RESUMO
Capacity of using visual feedback by infants at the age of reaching onset has been controversial. In this investigation we assessed movement kinematics in the task of reaching for a toy in 5-month-olds, comparing movements performed with the preferred arm under full vision versus visual occlusion. That comparison was made in consecutive periods of visual occlusion. Analysis of results revealed that visual occlusion led to decreased straightness of arm displacement toward the toy as compared to full vision. Longer periods of occlusion did not augment that effect. These results offer preliminary evidence for use of visual feedback early in infants' reaching development. Reconciliation of previous and current findings is made by proposing a hybrid mode of feedback processing for manual control reweighting the roles of vision and proprioception as a function of availability of environmental information.
RESUMO
Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the quantitative functional variables used in this classifier, and of the validation method. In addition, our findings suggest that by investigating the distribution of classifier coefficients, it is possible to infer knowledge and construct functional interpretations of the underlying neural mechanisms of the performed tasks. Finally, the promising results reported here (up to 97 percent classification accuracy on 1-second time windows) reflect the considerable potential of MEG for the continuous classification of mental states.