Week 2 Flashcards
What are the main motor control models?
Look up table model
Reflex based model/sherringtons hypothesis
Equilibrium point hypothesis
Direct cortical control model
- Describe the look up table model?
(What is it, experiment causing rejection, hypothesis on results, actual results and why it was rejected?)
Proposes the brain stores precomputed muscle force patterns for specific movements and retrieves them when needed.
No experiment, just issues/rejection based off computational and storage impracticality.
The hypothesis states the brain can store and retrieve vast numbers of precomputed movements
Rejected due to complexity, storing all movements, velocities and adjustments is beyond the brain capacity, it also cant adapt to changes
- Describe Reflex based model (Sherringtons hypothesis) model?
(What is it, experiment causing rejection, hypothesis on results, actual results and why it was rejected?)
Movements arise from combining reflexes like the stretch reflex, controlled by sensory feedback.
Taub and Berman (1968): Deafferented monkeys (with sensory feedback disabled) could still reach visual targets.
Movements are generated by reflexes driven by sensory feedback. Gamma motor neurons initiate movement before alpha motor neurons.
Deafferented monkeys could reach accurately, indicating movement is not entirely reflex-driven. No gamma motor neuron lead observed in Vallbo’s (1970) study of voluntary contractions.
- Describe the Equilibrium Point Hypothesis (EPH) model?
(What is it, experiment causing rejection, hypothesis on results, actual results and why it was rejected?)
Proposes the brain controls a virtual equilibrium point (desired position) to guide movements, not caring about intermediate forces.
Lackner and DiZio (1994): Coriolis force experiments showed that trajectories mattered and were affected by forces during movement.
The brain only cares about the final equilibrium point, not the trajectory or forces during movement. Movements should remain straight under perturbations, with accurate final positions.
Subjects showed curved trajectories during Coriolis force application, contrary to predictions. Endpoint accuracy errors persisted, refuting the claim that only final positions matter.
- Describe the Direct Cortical Control model?
(What is it, experiment causing rejection, hypothesis on results, actual results and why it was rejected?)
Suggests that the brain plans entire movement trajectories instead of just specifying an endpoint.
Validation - Emerged as a response to failures of EPH in explaining trajectory planning and adaptation.
The brain computes and controls the entire movement trajectory in advance.
This model has not yet been explicitly rejected and is supported by trajectory-sensitive experiments.
What does the direct cortical control model extend into ?
Optimal Control Model of Reaching, which has 4 main properties:
Minimum jerk model.
Minimum energy model.
Minimum uncertainty model (signal-dependent noise).
Optimal feedback control.
- Key concepts of the Optimal Control Model of Reaching and its properties?
The brain minimizes or maximizes a specific cost/benefit associated with movement.
Minimum jerk model = smoothness: The brain minimizes jerk (rate of change of acceleration) to produce smooth, bell-shaped velocity profiles.
Minimum torque/force = energy: The brain minimizes changes in torque or energy expenditure during movement, accommodating forces like inertia
SIgnal dependent noise = uncertainty: Movements are optimized to minimize noise generated by larger or faster control signals, reducing variability and ensuring accuracy.
Optimal Feedback Control: The brain stores policies (rules for action based on the body’s state) rather than precomputed commands, allowing for robustness, real time adjustments and no planning. Flexible based on the task.
What is the most dominant model of motor control?
Optimal feedback control, incorporating signal-dependent noise as it is adaptable and explains variability.
Evolution of Models?
From simple reflex- and endpoint-based models to the complex optimal feedback control model.
- What is optimal control and how the cost is defined?
Optimal control is a mathematical approach used to find a control policy or strategy that will minimize/maximize a certain objective, often referred to as a “cost” or “performance” function, over time. It is typically applied in systems where a decision-maker (controller) influences the state of the system by adjusting variables (controls) in response to its current state.
The cost function represents the “price” or “penalty” of a system’s behavior. In an optimal control problem, the goal is to find the strategy that minimizes this cost (e.g., fuel usage or time) over time while achieving the desired outcome.
- What is signal-dependent noise and how that is related to the uncertainty / variability of movement?
Signal-dependent noise refers to noise that increases with the strength or intensity of the signal. In the context of movement, this means that as the force or effort of a movement increases, the variability or uncertainty in the movement also grows. Essentially, stronger actions tend to lead to greater uncertainty or error in the outcome.
- What is optimal feedback control and how the feedback system works?
Optimal feedback control adjusts control inputs based on the system’s current state to minimize a cost function over time. It uses feedback to correct actions, guiding the system toward the desired goal while handling disturbances.
The feedback system measures the current state, compares it to the desired state, and adjusts control inputs to optimize performance and reduce errors in real-time