Error Correction and the Structure of Inter-Trial Fluctuations in a Redundant Movement Task.
ABSTRACT: We study inter-trial movement fluctuations exhibited by human participants during the repeated execution of a virtual shuffleboard task. Focusing on skilled performance, theoretical analysis of a previously-developed general model of inter-trial error correction is used to predict the temporal and geometric structure of variability near a goal equivalent manifold (GEM). The theory also predicts that the goal-level error scales linearly with intrinsic body-level noise via the total body-goal sensitivity, a new derived quantity that illustrates how task performance arises from the interaction of active error correction and passive sensitivity properties along the GEM. Linear models estimated from observed fluctuations, together with a novel application of bootstrapping to the estimation of dynamical and correlation properties of the inter-trial dynamics, are used to experimentally confirm all predictions, thus validating our model. In addition, we show that, unlike "static" variability analyses, our dynamical approach yields results that are independent of the coordinates used to measure task execution and, in so doing, provides a new set of task coordinates that are intrinsic to the error-regulation process itself.
Project description:After committing an error, participants tend to perform more slowly. This phenomenon is called post-error slowing (PES). Although previous studies have explored the PES effect in the context of observed errors, the issue as to whether the slowing effect generalizes across tasksets remains unclear. Further, the generation mechanisms of PES following observed errors must be examined. To address the above issues, we employed an observation-execution task in three experiments. During each trial, participants were required to mentally observe the outcomes of their partners in the observation task and then to perform their own key-press according to the mapping rules in the execution task. In Experiment 1, the same tasksets were utilized in the observation task and the execution task, and three error rate conditions (20%, 50% and 80%) were established in the observation task. The results revealed that the PES effect after observed errors was obtained in all three error rate conditions, replicating and extending previous studies. In Experiment 2, distinct stimuli and response rules were utilized in the observation task and the execution task. The result pattern was the same as that in Experiment 1, suggesting that the PES effect after observed errors was a generic adjustment process. In Experiment 3, the response deadline was shortened in the execution task to rule out the ceiling effect, and two error rate conditions (50% and 80%) were established in the observation task. The PES effect after observed errors was still obtained in the 50% and 80% error rate conditions. However, the accuracy in the post-observed error trials was comparable to that in the post-observed correct trials, suggesting that the slowing effect and improved accuracy did not rely on the same underlying mechanism. Current findings indicate that the occurrence of PES after observed errors is not dependent on the probability of observed errors, consistent with the assumption of cognitive control account. Moreover, the PES effect appears across tasksets with distinct stimuli and response rules in the context of observed errors, reflecting a generic process. Additionally, the slowing effect and improved accuracy in the post-observed error trial do not occur together, suggesting that they are independent behavioral adjustments in the context of observed errors.
Project description:Analysis of motor performance variability in tasks with redundancy affords insight about synergies underlying central nervous system (CNS) control. Preferential distribution of variability in ways that minimally affect task performance suggests sophisticated neural control. Unfortunately, in the analysis of variability the choice of coordinates used to represent multi-dimensional data may profoundly affect analysis, introducing an arbitrariness which compromises its conclusions. This paper assesses the influence of coordinates. Methods based on analyzing a covariance matrix are fundamentally dependent on an investigator's choices. Two reasons are identified: using anisotropy of a covariance matrix as evidence of preferential distribution of variability; and using orthogonality to quantify relevance of variability to task performance. Both are exquisitely sensitive to coordinates. Unless coordinates are known a priori, these methods do not support unambiguous inferences about CNS control. An alternative method uses a two-level approach where variability in task execution (expressed in one coordinate frame) is mapped by a function to its result (expressed in another coordinate frame). An analysis of variability in execution using this function to quantify performance at the level of results offers substantially less sensitivity to coordinates than analysis of a covariance matrix of execution variables. This is an initial step towards developing coordinate-invariant analysis methods for movement neuroscience.
Project description:Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33 ± 6 years, 8 F/12 M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the continuous execution of a working memory n-back task. We found that task execution impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to continuous task execution, can contribute to a better understanding of how brain networks rearrange themselves in response to a task.
Project description:The maintenance of stable goal-directed behaviour is a hallmark of conscious executive control in humans. Notably, both correct and error human actions may have a subconscious activation-based determination. One possible source of subconscious interference may be the default mode network that, in contrast to attentional network, manifests intrinsic oscillations at very low (<0.1 Hz) frequencies. In the present study, we analyse the time dynamics of performance accuracy to search for multisecond periodic fluctuations of error occurrence. Attentional lapses in attention deficit/hyperactivity disorder are proposed to originate from interferences from intrinsically oscillating networks. Identifying periodic error fluctuations with a frequency<0.1 Hz in patients with attention deficit/hyperactivity disorder would provide a behavioural evidence for such interferences. Performance was monitored during a visual flanker task in 92 children (7- to 16-year olds), 47 with attention deficit/hyperactivity disorder, combined type and 45 healthy controls. Using an original approach, the time distribution of error occurrence was analysed in the frequency and time-frequency domains in order to detect rhythmic periodicity. Major results demonstrate that in both patients and controls, error behaviour was characterized by multisecond rhythmic fluctuations with a period of ?12 s, appearing with a delay after transition to task. Only in attention deficit/hyperactivity disorder, was there an additional 'pathological' oscillation of error generation, which determined periodic drops of performance accuracy each 20-30 s. Thus, in patients, periodic error fluctuations were modulated by two independent oscillatory patterns. The findings demonstrate that: (i) attentive behaviour of children is determined by multisecond regularities; and (ii) a unique additional periodicity guides performance fluctuations in patients. These observations may re-conceptualize the understanding of attentive behaviour beyond the executive top-down control and may reveal new origins of psychopathological behaviours in attention deficit/hyperactivity disorder.
Project description:In a postural-suprapostural task, appropriate prioritization is necessary to achieve task goals and maintain postural stability. A "posture-first" principle is typically favored by elderly people in order to secure stance stability, but this comes at the cost of reduced suprapostural performance. Using a postural-suprapostural task with a motor suprapostural goal, this study investigated differences between young and older adults in dual-task cost across varying task prioritization paradigms. Eighteen healthy young (mean age: 24.8 ± 5.2 years) and 18 older (mean age: 68.8 ± 3.7 years) adults executed a designated force-matching task from a stabilometer board using either a stabilometer stance (posture-focus strategy) or force-matching (supraposture-focus strategy) as the primary task. The dual-task effect (DTE: % change in dual-task condition; positive value: dual-task benefit, negative value: dual-task cost) of force-matching error and reaction time (RT), posture error, and approximate entropy (ApEn) of stabilometer movement were measured. When using the supraposture-focus strategy, young adults exhibited larger DTE values in each behavioral parameter than when using the posture-focus strategy. The older adults using the supraposture-focus strategy also attained larger DTE values for posture error, stabilometer movement ApEn, and force-matching error than when using the posture-focus strategy. These results suggest that the supraposture-focus strategy exerted an increased dual-task benefit for posture-motor dual-tasking in both healthy young and elderly adults. The present findings imply that the older adults should make use of the supraposture-focus strategy for fall prevention during dual-task execution.
Project description:In a hypothesis-and-theory paper, a functional approach to movement analysis in sports is introduced. In this approach, contrary to classical concepts, it is not anymore the "ideal" movement of elite athletes that is taken as a template for the movements produced by learners. Instead, movements are understood as the means to solve given tasks that in turn, are defined by to-be-achieved task goals. A functional analysis comprises the steps of (1) recognizing constraints that define the functional structure, (2) identifying sub-actions that subserve the achievement of structure-dependent goals, (3) explicating modalities as specifics of the movement execution, and (4) assigning functions to actions, sub-actions and modalities. Regarding motor-control theory, a functional approach can be linked to a dynamical-system framework of behavioral shaping, to cognitive models of modular effect-related motor control as well as to explicit concepts of goal setting and goal achievement. Finally, it is shown that a functional approach is of particular help for sports practice in the context of structuring part practice, recognizing functionally equivalent task solutions, finding innovative technique alternatives, distinguishing errors from style, and identifying root causes of movement errors.
Project description:In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.
Project description:Detecting and evaluating errors in action execution is essential for learning. Through complex interactions of the inverse and the forward model, the human motor system can predict and subsequently adjust ongoing or subsequent actions. Inputs to such a prediction are efferent and afferent signals from various sources. The aim of the current study was to examine the impact of visual as well as a combination of efferent and proprioceptive input signals to error prediction in a complex motor task. Predicting motor errors has been shown to be correlated with a neural signal known as the error-related negativity (Ne/ERN). Here, we tested how the Ne/ERN amplitude was modulated by the availability of different sensory signals in a semi-virtual throwing task where the action outcome (hit or miss of the target) was temporally delayed relative to movement execution allowing participants to form predictions about the outcome prior to the availability of knowledge of results. 19 participants practiced the task and electroencephalogram was recorded in two test conditions. In the Visual condition, participants received only visual input by passively observing the throwing movement. In the EffProp condition, participants actively executed the task while visual information about the real and the virtual effector was occluded. Hence, only efferent and proprioceptive signals were available. Results show a significant modulation of the Ne/ERN in the Visual condition while no effect could be observed in the EffProp condition. In addition, amplitudes of the feedback-related negativity in response to the actual outcome feedback were found to be inversely related to the Ne/ERN amplitudes. Our findings indicate that error prediction is modulated by the availability of input signals to the forward model. The observed amplitudes were found to be attenuated in comparison to previous studies, in which all efferent and sensory inputs were present. Furthermore, we assume that visual signals are weighted higher than proprioceptive signals, at least in goal-oriented tasks with visual targets.
Project description:Maintenance of movement accuracy relies on motor learning, by which prior errors guide future behavior. One aspect of this learning process involves the accurate generation of predictions of movement outcome. These predictions can, for example, drive anticipatory movements during a predictive-saccade task. Predictive saccades are rapid eye movements made to anticipated future targets based on error information from prior movements. This predictive process exhibits long-memory (fractal) behavior, as suggested by inter-trial fluctuations. Here, we model this learning process using a regime-switching approach, which avoids the computational complexities associated with true long-memory processes. The resulting model demonstrates two fundamental characteristics. First, long-memory behavior can be mimicked by a system possessing no true long-term memory, producing model outputs consistent with human-subjects performance. In contrast, the popular two-state model, which is frequently used in motor learning, cannot replicate these findings. Second, our model suggests that apparent long-term memory arises from the trade-off between correcting for the most recent movement error and maintaining consistent long-term behavior. Thus, the model surprisingly predicts that stronger long-memory behavior correlates to faster learning during adaptation (in which systematic errors drive large behavioral changes); greater apparent long-term memory indicates more effective incorporation of error from the cumulative history across trials.
Project description:BACKGROUND: The anterior prefrontal cortex (PFC) exhibits activation during some cognitive tasks, including episodic memory, reasoning, attention, multitasking, task sets, decision making, mentalizing, and processing of self-referenced information. However, the medial part of anterior PFC is part of the default mode network (DMN), which shows deactivation during various goal-directed cognitive tasks compared to a resting baseline. One possible factor for this pattern is that activity in the anterior medial PFC (MPFC) is affected by dynamic allocation of attentional resources depending on task demands. We investigated this possibility using an event related fMRI with a face working memory task. METHODOLOGY/PRINCIPAL FINDINGS: Sixteen students participated in a single fMRI session. They were asked to form a task set to remember the faces (Face memory condition) or to ignore them (No face memory condition), then they were given 6 seconds of preparation period before the onset of the face stimuli. During this 6-second period, four single digits were presented one at a time at the center of the display, and participants were asked to add them and to remember the final answer. When participants formed a task set to remember faces, the anterior MPFC exhibited activation during a task preparation period but deactivation during a task execution period within a single trial. CONCLUSIONS/SIGNIFICANCE: The results suggest that the anterior MPFC plays a role in task set formation but is not involved in execution of the face working memory task. Therefore, when attentional resources are allocated to other brain regions during task execution, the anterior MPFC shows deactivation. The results suggest that activation and deactivation in the anterior MPFC are affected by dynamic allocation of processing resources across different phases of processing.