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Related Experiment Video

Updated: May 16, 2026

Brain Imaging Investigation of the Memory-Enhancing Effect of Emotion

Brain Imaging Investigation of the Memory-Enhancing Effect of Emotion

Published on: May 4, 2011

Emotion as morphofunctionality.

Carlos Herrera Pérez¹, Ricardo Sanz

¹Universidad Politécnica de Madrid, Spain. carl.hr@gmail.com

Artificial Life

|November 29, 2012

Summary

This study proposes a morphofunctional approach to model emotions and design adaptive embodied systems. It suggests emotions emerge from a control system with loosely coupled action and readiness, and patterned metacontrol.

Area of Science:

Robotics and Artificial Intelligence
Control Theory
Computational Neuroscience

Background:

Emotions in animals are linked to physiological changes preparing the body for action.
Existing models often lack a dynamic, structure-function perspective.
Adaptive embodied systems require robust control mechanisms for real-time adaptation.

Purpose of the Study:

To introduce a morphofunctional approach for modeling emotions.
To explore control requirements for adaptive embodied systems.
To investigate the role of changing system dynamics in emotional phenomena.

Main Methods:

Defining morphofunctionality as online changes in system structure and function.
Applying variable structure system (VSS) control theory.

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Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Published on: August 26, 2011

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Last Updated: May 16, 2026

Brain Imaging Investigation of the Memory-Enhancing Effect of Emotion

Brain Imaging Investigation of the Memory-Enhancing Effect of Emotion

Published on: May 4, 2011

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Published on: August 26, 2011

Developing a control architecture with loosely coupled action and action readiness systems.

Investigating patterned metacontrol mechanisms.

Main Results:

Morphofunctional changes alter system dynamics, creating a variable structure system (VSS).
A control setup with loose coupling and patterned metacontrol is proposed.
Emotional phenomena are hypothesized to emerge from this control architecture.

Conclusions:

The morphofunctional approach offers a novel perspective on emotion modeling.
This framework aids in designing more adaptive and biologically plausible embodied systems.
Control theory provides tools to analyze and implement dynamic emotion-like behaviors.