0.003553838,0.004520187,0.001403106,0.001164064,0.0005652134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004017898,"about_ca_system_score_gemma":0.0004271886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004777113,"about_ca_topic_score_gemma":0.0003976767,"domain_scores_codex":[0.9983381,0.0006889694,0.00008589843,0.0003474663,0.0004470709,0.0000923855],"domain_scores_gemma":[0.9955776,0.002213951,0.0002618051,0.001296559,0.0003675734,0.0002825122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001675379,0.0003509195,0.007819636,0.000793019,0.0002020932,0.0003387538,0.005839626,0.002135328,0.1217312,0.6993059,0.006617448,0.1531907],"study_design_scores_gemma":[0.0005168743,0.001844884,0.05088957,0.0004773558,0.0001935166,0.003168864,0.00473499,0.009305201,0.02951716,0.7965453,0.1026064,0.00019981],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6860346,0.009334687,0.1392858,0.03166202,0.0008940419,0.0001580963,0.0004214804,0.0005160475,0.1316932],"genre_scores_gemma":[0.980339,0.0008646576,0.01315412,0.002548913,0.0001158706,0.00007692654,0.0001099729,0.00008913173,0.002701422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004980039,"threshold_uncertainty_score":0.01665986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1184754975404089,"score_gpt":0.3556952749898121,"score_spread":0.2372197774494033,"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."}}