Biology subjects

Blustein, D.

Publications and source records attributed to Blustein, D..

5 recordsLinked to original sources

bioRxiv2023

Sensory prediction error drives subconscious motor learning outside of the laboratory

Sensorimotor adaptation is supported by at least two parallel learning systems: an intentionally controlled explicit strategy, and an involuntary implicit learning system. To investigate the error sources driving these two systems, past work focused on constrained reaches or finger movements in laboratory environments has shown subconscious learning systems to be driven in part by sensory prediction error (SPE), i.e., the mismatch between the realized and expected outcome of an action. We designed a ball rolling task to explore whether SPEs can drive implicit motor adaptation during complex whole-body movements that impart physical motion on external objects. After applying a visual shift, participants rapidly adapted their rolling angles to reduce the error between the ball and target. We removed all visual feedback and told participants to aim their throw directly toward the primary target, revealing an unintentional 5.06{degrees} implicit adjustment to reach angles that decayed over time. To determine whether this implicit adaptation was driven by SPE, we gave participants a second aiming target that would solve the visual shift, as in Mazzoni and Krakauer (2006). Remarkably, after rapidly reducing ball rolling error to zero (due to enhancements in strategic aiming), the additional aiming target caused rolling angles to deviate beyond the primary target by 3.15{degrees}. This involuntary overcompensation, which worsened task performance, is a hallmark of SPE-driven implicit learning. These results show that SPE-driven implicit processes, previously observed within simplified finger or planar reaching movements, actively contribute to motor adaptation in more complex naturalistic skill-based tasks. New and NoteworthyImplicit and explicit learning systems have been detected using simple, constrained movements inside the laboratory. How these systems impact movements during complex whole-body, skill-based tasks has not been established. Here we demonstrate that sensory prediction errors significantly impact how a person updates their movements, replicating findings from the laboratory in an unconstrained ball-rolling task. This real-world validation is an important step towards explaining how subconscious learning helps humans execute common motor skills in dynamic environments.

neuroscience↗

bioRxiv2022

Alpha suppression during prehension indicates neural motor drive inhibition

Changes in alpha band activity (8-12 Hz) have been shown to indicate the inhibition of engagement of brain regions during cognitive tasks, reflecting real-time cognitive load. Despite this, its feasibility to be used in a more dynamic environment with ongoing motor corrections has not been studied. This research used electroencephalography (EEG) to explore how different brain regions are engaged during a simple grasp and lift task where unexpected changes to the objects properties are introduced. To our knowledge, this is the first study to show alpha activity changes related to motor error correction occur only in motor-related areas (i.e. central areas), but not in error processing areas (ie. fronto-parietal network). This suggests that oscillations over motor areas could reflect inhibition of motor drive related to motor error correction, thus being a potential cortical electrophysiological biomarker for the process, and not solely as a proxy for cognitive demands. This observation is particularly relevant in scenarios where these signals are used to evaluate high cognitive demands co-occurring with high levels of motor errors and corrections, such as prosthesis use. The establishment of electrophysiological biomarkers of mental resource allocation during movement and cognition can help identify indicators of mental workload and motor drive, which may be useful for improving brain-machine interfaces. New and NoteworthyThis research expands on previous fMRI literature by demonstrating that alpha band suppression, an EEG metric with high temporal resolution, occurs over the primary sensorimotor area during error correction of hand movements. This furthers our understanding of alpha suppression beyond processes related to cognitive demands by highlighting how motor control also influences this frequency band. Recognizing that alpha band activity is modulated by both motor and cognitive processes is important in situations where high cognitive demands can lead to a high level of movement errors. Interpretations of such modulation are often attributed only to cognitive demands, whereas a motor process may also play a factor. Furthermore, alpha suppression could be used as a biomarker for error correction with applications in human machine interfaces, such as neuroprostheses.

neuroscience↗

bioRxiv2020

Cutting through the noise: reducing bias in motor adaptation analysis

During goal-directed movements, the magnitude of error correction by a person on a subsequent movement provides important insight into a persons motor learning dynamics. Observed differences in trial-by-trial adaptation rates might indicate different relative weighting placed on the various sources of information that inform a movement, e.g. sensory feedback, control predictions, or internal model expectations. Measuring this trial-by-trial adaptation rate is not straightforward, however, since externally observed data are masked by noise from several sources and influenced by inaccessible internal processes. Adaptation to perturbation has been used to measure error adaptation as the introduced external disturbance is sufficiently large to overshadow other noise sources. However, perturbation analysis is difficult to implement in real-world scenarios, requires a large number of movement trials to accommodate infrequent perturbations, and the paradigm itself might affect the movement dynamics being observed. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, the conventional estimation of trial-by-trial adaptation increases, a counterintuitive finding that is the consequence of systematic bias in the estimate due to noise masking the learners intention. We present an analytic solution relying on stochastic signal processing to reduce this effect of noise, producing an estimate of motor adaptation with reduced bias. The result is an improved estimate of trial-by-trial adaptation in a human learner compared to conventional methods. We demonstrate the effectiveness of the new method in analyzing simulated and empirical movement data under different noise conditions. The analytic approach is applicable across different types of movements in varied contexts and should replace the regression analysis method in future motor analysis studies. Author SummaryWhen a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction provides insight into how the nervous system is operating, particularly in regard to how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis of movement has required carefully controlled laboratory conditions, limiting the usefulness of motor analysis in clinical and everyday environments. Here we present a new computational method that can be accurately applied to typical movements. Counterintuitive findings of the established approach are corrected by the proposed method. This method will provide a common framework for researchers to analyze movements while extending dynamic motor adaptation analysis capabilities to clinical and non-laboratory settings.

neuroscience↗

bioRxiv2017

Assessing the quality of supplementary sensory feedback using a crossmodal congruency task

Peripheral nerve interfaces show promise in making prosthetic limbs more biomimetic and ultimately more intuitive and useful for patients. However, approaches to assess these emerging technologies are limited in their scope and the insight they provide. When outfitting a prosthesis with a new feedback system it would be helpful to quantify its physiological correspondence, i.e. how well the experimental feedback mimics the perceived feedback in an intact limb. Here we present an approach to quantify physiological correspondence using a modified crossmodal congruency task. We trained 60 able-bodied subjects to control a bypass prosthesis under different feedback conditions and training durations. We find that the crossmodal congruency effect (CCE) score is sensitive to changes in feedback modality (multi-way ANOVA; F(2,48) = 6.02, p<0.05). After extended training, the CCE score increased as the spatial separation between expected and perceived feedback decreased (unpaired t-test, p<0.05). We present a model that can quantitatively estimate physiological correspondence given the CCE result and the measured spatial separation of the feedback. This quantification approach gives researchers a tool to assess an aspect of emerging augmented feedback systems that is not measurable with current motor assessments.

neuroscience↗

bioRxiv2017

The crossmodal congruency effect, a tool incorporation metric, suffers from a learning effect with repeated exposures

The incorporation of feedback into a persons body schema is well established. The crossmodal congruency effect (CCE) task is used to objectively quantify incorporation without being susceptible to experimenter biases. This visual-tactile interference task is used to calculate the CCE score as a difference in response time for incongruent and congruent trials. Here we show that this metric is susceptible to a learning effect that causes attenuation of the CCE score due to repeated task exposure sessions. We demonstrate that this learning effect is persistent, even after a 6 month hiatus in testing. Two mitigation strategies are proposed: 1. Only use CCE scores that are taken after learning has stabilized, or 2. Use a modified CCE protocol that decreases the task exposure time. We show that the modified and shortened CCE protocol, which may be required to meet time or logistical constraints in laboratory or clinical settings, reduced the impact of the learning effect on CCE results. Importantly, the CCE scores from the modified protocol were not significantly more variable than results obtained with the original protocol. This study highlights the importance of considering exposure time to the CCE task when designing experiments and suggests two mitigation strategies to improve the utility of this psychophysical assessment.

neuroscience↗