{"id":"W2932161991","doi":"10.1101/598466","title":"Behavioural and neural interactions between objective and subjective performance in a Matching Pennies game","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Alberta","funders":"","keywords":"Outcome (game theory); Optimism; Contingency; Psychology; Matching (statistics); Negativity effect; Social psychology; Contrast (vision); Competition (biology); Trustworthiness; Contingency table; Ultimatum game; Statistics; Cognitive psychology; Econometrics; Economics; Mathematics; Microeconomics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001805,0.0005703454,0.001009268,0.0009901286,0.0002058716,0.001186445,0.0007355291,0.0003828466,0.00003067972],"category_scores_gemma":[0.0005925199,0.0005325765,0.0001396577,0.0005602333,0.0002396904,0.0009898099,0.001362,0.001435639,0.00006327777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000333219,"about_ca_system_score_gemma":0.0002727994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000387597,"about_ca_topic_score_gemma":0.00008564871,"domain_scores_codex":[0.9960046,0.000257848,0.001061501,0.001567,0.0005856428,0.0005234506],"domain_scores_gemma":[0.9965301,0.001004864,0.0007336068,0.001050789,0.0004436658,0.0002370326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005416078,0.00005770794,0.9965994,0.00003198029,0.00002988619,0.00002360674,0.0002876587,0.0002485857,0.00230722,0.00002828111,0.00001407033,0.0003174533],"study_design_scores_gemma":[0.0004266696,0.00008332396,0.9942747,0.0003121797,0.00006523091,2.052215e-7,0.0001409098,0.002170293,0.001682161,0.000123043,0.0001056429,0.0006155965],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971324,0.0003030227,0.00020526,0.0001308395,0.001263445,0.0006228165,0.0002253293,0.00009994684,0.00001695315],"genre_scores_gemma":[0.9987305,0.00008930538,0.0008230326,0.00005427522,0.0001552511,0.00006833214,2.720128e-7,0.0000673283,0.00001172967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002324645,"threshold_uncertainty_score":0.9998504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06622057945594605,"score_gpt":0.3171638347198824,"score_spread":0.2509432552639363,"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."}}