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February 28, 2026PeerJ Computer ScienceOpen Access

Biologically-inspired emotional processing for adaptive decision-making in non-stationary environments

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Authors

JKJaeyeon KimDKDaihun Kang

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Overview

A computational approach enhances decision-making in shifting environments, suggesting new AI applications.

Key Points

  • This research aims to explore the advantages of biology-inspired emotional processes in AI decision-making under unpredictable conditions.
  • Developed the Emotional-Cognition Integration Architecture (ECIA) with emotion signal analogs.
  • Evaluated ECIA against traditional and advanced algorithms across different environments.
  • Conducted large scale experiments (3,600 runs) to assess performance across various adaptive challenges.
  • ECIA outperformed all baseline algorithms in unpredictable environments (p < 0.001).
  • In stable environments, ECIA showed inferior performance compared to Naive UCB (0.8014 vs 0.8522).
  • Ablation studies revealed critical integration among emotional processing components.

Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/69a287f20a974eb0d3c03c61https://doi.org/10.7717/peerj-cs.3688
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