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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

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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Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...
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Associative Learning

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A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
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A computational developmental model for specificity and transfer in perceptual learning.

Mojtaba Solgi1, Taosheng Liu, Juyang Weng

  • 1Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA. solgi@cse.msu.edu

Journal of Vision
|January 8, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational model explaining how perceptual learning (PL) effects transfer to new situations. The model, inspired by brain function, clarifies both the specificity and transferability of PL through neuronal recruitment and self-organization.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Perceptual learning (PL) is typically stimulus-specific, but recent studies show transfer under certain conditions.
  • Understanding the generalization and abstraction of PL is crucial for learning.
  • Existing models do not fully explain both specificity and transfer of PL.

Purpose of the Study:

  • To present a brain-inspired neuromorphic computational model of visuomotor pathways.
  • To explain the specificity and transfer of perceptual learning.
  • To propose a novel mechanism for transfer effects in PL.

Main Methods:

  • Developed a computational model of Where-What visuomotor pathways.
  • Incorporated sensory and motor inputs for each feature neuron.
  • Used a refined Hebbian-learning rule and lateral competition for autonomous network development and neuronal recruitment.

Main Results:

  • The model successfully explains both specificity and transfer of perceptual learning.
  • Simulations demonstrated transfer of learning across retinal locations in a Vernier discrimination task.
  • Results align with previous psychophysical data from double-training procedures.

Conclusions:

  • The model provides a neurally-plausible explanation for perceptual learning specificity and transfer.
  • Gated self-organization during off-task processes is proposed as the mechanism for observed transfer effects.
  • This work offers the first neurally-plausible model to account for both transfer and specificity in PL.