{"id":"W2967757629","doi":"10.1002/hbm.24757","title":"How are visual words represented? Insights from EEG‐based visual word decoding, feature derivation and image reconstruction","year":2019,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electroencephalography; Decoding methods; Word (group theory); Visual Word; Orthographic projection; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Reading (process); Psychology; Computer science; Representation (politics); Cognitive psychology; Speech recognition; Natural language processing; Neuroscience; Image (mathematics); Image retrieval; Linguistics; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002476986,0.0001993509,0.000130748,0.0003514098,0.000072394,0.0006722372,0.0001527944,0.0002671087,0.001333047],"category_scores_gemma":[0.003806653,0.0001150536,0.0001083621,0.0003503245,0.0003721893,0.0009043265,0.0002568129,0.0002527649,0.0003041954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006409703,"about_ca_system_score_gemma":0.00008236235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004842613,"about_ca_topic_score_gemma":0.0005882246,"domain_scores_codex":[0.9998922,0.00003153269,0.000008097105,0.00002788323,0.00002683131,0.00001345035],"domain_scores_gemma":[0.9995688,0.0002621492,0.0000665773,0.00004007652,0.00004756183,0.00001487715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005249984,0.00005006457,0.02440624,0.0003323319,0.00006701732,0.00033587,0.001708503,0.001871297,0.7056522,0.00466067,0.000630141,0.2597607],"study_design_scores_gemma":[0.00006998787,0.0005350925,0.7061172,0.0001050763,0.0001285429,0.002164503,0.002194477,0.06518841,0.192369,0.02705608,0.003994689,0.00007704835],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9189286,0.0008719465,0.07283486,0.0003206152,0.00004148307,0.00005132735,0.0004778217,0.0001943217,0.006279033],"genre_scores_gemma":[0.9912547,0.0003393627,0.00770658,0.00002346371,0.00001219694,0.00001238147,0.0001402117,0.00003173698,0.000479378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001333047,"threshold_uncertainty_score":0.0044595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02709802042615703,"score_gpt":0.2800748927572617,"score_spread":0.2529768723311047,"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."}}