{"id":"W4416388619","doi":"10.1038/s42256-025-01142-3","title":"Convolutional architectures are cortex-aligned de novo","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Convolutional neural network; Representation (politics); Feature (linguistics); Architecture; Key (lock); Visual cortex; Deep learning; Network architecture","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001644426,0.0001812905,0.0001538307,0.0002155061,0.0002002401,0.00005622705,0.0003510902,0.0002635636,0.001169624],"category_scores_gemma":[0.001161076,0.0001601378,0.0001004135,0.0005108679,0.0001695813,0.00004681661,0.0000704488,0.0006855729,0.0002558779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009165346,"about_ca_system_score_gemma":0.00009570095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003002999,"about_ca_topic_score_gemma":0.0001195141,"domain_scores_codex":[0.9986448,0.0001453736,0.0002182338,0.0004286707,0.0002693253,0.0002936043],"domain_scores_gemma":[0.9991946,0.0003554939,0.00007442277,0.00021633,0.00007438892,0.00008481854],"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":[0.0004863564,0.0004577429,0.01189959,0.0001858373,0.00003158467,0.0001267928,0.0006014,0.001783602,0.7206487,0.1125544,0.005683678,0.1455403],"study_design_scores_gemma":[0.0003968366,0.00009851217,0.0600086,0.0002604765,0.00003578175,0.0003330944,0.0001460021,0.0161844,0.8149251,0.07249729,0.03450778,0.0006060803],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7459374,0.001728608,0.1763578,0.01093716,0.002837422,0.0008313784,0.0004046457,0.0007271452,0.06023844],"genre_scores_gemma":[0.986554,0.0001038667,0.0006067528,0.01047151,0.00008679937,0.00001785611,0.00001411935,0.00001086102,0.002134238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2406166,"threshold_uncertainty_score":0.9997435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614258252801474,"score_gpt":0.3235383940496456,"score_spread":0.3073958115216309,"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."}}