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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Beyond performance: A POMDP-based machine learning framework for expert cognition.

Hao He1, Yucheng Duan2,3

  • 1Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, 100084, China.

Behavior Research Methods
|November 25, 2025
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Summary
This summary is machine-generated.

Expertise in sports involves enhanced sensory precision (SP) and adaptive prior belief (pB) calibration, not just skill. This cognitive reorganization allows experts to better anticipate actions under uncertainty.

Keywords:
Cognitive modelingExpertiseMachine learningPrior beliefSensory precision

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Area of Science:

  • Cognitive Science
  • Sports Science
  • Machine Learning

Background:

  • Understanding expert-novice differences is crucial for skill acquisition.
  • Anticipation under uncertainty presents a significant cognitive challenge.

Purpose of the Study:

  • To investigate cognitive parameters differentiating experts and novices in anticipation.
  • To apply partially observable Markov decision process (POMDP) modeling and machine learning for this investigation.

Main Methods:

  • Forty-eight participants (24 experts, 24 novices) performed a basketball anticipation task.
  • POMDP modeling extracted sensory precision (SP) and prior belief (pB) parameters.
  • Machine learning classifiers analyzed the distinctiveness of expert and novice parameter profiles.

Main Results:

  • Experts showed higher SP and more neutral pB compared to novices.
  • Experts' cognitive parameters aligned more closely with the POMDP model.
  • Machine learning achieved >90% classification accuracy distinguishing experts from novices based on SP and pB.

Conclusions:

  • Expertise involves enhanced perceptual filtering (SP) and flexible prior knowledge use (pB).
  • Cognitive reorganization, not just skill increment, defines expertise.
  • The dual-parameter approach provides a model-based view of expert cognition.