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Modelling human memory: connectionism and convolution

G D Brown1, C Hulme, P Dalloz

  • 1Department of Psychology, University of Warwick, Coventry, UK.

The British Journal of Mathematical and Statistical Psychology
|May 1, 1996
PubMed
Summary
This summary is machine-generated.

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Convolution, a mathematical operation, is key to new human memory models. This study shows a connectionist-like network can perform single-trial learning like convolution, offering improved retrieval and linking both approaches.

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Convolution is an associative mechanism in influential human memory models.
  • Convolution enables associating vectors into memory traces for retrieval.
  • Current connectionist models struggle with one-trial learning, limiting their use in memory research.

Purpose of the Study:

  • To demonstrate a connectionist-like architecture capable of single-trial learning.
  • To link convolution and connectionist approaches in memory modeling.
  • To develop a memory model with the benefits of both connectionist and convolution methods.

Main Methods:

  • A connectionist-like architecture was trained using a gradient-descent algorithm.
  • The network learned to perform single-trial learning, mimicking convolution.

Related Experiment Videos

  • The study analyzed the network's retrieval variability and its ability to learn convolution.

    • Main Results:

    • The connectionist-like architecture successfully performed single-trial learning.
    • The network's retrieval was less variable compared to standard convolution.
    • The network demonstrated the capacity to learn the convolution operation itself.

    Conclusions:

    • A unified approach combining connectionist and convolution principles is feasible for memory modeling.
    • This architecture offers a potential solution for one-trial learning in connectionist models.
    • The findings bridge connectionist and convolution memory models, enhancing their applicability.