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Related Concept Videos

Operant Conditioning Intervention01:24

Operant Conditioning Intervention

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Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
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Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Reinforcement Schedules01:24

Reinforcement Schedules

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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Behavior Modification01:21

Behavior Modification

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Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
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Related Experiment Video

Updated: Jan 8, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Towards AI-based precision rehabilitation via contextual model-based reinforcement learning.

Dongze Ye1, Haipeng Luo1, Carolee Winstein2

  • 1Computer Science, University of Southern California, Los Angeles, CA, USA.

Journal of Neuroengineering and Rehabilitation
|December 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI system using reinforcement learning to personalize stroke rehabilitation plans. The AI system continuously refines treatment timing, dosage, and intensity for better patient outcomes.

Keywords:
Bayesian modelingDigital twinNeurorehabilitationPatient modelPrecision rehabilitationReinforcement learningStroke

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Area of Science:

  • Neuroscience
  • Rehabilitation Medicine
  • Artificial Intelligence

Background:

  • Stroke recovery varies significantly due to differences in lesions, recovery trajectories, and treatment responses.
  • Precision medicine is crucial for optimizing stroke recovery by tailoring interventions to individual patient needs.
  • Current artificial intelligence (AI) systems lack the capability to personalize and continuously adapt post-stroke rehabilitation plans.

Purpose of the Study:

  • To develop a novel AI-based decision-support system for precision rehabilitation in stroke patients.
  • To personalize sequential treatment plans, including timing, dosage, and intensity, to maximize long-term patient outcomes.
  • To create a system that collaborates with clinicians and patients to customize rehabilitation based on judgment, constraints, and preferences.

Main Methods:

  • Utilized a contextual Markov decision process (CMDP) framework.
  • Developed a novel hierarchical Bayesian model-based reinforcement learning (RL) algorithm, posterior sampling for contextual RL (PSCRL).
  • The algorithm balances exploitation and exploration to discover and adjust near-optimal sequential treatments while respecting constraints.

Main Results:

  • The AI system was implemented and validated using simulations with 150 diverse synthetic patients.
  • The system demonstrated continuous learning from current patient data and a database of past patients via Bayesian hierarchical modeling.
  • The AI-driven adaptive treatments significantly improved functional gains compared to non-adaptive, "one-size-fits-all" approaches.

Conclusions:

  • The developed AI-based precision rehabilitation system shows potential for integration into learning health systems.
  • This novel approach, utilizing contextual model-based RL, can advance personalized stroke rehabilitation.
  • The system's ability to adapt treatments offers a promising direction for improving stroke recovery outcomes.