Self-organizing high-order cognitive functions in artificial agents: implications for possible prefrontal cortex
Michail Maniadakis1, Panos Trahanias, Jun Tani
1Brain Science Institute, RIKEN, Wako-Shi, Saitama, Japan. mmaniada@ics.forth.gr
Summary
This study models executive control in artificial neural networks, finding that behavioral rules encoded in neuro-dynamic attractors influence confidence. This offers insights into brain mechanisms for adapting to changing circumstances.
Area of Science:
- Cognitive Neuroscience
- Artificial Intelligence
- Computational Neuroscience
Background:
- The prefrontal cortex (PFC) is crucial for executive control, enabling adaptation, context switching, and confidence monitoring in dynamic environments.
- The precise neural mechanisms underlying executive control and confidence remain incompletely understood.
- Investigating artificial cognitive systems can provide insights into biological cognitive functions.
Purpose of the Study:
- To explore mechanisms of executive control by implementing them in artificial cognitive systems.
- To model the self-organization of artificial neural networks in a rule-switching task analogous to the Wisconsin Card Sorting Test.
- To investigate how behavioral rules are encoded and how confidence is shaped within these systems.
Main Methods:
- Utilized artificial neural networks to simulate a robotic rule-switching task.
- Employed computational modeling to study the self-organization of network dynamics.
- Analyzed neuro-dynamic attractors and their geometric arrangements in phase space.
Main Results:
- Demonstrated that behavioral rules can be encoded in neuro-dynamic attractors within the artificial neural networks.
- Found that the geometric arrangement of these attractors influences the shaping of confidence.
- Observed emergent dynamical structures offering potential explanations for brain processes.
Conclusions:
- Behavioral rules and confidence may arise from the self-organization of neuro-dynamic attractors.
- The study provides a computational framework for understanding executive control and confidence monitoring.
- Findings suggest a link between high-level cognitive functions and low-level neural dynamics in the brain.
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