Literature DB >> 18923050

Neural correlates of predictive and postdictive switching mechanisms for internal models.

Hiroshi Imamizu1, Mitsuo Kawato.   

Abstract

Switching of sensorimotor tasks may be classified into predictive switching based on contextual information and postdictive switching based on the error between sensorimotor feedback and predictions. We used functional neuroimaging to study the brain regions involved in each type of switching of internal models for visuomotor rotations (clockwise and counterclockwise rotations of visual feedback). The color of a cue presented before movement initiation corresponded to direction of rotation of the feedback in an instructed condition, but not in a noninstructed condition. Switching-related activity was identified as activity that transiently increased after the direction of rotation was changed. The switching-related activity in cue periods in the instructed condition, when a predictive switch is possible, was observed in the superior parietal lobule (SPL). However, the switching-related activity in feedback periods in the noninstructed condition, when prediction error is crucial for the postdictive switch, was observed in the inferior parietal lobule (IPL) and prefrontal cortex. The functional influence of the SPL on the lateral cerebellum, namely, a possible neural correlate for internal models, increased in the instructed condition, but the influence of the IPL on the cerebellum was increased in the noninstructed condition. We observed a rapid activity increase in the instructed condition and a gradual activity increase in the noninstructed condition mainly in the lateral occipito-temporal cortices (LOTC) and supplementary motor cortex (SMA). These results are consistent with separate mechanisms for predictive and postdictive switches and suggest that the LOTC and SMA receive output signals from appropriate internal models.

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Year:  2008        PMID: 18923050      PMCID: PMC6671335          DOI: 10.1523/JNEUROSCI.1106-08.2008

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  61 in total

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