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Related Concept Videos

Forgetting01:21

Forgetting

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Forgetting is an intrinsic aspect of human memory, characterized by the gradual loss or inaccessibility of information over time. Hermann Ebbinghaus, a pioneering psychologist, extensively studied this phenomenon and formulated the forgetting curve. This curve illustrates that memory loss occurs rapidly immediately after learning and then decelerates over time. Several mechanisms contribute to forgetting, including encoding failure, storage decay, retrieval failure, and interference.
Encoding...
391
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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Corrosion of Reinforcement01:27

Corrosion of Reinforcement

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The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
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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.
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Reinforcements in Concrete01:25

Reinforcements in Concrete

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Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
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Fiber Reinforced Concrete01:22

Fiber Reinforced Concrete

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Fiber-reinforced concrete significantly enhances the structural and nonstructural properties of traditional concrete by incorporating fibers like steel, glass, and polymers. These fibers, varying from natural ones such as sisal and cellulose to manufactured ones like polypropylene and Kevlar, are mixed into hydraulic cement with aggregates. Steel fibers, often preferred for their robustness, contribute to improved ductility, toughness, and post-cracking performance. The concrete is classified...
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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Reinforcement Learning With Parsimonious Computation and a Forgetting Process.

Asako Toyama1, Kentaro Katahira1, Hideki Ohira1

  • 1Department of Psychology, Graduate School of Informatics, Nagoya University, Nagoya, Japan.

Frontiers in Human Neuroscience
|May 31, 2019
PubMed
Summary
This summary is machine-generated.

This study reveals that people update action sequence values, not individual actions, in decision-making. Incorporating a forgetting process in computational models significantly impacts understanding model-free and model-based learning contributions.

Keywords:
action sequencecomputational modeldecision-makingforgetting processmodel-based learningreinforcement learning

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychiatry
  • Decision Science

Background:

  • Human decision-making relies on both experience-based (model-free) and knowledge-based (model-based) systems.
  • Multistep decision-making tasks and computational models are crucial for dissociating these systems' contributions.
  • Understanding value-based learning and refining computational models are essential for accurate dissociation.

Purpose of the Study:

  • To investigate value-based learning processes using a multistep decision-making task.
  • To test alternative algorithms for model-free and model-based learning systems.
  • To estimate the contribution of the model-based system in a deterministic environment.

Main Methods:

  • Utilized a multistep decision-making task with a deterministic transition structure.
  • Collected data from 29 participants and fitted computational models with varying assumptions.
  • Employed model comparison and parameter estimation to analyze learning mechanisms.

Main Results:

  • Participants update values for action sequences rather than individual actions.
  • Model fit improved significantly with the inclusion of a forgetting process in learning.
  • Model assumptions, particularly forgetting, substantially influenced the estimated weighting of model-free and model-based systems.

Conclusions:

  • Value-based learning involves updating action sequences, influenced by a forgetting mechanism.
  • Computational model assumptions critically affect the interpretation of model-free and model-based system contributions.
  • Future research should carefully consider forgetting processes when modeling decision-making.