{"id":"W2947967193","doi":"10.3758/s13414-019-01752-1","title":"Taking it out of context: The role of contextual coherence during social event segmentation","year":2019,"lang":"en","type":"article","venue":"Attention Perception & Psychophysics","topic":"Action Observation and Synchronization","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Perception; Psychology; Event (particle physics); Cognitive psychology; Context (archaeology); Coherence (philosophical gambling strategy); Segmentation; Social cue; Social environment; Computer science; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002052784,0.0001444832,0.0002132931,0.00007020992,0.0001509632,0.00001972602,0.0001560469,0.0001113074,0.006808989],"category_scores_gemma":[0.00001040699,0.0001320554,0.0001768956,0.0002356988,0.0001011069,0.0002726278,0.00002107586,0.0001478843,0.0005364316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000077929,"about_ca_system_score_gemma":0.00002130526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007792054,"about_ca_topic_score_gemma":0.00002080034,"domain_scores_codex":[0.998431,0.000192261,0.0005858878,0.0002664962,0.0003662072,0.0001581711],"domain_scores_gemma":[0.9984728,0.00004365414,0.0008705545,0.0002627146,0.0003237318,0.00002653729],"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":[0.0002942305,0.0004813063,0.1235378,0.00006307061,0.0001623549,1.676096e-7,0.02702396,0.0001311825,0.7132069,0.007624283,0.001005087,0.1264696],"study_design_scores_gemma":[0.001756435,0.0001119026,0.9473403,0.00005042192,0.0000470031,0.000001564462,0.04721332,0.0006370959,0.001018778,0.000404132,0.001248666,0.0001703573],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841699,0.00002109856,0.007503972,0.0004004179,0.001149812,0.0005217039,0.00002014395,0.00003763157,0.006175331],"genre_scores_gemma":[0.9966699,0.000009022079,0.000049281,0.0002494313,0.0001908106,0.00004316283,0.0001057101,0.00002128062,0.002661362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8238025,"threshold_uncertainty_score":0.9940989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02907722622406803,"score_gpt":0.3297573997962265,"score_spread":0.3006801735721584,"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."}}