{"id":"W2028076044","doi":"10.1080/17470919.2014.1003272","title":"Characterizing an ERP correlate of intentions understanding using a sequential comic strips paradigm","year":2015,"lang":"en","type":"article","venue":"Social Neuroscience","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut Universitaire en Santé Mentale de Québec; Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Psychology; Attribution; Cognitive psychology; Event-related potential; Magnetoencephalography; Comic strip; Stimulus (psychology); Electroencephalography; Social psychology; Neuroscience; Comics; Computer science; Artificial intelligence","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":[0.0002236399,0.0002708556,0.0001085504,0.0002106819,0.00005615117,0.0001961196,0.0001192503,0.000293974,0.00224229],"category_scores_gemma":[0.001907889,0.0001028197,0.00007536734,0.0001378398,0.0002380081,0.0001892276,0.000240321,0.0002524037,0.0001643358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004277863,"about_ca_system_score_gemma":0.00005609289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001455301,"about_ca_topic_score_gemma":0.0002824121,"domain_scores_codex":[0.9999214,0.00001890122,0.000004901049,0.00002533444,0.00002035613,0.00000908582],"domain_scores_gemma":[0.9995078,0.0002800714,0.00008851473,0.00004376898,0.00004769924,0.00003222983],"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":[0.0014631,0.0001998921,0.01322834,0.0002022,0.00004227736,0.0004186657,0.0004530023,0.0002978031,0.9539772,0.0006974139,0.0001562556,0.02886384],"study_design_scores_gemma":[0.0001532712,0.002288713,0.8925203,0.00001900499,0.0001016165,0.001956297,0.0004000154,0.003168785,0.09530646,0.002764318,0.001293524,0.00002759182],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896923,0.00007759171,0.007782126,0.00003691834,0.00001010883,0.00007780356,0.0001324036,0.00003178387,0.002159022],"genre_scores_gemma":[0.9947744,0.00007549259,0.004345412,0.00003358697,0.00002089747,0.00006573476,0.0001635345,0.00001173454,0.000509279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00224229,"threshold_uncertainty_score":0.007501245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.65543724727221,"score_gpt":0.4514907159021775,"score_spread":0.2039465313700324,"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."}}