{"id":"W6894177092","doi":"10.5683/sp3/noldfb","title":"Context Matters: Hyperscanning the N400 Lexical Frequency Effect During Conversation and Passive Listening","year":2025,"lang":"en","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Conversation; N400; Active listening; Context (archaeology); Conversation analysis; Context effect","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":[0.002202582,0.002741674,0.001704807,0.001895277,0.001385146,0.002580804,0.003643691,0.003470776,0.08118036],"category_scores_gemma":[0.01620559,0.0008726402,0.002266408,0.001740522,0.0006078911,0.001488542,0.003253212,0.002033719,0.06815916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001069692,"about_ca_system_score_gemma":0.001897801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01737918,"about_ca_topic_score_gemma":0.04094407,"domain_scores_codex":[0.9986088,0.0002857687,0.0001707547,0.0005200115,0.0002241149,0.0001906172],"domain_scores_gemma":[0.9958164,0.001880816,0.0003712691,0.000912664,0.0007982127,0.0002205521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007953123,0.0001217075,0.003307946,0.001954542,0.0002036361,0.00008670708,0.0001519141,0.000312492,0.0006264876,0.0005894006,0.9854423,0.006407483],"study_design_scores_gemma":[0.005541902,0.0002268436,0.04562111,0.001280232,0.0008851708,0.0005175315,0.0004714482,0.002063826,0.003407969,0.007593025,0.93209,0.0003009945],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002025142,0.0001594909,0.0004242201,0.0001991098,0.0001065928,0.0001353316,0.9949462,0.0007497047,0.001254307],"genre_scores_gemma":[0.005839361,0.000062128,0.001427737,0.0001744355,0.00005457996,0.001759487,0.9867842,0.0005607666,0.003337334],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08118036,"threshold_uncertainty_score":0.2715754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713902916710375,"score_gpt":0.2992073395351801,"score_spread":0.2820683103680763,"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."}}