scholarly journals Measures of explicit and implicit in motor learning: what we know and what we don’t

2020 ◽  
Author(s):  
Jana Maresch ◽  
Liad Mudrik ◽  
Opher Donchin
Keyword(s):  
Cognitive Science ◽  
Implicit Learning ◽  
Time Constant ◽  
Motor Adaptation ◽  
Visuomotor Adaptation ◽  
Fast Time ◽  
Implicit System ◽  
Learning And Adaptation ◽  
Underlying Processes

Multiple different processes are known to contribute to sensorimotor learning, and adaptation tasks have been a key tool in characterizing these underlying processes. Recently, much interest has focused on quantifying the explicit and implicit components of motor adaptation using a variety of methods. The methods differ in their underlying assumptions and ideas. In some cases, they yield similar findings, in others they do not. We review the literature with a focus on the agreement and inconsistencies between different measures of explicit adaptation. Some aspects of explicit adaptation seem robust across different measurements: the fast time constant of the explicit system and the slow time constant of the implicit system, for instance. Other aspects seem to reflect quite differently across measures: for example, the extent to which explicit and implicit combine linearly. To help understand these differences, we explored ideas of explicit and implicit learning in the context of the larger field of cognitive science. We found that non-linearity and a possible bias in the measurements make explicit and implicit learning difficult to measure across different fields within cognitive science. We relate this back to the study of motor adaptation, arguing that the only way forward is through a strong experimental characterization of the phenomenology of our visuomotor adaptation and a rich set of models to test on it.

scholarly journals An implicit memory of errors limits human sensorimotor adaptation

2019 ◽  
Author(s):  
Scott T. Albert ◽  
Jihoon Jang ◽  
Hannah Sheahan ◽  
Lonneke Teunissen ◽  
Koenraad Vandevoorde ◽  
...  
Keyword(s):  
Implicit Learning ◽  
Motor Adaptation ◽  
Learning System ◽  
Second Order Statistics ◽  
Error Sensitivity ◽  
Implicit System ◽  
Extended Practice ◽  
History Of ◽  
Residual Errors ◽  
The Brain

AbstractAfter extended practice, motor adaptation reaches a limit in which learning appears to stop, despite the fact that residual errors persist. What prevents the brain from eliminating the residual errors? Here we found that the adaptation limit was causally dependent on the second order statistics of the perturbation; when variance was high, learning was impaired and large residual errors persisted. However, when learning relied solely on explicit strategy, both the adaptation limit and its dependence on perturbation variability disappeared. In contrast, when learning depended entirely, or in part on implicit learning, residual errors developed. Residual errors in implicit performance were caused by variance-dependent modifications to error sensitivity, not forgetting. These observations are consisted with a model of learning in which the implicit system becomes more sensitive to error when errors are consistent, but forgets this memory of errors over time. Thus, residual errors in motor adaptation are a signature of the implicit learning system, caused by an error sensitivity that depends on the history of past errors.

scholarly journals GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gwenaëlle G. Lemoine ◽  
Marie-Pier Scott-Boyer ◽  
Bathilde Ambroise ◽  
Olivier Périn ◽  
Arnaud Droit
Keyword(s):  
Muscle Development ◽  
R Package ◽  
Topological Information ◽  
Old Patients ◽  
Network Modules ◽  
Extended Analysis ◽  
Networks Analysis ◽  
Underlying Processes

Abstract Background Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline. Results Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions. Conclusion GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

scholarly journals On the characterization of glacier response by a single time-scale

2001 ◽  
Vol 47 (159) ◽  
pp. 659-664 ◽  
Author(s):  
W. D. Harrison ◽  
D. H. Elsberg ◽  
K. A. Echelmeyer ◽  
R. M. Krimmel
Keyword(s):  
Climate Change ◽  
Mass Balance ◽  
Time Constant ◽  
Time Scale ◽  
Response Models ◽  
Single Time ◽  
Glacier Changes ◽  
Major Increase ◽  
Effect Of Surface

AbstractGlacier response to climate can be characterized by a single time-scale when the glacier changes sufficiently slowly. Then the derivative of volume with respect to area defines a thickness scale similar to that of Jóhannesson and others, and the time-scale follows from it. Our version of the time-scale is different from theirs because it explicitly includes the effect of surface elevation on mass-balance rate, which can cause a major increase in the time-scale or even lead to unstable response. The time constant has a dual role, controlling both the rate and magnitude of response to a given climate change. Data from South Cascade Glacier, Washington, U.S.A., illustrate the ideas, some of the difficulty in obtaining accurate values for the thickness and time-scales, and the susceptibility of all response models to potentially large errors.

Sensorimotor knowledge from task-irrelevant feedback contributes to motor learning

2021 ◽  
Author(s):  
Wanying Jiang ◽  
Yajie Liu ◽  
Yuqing Bi ◽  
Kunlin Wei
Keyword(s):  
Motor Learning ◽  
Perceptual Learning ◽  
Implicit Learning ◽  
Motor Adaptation ◽  
Strategic Learning ◽  
Visuomotor Rotation ◽  
Saving Effect ◽  
Sensorimotor Knowledge ◽  
Sensory Processes ◽  
Task Irrelevant

Exposure to task-irrelevant feedback leads to perceptual learning, but its effect on motor learning has been understudied. Here we asked human participants to reach a visual target with a hand-controlled cursor while observing another cursor moving independently in a different direction. While the task-irrelevant feedback did not change the main task's performance, it elicited robust savings in subsequent adaptation to classical visuomotor rotation perturbation. We demonstrated that the saving effect resulted from a faster formation of strategic learning through a series of experiments, not from gains in the implicit learning process. Furthermore, the saving effect was robust against drastic changes in stimulus features (i.e., rotation size or direction) or task types (i.e., for motor adaptation and skill learning). However, the effect was absent when the task-irrelevant feedback did not carry the visuomotor relationship embedded in visuomotor rotation. Thus, though previous research on perceptual learning has related task-irrelevant feedback to changes in early sensory processes, our findings support its role in acquiring abstract sensorimotor knowledge during motor learning. Motor learning studies have traditionally focused on task-relevant feedback, but our study extends the scope of feedback processes and sheds new light on the dichotomy of explicit and implicit learning in motor adaptation as well as motor structure learning.

Comparative effects of vasodilator drugs on flow distribution and venous return

1985 ◽  
Vol 63 (11) ◽  
pp. 1345-1355 ◽  
Author(s):  
R. I. Ogilvie
Keyword(s):  
Blood Flow ◽  
Blood Volume ◽  
Time Constant ◽  
Venous Return ◽  
Flow Distribution ◽  
Fast Time ◽  
Central Blood Volume ◽  
Slow Time ◽  
Arterial Resistance ◽  
Slow Time Constant

Systemic vascular effects of hydralazine, prazosin, captopril, and nifedipine were studied in 115 anesthetized dogs. Blood flow [Formula: see text] and right atrial pressure (Pra) were independently controlled by a right heart bypass. Transient changes in central blood volume after an acute reduction in Pra at a constant [Formula: see text] showed that blood was draining from two vascular compartments with different time constants, one fast and the other slow. At three dose levels producing comparable reductions in systemic arterial pressure (30–40% at the highest dose), these drugs had different effects on flow distribution and venous return. Hydralazine and prazosin had parallel and balanced effects on arterial resistance of the two vascular compartments, and flow distribution was unaltered. Captopril preferentially reduced arterial resistance of the compartment with a slow time constant for venous return (−26 ± 6%, −30 ± 6%, −50 ± 5% at 0.02, 0.10, and 0.50 mg∙kg−1∙h−1, respectively; [Formula: see text]) without altering arterial resistance of the fast time-constant compartment. Blood flow to the slow time-constant compartment was increased 43 ± 14% at the highest dose, and central blood volume was reduced 108 ± 15 mL. In contrast, nifedipine had a balanced effect on arterial resistance with the lowest dose (0.025 mg/kg) but caused a preferential reduction in arterial resistance of the fast time-constant compartment at higher doses (−38 ± 4% and −55 ± 2% at 0.05 and 0.10 mg/kg, respectively). Blood flow to the slow time-constant compartment was reduced 36 ± 5% at the highest dose of nifedipine, and central blood volume was increased 66 ± 12 mL. Total systemic venous compliance was unaltered or slightly reduced by each of the four drugs. These results add further evidence to the hypothesis that peripheral blood flow distribution is a major determinant of venous return to the heart.

Analysis of venous flow transients for estimation of vascular resistance, compliance, and blood flow distribution

1987 ◽  
Vol 65 (9) ◽  
pp. 1884-1890 ◽  
Author(s):  
Richard I. Ogilvie ◽  
Danuta Zborowska-Sluis
Keyword(s):  
Time Constant ◽  
Venous Return ◽  
Fast Time ◽  
Slow Time ◽  
Venous Outflow ◽  
Venous Flow ◽  
Time Constants ◽  
Slow Time Constant ◽  
Over Time ◽  
Flow Transients

We analysed venous flow transients using a long venous circuit and right heart bypass in 17 dogs after a rapid decrease in atrial pressure. A biphase curve was obtained which we decomposed into a two-compartmental model, one with a fast time constant for venous return (0.069 min) and 52% of total circulating flow [Formula: see text], and one with a slower time constant (0.456 min) and 48% of [Formula: see text]. Subsequently, separate drainage from splanchnic and peripheral beds (with the renal venous return in the peripheral bed drainage) allowed comparison of time constants and venous outflow in these beds. The sum of the venous outflow volumes over time during separate drainage was indistinguishable from the single biphasic venous outflow volume curve over time observed with a long circuit and single reservoir. The fast time constant of the biphasic curve was not different from that determined by separate drainage from the peripheral circulation. The slow time constant of the single biphasic curve of 0.456 min was hybrid of two time constants, 0.216 min in the splanchnic bed and 0.862 min in the peripheral bed. Separate drainage from peripheral and splanchnic vascular beds demonstrated that the peripheral bed constituted 70% of venous outflow in the fast time constant compartment using Caldini's technique, whereas the splanchnic bed constituted 63% of venous outflow in the slow time constant compartment. It is concluded that, although Caldini's technique demonstrates biphasic venous flow transients, neither the fast nor the slow time constant compartments resolved from this analysis represent a particular anatomical region or vascular bed.

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