Properties of intermodal transfer after dual visuo- and auditory-motor adaptation

Visual but Not Auditory-Verbal Feedback Induces Aftereffects Following Adaptation to Virtual Prisms

Visuo-motor adaptation with optical prisms that displace the visual scene (prism adaptation, PA) has been widely used to study visuo-motor plasticity in healthy individuals and to decrease the lateralized bias of brain-damaged patients suffering from spatial neglect. Several factors may influence PA aftereffects, such as the degree of optical deviation (generally measured in dioptres of wedge prisms) or the direction of the prismatic shift (leftward vs. rightward). However, the mechanisms through which aftereffects of adaptation in healthy individuals and in neglect affect performance in tasks probing spatial cognition remain controversial. For example, some studies have reported positive effects of PA on auditory neglect, while other studies failed to obtain any changes of performance even in the visual modality. We here tested a new adaptation method in virtual reality to evaluate how sensory parameters influence PA aftereffects. Visual vs. auditory-verbal feedback of optical deviations were contrasted to assess whether rightward deviations influence manual and perceptual judgments in healthy individuals. Our results revealed that altered visual, but not altered auditory-verbal feedback induces aftereffects following adaptation to virtual prisms after 30-degrees of deviation. These findings refine current models of the mechanisms underlying the cognitive effects of virtual PA in emphasizing the importance of visual vs. auditory-verbal feedback during the adaptation phase on visuospatial judgments. Our study also specifies parameters which influence virtual PA and its aftereffect, such as the sensory modality used for the feedback.

A tale of too many tasks: task fragmentation in motor learning and a call for model task paradigms

Motor learning encompasses a broad set of phenomena that requires a diverse set of experimental paradigms. However, excessive variation in tasks across studies creates fragmentation that can adversely affect the collective advancement of knowledge. Here, we show that motor learning studies tend toward extreme fragmentation in the choice of tasks, with almost no overlap between task paradigms across studies. We argue that this extreme level of task fragmentation poses serious theoretical and methodological barriers to advancing the field. To address these barriers, we propose the need for developing common 'model' task paradigms which could be widely used across labs. Combined with the open sharing of methods and data, we suggest that these model task paradigms could be an important step in increasing the robustness of the motor learning literature and facilitate the cumulative process of science.

Modeling Expected Reaching Error and Behaviors for Motor Adaptation

Motor adaptation studies can provide insight into how the brain handles ascending and descending neural signals during motor tasks, revealing how neural pathologies affect the capacity to learn and adapt to movement errors. Such studies often involve reaches towards prompted target locations, with adaptation and learning quantified according to Euclidean distance between reach endpoint and target location. This paper describes methods to calculate steady-state error using knowledge of the distribution of univariate, bivariate, and multivariate steady-state reaches. Additionally, in cases where steady-state error is known or estimated, it does not fully describe underlying reach distributions that could be observed at steady-state. Thus, this paper also investigates methods to describe univariate, bivariate, and multivariate steady-state reaching behavior using knowledge of the estimated steady-state error. These methods may yield a clearer understanding of adaptation and steady-state reaching behavior, allowing greater opportunities for inter-study comparison and modeling.

Early stages of sensorimotor map acquisition: learning with free exploration, without active movement or global structure

One of the puzzles of learning to talk or play a musical instrument is how we learn which movement produces a particular sound: an audiomotor map. The initial stages of map acquisition can be studied by having participants learn arm movements to auditory targets. The key question is what mechanism drives this early learning. Three learning processes from previous literature were tested: map learning may rely on active motor outflow (target), on error correction, and on the correspondence between sensory and motor distances (i.e., that similar movements map to similar sounds). Alternatively, we hypothesized that map learning can proceed without these. Participants made movements that were mapped to sounds in a number of different conditions that each precluded one of the potential learning processes. We tested whether map learning relies on assumptions about topological continuity by exposing participants to a permuted map that did not preserve distances in auditory and motor space. Further groups were tested who passively experienced the targets, kinematic trajectories produced by a robot arm, and auditory feedback as a yoked active participant (hence without active motor outflow). Another group made movements without receiving targets (thus without experiencing errors). In each case we observed substantial learning, therefore none of the three hypothesized processes is required for learning. Instead early map acquisition can occur with free exploration without target error correction, is based on sensory-to-sensory correspondences, and possible even for discontinuous maps. The findings are consistent with the idea that early sensorimotor map formation can involve instance-specific learning.NEW & NOTEWORTHY This study tested learning of novel sensorimotor maps in a variety of unusual circumstances, including learning a mapping that was permuted in such as way that it fragmented the sensorimotor workspace into discontinuous parts, thus not preserving sensory and motor topology. Participants could learn this mapping, and they could learn without motor outflow or targets. These results point to a robust learning mechanism building on individual instances, inspired from machine learning literature.