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Related Concept Videos

Reinforcement Schedules01:24

Reinforcement Schedules

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,...
Reinforcement01:23

Reinforcement

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.
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Role of Shaping in Operant Conditioning01:19

Role of Shaping in Operant Conditioning

Shaping is a technique used in operant conditioning to train complex behaviors by rewarding successive approximations toward the target behavior. This method is necessary because organisms are unlikely to perform complex behaviors spontaneously. Instead, shaping breaks down the desired behavior into small, manageable steps.
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Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

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Observational Learning01:12

Observational Learning

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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

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Updated: Jun 23, 2026

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

Regulating recognition decisions through incremental reinforcement learning.

Sanghoon Han1, Ian G Dobbins

  • 1Duke University, Durham, North Carolina, USA. sanghoon.han@duke.edu

Psychonomic Bulletin & Review
|May 20, 2009
PubMed
Summary
This summary is machine-generated.

Incremental reinforcement learning significantly impacts recognition memory judgments by altering decision criteria. These changes persist even after feedback removal, showing learning influences memory evaluation.

Related Experiment Videos

Last Updated: Jun 23, 2026

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Memory Research

Background:

  • Recognition memory judgments rely on evaluating past experiences.
  • Feedback mechanisms play a crucial role in refining memory evaluation strategies.

Purpose of the Study:

  • To investigate the influence of incremental reinforcement learning on recognition memory judgments.
  • To examine how manipulating feedback validity and availability affects decision criteria.

Main Methods:

  • Experiment 1: Introduced probabilistic, incorrect feedback for recognition errors (misses or false alarms).
  • Experiment 2: Selectively withheld feedback for specific error types (misses or false alarms).
  • Assessed shifts in recognition memory decision criteria before and after feedback manipulation.

Main Results:

  • Both feedback manipulation methods induced significant shifts in decision criteria for recognition memory.
  • These criterion shifts demonstrated considerable persistence, lasting even after feedback was discontinued.
  • Reinforcement learning mechanisms were shown to alter the caution levels in explicit memory evaluation.

Conclusions:

  • Incremental reinforcement learning plays a key role in shaping recognition memory judgments.
  • The study highlights the adaptive nature of decision-making in memory evaluation based on feedback.
  • Findings suggest that learning processes dynamically adjust the threshold for making recognition memory decisions.