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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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Related Experiment Video

Updated: Jan 9, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Using dynamic Bayesian optimization to induce desired effects in the presence of motor learning: a simulation study.

GilHwan Kim1, Haider Ali Chishty1, Fabrizio Sergi1,2

  • 1Department of Mechanical Engineering, University of Delaware, Newark, DE, USA.

Computer Methods in Biomechanics and Biomedical Engineering
|December 3, 2025
PubMed
Summary

Dynamic Bayesian optimization (DBO) effectively optimizes human-in-the-loop control systems, outperforming standard Bayesian optimization (BO) in simulations involving motor learning. DBO shows promise for adaptive device control when sufficient data is gathered.

Keywords:
Human-in-the-loopbayesian optimizationmachine learningmotor learning

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Area of Science:

  • Robotics and Human-Computer Interaction
  • Computational Neuroscience and Motor Learning

Background:

  • Human-in-the-loop optimization (HILO) is crucial for adaptive device control.
  • Motor learning introduces dynamic changes in user output during optimization.
  • Standard Bayesian optimization (BO) may struggle with time-varying user responses.

Purpose of the Study:

  • To evaluate dynamic Bayesian optimization (DBO) for HILO of device control inputs.
  • To assess DBO's suitability when user output changes due to motor learning.
  • To compare DBO against standard BO in simulated adaptive control scenarios.

Main Methods:

  • Simulations using time-dependent participant responses.
  • Simulations incorporating state-space models of motor learning.
  • Comparative analysis of DBO and standard BO convergence rates.

Main Results:

  • DBO demonstrated superior convergence to optimal inputs and outputs compared to standard BO after a set number of iterations.
  • DBO's performance advantage increased with sufficient iterations to differentiate learning from variability.
  • Simulations confirmed DBO's efficacy in handling dynamic user responses.

Conclusions:

  • Dynamic Bayesian optimization (DBO) is a suitable algorithm for HILO in adaptive device control.
  • DBO offers improved performance over standard BO when dealing with motor learning and dynamic user states.
  • Effective implementation of DBO requires sufficient iterations for accurate learning assessment.