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Related Concept Videos

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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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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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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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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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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Related Experiment Video

Updated: Nov 2, 2025

Pavlovian Conditioned Approach Training in Rats
06:57

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Published on: February 4, 2016

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A Sentence-Level Joint Relation Classification Model Based on Reinforcement Learning.

Zhen Liu1,2, XiaoQiang Di1,2,3, Wei Song1,2

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.

Computational Intelligence and Neuroscience
|June 17, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel joint relation classification model using reinforcement learning (RL) and a Bi-LSTM attention network to effectively handle noisy data and improve sentence-level relation extraction accuracy.

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Last Updated: Nov 2, 2025

Pavlovian Conditioned Approach Training in Rats
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Published on: February 4, 2016

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Area of Science:

  • Natural Language Processing (NLP)
  • Machine Learning
  • Artificial Intelligence

Background:

  • Relation classification is a key NLP task for understanding semantic relationships.
  • Automatic data generation for training often introduces label noise, hindering performance.
  • Identifying crucial information scattered across sentences presents a significant challenge.

Purpose of the Study:

  • To develop a robust sentence-level joint relation classification model.
  • To address the issue of label noise in large-scale training datasets.
  • To enhance the extraction of important information regardless of its position within a sentence.

Main Methods:

  • A novel model combining a reinforcement learning (RL) agent and a joint network.
  • Utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism for text feature processing.
  • Implementing an attention mechanism to uncover hidden information within sentences.

Main Results:

  • The joint training approach effectively mitigates noise in relation extraction.
  • The model demonstrates improved performance in sentence-level information extraction.
  • Experimental results confirm superior relation classification accuracy at the sentence level.

Conclusions:

  • The proposed joint model successfully tackles data noise in relation classification.
  • The integration of RL and Bi-LSTM with attention enhances semantic understanding.
  • This approach offers a significant advancement for accurate sentence-level relation extraction.