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Related Concept Videos

Structural Joints: Synovial Joints01:16

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
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As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
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Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
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Fibrous Joints Are Immovable
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Psychology is the scientific discipline dedicated to understanding both observable behavior and the internal mental processes underlying such behavior. It aims to comprehend human nature and apply this understanding to solve practical problems, enhance well-being, and improve societal outcomes. An example of psychology's application is the study of prosocial behavior, such as why and under what conditions individuals might help strangers in need. This process involves describing observed...
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Psychology, as a scientific discipline, aims to understand the mind and behavior through rigorous and systematic methods. The foundation of psychological research is evidence-based, relying heavily on the scientific method to derive and validate knowledge. This structured approach ensures that findings are reliable, valid, and applicable to broader contexts.
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Obtaining psychological embeddings through joint kernel and metric learning.

Brett D Roads1,2, Michael C Mozer3,4

  • 1Department of Computer Science, University of Colorado Boulder, Boulder, CO, 80309-0430, USA. brett.roads@colorado.edu.

Behavior Research Methods
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Summary

This study introduces PsiZ, a Python package for creating psychological embeddings efficiently. It offers flexible data collection and advanced modeling for better insights into human perception.

Keywords:
Active learningCognitive modelingPsychological embeddingPython packageSimilarity kernel

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Area of Science:

  • Cognitive Science
  • Computational Psychology
  • Machine Learning

Background:

  • Psychological embeddings map human-perceived similarity but are costly to obtain.
  • Existing methods require significant resources for algorithm design and participant compensation.

Purpose of the Study:

  • To advance psychological embedding techniques for easier and more efficient high-quality embedding inference.
  • To provide a comprehensive software package, PsiZ, for practical application.

Main Methods:

  • Developed an embedding procedure supporting diverse trial configurations (e.g., triplets, sorting).
  • Implemented a likelihood model for three similarity kernel classes with gradient descent parameter inference.
  • Introduced an active selection algorithm to optimize data collection efficiency.
  • Enabled group-specific attention weight parameters within the likelihood model.

Main Results:

  • Demonstrated the effectiveness of multiple trial configurations for similarity judgment collection.
  • Showcased efficient parameter inference using gradient descent for various similarity models.
  • Validated the active selection algorithm's ability to reduce data collection costs.
  • Highlighted the impact of group-specific attention weights on embedding quality.

Conclusions:

  • PsiZ offers a powerful and complete toolkit for inferring psychological embeddings.
  • The integrated approach enhances efficiency and accessibility in psychological embedding research.
  • The software package is publicly available on GitHub for broader adoption.