Related Experiment Video
Updated: Jul 17, 2026
07:30
Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum
Published on: March 21, 2019
An event-based account of coordination stability.
Rebecca M C Spencer1, Andras Semjen, Stephanie Yang
1Department of Psychology, University of California, 3210 Tolman Hall No. 1650, Berkeley, CA 94270-1650, USA. rspencer@socrates.berkeley.edu
Psychonomic Bulletin & Review
|January 5, 2007
Summary
Cognitive task goals, not just effector dynamics, constrain bimanual coordination. The complexity of task event structures, like vocalizations during movements, significantly impacts coordination stability.
Area of Science:
- Motor control
- Cognitive psychology
- Human movement science
Background:
- Bimanual coordination is traditionally explained by dynamic interactions between effectors.
- Cognitive representations of task goals may also play a crucial role in motor constraints.
Purpose of the Study:
- To investigate the role of cognitive task goal representation in bimanual coordination.
- To determine if the event structure of task goals influences coordination stability.
Main Methods:
- Participants performed in-phase and anti-phase bimanual movements while vocalizing.
- Vocalization patterns were analyzed for temporal coupling with manual movements.
- Task event structure was manipulated by requiring one or two vocalizations per hand cycle.
Main Results:
- Spontaneous vocalizations revealed different temporal coupling patterns for in-phase versus anti-phase movements.
- Anti-phase movements showed a bias towards two vocalizations per cycle, indicating a more complex event structure.
- Coordination stability decreased when the task's event structure was experimentally made more complex.
Conclusions:
- Cognitive representation of task goals is a fundamental constraint on bimanual coordination.
- The complexity of the event structure within task goals significantly impacts the stability of bimanual movements.
- Future research should consider cognitive factors alongside dynamic interactions in understanding motor control.
Related Concept Videos
01:23Stability of Equilibrium Configuration
Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
01:13Stability of Equilibrium Configuration: Problem Solving
The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...
Problem-solving in the context of the stability of equilibrium configuration...
01:24Pole and System Stability
The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's response.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's response.
01:14Stability of structures
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
01:28Stability
The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...
01:24BIBO stability of continuous and discrete -time systems
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.