{"id":"W2096267981","doi":"10.1509/jmr.13.0564","title":"Using EEG to Predict Consumers’ Future Choices","year":2015,"lang":"en","type":"article","venue":"Journal of Marketing Research","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":263,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electroencephalography; Neural correlates of consciousness; Predictive power; Computer science; Product (mathematics); Artificial intelligence; Neural activity; Econometrics; Neuromarketing; Psychology; Machine learning; Pattern recognition (psychology); Cognitive psychology; Cognition; Mathematics; Neuroscience","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":[],"consensus_categories":[],"category_scores_codex":[0.00978609,0.00009883683,0.0002045771,0.000329482,0.0002535895,0.00008165766,0.0004709918,0.00006014499,0.00004686441],"category_scores_gemma":[0.007442629,0.00006871567,0.0000669798,0.0006034324,0.000221222,0.0001971534,0.0002288875,0.0008487399,0.00003430634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009144322,"about_ca_system_score_gemma":0.0001330084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001306856,"about_ca_topic_score_gemma":0.000004845763,"domain_scores_codex":[0.9962098,0.001621016,0.0003271362,0.0002221043,0.001125802,0.0004941136],"domain_scores_gemma":[0.9976779,0.001150771,0.0001484895,0.0001447076,0.0005572445,0.000320857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003385318,0.000254404,0.1460937,0.00003674659,0.00002088088,0.000770978,0.0007826801,0.00002297244,0.7548622,0.00001453775,0.07355832,0.02019718],"study_design_scores_gemma":[0.00511401,0.003909155,0.3212516,0.0009684014,0.0001014456,0.00368179,0.01216168,0.0001891054,0.1205224,0.0008897692,0.5303354,0.0008752452],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913644,0.0002831105,0.000004786099,0.004379507,0.001013113,0.0001264529,0.000003010059,0.00001175559,0.002813872],"genre_scores_gemma":[0.9976652,0.0001013357,0.0004121858,0.0003149442,0.0006083777,0.000001657112,4.598695e-8,0.00001244264,0.0008838557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6343399,"threshold_uncertainty_score":0.8910059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.555237383443883,"score_gpt":0.5313752920203849,"score_spread":0.02386209142349804,"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."}}