ViMoAct · Modelling cortical information flow during visuomotor adaptation as active inference in the human brain

Modelling cortical information flow during visuomotor adaptation as active inference in the human brain

Controlling the body’s actions in a constantly changing environment is one of the earliest and most important tasks of the human brain. Failure of the underlying mechanisms would have profound implications for our experience of selfhood and self-other distinction—and thus, for our normal functioning in society. But the mechanisms by which the brain uses information from various senses to control bodily actions remain unclear. This project was aimed at addressing this question—using the specific example of visual (seen) and proprioceptive (felt) sensory feedback from the moving hand. We used a virtual reality environment to decouple seen and felt hand postures during a task requiring target-tracking with either hand (Figure 1). Participants had to match the phase of grasping movements—sensed from their unseen real hand or a seen virtual hand—to a virtual target, under varying congruence of proprioceptive (real) and visual (i.e., virtual) signals. Thus, either visual or proprioceptive information was task-relevant (while the respective other modality was a distractor). This experimental design was unique and novel in that it implemented a manipulation of our participants’ ‘cognitive-attentional set’; in other words, we manipulated how seen vs felt feedback from the moving hand was weighted depending on cognitive-attentional factors (in this case, task-relevance). The specific objectives of this project were to illuminate the neuronal mechanisms underlying the above processes, using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) recordings. Crucially, we aimed at modelling these data with a computational network analysis called ‘Dynamic causal modelling’ (DCM). DCM is a computational framework that allows one to compare multiple alternative hypotheses (models) about how some observed data feature (in our case: fMRI signal activation or MEG spectral power across the scalp) was most likely generated by underlying interactions between and/or within neuronal populations across a network of brain sources. The design and methods used allowed us, furthermore, to interpret the results within the framework of ‘active inference’. In brief, active inference is a neurobiologically inspired computational account of perception and action. For us, the framework provided concrete predictions how visual vs proprioceptive sensory inputs should be weighted depending on cognitive-attentional set, and how this should manifest itself in behaviour and brain data. Finally, the active inference framework provided us with an opportunity to relate our experimental findings to philosophical accounts of minimal selfhood.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз