{"id":"W3162963716","doi":"10.1002/acp.3837","title":"Who can best find Waldo? Exploring individual differences that bolster performance in a security surveillance microworld","year":2021,"lang":"en","type":"article","venue":"Applied Cognitive Psychology","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Thales (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bolster; Flexibility (engineering); Cognition; Cognitive flexibility; Working memory; Psychology; Context (archaeology); Cognitive psychology; Selection (genetic algorithm); Applied psychology; Computer science; Artificial intelligence; Engineering","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":[0.001235598,0.0001756399,0.0001982961,0.0004979353,0.000424308,0.001634036,0.0002566896,0.0003911162,0.002117347],"category_scores_gemma":[0.0080585,0.0001243283,0.0001830996,0.0002304305,0.000534473,0.0008070375,0.0005426187,0.0004714707,0.0003483197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002394361,"about_ca_system_score_gemma":0.0002365137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002263396,"about_ca_topic_score_gemma":0.004254896,"domain_scores_codex":[0.9994547,0.0001996559,0.00003673171,0.00008793396,0.0001000982,0.0001209411],"domain_scores_gemma":[0.9957559,0.001615366,0.001178458,0.0002476313,0.0003463346,0.0008562983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003726151,0.0007182121,0.9643698,0.00003128092,0.0001008807,0.0001960453,0.01300785,0.00037695,0.001894737,0.0002635647,0.0005022893,0.01816585],"study_design_scores_gemma":[0.000007559268,0.0004149966,0.9812269,0.000017883,0.00003107963,0.0001398113,0.01507603,0.00124127,0.0005297768,0.0004951633,0.000797031,0.00002254367],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993494,0.00001922296,0.0000608683,0.00003391846,0.000002447954,0.000003878235,0.00001165228,0.000001351873,0.00051716],"genre_scores_gemma":[0.9996995,0.00001612751,0.00007533535,0.00001260219,0.000001330502,0.000003175759,0.00001426844,0.000001165916,0.0001764619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002263396,"threshold_uncertainty_score":0.007083178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1129926272616519,"score_gpt":0.3574918814263377,"score_spread":0.2444992541646858,"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."}}