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Related Concept Videos

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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Reinforcement01:23

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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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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Methods to Test Visual Attention Online
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Switching to online: Testing the validity of supervised remote testing for online reinforcement learning experiments.

Gibson Weydmann1, Igor Palmieri2, Reinaldo A G Simões2

  • 1Programa de Pós-Graduação em Psicologia, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil. gibson.weydmann@ufrgs.br.

Behavior Research Methods
|October 11, 2022
PubMed
Summary

This study validated remote, supervised reinforcement learning (RL) experiments. Researchers can now conduct complex behavioral tasks online, replicating in-person findings with working memory (WM) load effects.

Keywords:
Computational modelingOnline remote experimentReinforcement learning

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Behavioral Research Methods

Background:

  • Online experiments offer alternatives to traditional lab settings.
  • Conducting complex, supervised behavioral research online presents challenges.
  • The COVID-19 pandemic necessitated remote research methodologies.

Purpose of the Study:

  • To assess the computational validity and feasibility of remote, synchronous reinforcement learning (RL) experiments.
  • To detail the adaptation of an in-person behavioral experiment for online, supervised delivery.
  • To demonstrate the replication of working memory (WM) load effects on RL performance in a remote setting.

Main Methods:

  • Utilized open-source software for data collection, statistical analysis, and computational modeling.
  • Developed Python code to simulate computational models of working memory (WM) load's impact on RL.
  • Adapted an existing in-person behavioral task for supervised remote execution.

Main Results:

  • Behavioral data successfully replicated previously observed effects of working memory (WM) load on reinforcement learning (RL) performance.
  • Computational analyses using Python code confirmed the accurate simulation of WM load effects on RL.
  • The study demonstrated the reliability of algorithms and optimization methods in reproducing observed behaviors.

Conclusions:

  • Remote, supervised RL experiments are computationally valid and feasible for complex behavioral research.
  • The methodology allows for the replication of in-person findings in online settings.
  • This approach provides a valuable resource for researchers adapting long and complex experiments for online delivery.