The Mosaic Mind Theory of Inner Monologues: A Framework for Contextually Adaptive Multi-Agent AI Systems

Author: Matthew H. Maxwell, DBA, MS

Abstract

The Mosaic Mind Theory posits that human cognition is characterized by multiple internal “self-states” that adapt to specific contexts. While originally developed in clinical psychology, this framework provides a novel blueprint for constructing multi-agent AI systems capable of context-sensitive decision-making. By mapping self-states to specialized computational agents, these systems can dynamically switch between modules to handle diverse tasks. Drawing on theories from Dialogical Self Theory (Hermans, 2001), Schema Therapy (Young, Klosko, & Weishaar, 2003), and predictive processing (Wilkinson, Linson, & Friston, 2019), this work outlines a modular architecture for AI that leverages Bayesian inference (Hohwy & Michael, 2017) and ecological momentary assessment methods (Kross, Ayduk, & Mischel, 2014) for empirical validation. The proposed framework promises enhancements in adaptability, resilience, and user engagement for cognitive systems.

Keywords

Multi-agent systems; Contextual alignment; Predictive processing; Cognitive architecture; AI companion

Highlights

  • Proposes mapping human self-states to specialized AI agents.
  • Integrates Bayesian inference for dynamic agent switching.
  • Outlines empirical validation strategies for cognitive architectures.

1. Introduction

Building AI systems that adapt seamlessly to changing contexts remains a critical challenge. Conventional monolithic models often encounter difficulties when faced with domain shifts, ambiguous user inputs, or conflicting objectives (Dulberg et al., 2023). In contrast, the Mosaic Mind Theory—which conceptualizes human cognition as comprising multiple, context-dependent self-states (Hermans, 2001)—offers a natural model for modular, multi-agent AI architectures. This paper presents a framework that leverages the theory to develop AI systems capable of context-sensitive decision-making and robust performance across varied environments.

2. Theoretical Framework

2.1 Foundations of the Mosaic Mind Theory

The Mosaic Mind Theory originates from psychological models that depict the self as a collection of interacting “voices” or subpersonalities (Hermans, 2001; Young, Klosko, & Weishaar, 2003). These internal states are thought to emerge in response to diverse situational demands, thereby facilitating adaptive behavior. Translating this concept to AI, each self-state can be mapped to a specialized module—enabling the system to invoke the most appropriate agent based on real-time contextual cues (Kross, Ayduk, & Mischel, 2014).

2.2 Adaptive Modularity and Contextual Alignment

Adaptive modularity in human cognition allows rapid role switching and nuanced responses to complex environments (Snyder, 1974; Singer, 1975). In AI, multi-agent systems replicate this behavior by distributing tasks among distinct modules (Dulberg et al., 2023). Contextual alignment is achieved by embedding mechanisms—such as user intent classifiers and sensor-driven environment trackers—that enable the AI to dynamically select the most relevant agent for a given task.

2.3 Predictive Processing and Bayesian Inference

The predictive processing framework contends that the brain continuously updates its internal models to minimize prediction errors (Wilkinson, Linson, & Friston, 2019). By equipping each AI module with Bayesian models (Hohwy & Michael, 2017), the system can evaluate predictive errors and switch to the module that best fits the current context. This mechanism ensures optimal performance even when conditions change rapidly.

3. System Architecture and Agent Taxonomy

3.1 Multi-Agent Architecture

The proposed architecture consists of multiple specialized agents, each designed to handle a specific set of functions. For example, “Protective Agents” monitor for anomalies, while “Performance-Driven Agents” focus on goal optimization. “Social-Relational Agents” facilitate user interactions, and “Creative Agents” support innovation and open-ended problem solving. A regulatory module (Mediator) oversees the coordination and integration of outputs from these agents.

3.2 Taxonomy of Agents

  • Protective Agents: Responsible for risk detection and anomaly monitoring.
  • Performance-Driven Agents: Optimize task execution and resource allocation.
  • Social-Relational Agents: Manage user communication and engagement.
  • Creative/Exploratory Agents: Generate novel solutions and ideas.
  • Regulatory Agents: Coordinate inter-agent interactions and resolve conflicts.
  • Contextual-Cultural Agents: Adapt system behavior based on cultural and domain-specific norms.

4. Empirical Validation Approaches

To verify the effectiveness of the proposed modular AI framework, several validation methodologies are recommended:

  • Simulation Studies: Virtual environments with dynamic conditions will test the system’s ability to switch agents effectively (Dulberg et al., 2023).
  • Ecological Momentary Assessments: Real-world deployments collecting high-frequency user interaction data will evaluate context alignment (Kross, Ayduk, & Mischel, 2014).
  • Longitudinal Field Studies: Extended observation periods will determine the system’s learning and adaptation over time (Costa & McCrae, 1992).
  • Cross-Context Benchmarking: Testing across multiple domains (e.g., healthcare, finance) will assess transfer learning capabilities.

5. Discussion

The integration of Mosaic Mind Theory with multi-agent system design offers a promising avenue for developing cognitive architectures that are both adaptive and resilient. By mirroring the modularity of human cognition, the proposed system can flexibly respond to varying contextual demands. However, challenges remain in achieving seamless coordination among agents and ensuring ethical transparency in system decisions. Future research should focus on refining the regulatory mechanisms and exploring advanced methods for dynamic agent selection.

6. Conclusion

Reconceptualizing AI architectures through the lens of Mosaic Mind Theory yields a novel framework for contextually adaptive, multi-agent systems. By assigning specialized roles to individual agents and employing Bayesian predictive models, the system can robustly navigate dynamic environments. This approach not only enhances performance and user engagement but also contributes to our understanding of cognitive processes in both natural and artificial systems. Ongoing empirical validation and interdisciplinary research will be essential to fully realize this potential.

Declaration of Generative AI and AI-assisted Technologies in the Writing Process

During the preparation of this work, the authors used OpenAI for language editing purposes. All content was reviewed and edited by the authors, who take full responsibility for the final manuscript.

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