Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic...

Area of Science:

• Neuroscience and Robotics
• Computational Psychiatry
• Developmental Psychology

Background:

• Neurodevelopmental disorders research yields fragmented knowledge across molecular, neural, computational, and behavioral domains.
• Heterogeneity in developmental pathways complicates understanding of clinical phenotypes.
• Distinguishing primary causes from consequences in developmental learning processes is challenging.

Purpose of the Study:

• To review developmental neurorobotic experiments addressing the complex dynamics of neurodevelopmental disorders.
• To focus on neurorobotic models employing predictive processing for studying developmental disorders.
• To explore how these models can integrate diverse characteristics and developmental learning.

Main Methods:

• Constructing neurorobotic models incorporating predictive processing for learning, perception, and action.
• Simulating integrated causal relationships between neurodynamics, computation, and behavior in robot agents.
• Considering developmental learning processes within the neurorobotic framework.

Main Results:

• Neurorobotic models can simulate the formation of integrated causal relationships.
• The predictive processing framework can link neurobiological hypotheses (e.g., excitation-inhibition imbalance) with computational accounts (e.g., uncertainty encoding) and clinical symptoms.
• This approach offers a method to connect fragmented findings in neurodevelopmental disorder research.

Conclusions:

• Developmental neurorobotics provides a complementary framework for integrating knowledge on neurodevelopmental disorders.
• This approach can help overcome the challenge of heterogeneity in developmental pathways and clinical presentations.
• Neurorobotic models offer a promising avenue for a unified understanding of neurodevelopmental disorders.