Motor learning without movement.

Prediction errors guide many forms of learning, providing teaching signals that help us improve our performance. Implicit motor adaptation, for instance, is thought to be driven by sensory prediction errors (SPEs), which occur when the expected and observed consequences of a movement differ. Traditionally, SPE computation is thought to require movement execution. However, recent work suggesting that the brain can generate sensory predictions based on motor imagery or planning alone calls this assumption into question. Here, by measuring implicit motor adaptation during a visuomotor task, we tested whether motor planning and well-timed sensory feedback are sufficient for adaptation. Human participants were cued to reach to a target and were, on a subset of trials, rapidly cued to withhold these movements. Errors displayed both on trials with and without movements induced single-trial adaptation. Learning following trials without movements persisted even when movement trials had never been paired with errors and when the direction of movement and sensory feedback trajectories were decoupled. These observations indicate that the brain can compute errors that drive implicit adaptation without generating overt movements, leading to the adaptation of motor commands that are not overtly produced.

Sensorimotor prediction is used to direct gaze toward task-relevant locations in a goal-directed throwing task.

Previous research has shown that action effects of self-generated movements are internally predicted before outcome feedback becomes available. To test whether these sensorimotor predictions are used to facilitate visual information uptake for feedback processing, we measured eye movements during the execution of a goal-directed throwing task. Participants could fully observe the effects of their throwing actions (ball trajectory and either hitting or missing a target) in most of the trials. In a portion of the trials, the ball trajectory was not visible, and participants only received static information about the outcome. We observed a large proportion of predictive saccades, shifting gaze toward the goal region before the ball arrived and outcome feedback became available. Fixation locations after predictive saccades systematically covaried with future ball positions in trials with continuous ball flight information, but notably also in trials with static outcome feedback and only efferent and proprioceptive information about the movement that could be used for predictions. Fixation durations at the chosen positions after feedback onset were modulated by action outcome (longer durations for misses than for hits) and outcome uncertainty (longer durations for narrow vs. clear outcomes). Combining both effects, durations were longest for narrow errors and shortest for clear hits, indicating that the chosen locations offer informational value for feedback processing. Thus, humans are able to use sensorimotor predictions to direct their gaze toward task-relevant feedback locations. Outcome-dependent saccade latency differences (miss vs. hit) indicate that also predictive valuation processes are involved in planning predictive saccades.NEW & NOTEWORTHY We elucidate the potential benefits of sensorimotor predictions, focusing on how the system actually uses this information to optimize feedback processing in goal-directed actions. Sensorimotor information is used to predict spatial parameters of movement outcomes, guiding predictive saccades toward future action effects. Saccade latencies and fixation durations are modulated by outcome quality, indicating that predictive valuation processes are considered and that the locations chosen are of high informational value for feedback processing.

The impact of CSF-filled cavities on scalp EEG and its implications.

Previous studies have found electroencephalogram (EEG) amplitude and scalp topography differences between neurotypical and neurological/neurosurgical groups, being interpreted at the cognitive level. However, these comparisons are invariably accompanied by anatomical changes. Critical to EEG are the so-called volume currents, which are affected by the spatial distribution of the different tissues in the head. We investigated the effect of cerebrospinal fluid (CSF)-filled cavities on simulated EEG scalp data. We simulated EEG scalp potentials for known sources using different volume conduction models: a reference model (i.e., unlesioned brain) and models with realistic CSF-filled cavities gradually increasing in size. We used this approach for a single source close or far from the CSF-lesion cavity, and for a scenario with a distributed configuration of sources (i.e., a "cognitive event-related potential effect"). The magnitude and topography errors between the reference and lesion models were quantified. For the single-source simulation close to the lesion, the CSF-filled lesion modulated signal amplitude with more than 17% magnitude error and topography with more than 9% topographical error. Negligible modulation was found for the single source far from the lesion. For the multisource simulations of the cognitive effect, the CSF-filled lesion modulated signal amplitude with more than 6% magnitude error and topography with more than 16% topography error in a nonmonotonic fashion. In conclusion, the impact of a CSF-filled cavity cannot be neglected for scalp-level EEG data. Especially when group-level comparisons are made, any scalp-level attenuated, aberrant, or absent effects are difficult to interpret without considering the confounding effect of CSF.

Impairment of the internal forward model and feedback mechanisms for vocal sensorimotor control in post-stroke aphasia: evidence from directional responses to altered auditory feedback.

The present study examined opposing and following vocal responses to altered auditory feedback (AAF) to determine how damage to left-hemisphere brain networks impairs the internal forward model and feedback mechanisms in post-stroke aphasia. Forty-nine subjects with aphasia and sixty age-matched controls performed speech vowel production tasks while their auditory feedback was altered using randomized ± 100 cents upward and downward pitch-shift stimuli. Data analysis revealed that when vocal responses were averaged across all trials (i.e., opposing and following), the overall magnitude of vocal compensation was significantly reduced in the aphasia group compared with controls. In addition, when vocal responses were analyzed separately for opposing and following trials, subjects in the aphasia group showed a significantly lower percentage of opposing and higher percentage of following vocal response trials compared with controls, particularly for the upward pitch-shift stimuli. However, there was no significant difference in the magnitude of opposing and following vocal responses between the two groups. These findings further support previous evidence on the impairment of vocal sensorimotor control in aphasia and provide new insights into the distinctive impact of left-hemisphere stroke on the internal forward model and feedback mechanisms. In this context, we propose that the lower percentage of opposing responses in aphasia may be accounted for by deficits in feedback-dependent mechanisms of audio-vocal integration and motor control. In addition, the higher percentage of following responses may reflect aberrantly increased reliance of the speech system on the internal forward model for generating sensory predictions during vocal error detection and motor control.

Neurophysiological evidence of motor preparation in inner speech and the effect of content predictability.

Self-generated overt actions are preceded by a slow negativity as measured by electroencephalogram, which has been associated with motor preparation. Recent studies have shown that this neural activity is modulated by the predictability of action outcomes. It is unclear whether inner speech is also preceded by a motor-related negativity and influenced by the same factor. In three experiments, we compared the contingent negative variation elicited in a cue paradigm in an active vs. passive condition. In Experiment 1, participants produced an inner phoneme, at which an audible phoneme whose identity was unpredictable was concurrently presented. We found that while passive listening elicited a late contingent negative variation, inner speech production generated a more negative late contingent negative variation. In Experiment 2, the same pattern of results was found when participants were instead asked to overtly vocalize the phoneme. In Experiment 3, the identity of the audible phoneme was made predictable by establishing probabilistic expectations. We observed a smaller late contingent negative variation in the inner speech condition when the identity of the audible phoneme was predictable, but not in the passive condition. These findings suggest that inner speech is associated with motor preparatory activity that may also represent the predicted action-effects of covert actions.

More than fulfilled expectations: An electrophysiological investigation of varying cause-effect relationships and schizotypal personality traits as related to the sense of agency.

The sense of agency (SoA) is central to human experience. The comparator model, contrasting sensory prediction and action feedback, is influential but limited in explaining SoA. We investigated mechanisms beyond the comparator model, focusing on the processing of unpredictable stimuli, perimotor components of SoA, and their relation to schizotypy. ERPs were recorded from 18 healthy participants engaged in button-pressing tasks while perceiving tones with varying causal relationships with their actions. We investigated the processing of non-causally related tones, contrasted this to causally related tones, and examined perimotor correlates of subjective expectancy and experience of agency. We confirmed N100 attenuation for self-generated stimuli but found similar effects for expectancy-dependent processing of random tones. SoA also correlated with perimotor ERP components, modulated by schizotypy. Thus, neural processes preceding actions contribute to the formation of SoA and are associated with schizotypy. Unpredictable events also undergo sensory attenuation, implying additional mechanisms contributing to SoA.

Evaluating the influence of anatomical accuracy and electrode positions on EEG forward solutions.

Generating realistic volume conductor models for forward calculations in electroencephalography (EEG) is not trivial and several factors contribute to the accuracy of such models, two of which are its anatomical accuracy and the accuracy with which electrode positions are known. Here, we investigate effects of anatomical accuracy by comparing forward solutions from SimNIBS, a tool which allows state-of-the-art anatomical modeling, with well-established pipelines in MNE-Python and FieldTrip. We also compare different ways of specifying electrode locations when digitized positions are not available such as transformation of measured positions from standard space and transformation of a manufacturer layout. Substantial effects of anatomical accuracy were seen throughout the entire brain both in terms of field topography and magnitude with SimNIBS generally being more accurate than the pipelines in MNE-Python and FieldTrip. Topographic and magnitude effects were particularly pronounced for MNE-Python which uses a three-layer boundary element method (BEM) model. We attribute these mainly to the coarse representation of the anatomy used in this model, in particular differences in skull and cerebrospinal fluid (CSF). Effects of electrode specification method were evident in occipital and posterior areas when using a transformed manufacturer layout whereas transforming measured positions from standard space generally resulted in smaller errors. We suggest modeling the anatomy of the volume conductor as accurately possible and we hope to facilitate this by making it easy to export simulations from SimNIBS to MNE-Python and FieldTrip for further analysis. Likewise, if digitized electrode positions are not available, a set of measured positions on a standard head template may be preferable to those specified by the manufacturer.

Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity.

Human brain activity generates scalp potentials (electroencephalography - EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography - MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community.

Aging exerts a limited influence on the perception of self-generated and externally generated touch.

Touch generated by our voluntary movements is attenuated both at the perceptual and neural levels compared with touch of the same intensity delivered to our body by another person or machine. This somatosensory attenuation phenomenon relies on the integration of somatosensory input and predictions about the somatosensory consequences of our actions. Previous studies have reported increased somatosensory attenuation in elderly people, proposing an overreliance on sensorimotor predictions to compensate for age-related declines in somatosensory perception; however, recent results have challenged this direct relationship. In a preregistered study, we used a force-discrimination task to assess whether aging increases somatosensory attenuation and whether this increase is explained by decreased somatosensory precision in elderly individuals. Although 94% of our sample (n = 108, 21-77 yr old) perceived their self-generated touches as weaker than externally generated touches of identical intensity (somatosensory attenuation) regardless of age, we did not find a significant increase in somatosensory attenuation in our elderly participants (65-77 yr old), but a trend when considering only the oldest subset (69-77 yr old). Moreover, we did not observe a significant age-related decline in somatosensory precision or a significant relationship of age with somatosensory attenuation. Together, our results suggest that aging exerts a limited influence on the perception of self-generated and externally generated touch and indicate a less direct relationship between somatosensory precision and attenuation in the elderly individuals than previously proposed.NEW & NOTEWORTHY Self-generated touch is attenuated compared with externally generated touch of identical intensity. This somatosensory attenuation has been previously shown to be increased in elderly participants, but it remains unclear whether it is related to age-related somatosensory decline. In our preregistered study, we observed a trend for increased somatosensory attenuation in our oldest participants (≥69 yr), but we found no evidence of an age-related decline in somatosensory function or a relationship of age with somatosensory attenuation.

Sensitivity to syllable stress regularities in externally but not self-triggered speech in Dutch.

Several theories of predictive processing propose reduced sensory and neural responses to anticipated events. Support comes from magnetoencephalography/electroencephalography (M/EEG) studies, showing reduced auditory N1 and P2 responses to self-generated compared to externally generated events, or when the timing and form of stimuli are more predictable. The current study examined the sensitivity of N1 and P2 responses to statistical speech regularities. We employed a motor-to-auditory paradigm comparing event-related potential (ERP) responses to externally and self-triggered pseudowords. Participants were presented with a cue indicating which button to press (motor-auditory condition) or which pseudoword would be presented (auditory-only condition). Stimuli consisted of the participant's own voice uttering pseudowords that varied in phonotactic probability and syllable stress. We expected to see N1 and P2 suppression for self-triggered stimuli, with greater suppression effects for more predictable features such as high phonotactic probability and first-syllable stress in pseudowords. In a temporal principal component analysis (PCA), we observed an interaction between syllable stress and condition for the N1, where second-syllable stress items elicited a larger N1 than first-syllable stress items, but only for externally generated stimuli. We further observed an effect of syllable stress on the P2, where first-syllable stress items elicited a larger P2. Strikingly, we did not observe motor-induced suppression for self-triggered stimuli for either the N1 or P2 component, likely due to the temporal predictability of the stimulus onset in both conditions. Taking into account previous findings, the current results suggest that sensitivity to syllable stress regularities depends on task demands.

Pre-movement event-related potentials and multivariate pattern of EEG encode action outcome prediction.

Self-initiated movements are accompanied by an efference copy, a motor command sent from motor regions to the sensory cortices, containing a prediction of the movement's sensory outcome. Previous studies have proposed pre-motor event-related potentials (ERPs), including the readiness potential (RP) and its lateralized sub-component (LRP), as potential neural markers of action feedback prediction. However, it is not known how specific these neural markers are for voluntary (active) movements as compared to involuntary (passive) movements, which produce much of the same sensory feedback (tactile, proprioceptive) but are not accompanied by an efference copy. The goal of the current study was to investigate how active and passive movements are distinguishable from premotor electroencephalography (EEG), and to examine if this change of neural activity differs when participants engage in tasks that differ in their expectation of sensory outcomes. Participants made active (self-initiated) or passive (finger moved by device) finger movements that led to either visual or auditory stimuli (100 ms delay), or to no immediate contingency effects (control). We investigated the time window before the movement onset by measuring pre-movement ERPs time-locked to the button press. For RP, we observed an interaction between task and movement. This was driven by movement differences in the visual and auditory but not the control conditions. LRP conversely only showed a main effect of movement. We then used multivariate pattern analysis to decode movements (active vs. passive). The results revealed ramping decoding for all tasks from around -800 ms onwards up to an accuracy of approximately 85% at the movement. Importantly, similar to RP, we observed lower decoding accuracies for the control condition than the visual and auditory conditions, but only shortly (from -200 ms) before the button press. We also decoded visual vs. auditory conditions. Here, task is decodable for both active and passive conditions, but the active condition showed increased decoding shortly before the button press. Taken together, our results provide robust evidence that pre-movement EEG activity may represent action-feedback prediction in which information about the subsequent sensory outcome is encoded.

Neural correlates of temporal recalibration to delayed auditory feedback of active and passive movements.

When we perform an action, its sensory outcomes usually follow shortly after. This characteristic temporal relationship aids in distinguishing self- from externally generated sensory input. To preserve this ability under dynamically changing environmental conditions, our expectation of the timing between action and outcome must be able to recalibrate, for example, when the outcome is consistently delayed. Until now, it remains unclear whether this process, known as sensorimotor temporal recalibration, can be specifically attributed to recalibration of sensorimotor (action-outcome) predictions, or whether it may be partly due to the recalibration of expectations about the intersensory (e.g., audio-tactile) timing. Therefore, we investigated the behavioral and neural correlates of temporal recalibration and differences in sensorimotor and intersensory contexts. During fMRI, subjects were exposed to delayed or undelayed tones elicited by actively or passively generated button presses. While recalibration of the expected intersensory timing (i.e., between the tactile sensation during the button movement and the tones) can be expected to occur during both active and passive movements, recalibration of sensorimotor predictions should be limited to active movement conditions. Effects of this procedure on auditory temporal perception and the modality-transfer to visual perception were tested in a delay detection task. Across both contexts, we found recalibration to be associated with activations in hippocampus and cerebellum. Context-dependent differences emerged in terms of stronger behavioral recalibration effects in sensorimotor conditions and were captured by differential activation pattern in frontal cortices, cerebellum, and sensory processing regions. These findings highlight the role of the hippocampus in encoding and retrieving newly acquired temporal stimulus associations during temporal recalibration. Furthermore, recalibration-related activations in the cerebellum may reflect the retention of multiple representations of temporal stimulus associations across both contexts. Finally, we showed that sensorimotor predictions modulate recalibration-related processes in frontal, cerebellar, and sensory regions, which potentially account for the perceptual advantage of sensorimotor versus intersensory temporal recalibration.

Did We Get Sensorimotor Adaptation Wrong? Implicit Adaptation as Direct Policy Updating Rather than Forward-Model-Based Learning.

The human motor system can rapidly adapt its motor output in response to errors. The prevailing theory of this process posits that the motor system adapts an internal forward model that predicts the consequences of outgoing motor commands and uses this forward model to plan future movements. However, despite clear evidence that adaptive forward models exist and are used to help track the state of the body, there is no definitive evidence that such models are used in movement planning. An alternative to the forward-model-based theory of adaptation is that movements are generated based on a learned policy that is adjusted over time by movement errors directly ("direct policy learning"). This learning mechanism could act in parallel with, but independent of, any updates to a predictive forward model. Forward-model-based learning and direct policy learning generate very similar predictions about behavior in conventional adaptation paradigms. However, across three experiments with human participants (N = 47, 26 female), we show that these mechanisms can be dissociated based on the properties of implicit adaptation under mirror-reversed visual feedback. Although mirror reversal is an extreme perturbation, it still elicits implicit adaptation; however, this adaptation acts to amplify rather than to reduce errors. We show that the pattern of this adaptation over time and across targets is consistent with direct policy learning but not forward-model-based learning. Our findings suggest that the forward-model-based theory of adaptation needs to be re-examined and that direct policy learning provides a more plausible explanation of implicit adaptation.SIGNIFICANCE STATEMENT The ability of our brain to adapt movements in response to error is one of the most widely studied phenomena in motor learning. Yet, we still do not know the process by which errors eventually result in adaptation. It is known that the brain maintains and updates an internal forward model, which predicts the consequences of motor commands, and the prevailing theory of motor adaptation posits that this updated forward model is responsible for trial-by-trial adaptive changes. Here, we question this view and show instead that adaptation is better explained by a simpler process whereby motor output is directly adjusted by task errors. Our findings cast doubt on long-held beliefs about adaptation.

Identification of a Brain Network Underlying the Execution of Freely Chosen Movements.

Action selection refers to the decision regarding which action to perform in order to reach a desired goal, that is, the "what" component of intention. Whether the action is freely chosen or externally instructed involves different brain networks during the selection phase, but it is assumed that the way an action is selected should not influence the subsequent execution phase of the same movement. Here, we aim to test this hypothesis by investigating whether the modality of movement selection influences the brain networks involved during the execution phase of the movement. Twenty healthy volunteers performed a delayed response task in an event-related functional magnetic resonance imaging design to compare freely chosen and instructed unimanual or bimanual movements during the execution phase. Using activation analyses, we found that the pre-supplementary motor area (preSMA) and the parietal and cerebellar areas were more activated during the execution phase of freely chosen as compared to instructed movements. Connectivity analysis showed an increase of information flow between the right posterior parietal cortex and the cerebellum for freely chosen compared to instructed movements. We suggest that the parieto-cerebellar network is particularly engaged during freely chosen movement to monitor the congruence between the intentional content of our actions and their outcome.

A New Framework of Designing Iterative Techniques for Image Deblurring.

In this work we present a framework of designing iterative techniques for image deblurring in inverse problem. The new framework is based on two observations about existing methods. We used Landweber method as the basis to develop and present the new framework but note that the framework is applicable to other iterative techniques. First, we observed that the iterative steps of Landweber method consist of a constant term, which is a low-pass filtered version of the already blurry observation. We proposed a modification to use the observed image directly. Second, we observed that Landweber method uses an estimate of the true image as the starting point. This estimate, however, does not get updated over iterations. We proposed a modification that updates this estimate as the iterative process progresses. We integrated the two modifications into one framework of iteratively deblurring images. Finally, we tested the new method and compared its performance with several existing techniques, including Landweber method, Van Cittert method, GMRES (generalized minimal residual method), and LSQR (least square), to demonstrate its superior performance in image deblurring.

Commonalities and differences in predictive neural processing of discrete vs continuous action feedback.

Sensory action consequences are highly predictable and thus engage less neural resources compared to externally generated sensory events. While this has frequently been observed to lead to attenuated perceptual sensitivity and suppression of activity in sensory cortices, some studies conversely reported enhanced perceptual sensitivity for action consequences. These divergent findings might be explained by the type of action feedback, i.e., discrete outcomes vs. continuous feedback. Therefore, in the present study we investigated the impact of discrete and continuous action feedback on perceptual and neural processing during action feedback monitoring. During fMRI data acquisition, participants detected temporal delays (0-417 ms) between actively or passively generated wrist movements and visual feedback that was either continuously provided during the movement or that appeared as a discrete outcome. Both feedback types resulted in (1) a neural suppression effect (active

Structural templates for imaging EEG cortical sources in infants.

Electroencephalographic (EEG) source reconstruction is a powerful approach that allows anatomical localization of electrophysiological brain activity. Algorithms used to estimate cortical sources require an anatomical model of the head and the brain, generally reconstructed using magnetic resonance imaging (MRI). When such scans are unavailable, a population average can be used for adults, but no average surface template is available for cortical source imaging in infants. To address this issue, we introduce a new series of 13 anatomical models for subjects between zero and 24 months of age. These templates are built from MRI averages and boundary element method (BEM) segmentation of head tissues available as part of the Neurodevelopmental MRI Database. Surfaces separating the pia mater, the gray matter, and the white matter were estimated using the Infant FreeSurfer pipeline. The surface of the skin as well as the outer and inner skull surfaces were extracted using a cube marching algorithm followed by Laplacian smoothing and mesh decimation. We post-processed these meshes to correct topological errors and ensure watertight meshes. Source reconstruction with these templates is demonstrated and validated using 100 high-density EEG recordings from 7-month-old infants. Hopefully, these templates will support future studies on EEG-based neuroimaging and functional connectivity in healthy infants as well as in clinical pediatric populations.

Predicting neuronal response properties from hemodynamic responses in the auditory cortex.

Recent functional MRI (fMRI) studies have highlighted differences in responses to natural sounds along the rostral-caudal axis of the human superior temporal gyrus. However, due to the indirect nature of the fMRI signal, it has been challenging to relate these fMRI observations to actual neuronal response properties. To bridge this gap, we present a forward model of the fMRI responses to natural sounds combining a neuronal model of the auditory cortex with physiological modeling of the hemodynamic BOLD response. Neuronal responses are modeled with a dynamic recurrent firing rate model, reflecting the tonotopic, hierarchical processing in the auditory cortex along with the spectro-temporal tradeoff in the rostral-caudal axis of its belt areas. To link modeled neuronal response properties with human fMRI data in the auditory belt regions, we generated a space of neuronal models, which differed parametrically in spectral and temporal specificity of neuronal responses. Then, we obtained predictions of fMRI responses through a biophysical model of the hemodynamic BOLD response (P-DCM). Using Bayesian model comparison, our results showed that the hemodynamic BOLD responses of the caudal belt regions in the human auditory cortex were best explained by modeling faster temporal dynamics and broader spectral tuning of neuronal populations, while rostral belt regions were best explained through fine spectral tuning combined with slower temporal dynamics. These results support the hypotheses of complementary neural information processing along the rostral-caudal axis of the human superior temporal gyrus.

What tool representation, intuitive physics, and action have in common: The brain's first-person physics engine.

An overlapping set of brain regions in parietal and frontal cortex are engaged by different types of tasks and stimuli: (i) making inferences about the physical structure and dynamics of the world, (ii) passively viewing, or actively interacting with, manipulable objects, and (iii) planning and execution of reaching and grasping actions. We suggest the observed neural overlap is because a common superordinate computation is engaged by each of those different tasks: A forward model of physical reasoning about how first-person actions will affect the world and be affected by unfolding physical events. This perspective offers an account of why some physical predictions are systematically incorrect - there can be a mismatch between how physical scenarios are experimentally framed and the native format of the inferences generated by the brain's first-person physics engine. This perspective generates new empirical expectations about the conditions under which physical reasoning may exhibit systematic biases.

Multiple behavior-specific cancellation signals contribute to suppressing predictable sensory reafference in a cerebellum-like structure.

Movement induces sensory stimulation of an animal's own sensory receptors, termed reafference. With a few exceptions, notably vestibular and proprioception, this reafference is unwanted sensory noise and must be selectively filtered in order to detect relevant external sensory signals. In the cerebellum-like electrosensory nucleus of elasmobranch fish, an adaptive filter preserves novel signals by generating cancellation signals that suppress predictable reafference. A parallel fiber network supplies the principal Purkinje-like neurons (called ascending efferent neurons, AENs) with behavior-associated internal reference signals, including motor corollary discharge and sensory feedback, from which predictive cancellation signals are formed. How distinct behavior-specific cancellation signals interact within AENs when multiple behaviors co-occur and produce complex, changing patterns of reafference is unknown. Here, we show that when multiple streams of internal reference signals are available, cancellation signals form that are specific to parallel fiber inputs temporally correlated with, and therefore predictive of, sensory reafference. A single AEN has the capacity to form more than one cancellation signal, and AENs form multiple cancellation signals simultaneously and modify them independently during co-occurring behaviors. Cancellation signals update incrementally during continuous behaviors, as well as episodic bouts of behavior that last minutes to hours. Finally, individual AENs, independently of their neighbors, form unique AEN-specific cancellation signals that depend on the particular sensory reafferent input it receives. Together, these results demonstrate the capacity of the adaptive filter to utilize multiple cancellation signals to suppress dynamic patterns of reafference arising from complex co-occurring and intermittently performed behaviors.