{"id":"W2088810634","doi":"10.1016/j.cub.2014.08.046","title":"Motor, not visual, encoding of potential reach targets","year":2014,"lang":"en","type":"letter","venue":"Current Biology","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ontario Innovation Trust","keywords":"Affordance; Premotor cortex; Action (physics); Action selection; Selection (genetic algorithm); Perception; Visual cortex; Encoding (memory); Neuroscience; Cognitive psychology; Biology; Cognitive science; Psychology; Dorsum; Computer science; Artificial intelligence","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":[0.0006751415,0.0005075891,0.0007978992,0.0003731699,0.0009873487,0.001654078,0.001388748,0.01660515,0.01478443],"category_scores_gemma":[0.006521279,0.0003086548,0.0005818462,0.0002264858,0.001263882,0.001159139,0.0005175903,0.01058238,0.006193292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001189819,"about_ca_system_score_gemma":0.000585938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001913259,"about_ca_topic_score_gemma":0.003629673,"domain_scores_codex":[0.9996809,0.00006167417,0.00003686658,0.00006440121,0.00009508176,0.00006116219],"domain_scores_gemma":[0.9979333,0.001293688,0.0001148442,0.0001877312,0.000330847,0.0001396538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001530005,0.0001542926,0.001155753,0.0003714941,0.00007754021,0.01329091,0.0002474776,0.0008254905,0.008066502,0.02827422,0.8609261,0.08508034],"study_design_scores_gemma":[0.0004951534,0.000338709,0.008109013,0.0004491361,0.0001014146,0.01837706,0.0002822021,0.00886087,0.01239694,0.1028631,0.847621,0.0001054165],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.008217353,0.004366179,0.008056014,0.8364221,0.09494058,0.00006587328,0.0008119108,0.000511307,0.04660867],"genre_scores_gemma":[0.1822138,0.009534823,0.006772742,0.4048794,0.2119849,0.0002514263,0.0007149919,0.0003350266,0.1833129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01660515,"threshold_uncertainty_score":0.0494588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0649981595908951,"score_gpt":0.3180384261428148,"score_spread":0.2530402665519197,"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."}}