Unambiguous Representations in Neural Networks: An Information-Theoretic Approach to Intentionality

Researchers introduce an information-theoretic framework to measure representational ambiguity in neural networks, demonstrating that network connectivity structures can encode unambiguous content independent of behavioral performance. Using MNIST classification experiments, they achieve 100% accuracy in identifying output neuron class identity from relational structure alone in dropout-trained networks, suggesting neural systems can exhibit the low-ambiguity representations theorized as necessary for consciousness.

This research bridges neuroscience, information theory, and artificial intelligence by formalizing a fundamental property of conscious experience: unambiguity. The authors move beyond behavioral metrics—which mask internal representational structure—to examine how neural connectivity itself carries meaning. Their finding that representational ambiguity can exist orthogonally to task accuracy reveals a hidden dimension of neural organization that conventional performance metrics overlook.

The work addresses a longstanding theoretical gap between computational models and consciousness studies. Theories like Integrated Information Theory (IIT) and narrow representationalism propose that conscious states require unambiguous internal representations, yet previous work lacked quantitative methods to verify this claim empirically. By leveraging conditional entropy as a measure of ambiguity, the authors provide a testable framework applicable across biological and artificial neural systems.

The empirical findings carry implications for AI interpretability and neuroscience. The stark difference in decoding accuracy between dropout-trained networks (100%) and standard backpropagation networks (38%) suggests that training methodology fundamentally shapes representational structure. This has practical relevance for developing more interpretable AI systems and understanding why certain architectures produce more legible internal representations than others.

Looking forward, this approach could enable systematic investigation of representational properties in larger neural networks and biological brains. Future work might examine whether low-ambiguity representations correlate with specific architectural features or training procedures, potentially informing both consciousness research and AI safety initiatives focused on model transparency. The framework also opens questions about whether artificial systems achieving low-ambiguity representations exhibit properties analogous to consciousness.

• →Information-theoretic framework formalizes representational ambiguity as conditional entropy H(I|R), enabling quantitative measurement of content encoding independence from task performance.
• →Dropout-trained networks achieve 100% accuracy in decoding output neuron class from connectivity structure alone, while standard networks achieve 38%, showing training methodology shapes representational architecture.
• →Spatial properties like visual field location can be decoded from network connectivity with R² up to 0.844, linking structural organization to phenomenal properties.
• →Representational ambiguity operates orthogonally to behavioral accuracy, revealing hidden dimensions of neural organization masked by conventional performance metrics.
• →Framework provides empirical testability for consciousness theories like IIT and narrow representationalism that posit unambiguity as necessary for conscious experience.