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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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Information Processing Approach01:30

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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
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According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
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Piaget's Theory of Cognitive Development from Childhood into Adulthood01:25

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Jean Piaget's theory of cognitive development emphasizes the role of thinking in a child's learning process, suggesting that children are naturally curious about their environment. His approach to development is discontinuous, proposing that cognitive abilities progress through distinct stages, each with unique characteristics. Central to Piaget's theory is schemata—mental structures that allow individuals to understand and interpret the world.
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Concepts and Prototypes01:24

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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.
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Updated: Sep 10, 2025

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Individual Differences in Student Learning: A Comparison Between the Student Approaches to Learning and

Mark A McDaniel1, Christopher M Wally1, Regina F Frey2

  • 1Center for Integrative Research on Cognition, Learning, and Education (CIRCLE), Washington University in St. Louis, St. Louis, MO 63130, USA.

Behavioral Sciences (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

This study found no direct link between cognitive science

Keywords:
Modified Approaches and Study Skills Inventory (M-ASSIST)Revised Study Process Questionnaire (R-SPQ-2F)abstraction learnerconcept-buildingdeep approachexemplar learnerfunction learningstudent approaches to learningsurface approach

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Area of Science:

  • Cognitive Science
  • Educational Psychology
  • Learning Sciences

Background:

  • Learning is conceptualized as surface or deep.
  • Cognitive science uses concept-building tasks (exemplar/abstraction).
  • Education uses self-report surveys (surface/deep approaches).

Purpose of the Study:

  • To directly compare cognitive science and education frameworks for assessing learning approaches.
  • To investigate links between exemplar/abstraction learning and surface/deep learning approaches.

Main Methods:

  • Two studies were conducted.
  • Self-report data from M-ASSIST and R-SPQ-2F were collected.
  • Objective classifications from a computer-based function learning task were obtained.

Main Results:

  • No significant links were found between exemplar learning and surface approaches.
  • No significant links were found between abstraction learning and deep approaches.
  • Direct comparisons between the two frameworks yielded null results.

Conclusions:

  • Current frameworks for assessing surface and deep learning may not align empirically.
  • Student metacognition, measurement inaccuracies, or content dependencies may explain discrepancies.
  • Future research should focus on reconciling these different assessment approaches.