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This review synthesizes advances in joint brain-behaviour modeling, highlighting how artificial intelligence reveals shared neural and behavioral structures. Future work should focus on trustworthiness and interpretability for robust scientific and engineering applications.

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Area of Science:

  • Neuroscience and Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning for Brain-Data Analysis

Background:

  • Artificial intelligence (AI) drives significant progress in scientific and engineering fields.
  • Joint brain-behaviour modeling integrates neural and behavioral data to understand brain function.
  • Recent methodological innovations are crucial for advancing this interdisciplinary area.

Purpose of the Study:

  • To synthesize recent advances in joint brain-behaviour modeling.
  • To focus on methodological innovations, scientific motivations, and future research directions.
  • To explore how these models reveal shared brain-behavior structures for science and engineering.

Main Methods:

  • Review of recent literature on joint brain-behaviour modeling.
  • Categorization of modeling approaches into discriminative, generative, and contrastive classes.
  • Discussion of advancements in behavioral analysis, including pose estimation and multimodal-language models.

Main Results:

  • Joint modeling approaches are shaped by discriminative, generative, and contrastive frameworks.
  • Advanced behavioral analysis techniques like pose estimation and multimodal-language models offer new possibilities.
  • The integration of neural and behavioral data provides insights into shared underlying structures.

Conclusions:

  • Joint brain-behaviour modeling offers powerful tools for both scientific discovery and engineering applications.
  • Future innovations should consider model trustworthiness and interpretability alongside performance.
  • Continued development in AI and behavioral analysis will enhance the next generation of joint models.