Brain reorganization as a function of walking experience in 12-month-old infants: implications for the development of manual laterality

Hand preference in infancy is marked by many developmental shifts in hand use and arm coupling as infants reach for and manipulate objects. Research has linked these early shifts in hand use to the emergence of fundamental postural–locomotor milestones. Specifically, it was found that bimanual reaching declines when infants learn to sit; increases if infants begin to scoot in a sitting posture; declines when infants begin to crawl on hands and knees; and increases again when infants start walking upright. Why such pattern fluctuations during periods of postural–locomotor learning? One proposed hypothesis is that arm use practiced for the specific purpose of controlling posture and achieving locomotion transfers to reaching via brain functional reorganization. There has been scientific support for functional cortical reorganization and change in neural connectivity in response to motor practice in adults and animals, and as a function of crawling experience in human infants. In this research, we examined whether changes in neural connectivity also occurred as infants coupled their arms when learning to walk and whether such coupling mapped onto reaching laterality. Electroencephalogram (EEG) coherence data were collected from 43 12-month-old infants with varied levels of walking experience. EEG was recorded during quiet, attentive baseline. Walking proficiency was laboratory assessed and reaching responses were captured using small toys presented at mid-line while infants were sitting. Results revealed greater EEG coherence at homologous prefrontal/central scalp locations for the novice walkers compared to the prewalkers or more experienced walkers. In addition, reaching laterality was low in prewalkers and early walkers but high in experienced walkers. These results are consistent with the interpretation that arm coupling practiced during early walking transferred to reaching via brain functional reorganization, leading to the observed developmental changes in manual laterality

A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot

Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)