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Related Concept Videos

Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Structural Classification of Joints01:20

Structural Classification of Joints

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.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Concepts and Prototypes01:24

Concepts and Prototypes

The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
Functional Classification of Joints01:09

Functional Classification of Joints

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.
Synarthrosis
An immobile...

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Related Experiment Videos

Strudel: a corpus-based semantic model based on properties and types.

Marco Baroni1, Brian Murphy, Eduard Barbu

  • 1Center for Mind Brain Sciences, University of Trento.

Cognitive Science
|May 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method to extract structured concept descriptions from text, improving psychological plausibility for meaning models. The property-based approach enhances understanding of concept relationships and internal structures.

Related Experiment Videos

Area of Science:

  • Computational linguistics
  • Cognitive science
  • Natural language processing

Background:

  • Current computational models of meaning use "bags of words" or topical dimensions, limiting psychological plausibility.
  • Existing methods struggle to capture the nuanced, structured nature of human concept representation.

Purpose of the Study:

  • To develop a fully automatic method for extracting structured concept descriptions from text.
  • To characterize concepts using weighted properties and property types approximating classical relations.
  • To validate the psychological plausibility of a property-based approach to computational semantics.

Main Methods:

  • Utilized an English part-of-speech-tagged corpus for concept extraction.
  • Developed a method to represent concepts via weighted properties and enriched property types (e.g., hypernymy, function).
  • Evaluated the model on cognitive tasks assessing concept-internal structures and inter-concept relations.

Main Results:

  • The proposed method successfully extracts structured and comprehensive concept descriptions.
  • The property-based model demonstrated superior performance in discovering concept properties and grouping them by type.
  • The model excelled in inter-concept relation tasks, such as clustering into superordinates, outperforming comparable algorithms.

Conclusions:

  • The property-based approach offers a more psychologically plausible model of meaning compared to "bag-of-words" models.
  • This method provides a robust framework for computational semantics, enhancing understanding of concept representation.
  • Empirical validation supports the effectiveness of structured, property-based concept descriptions in computational models.