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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
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Latent Relations at Steady-state with Associative Nets.

Kevin D Shabahang1, Hyungwook Yim2, Simon J Dennis1

  • 1School of Psychological Sciences, The University of Melbourne.

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Summary

A new Dynamic-Eigen-Net model constructs word meaning online, outperforming topic models and matching word2vec in predicting human associations. This fast-learning network avoids limitations of latent representations in semantic modeling.

Keywords:
Associative netProcess modelRetrieval dynamicsSemantic memoryWords

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Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Current word meaning models like topic models and word2vec use latent representations, which can be slow, prone to interference, and imply dual memory systems.
  • These models condense word-context co-occurrence statistics, organizing words along semantic dimensions but facing limitations with unstructured data.

Purpose of the Study:

  • To develop a novel method for constructing word meaning online during retrieval, overcoming limitations of latent representation models.
  • To introduce and evaluate the Dynamic-Eigen-Net, a recurrent associative network designed for natural language processing.

Main Methods:

  • Implemented a spreading activation account within a one-layer, highly recurrent associative network (Dynamic-Eigen-Net).
  • Utilized a one-hot coded Dynamic-Eigen-Net for processing unstructured text data.
  • Compared the Dynamic-Eigen-Net's performance against topic models, word2vec, and Latent Semantic Analysis (LSA) on predicting human free associations and word similarity.

Main Results:

  • The Dynamic-Eigen-Net demonstrated superior performance compared to the topic model and comparable performance to word2vec in predicting human free associations and word similarity.
  • Latent Semantic Analysis (LSA) showed similar performance when using Shifted Positive Pointwise Mutual Information but poorer predictability for free associations with entropy-based normalization.
  • The Dynamic-Eigen-Net exhibited faster learning rates, reaching asymptotic performance more quickly than word2vec.

Conclusions:

  • The Dynamic-Eigen-Net offers a viable alternative to latent representation models, functioning as a fast learner without susceptibility to catastrophic interference.
  • This model supports a single-store account of memory and constructs word meaning dynamically during retrieval, avoiding issues associated with latent representations.