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Related Experiment Video

Updated: Nov 17, 2025

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Self-incremental learning vector quantization with human cognitive biases.

Nobuhito Manome^1,2, Shuji Shinohara³, Tatsuji Takahashi⁴

¹Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. manome@bioeng.t.u-tokyo.ac.jp.

Scientific Reports

|February 17, 2021

Summary

New self-incremental learning vector quantization (SILVQ) methods leverage human cognitive biases for efficient concept learning. These SILVQ methods improve accuracy with small datasets and reduce parameter tuning, outperforming traditional LVQ algorithms.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Machine Learning
Cognitive Science
Data Science

Background:

Humans possess cognitive biases enabling efficient concept acquisition from limited data.
Learning Vector Quantization (LVQ) is a prototype-based online machine learning technique.
Traditional LVQ methods often require extensive parameter tuning and struggle with complex datasets.

Purpose of the Study:

To develop interpretable self-incremental LVQ (SILVQ) methods inspired by human cognitive biases.
To introduce an automatic learning rate adjustment mechanism incorporating these biases.
To enable SILVQ to self-increase prototypes for adaptive learning.

Main Methods:

Incorporation of human cognitive biases into the LVQ framework.
Development of a novel automatic learning rate adjustment method.
Implementation of a self-incremental prototype increase mechanism within SILVQ.
Evaluation using four real-world and two artificial datasets.

Main Results:

Proposed methods eliminate the need for manual parameter tuning.
Achieved higher accuracy compared to original LVQ algorithms, especially with small datasets.
SILVQ demonstrated comparable or superior accuracy to existing LVQ algorithms with larger datasets.
Successfully learned linearly inseparable concepts without overfitting, using an optimal number of prototypes.

Conclusions:

SILVQ methods offer an interpretable and efficient approach to machine learning.
The integration of cognitive biases enhances LVQ performance and adaptability.
SILVQ provides a robust solution for concept learning across various data sizes and complexities.