{"id":1071,"date":"2021-04-21T09:17:00","date_gmt":"2021-04-21T00:17:00","guid":{"rendered":"http:\/\/cinetjp-static3.nict.go.jp\/english\/?p=1071"},"modified":"2022-08-27T21:30:33","modified_gmt":"2022-08-27T12:30:33","slug":"20210421_4210","status":"publish","type":"news","link":"http:\/\/cinetjp-static3.nict.go.jp\/english\/news\/20210421_4210\/","title":{"rendered":"CiNet Researcher Tsuyoshi Ikegami (Naito Group) published a research article in PLOS Computational Biology"},"content":{"rendered":"\n<p>CiNet Researcher\u00a0<a href=\"http:\/\/cinetjp-static3.nict.go.jp\/english\/people\/2014482\/\">Tsuyoshi Ikegami<\/a>\u00a0(<a href=\"http:\/\/cinetjp-static3.nict.go.jp\/english\/people\/2014899\/\">Naito Group<\/a>) published a research article in PLOS Computational Biology<\/p>\n\n\n\n<p>Outline:<br>How do we improve actions after a movement failure? Although negotiating movement failures is obviously crucial, previous motor-control studies have predominantly examined human movement adaptations in the absence of failures, and it remains unclear how failures affect subsequent movement adaptations. Here we examined this issue by developing a novel force field adaptation task where the hand movement during an arm reaching is perturbed by novel forces that induce a large target error, that is a failure. Our experimental observation and computational modeling show that, in addition to the popular \u2018internal model learning\u2019 process of motor adaptations, humans also utilize a \u2018failure-negotiating\u2019 process, that enables them to quickly improve movements in the presence of failure, even at the expense of increased arm trajectory deflections, which are subsequently reduced gradually with training after the achievement of the task success. Our results suggest that a hierarchical interaction between these two processes is a key for humans to negotiate movement failures.<\/p>\n\n\n\n<p>Publication:<br>Ikegami T, Ganesh G, Gibo TL, Yoshioka T, Osu R, Kawato M (2021) Hierarchical motor adaptations negotiate failures during force field learning. PLoS Comput Biol 17(4): e1008481.<\/p>\n\n\n\n<figure class=\"wp-block-embed\"><div class=\"wp-block-embed__wrapper\">\nhttps:\/\/doi.org\/10.1371\/journal.pcbi.1008481\n<\/div><\/figure>\n","protected":false},"featured_media":0,"template":"","acf":[],"_links":{"self":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/english\/wp-json\/wp\/v2\/news\/1071"}],"collection":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/english\/wp-json\/wp\/v2\/news"}],"about":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/english\/wp-json\/wp\/v2\/types\/news"}],"wp:attachment":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/english\/wp-json\/wp\/v2\/media?parent=1071"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}