{"id":"W164810205","doi":"10.1038/npre.2008.2415.1","title":"Positive and Negative Congruency Effects in Masked Priming: A Neuro-computational Model Based on Representation Strength and Attention","year":2008,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Priming (agriculture); Prime (order theory); Negative priming; Response priming; Psychology; Cognitive psychology; Representation (politics); Neuroscience; Contrast (vision); Computer science; Mathematics; Lexical decision task; Cognition; Artificial intelligence; Biology; Selective attention; Combinatorics","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"],"consensus_categories":[],"category_scores_codex":[0.00008608808,0.000343717,0.0003500743,0.0002844468,0.0001685913,0.00006345256,0.0001345225,0.0004589272,0.000001142877],"category_scores_gemma":[0.0008043919,0.0003118423,0.000063492,0.0002029714,0.0002581703,0.0001627954,0.0002394446,0.001655119,0.000001056136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005997086,"about_ca_system_score_gemma":0.00003368539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002671727,"about_ca_topic_score_gemma":0.000006365303,"domain_scores_codex":[0.9977959,0.0001909369,0.0002593892,0.001170672,0.0003476046,0.0002354815],"domain_scores_gemma":[0.9984048,0.001054268,0.0002380213,0.0001402786,0.0000906209,0.00007201088],"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":[0.00472224,0.002544621,0.137357,0.001240825,0.0001019056,0.001037952,0.01233167,0.06177205,0.7352504,0.003002448,0.002821278,0.03781767],"study_design_scores_gemma":[0.003739862,0.000589844,0.5077346,0.0008610961,0.0001168792,0.00006095657,0.00006301148,0.382826,0.0795958,0.02355825,0.000005245657,0.0008484616],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995739,0.00005840444,0.0001927974,0.001834433,0.0004348775,0.001015457,0.00005831618,0.0000696019,0.0005970977],"genre_scores_gemma":[0.9967378,0.0001204629,0.001441775,0.001461016,0.00003533649,0.00009210779,0.0000309766,0.00002204484,0.00005842429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6556545,"threshold_uncertainty_score":0.9999334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04772284819086457,"score_gpt":0.3554393330304685,"score_spread":0.307716484839604,"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."}}