{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001288659,0.0002728339,0.0002857983,0.0002943984,0.0004085755,0.0007342327,0.0002358702,0.0001567425,0.00007451209],"category_scores_gemma":[0.0003143234,0.0002653941,0.00007858573,0.0003064202,0.0001488135,0.0007924663,0.0001483737,0.0003706541,0.00001933525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005961201,"about_ca_system_score_gemma":0.00002086557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001714065,"about_ca_topic_score_gemma":0.00003429385,"domain_scores_codex":[0.9980391,0.0002398483,0.0002253462,0.0008863984,0.0002972949,0.0003120589],"domain_scores_gemma":[0.9987594,0.000506964,0.0003015338,0.0002871233,0.00004786785,0.00009712932],"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":[0.0000243065,0.0000343764,0.007897465,0.00004522878,0.00001010485,0.00001738998,0.0006688178,0.00001030695,0.9844673,0.0002490466,0.001522469,0.005053241],"study_design_scores_gemma":[0.004504968,0.0003994967,0.2698563,0.002147001,0.00003303848,0.0001022149,0.005390008,0.106263,0.5908206,0.004365092,0.01430898,0.001809381],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882528,0.00005233508,0.007793415,0.002227045,0.000570424,0.0003488524,0.000006948515,0.0002236867,0.0005245241],"genre_scores_gemma":[0.9950029,0.000003588842,0.001760973,0.001326855,0.0003007498,0.00001174905,0.00002408531,0.00003231117,0.001536764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3936467,"threshold_uncertainty_score":0.9999799,"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."}}