{"id":"W2951877485","doi":"10.1371/journal.pcbi.1005508","title":"Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis","year":2017,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":357,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; James S. McDonnell Foundation","keywords":"Component (thermodynamics); Similarity (geometry); Computer science; Computational biology; Evolutionary biology; Artificial intelligence; Biology","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":[0.01648553,0.002893638,0.00301018,0.007390152,0.001358521,0.01137016,0.008753589,0.004728436,0.006236567],"category_scores_gemma":[0.0322808,0.001467577,0.005666871,0.0045594,0.01087641,0.01623267,0.005435554,0.008059811,0.001698534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003924124,"about_ca_system_score_gemma":0.003060696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004697959,"about_ca_topic_score_gemma":0.001617522,"domain_scores_codex":[0.9920977,0.00402831,0.0007580366,0.001203705,0.001533067,0.0003790614],"domain_scores_gemma":[0.9811668,0.01196057,0.001503992,0.003361451,0.001620109,0.0003870924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001739835,0.00002270299,0.0002442906,0.0001791096,0.00009177574,0.00007173677,0.000331659,0.01932507,0.0007717963,0.9609684,0.0009461744,0.01703003],"study_design_scores_gemma":[0.000009388934,0.00002600897,0.000189085,0.0000709124,0.00002237085,0.0000781583,0.00006067235,0.0637714,0.0003318913,0.9315645,0.003843294,0.00003226235],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008986788,0.0006615957,0.9956107,0.0008236994,0.00003430109,0.00005143305,0.0001290982,0.0001168247,0.001673722],"genre_scores_gemma":[0.1370346,0.004034821,0.8515561,0.001313368,0.0007037421,0.001196177,0.0007802681,0.0004184587,0.002962531],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01648553,"threshold_uncertainty_score":0.08718485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3497640248191166,"score_gpt":0.4126348970425274,"score_spread":0.06287087222341076,"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."}}