Grounded Computation & Consciousness: A Framework for Exploring Consciousness in Machines & Other Organisms (2409.16036v1)

Abstract: Computational modeling is a critical tool for understanding consciousness, but is it enough on its own? This paper discusses the necessity for an ontological basis of consciousness, and introduces a formal framework for grounding computational descriptions into an ontological substrate. Utilizing this technique, a method is demonstrated for estimating the difference in qualitative experience between two systems. This framework has wide applicability to computational theories of consciousness.

• The paper presents a novel framework that grounds computational models in concrete ontological states to reduce implementational and interpretational ambiguities.
• It introduces state divergence metrics to quantitatively assess qualitative experience differences between systems, enhancing mapping precision.
• The work bridges computational theories like IIT and GWT with physical instantiation, offering a rigorous approach for evaluating conscious phenomena in machines and organisms.

Grounded Computation and Consciousness: A Framework for Exploring Consciousness in Machines and Other Organisms

The paper "Grounded Computation and Consciousness: A Framework for Exploring Consciousness in Machines and Other Organisms" by Ryan Williams addresses a critical aspect of computational modeling in the paper of consciousness. The author argues that while computational methods have been invaluable for understanding various functions of the mind, they are insufficient for fully explaining conscious experience without considering the ontological basis of consciousness.

Primary Contributions

1. Ontology Importance: The paper posits that computational descriptions demand an ontological substrate for a complete account of consciousness. This assertion challenges the computationalist thesis that any system described by algorithm $X$ can be considered conscious, emphasizing the necessity of a physical basis for subjective experiences.
2. Framework for Grounding Computation: Williams proposes a formal framework to ground computational systems within a specific ontology. By fixing mappings and grounding representations to concrete ontological states, computational ambiguity, including implementational and interpretational ambiguity, is significantly mitigated.
3. Estimating Qualitative Experience Differences: A novel method is suggested for estimating qualitative experience differences between systems by employing divergence metrics. These metrics assess the fidelity of representations and inform optimal choices of computational mappings, thus enhancing the granularity and robustness of comparisons between conscious experiences.

Detailed Examination

Computational Accounts of Consciousness

The paper reviews several computational theories of consciousness, such as:

• Global Workspace Theory (GWT), which posits that consciousness arises from the integration of information across distinct modules within the brain.
• Integrated Information Theory (IIT), which quantifies consciousness using a measure $\Phi$, representing the degree of causal interdependence within a system.

Despite their insights, Williams argues these theories lack grounding in an ontological context, thereby failing to fully capture qualitative phenomena.

Ontological Implications and Framework

The crux of Williams' argument lies in the necessity of grounding computational states in concrete ontological realities. The proposed framework involves:

• Choosing specific mappings ($\phi$ and $\gamma$) to represent both the structure and semiotics of system states, respectively.
• Associating these computational descriptions with physical states to reduce ambiguities and ensure a consistent ontological basis.

This approach serves to bridge the gap between abstract computational models and the physical nature of conscious systems.

Computational Ambiguity Addressed

Implementational Ambiguity allows different physical systems to implement the same computation, requiring an assumption that these diverse systems share similar qualitative experiences which is problematic.

Interpretational Ambiguity deals with the multiple valid computational interpretations that can be derived from a single physical system. A significant challenge here is determining which computational descriptions are correct, highlighting the need for grounding.

Formal Framework and Metrics

Williams develops a metric-based approach to assess the equivalence and fidelity of representations. These include:

• State Divergence Metrics ($\mathbb{D}_s$), such as earth mover's distance, to quantify differences between states and transitions.
• Higher-Level Metrics ($\mathbb{D}_L$) that integrate multiple levels of description, providing a holistic view across various scales of computational representation.

The framework also encompasses methods for defining and selecting optimal mappings $\phi^*$ and $\gamma^*$ using divergence measures that align with empirical data and theoretical constraints.

Implications and Future Directions

The practical implications of this research are profound, extending beyond computational theories of consciousness to general AI system design and evaluation. The grounding framework can be applied to ensure that AI systems with claims to conscious-like behaviors are more rigorously scrutinized within a concrete ontological context.

From a theoretical standpoint, this work marks a significant step towards integrating computational and physical theories of mind, potentially informing new models that concurrently address the abstract computational processes and the concrete physical states underpinning consciousness.

Future research might focus on refining these grounding techniques, further empirical validation of qualitative experience estimates, and broadening the application of these principles across diverse AI and biological systems. This could lead to a more comprehensive understanding of consciousness that transcends the limitations of purely computational or purely physical models.

In summary, "Grounded Computation and Consciousness" provides a robust framework for reconciling computational accounts with physical ontology, thus offering a more nuanced and potentially complete understanding of conscious phenomena.