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When repeatedly switching between two visuomotor mappings, e.g. in a reaching or pointing task, adaptation tends to speed up over time. That is, when the error in the feedback corresponds to a mapping switch, fast adaptation occurs. Yet, what is learned, the relative error or the absolute mappings? When switching between mappings, errors with a size corresponding to the relative difference between the mappings will occur more often than other large errors. Thus, we could learn to correct more for errors with this familiar size (Error Learning). On the other hand, it has been shown that the human visuomotor system can store several absolute visuomotor mappings (Mapping Learning) and can use associated contextual cues to retrieve them. Thus, when contextual information is present, no error feedback is needed to switch between mappings. Using a rapid pointing task, we investigated how these two types of learning may each contribute when repeatedly switching between mappings in the absence of task-irrelevant contextual cues. After training, we examined how participants changed their behaviour when a single error probe indicated either the often-experienced error (Error Learning) or one of the previously experienced absolute mappings (Mapping Learning). Results were consistent with Mapping Learning despite the relative nature of the error information in the feedback. This shows that errors in the feedback can have a double role in visuomotor behaviour: they drive the general adaptation process by making corrections possible on subsequent movements, as well as serve as contextual cues that can signal a learned absolute mapping.

Human touch is an inherently active sense: to estimate an object’s shape humans often move their hand across its surface. This way the object is sampled both in a serial (sampling different parts of the object across time) and parallel fashion (sampling using different parts of the hand simultaneously). Both the serial (moving a single finger) and parallel (static contact with the entire hand) exploration modes provide reliable and similar global shape information, suggesting the possibility that this information is shared early in the sensory cortex. In contrast, we here show the opposite. Using an adaptation-and-transfer paradigm, a change in haptic perception was induced by slant-adaptation using either the serial or parallel exploration mode. A unified shape-based coding would predict that this would equally affect perception using other exploration modes. However, we found that adaptation-induced perceptual changes did not transfer between exploration modes. Instead, serial and parallel exploration components adapted simultaneously, but to different kinaesthetic aspects of exploration behaviour rather than object-shape per se. These results indicate that a potential combination of information from different exploration modes can only occur at down-stream cortical processing stages, at which adaptation is no longer effective.

In our daily life, we often interact with objects using both hands raising the question the question to what extent information between the hands is shared. It has, for instance, been shown that curvature adaptation aftereffects can transfer from the adapted hand to the non-adapted hand. However, this transfer only occurred for dynamic exploration, e.g. by moving a single finger over a surface, but not for static exploration when keeping static contact with the surface and combining the information from different parts of the hand. This raises the question to what extent adaptation to object shape is shared between the hands when both hands are used in static fashion simultaneously and the object shape estimates require information from both hands. Here we addressed this question in three experiments using a slant adaptation paradigm. In Experiment 1 we investigated whether an aftereffect of static bimanual adaptation occurs at all and whether it transfers to conditions in which one hand was moving. In Experiment 2 participants adapted either to a felt slanted surface or simply be holding their hands in mid-air at similar positions, to investigate to what extent the effects of static bimanual adaptation are posture-based rather than object based. Experiment 3 further explored the idea that bimanual adaptation is largely posture based. We found that bimanual adaptation using static touch did lead to aftereffects when using the same static exploration mode for testing. However, the aftereffect did not transfer to any exploration mode that included a dynamic component. Moreover, we found similar aftereffects both with and without a haptic surface. Thus, we conclude that static bimanual adaptation is of proprioceptive nature and does not occur at the level at which the object is represented.

Random errors are omnipresent in sensorimotor tasks due to perceptual and motor noise. The question is, are humans aware of their random errors on an instance-by-instance basis? The appealing answer would be ‘no’ because it seems intuitive that humans would otherwise immediately correct for the errors online, thereby increasing sensorimotor precision. However, here we show the opposite. Participants pointed to visual targets with varying degree of feedback. After movement completion participants indicated whether they believed they landed left or right of target. Surprisingly, participants' left/right-discriminability was well above chance, even without visual feedback. Only when forced to correct for the error after movement completion did participants loose knowledge about the remaining error, indicating that random errors can only be accessed offline. When correcting, participants applied the optimal correction gain, a weighting factor between perceptual and motor noise, minimizing end-point variance. Together these results show that humans optimally combine direct information about sensorimotor noise in the system (the current random error), with indirect knowledge about the variance of the perceptual and motor noise distributions. Yet, they only appear to do so offline after movement completion, not while the movement is still in progress, suggesting that during movement proprioceptive information is less precise.

Humans can learn and store multiple visuomotor mappings (dual-adaptation) when feedback for each is provided alternately. Moreover, learned context cues associated with each mapping can be used to switch between the stored mappings. However, little is known about the associative learning between cue and required visuomotor mapping, and how learning generalises to novel but similar conditions. To investigate these questions, participants performed a rapid target-pointing task while we manipulated the offset between visual feedback and movement end-points. The visual feedback was presented with horizontal offsets of different amounts, dependent on the targets shape. Participants thus needed to use different visuomotor mappings between target location and required motor response depending on the target shape in order to “hit” it. The target shapes were taken from a continuous set of shapes, morphed between spiky and circular shapes. After training we tested participants performance, without feedback, on different target shapes that had not been learned previously. We compared two hypotheses. First, we hypothesised that participants could (explicitly) extract the linear relationship between target shape and visuomotor mapping and generalise accordingly. Second, using previous findings of visuomotor learning, we developed a (implicit) Bayesian learning model that predicts generalisation that is more consistent with categorisation (i.e. use one mapping or the other). The experimental results show that, although learning the associations requires explicit awareness of the cues’ role, participants apply the mapping corresponding to the trained shape that is most similar to the current one, consistent with the Bayesian learning model. Furthermore, the Bayesian learning model predicts that learning should slow down with increased numbers of training pairs, which was confirmed by the present results. In short, we found a good correspondence between the Bayesian learning model