{"id":"W4311804465","doi":"10.1167/jov.22.14.4202","title":"Intention to grasp reactivates shape processing","year":2022,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"GRASP; Artificial intelligence; Object (grammar); Computer science; Stimulus (psychology); Cognitive neuroscience of visual object recognition; Computer vision; Pattern recognition (psychology); Visual Objects; Electroencephalography; Task (project management); Communication; Psychology; Perception; Cognitive psychology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003923051,0.0004016388,0.0002538447,0.0002964965,0.0001227415,0.0007135135,0.0002721518,0.0004688131,0.00313243],"category_scores_gemma":[0.003927981,0.000304424,0.0004623113,0.0001923687,0.0003902058,0.0005719916,0.0006428822,0.0005221285,0.0003470841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002428649,"about_ca_system_score_gemma":0.000264583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009318171,"about_ca_topic_score_gemma":0.0008541258,"domain_scores_codex":[0.999643,0.00004174641,0.00002998053,0.0001132604,0.00009268225,0.000079308],"domain_scores_gemma":[0.9986665,0.0005963212,0.0002688311,0.0001867545,0.0001635364,0.0001180538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000319977,0.00006417515,0.0045387,0.0001497731,0.00004131984,0.0001900854,0.0003161141,0.0004261023,0.9697833,0.0006407425,0.0002101212,0.02331958],"study_design_scores_gemma":[0.0001164457,0.001709512,0.7479409,0.00006800914,0.0001800322,0.000867176,0.0003905885,0.0165435,0.2209034,0.007695054,0.003520238,0.00006505551],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663897,0.0002781255,0.0244434,0.0002544359,0.0001098623,0.0001371072,0.0001798639,0.000247653,0.007959776],"genre_scores_gemma":[0.9923299,0.0001520674,0.005410816,0.0001719337,0.00003028698,0.00006564167,0.0001445971,0.00006133597,0.001633524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00313243,"threshold_uncertainty_score":0.01047903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06198733710180596,"score_gpt":0.3658746274267963,"score_spread":0.3038872903249903,"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."}}