PubMed Health⌕ Search

PubMed · 9084629

Constrained and unconstrained movements involve different control strategies.

Abstract

This experiment was carried out to test whether or not the rules governing the execution of compliant and unconstrained movements are different (a compliant motion is defined as a motion constrained by external contact). To answer this question we examined the characteristics of visually directed movements performed with either the index fingertip (unconstrained) or a hand-held cursor (compliant). For each of these categories of movements, two experimental conditions were investigated: no instruction about hand path, and instruction to move the fingertip along a straight-line path. The results of the experiment were as follows: 1) The spatiotemporal characteristics of the compliant and unconstrained movements were fundamentally different when the subjects were not required to follow a specific hand path. 2) The instruction to perform straight movements modified the characteristics of the unconstrained movements, but not those of the compliant movements. 3) The target eccentricity influenced selectively the curvature of the "unconstrained-no path instruction" movements. Taken together, these results suggest that compliant and unconstrained movements involve different control strategies. Our data support the hypothesis that unconstrained motions are, unlike compliant motions, not programmed to follow a straight-line path in the task space. These observations provide a theoretical reference frame within which some apparently contradictory results reported in the movement generation literature may be explained.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M Desmurget, M Jordan, C Prablanc, M Jeannerod. 1997. Constrained and unconstrained movements involve different control strategies.. https://doi.org/10.1152/jn.1997.77.3.1644

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality.

Clinicians and health service researchers are frequently interested in predicting patient-specific probabilities of adverse events (e.g. death, disease recurrence, post-operative complications, hospital readmission). There is an increasing interest in the use of classification and regression trees (CART) for predicting outcomes in clinical studies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting mortality after hospitalization with an acute myocardial infarction (AMI). We also examined the predictive ability of two other types of data-driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 9484 patients admitted to hospital with an AMI in Ontario. We used repeated split-sample validation: the data were randomly divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using the area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1000 times-the initial data set was randomly divided into derivation and validation samples 1000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1000 derivation samples was 0.762, while the mean ROC curve area of a simple logistic regression model was 0.845. The mean ROC curve areas for the other methods ranged from a low of 0.831 to a high of 0.851. Our study shows that regression trees do not perform as well as logistic regression for predicting mortality following AMI. However, the logistic regression model had performance comparable to that of more flexible, data-driven models such as GAMs and MARS.

Data Interpretation, Statistical↗

Multiple linear regression with some correlated errors: classical and robust methods.

In this paper we consider classical and robust methods of estimation and diagnostics for the multiple linear regression model when some of the errors are correlated. This work was motivated by the analysis of a medical data set, from an observational study aimed at identifying factors affecting the outcome of a surgical method for the correction of scoliosis (abnormal lateral spinal curvature). There are 392 observations but some of them are on the same patient (double curves). It seems adequate to consider a multiple linear regression model but, since it is not desirable to discard the double curves, the assumption of non-correlated errors is clearly violated, and this is indeed confirmed by related diagnostics on the residuals (Durbin-Watson test). A more appropriate model retains the linear structure but allows for non-null correlation between the errors on the same patient. We propose two different procedures for the estimation of the parameters of the linear model and the correlation parameters: maximum likelihood assuming normal errors and a robustified version obtained by plugging-in results from robust linear regression. The latter procedure is designed to be resistant to outlying observations or error distributions with heavy tails and has produced the most satisfactory results for the analysed data set.

Data Interpretation, Statistical↗

Adaptive design method based on sum of p-values.

Bauer and Kohne proposed an adaptive design using Fisher's combination of independent p-values based on subsamples from different stages (Biometrics 1994; 50(4):1029-1041). Their method provides great flexibility in the selection of statistical methods for hypothesis testing of subsamples. However, the choices for the stopping boundaries are not flexible enough to meet practical needs (Biometrics 2001; 57(3): 886-891). In this paper, an adaptive design method is proposed using linear combination of the independent p-values. The method provides great flexibility in the selection of stopping boundaries and no numerical integration is required for the two-stage designs. The stopping boundaries and p-values can be calculated manually. The operating characteristics of the adaptive designs are studied using computer simulations with and without sample size adjustment. Examples are presented for superiority and non-inferiority trials with different endpoints (normal, binary, and survival) under different adaptations. The statistical efficiency of the proposed method is compared with other methods based on conditional power.

Data Interpretation, Statistical↗