Biology subjects

McDougle, S. D.

Publications and source records attributed to McDougle, S. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Temporal Indexing of Motor Memory

Humans must store and retrieve distinct implicit motor memories in different contexts. Recent research has revealed the motor adaptation system's sensitivity to contextual cues related to posture, spatial goals, and visual inputs. However, to date, the role of one critical variable in implicit motor memory retrieval - the passage of time - remains underexplored, even though it is arguably more fundamental. Here we ask if different temporal intervals can act as effective contextual cues for retrieving competing motor adaptation memories at the subsecond level. We combined a visuomotor dual adaptation paradigm with a 'set-go' task to link opposite perturbation contexts with different movement preparation intervals. The preparation intervals (and their associated perturbations) were randomly interleaved during training, leading to consistent interference. After training, a testing phase revealed robust contextual modulation effects, such that distinct implicit motor memories were retrieved based on the time elapsed during the pre-movement preparatory interval. These contextual memory effects generalized to novel preparatory intervals in a highly structured fashion. Contextual motor memory separation was seen both when elapsed time had to be internally tracked (high uncertainty) and when it was aided by visual and symbolic cues (low uncertainty). Learning and generalization data across experiments could be parsimoniously explained by a computational model that indexed motor memories via a cerebellar granule-cell-like temporal basis set that tiled the delay interval. Our findings suggest that intrinsic timing processes in the cerebellum may function to index motor memories and generalize those memories to new temporal contexts.

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bioRxiv2026

Abstract Representations of Sensorimotor Transformations in Human Premotor Cortex

Humans flexibly adapt their movements across contexts to implement a vast repertoire of skills. This means the motor system should not only encode movement-specific information, but also context-specific representations linking goals to actions. Here we asked if and how the human sensorimotor cortex encodes different latent mappings between visual goals and movements. Participants learned to adapt wrist movements under two distinct visuomotor transformations, where they controlled a visual cursor that was either rotated or reflected away from their hand movement. Representational similarity analyses on fMRI data collected during the task dissociated neural activity patterns related to transformation context, movement direction, and target location. We observed distinct representational profiles within sensorimotor cortex: Premotor cortex activity patterns tracked both movement direction and transformation context, whereas primary motor cortex activity patterns tracked movement direction but not transformation context. Stronger transformation-context encoding in dorsal premotor cortex was associated with better task performance, linking our neural effects to behavior. These findings suggest that premotor cortex can represent sensorimotor context abstracted from movement particulars, providing a candidate neural interface between abstract action policies and motor commands.

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bioRxiv2026

Unsupervised nonmotor learning in the human cerebellum

A core motor function of the cerebellum is error-based learning: it uses prediction errors--mismatches between predicted and actual sensory feedback--to refine actions. One provocative hypothesis is that the cerebellum performs similar computations in cognitive domains. Here, we ask whether error-based learning computations in the cerebellum extend to a passive statistical learning task that requires neither action nor decision-making. Human participants viewed sequences of visual stimuli with varying probabilistic stimulus-stimulus transitions while undergoing fMRI. Subjects reported no explicit knowledge of the transitional probabilities after the scan. Nonetheless, putative cognitive regions of the cerebellum encoded prediction errors during statistical learning. Moreover, error signals in the cerebellum were encoded in a different manner than error signals in the anterior hippocampus, a well-known substrate of statistical learning: cerebellar activity covaried with trial-by-trial prediction errors that evolved slowly over time (echoing cerebellar computations in motor learning), whereas the hippocampus responded in a more fixed manner to the underlying transition structure. Computational modeling formalized this dissociation, with cerebellar activity explained by a delta-rule model that incrementally adjusted predictions based on recent experience, and hippocampal activity characterized as a form of Bayesian updating of a transition distribution held in memory. Our findings demonstrate that the cerebellum encodes prediction errors during passive nonmotor learning, and thus that it likely plays a domain-general role in error-based learning. SignificanceThe cerebellum is known to support motor learning by using prediction errors to refine behavior. Here, we test whether these computations extend to nonmotor learning contexts that require neither action nor decision-making. We report the discovery of prediction error signals in the cerebellum during statistical learning, an unsupervised process that extracts regularities from the sensory environment. These findings support a domain-general role for the cerebellum in error-based learning, offering a possible explanation for why cerebellar damage is implicated in such wide-ranging outcomes, from impaired motor coordination to complex disorders like autism.

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bioRxiv2025

The human cerebellum encodes temporally sensitive reinforcement learning signals

In addition to supervised motor learning, the cerebellum also supports nonmotor forms of learning, including reinforcement learning (RL). Recent studies in animal models have identified core RL signals related to reward processing, reward prediction, and prediction errors in specific regions in cerebellar cortex. However, the constraints on these signals remain poorly understood, particularly in humans. Here, we investigated cerebellar RL signals in a computationally-driven fMRI study. Human participants performed an RL task without low-level sensorimotor contingencies. We observed robust RL signals related to reward processing and reward prediction errors in cognitive regions of the cerebellum (Crus I and II). These signals were not explained by oculomotor or physiological confounds. By manipulating the delay between choices and reward outcomes, we discovered that cerebellar RL signals are temporally sensitive: robust when feedback was delivered shortly following choices, but undetectable at supra-second feedback delays. Similar delay effects were not found in other areas implicated in reward processing, including the ventral striatum and hippocampus. Further, reward prediction error activity in the cerebellum was related to behavioral performance when feedback was delivered promptly, but not when it was delayed. Connectivity analyses revealed that during RL feedback, cognitive areas of the cerebellum coactivate with a network that includes the medial and lateral prefrontal cortex and caudate nucleus. Together, these results highlight a temporally constrained contribution of the human cerebellum to a cognitive learning task.

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bioRxiv2025

Linking Motor Working Memory to Explicit and Implicit Motor Learning

While explicit and implicit motor learning have been widely studied, the extent to which working memory for recent movements - motor working memory (MWM) - contributes to these processes remains unclear. Previous research suggests that visuospatial working memory may facilitate explicit motor learning but is either uninvolved in or detrimental to implicit learning. Here, we ask if and how these findings extend to non-visual MWM. Based on recent work pointing to separate effector-independent and effector-specific MWM codes, we hypothesized that: (1) explicit motor learning processes would correlate with effector-independent MWM, and (2) implicit motor learning processes would correlate with effector-specific MWM. To test these hypotheses, human participants completed both a MWM task and a visuomotor adaptation task. Our results revealed significant correlations between the quality of effector-independent MWM and the degree of explicit motor learning, extending previous findings on visuospatial working memory. Additionally, we present evidence supporting our second hypothesis, that effector-specific MWM correlates with implicit motor learning.

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bioRxiv2024

Audiomotor prediction errors drive speech adaptation even in the absence of overt movement

Observed outcomes of our movements sometimes differ from our expectations. These sensory prediction errors recalibrate the brains internal models for motor control, reflected in alterations to subsequent movements that counteract these errors (motor adaptation). While leading theories suggest that all forms of motor adaptation are driven by learning from sensory prediction errors, dominant models of speech adaptation argue that adaptation results from integrating time-advanced copies of corrective feedback commands into feedforward motor programs. Here, we tested these competing theories of speech adaptation by inducing planned, but not executed, speech. Human speakers were prompted to speak a word and, on a subset of trials, were rapidly cued to withhold the prompted speech. On standard trials, speakers were exposed to real-time playback of their own speech with an auditory perturbation of the first formant to induce single-trial speech adaptation. Speakers experienced a similar sensory error on movement cancellation trials, hearing a perturbation applied to a recording of their speech from a previous trial at the time they would have spoken. Speakers adapted to auditory prediction errors in both contexts, altering the spectral content of spoken vowels to counteract formant perturbations even when no actual movement coincided with the perturbed feedback. Such adaptation was not observed when participants passively listened to perturbed feedback without the intention to speak, ruling out observational learning as the cause of adaptation in movement cancellation trials. These results build upon recent findings in reaching, and suggest that prediction errors, rather than corrective motor commands, drive audiomotor adaptation in speech.

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bioRxiv2024

Humans use local spectrotemporal correlations to detect rising and falling pitch

To discern speech or appreciate music, the human auditory system detects how pitch increases or decreases over time. However, the algorithms used to detect changes in pitch, or pitch motion, are incompletely understood. Here, using psychophysics, computational modeling, functional neuroimaging, and analysis of recorded speech, we ask if humans can detect pitch motion using computations analogous to those used by the visual system. We adapted stimuli from studies of vision to create novel auditory correlated noise stimuli that elicited robust pitch motion percepts. Crucially, these stimuli are inharmonic and possess no persistent features across frequency or time, but do possess positive or negative local spectrotemporal correlations in intensity. In psychophysical experiments, we found clear evidence that humans can judge pitch direction based only on positive or negative spectrotemporal intensity correlations. The key behavioral result--robust sensitivity to the negative spectrotemporal correlations--is a direct analogue of illusory "reverse-phi" motion in vision, and thus constitutes a new auditory illusion. Our behavioral results and computational modeling led us to hypothesize that human auditory processing may employ pitch direction opponency. fMRI measurements in auditory cortex supported this hypothesis. To link our psychophysical findings to real-world pitch perception, we analyzed recordings of English and Mandarin speech and found that pitch direction was robustly signaled by both positive and negative spectrotemporal correlations, suggesting that sensitivity to both types of correlations confers ecological benefits. Overall, this work reveals how motion detection algorithms sensitive to local correlations are deployed by the central nervous system across disparate modalities (vision and audition) and dimensions (space and frequency).

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bioRxiv2023

Evidence of an optimal error rate for motor skill learning

When acquiring a motor skill, learners must practice the skill at a difficulty that is challenging but still manageable to gradually improve their performance. In other words, during training, the learner must experience success as well as failure. Does there exist an optimal proportion of successes and failures to promote the fastest improvements in skill? Here, we build on a recent theoretical framework for optimal machine learning, extending it to the learning of motor skills. We then designed a custom task whose difficulty dynamically changed along with human subjects performance, constraining the error rate during training. In a large behavioral dataset (N=192), we observed evidence that learning is greatest at around a [~]30% error rate, matching our theoretical predictions.

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bioRxiv2021

Learning from the path not taken: Sensory prediction errors are sufficient for implicit adaptation of withheld movements

Prediction errors guide many forms of learning, providing teaching signals that help us improve our performance. Implicit motor adaptation, for instance, is 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 generates and accounts for sensory predictions based on motor imagery or planning alone calls this assumption into question. Here, by measuring implicit adaptation during a visuomotor task, we tested whether motor planning and well-timed sensory feedback are sufficient for SPE computation. 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 implicit learning. 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 demonstrate that the brain can compute SPEs without generating overt movements, leading to the adaptation of planned movements even when they are not performed. SIGNIFICANCE STATEMENTWe are always learning from our mistakes, because the brain is constantly generating predictions and monitoring the world for any surprises, which are also referred to as "prediction errors." Whenever a prediction error occurs, the brain learns to update future predictions and be more accurate. Here, we demonstrate that the brain predicts the consequences of movements, computes prediction errors, and updates future movements, even if we subsequently decide to withhold the movement. Thus, the brain can learn to update movements that are not performed, representing a mechanism for learning based only on movement planning and sensory expectation. These findings also provide further support for the role of prediction in motor control. SIGNIFICANCE STATEMENTOur brains control aspects of our movement without our conscious awareness - allowing many of us to effortlessly pick up a glass of water or wave "hello." Here, we demonstrate that this implicit motor system can learn to refine movements that we plan but ultimately decide not to perform. Participants planned to reach to a target, and they sometimes withheld these reaches. When reaches were withheld, an animation simulating a reach that missed the target played. Afterwards, participants reached opposite the direction of the mistake without awareness of this change in their movements, indicating that the implicit motor system had learned from the animated mistake. These findings indicate that movement is not strictly necessary for motor adaptation, and that we can learn to update our actions based only on movement planning and observation of related events in the world.

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bioRxiv2021

Generalization of motor learning in psychological space

To generate adaptive movements we must generalize what we have previously learned to novel situations. The generalization of adapted movements has typically been framed as a consequence of neural tuning functions that overlap for similar movement kinematics - what might be considered bottom-up generalization. However, as is true in many domains of human behavior, generalization can also be framed as the result of deliberate decisions about how to act (top-down generalization). Here we attempt to broaden the scope of theories about motor generalization, hypothesizing that part of the typical motor generalization function can be characterized as a consequence of top-down decisions concerning the subjective similarity of different movement contexts. We tested this proposal by having participants make explicit similarity ratings over both traditional kinematic contextual dimensions (movement direction) and more abstract contextual dimensions (target shape), and perform a visuomotor adaptation generalization task where trials varied over those dimensions. Across five experiments, we measured the relationship between subjective similarity ratings and motor generalization. In some cases this link was rather strong, though it was determined by both task-relevance and explicit instruction. These results support a broadening of the descriptive framework used to understand the generalization of motor behaviors and support a more careful deployment of instructions in generalization studies. Significance StatementGeneralization describes the transfer of knowledge from one context to another, and is typically thought to result from a higher-order inference process. However, in the motor adaptation domain, generalization is thought to arise from neural representations tuned to low-level kinematics. To bridge these differing views, we measured peoples subjective similarity judgements of different task contexts during sensorimotor adaptation. We found that motor generalization was closely linked to participants subjective judgements, and that explicit instructions about the consequential dimension of different contexts further shaped generalization. These findings emphasize that in addition to low level kinematic considerations, top-down inferences about which action to take in a given context should be considered as another key component of motor generalization.

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bioRxiv2020

Executive modulation of brain reward systems endows goals with value

People often learn from the outcomes of their actions, even when these outcomes do not involve material rewards or punishments. How does our brain provide this flexibility? We combined behavior, computational modeling, and functional neuroimaging to probe whether learning from transient goals harnesses the same circuitry that supports learning from secondary reinforcers. Behavior and neuroimaging revealed that "one-shot" transient goals (abstract fractal images seen once) can act as a substitute for rewards during instrumental learning, and produce reliable reward-like signals in dopaminergic reward circuits. Moreover, we found evidence that prefrontal correlates of executive control may play a role in shaping these responses in reward circuits. These results suggest that learning from abstract goal outcomes is supported by an interplay between high-level representations in prefrontal cortex and low-level responses in subcortical reward circuits. This interaction may allow humans to perform reinforcement learning over flexible, arbitrarily abstract reward functions.

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bioRxiv2020

An associative learning account of sensorimotor adaptation

Traditional associative learning tasks focus on the formation of associations between salient events and arbitrary stimuli that predict those events. This is exemplified in cerebellar-dependent delay eyeblink conditioning, where arbitrary cues such as a light or tone act as conditioning stimuli that predict aversive sensations at the cornea. Here we ask if a similar framework could be applied to another type of cerebellar-dependent sensorimotor learning - sensorimotor adaptation. Models of sensorimotor adaptation posit that the introduction of an environmental perturbation results in an error signal that is used to update an internal model of a sensorimotor map for motor planning. Here we take a step towards an integrative account of these two forms of cerebellar-dependent learning, examining the relevance of core concepts from associative learning for sensorimotor adaptation. Using a visuomotor adaptation reaching task, we paired movement-related feedback with neutral auditory or visual contextual cues that served as conditioning stimuli (CSs). Trial-by trial changes in feedforward movement kinematics exhibited three key signatures of associative learning: Differential conditioning, sensitivity to the CS-US interval, and compound conditioning. Moreover, after compound conditioning, a robust negative correlation was observed between responses to the two elemental CSs of the compound (i.e., overshadowing), consistent with the additivity principle posited by theories of associative learning. The existence of associative learning effects in sensorimotor adaptation provides a proof-of-concept for linking cerebellar- dependent learning paradigms within a common theoretical framework.

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bioRxiv2020

Cerebellar degeneration selectively disrupts continuous mental operations in visual cognition

Here we test the hypothesis that the cerebellum aids in the dynamic transformation of mental representations. We report a series of neuropsychological experiments comparing the performance of individuals with cerebellar degeneration (CD) on cognitive tasks that either entail continuous, movement-like mental operations or more discrete mental operations. In visual cognition, individuals with CD exhibited an impaired rate of mental rotation, an operation hypothesized to require the continuous manipulation of a visual representation. In contrast, individuals with CD showed a normal processing rate when scanning items in visual working memory, an operation hypothesized to require the maintenance and retrieval of representations. In mathematical cognition, individuals with CD were impaired at single-digit addition, an operation hypothesized to require iterative manipulations along a mental number-line; this group was not impaired on arithmetic tasks requiring memory retrieval (e.g., single-digit multiplication). These results, obtained in tasks from two disparate domains, suggest one potential constraint on the contribution of the cerebellum to cognitive tasks. This constraint may parallel the cerebellums role in motor control, involving coordinated dynamic transformations in a mental workspace.

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bioRxiv2020

Behavioral, physiological, and neural signatures of surprise during naturalistic sports viewing

Surprise signals a discrepancy between past and current beliefs. It is theorized to be linked to affective experiences, the creation of particularly resilient memories, and segmentation of the flow of experience into discrete perceived events. However, the ability to precisely measure naturalistic surprise has remained elusive. We used advanced basketball analytics to derive a quantitative measure of surprise and characterized its behavioral, physiological, and neural correlates in human subjects observing basketball games. We found that surprise was associated with segmentation of ongoing experiences, as reflected by subjectively perceived event boundaries and shifts in neocortical patterns underlying belief states. Interestingly, these effects differed by whether surprising moments contradicted or bolstered current predominant beliefs. Surprise also positively correlated with pupil dilation, activation in subcortical regions associated with dopamine, game enjoyment, and long-term memory. These investigations support key predictions from event segmentation theory and extend theoretical conceptualizations of surprise to real-world contexts.

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bioRxiv2019

Prolonged reaction times eliminate residual errors in visuomotor adaptation

One persistent curiosity in visuomotor adaptation tasks is that participants often do not reach maximal performance. This incomplete asymptote has been explained as a consequence of obligatory computations within the implicit adaptation system, such as an equilibrium between learning and forgetting. A body of recent work has shown that in standard adaptation tasks, cognitive strategies operate alongside implicit learning. We reasoned that incomplete learning in adaptation tasks may primarily reflect a speed-accuracy trade-off on time-consuming motor planning. Across three experiments, we find evidence supporting this hypothesis, showing that hastened motor planning may primarily lead to under-compensation. When an obligatory waiting period was administered before movement start, participants were able to fully counteract imposed perturbations (experiment 1). Inserting the same delay between trials - rather than during movement planning - did not induce full compensation, suggesting that the motor planning interval predicts the learning asymptote (experiment 2). In the last experiment, we asked participants to continuously report their movement intent. We show that emphasizing explicit re-aiming strategies (and concomitantly increasing planning time) also lead to complete asymptotic learning. Findings from all experiments support the hypothesis that incomplete adaptation is, in part, the result of an intrinsic speed-accuracy trade-off, perhaps related to cognitive strategies that require parametric attentional reorienting from the visual target to the goal.

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bioRxiv2019

Distinct Processing of Selection and Execution Errors in Neural Signatures of Outcome Monitoring

Losing a point in tennis could result from poor shot selection or faulty stroke execution. To explore how the brain responds to these different types of errors, we examined feedback-locked EEG activity while participants completed a modified version of a standard three-armed bandit probabilistic reward task. Our task framed unrewarded outcomes as either the result of errors of selection or errors of execution. We examined whether amplitude of a medial frontal negativity (the Feedback-Related Negativity; FRN) was sensitive to the different forms of error attribution. Consistent with previous reports, selection errors elicited a large FRN relative to rewards and amplitude of this signal correlated behavioral adjustment following these errors. A different pattern was observed in response to execution errors. These outcomes produced a larger FRN, a frontocentral attenuation in activity preceding this component, and a subsequent enhanced error positivity in parietal sites. Notably, the only correlations with behavioral adjustment were with the early frontocentral attenuation and amplitude of the parietal signal; FRN differences between execution errors and rewarded trials did not correlate with subsequent changes in behavior. Our findings highlight distinct neural correlates of selection and execution error processing, providing insight into how the brain responds to the different classes of error that determine future action.

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bioRxiv2019

Explicit strategies in force field adaptation

In recent years, it has become increasingly clear that a number of learning processes are at play in visuomotor adaptation tasks. In addition to the presumed formation of an internal model of the perturbation, learners can also develop explicit knowledge allowing them to select better actions in responding to a given perturbation. Advances in visuomotor rotation experiments have underscored the important role that such \"explicit learning\" plays in shaping adaptation to kinematic perturbations. Yet, in adaptation to dynamic perturbations, its contribution has been largely overlooked, potentially because compensation of a viscous force field, for instance, is difficult to assess by commonly-used verbalization-based approaches. We therefore sought to assess the contribution of explicit learning in learners adapting to a dynamic perturbation by two novel modifications of a force field experiment. First, via an elimination approach, we asked learners to abandon any cognitive strategy before selected force channel trials to expose consciously accessible parts of overall learning. Learners indeed reduced compensatory force compared to standard Catch channels. Second, via a manual reporting approach, we instructed a group of learners to mimic their right hands adaptation by moving with their naive left hand. While a control group displayed negligible left-hand force compensation, the Mimic group reported forces that approximated right-hand adaptation but appeared to under-report the velocity component of the force field in favor of a more position-based component. We take these results to clearly demonstrate the contribution of explicit learning to force adaptation, underscoring its relevance to motor learning in general.\n\nNew & NoteworthyWhile the role of explicit learning has recently been appreciated in visuomotor adaptation tasks, their contribution to force field adaptation has not been as widely acknowledged. To address this issue, we employed two novel methods to assay explicit learning in force field adaptation tasks and found that learners can voluntarily control aspects of force production and manually report them with their untrained limb. This suggests that an explicit component contributes to force field adaptation and may provide alternative explanations to behavioral phenomena commonly thought to reveal a complex organization of internal models in the brain.

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bioRxiv2018

Neural Signatures of Prediction Errors in a Decision-making Task are Modulated by Action Execution Failures

Decisions must be implemented through actions, and actions are prone to error. As such, when an expected outcome is not obtained, an individual should not only be sensitive to whether the choice itself was suboptimal, but also whether the action required to indicate that choice was executed successfully. The intelligent assignment of credit to action execution versus action selection has clear ecological utility for the learner. To explore this scenario, we used a modified version of a classic reinforcement learning task in which feedback indicated if negative prediction errors were, or were not, associated with execution errors. Using fMRI, we asked if prediction error computations in the human striatum, a key substrate in reinforcement learning and decision making, are modulated when a failure in action execution results in the negative outcome. Participants were more tolerant of non-rewarded outcomes when these resulted from execution errors versus when execution was successful but the reward was withheld. Consistent with this behavior, a model-driven analysis of neural activity revealed an attenuation of the signal associated with negative reward prediction error in the striatum following execution failures. These results converge with other lines of evidence suggesting that prediction errors in the mesostriatal dopamine system integrate high-level information during the evaluation of instantaneous reward outcomes.

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