{"id":"W4386050919","doi":"10.31234/osf.io/8yta9","title":"Fast and frequent corrections of saccadic decisions underlie a satisficing strategy when searching everyday scenes","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Institut de Valorisation des Données","keywords":"Visual search; Fixation (population genetics); Saccade; Eye movement; Latency (audio); Psychology; Perception; Computer science; Microsaccade; Communication; Cognitive psychology; Neuroscience; Saccadic masking; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005994698,0.0002330295,0.0003231433,0.0005505577,0.0003025846,0.0003800592,0.0005592066,0.0001799089,0.00004537127],"category_scores_gemma":[0.0001615437,0.0002133616,0.0001430501,0.0003831421,0.0000781582,0.0002639356,0.001162143,0.0005346123,0.00003951099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009200421,"about_ca_system_score_gemma":0.0002293751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002337623,"about_ca_topic_score_gemma":0.001714308,"domain_scores_codex":[0.9978006,0.000189316,0.0005319525,0.0007105724,0.0004839558,0.0002836326],"domain_scores_gemma":[0.9984875,0.0004022324,0.0002087551,0.000590127,0.0001629915,0.0001483588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001528545,0.0002695889,0.007323049,0.0003165402,0.0002669152,0.00002769764,0.00521767,0.05652777,0.00505309,0.06639561,0.001928637,0.8566582],"study_design_scores_gemma":[0.000493064,0.0003199638,0.09118017,0.001092175,0.00005863512,0.0000616314,0.001939246,0.7429143,0.001635394,0.1594416,0.0001001836,0.0007635821],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09388938,0.0001018232,0.9022333,0.0003902467,0.001531807,0.0003088151,0.00001188892,0.0004005106,0.001132224],"genre_scores_gemma":[0.9812456,0.000249563,0.01662133,0.00005106562,0.00005275797,0.00002906702,0.000009885501,0.00002130771,0.00171946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8873562,"threshold_uncertainty_score":0.8700632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1223879075814467,"score_gpt":0.3418072566062629,"score_spread":0.2194193490248162,"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."}}