» Articles » PMID: 19571194

Models for the Extrapolation of Target Motion for Manual Interception

Overview
Journal J Neurophysiol
Specialties Neurology
Physiology
Date 2009 Jul 3
PMID 19571194
Citations 17
Authors
Affiliations
Soon will be listed here.
Abstract

Intercepting a moving target requires a prediction of the target's future motion. This extrapolation could be achieved using sensed parameters of the target motion, e.g., its position and velocity. However, the accuracy of the prediction would be improved if subjects were also able to incorporate the statistical properties of the target's motion, accumulated as they watched the target move. The present experiments were designed to test for this possibility. Subjects intercepted a target moving on the screen of a computer monitor by sliding their extended finger along the monitor's surface. Along any of the six possible target paths, target speed could be governed by one of three possible rules: constant speed, a power law relation between speed and curvature, or the trajectory resulting from a sum of sinusoids. A go signal was given to initiate interception and was always presented when the target had the same speed, irrespective of the law of motion. The dependence of the initial direction of finger motion on the target's law of motion was examined. This direction did not depend on the speed profile of the target, contrary to the hypothesis. However, finger direction could be well predicted by assuming that target location was extrapolated using target velocity and that the amount of extrapolation depended on the distance from the finger to the target. Subsequent analysis showed that the same model of target motion was also used for on-line, visually mediated corrections of finger movement when the motion was initially misdirected.

Citing Articles

Humans Can Track But Fail to Predict Accelerating Objects.

Kreyenmeier P, Kammer L, Fooken J, Spering M eNeuro. 2023; 9(5).

PMID: 36635938 PMC: 9469915. DOI: 10.1523/ENEURO.0185-22.2022.


When intercepting moving targets, mid-movement error corrections reflect distinct responses to visual and haptic perturbations.

Gonzalez Polanco P, Mrotek L, Nielson K, Beardsley S, Scheidt R Exp Brain Res. 2022; 241(1):231-247.

PMID: 36469052 PMC: 10440829. DOI: 10.1007/s00221-022-06515-3.


Gravity and Known Size Calibrate Visual Information to Time Parabolic Trajectories.

Aguado B, Lopez-Moliner J Front Hum Neurosci. 2021; 15:642025.

PMID: 34497497 PMC: 8420811. DOI: 10.3389/fnhum.2021.642025.


Expectations affect the perception of material properties.

Alley L, Schmid A, Doerschner K J Vis. 2020; 20(12):1.

PMID: 33137175 PMC: 7645227. DOI: 10.1167/jov.20.12.1.


Sensorimotor delays in tracking may be compensated by negative feedback control of motion-extrapolated position.

Parker M, Weightman A, Tyson S, Abbott B, Mansell W Exp Brain Res. 2020; 239(1):189-204.

PMID: 33136186 PMC: 7884356. DOI: 10.1007/s00221-020-05962-0.


References
1.
Leung H, Kettner R . Predictive smooth pursuit of complex two-dimensional trajectories demonstrated by perturbation responses in monkeys. Vision Res. 1997; 37(10):1347-54. DOI: 10.1016/s0042-6989(96)00287-8. View

2.
Viviani P, Cenzato M . Segmentation and coupling in complex movements. J Exp Psychol Hum Percept Perform. 1985; 11(6):828-45. DOI: 10.1037//0096-1523.11.6.828. View

3.
Buneo C, Soechting J, Flanders M . Muscle activation patterns for reaching: the representation of distance and time. J Neurophysiol. 1994; 71(4):1546-58. DOI: 10.1152/jn.1994.71.4.1546. View

4.
Brouwer A, Middelburg T, Smeets J, Brenner E . Hitting moving targets: a dissociation between the use of the target's speed and direction of motion. Exp Brain Res. 2003; 152(3):368-75. DOI: 10.1007/s00221-003-1556-8. View

5.
Kramer K, Stubberud S . Analysis and implementation of a neural extended Kalman filter for target tracking. Int J Neural Syst. 2006; 16(1):1-13. DOI: 10.1142/S0129065706000457. View