{"id":"W4367837406","doi":"10.1101/2023.05.03.539191","title":"How well do models of visual cortex generalize to out of distribution samples?","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual cortex; Artificial intelligence; Computer science; Generalization; Artificial neural network; Cognitive neuroscience of visual object recognition; Robustness (evolution); Machine learning; Stimulus (psychology); Pattern recognition (psychology); Neuroscience; Object (grammar); Psychology; Biology; Cognitive psychology; Mathematics","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":[0.005434917,0.001182004,0.001084672,0.0008600467,0.0003197025,0.001871909,0.001468038,0.001764096,0.001388411],"category_scores_gemma":[0.02495634,0.0006050965,0.001329088,0.0005287154,0.001642573,0.004428914,0.001250173,0.002274465,0.0005517298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001229832,"about_ca_system_score_gemma":0.0006415766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005968928,"about_ca_topic_score_gemma":0.004649005,"domain_scores_codex":[0.9986202,0.0005314539,0.00006628824,0.0005081914,0.0001539124,0.0001199421],"domain_scores_gemma":[0.9927542,0.004268468,0.0008276801,0.001436649,0.0004210074,0.0002920536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003343955,0.00008035709,0.0221983,0.0001767324,0.0004996507,0.0001292152,0.0002041325,0.9136725,0.006466629,0.007962491,0.002010442,0.0462652],"study_design_scores_gemma":[0.000009041367,0.00006391342,0.005908822,0.00002931186,0.00002449214,0.00004883039,0.00003230423,0.9712215,0.001414397,0.02096897,0.0002584155,0.00001999017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.682932,0.002173683,0.3030437,0.004315726,0.0002581974,0.00006835672,0.0008831219,0.001463487,0.004861666],"genre_scores_gemma":[0.9921295,0.0003714424,0.005788933,0.0003475745,0.00007331483,0.00002733166,0.0004138093,0.0001107054,0.0007372461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005968928,"threshold_uncertainty_score":0.02874291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04687289794409823,"score_gpt":0.2559952981862987,"score_spread":0.2091224002422004,"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."}}