Enterprise AI Analysis
Use and usability: concepts of representation in philosophy, neuroscience, cognitive science, and computer science
Authors: Ben Baker, Richard D. Lange, Andrew Richmond, Nikolaus Kriegeskorte, Rosa Cao, Xaq Pitkow, Odelia Schwartz
Publication Date: 15 Apr 2026 | Source: arXiv:2604.13829v1
Executive Impact Summary
This paper provides a critical framework for understanding 'representation' across diverse scientific fields, offering profound implications for enterprise AI development, deployment, and governance.
Representations play a central role in the study of both biological and artificial intelligence, as well as philosophy of mind. Across neuroscience, computer science, and philosophy, a recurring theme is that representations not only carry information but should be "useful" for or "usable" by an agent in some sense. Here, we review how the "usefulness" of representations has been conceptualized and how it figures into different conceptions of representation. We identify and explore four aspects of use and usability: representations generally carry information; that information may or may not be useful and it may or may not be encoded in a usable format; and the representations may or may not be used downstream. Building on these four aspects of information and use, we then organize existing perspectives on neural representations into three levels: Representations as Information (Level 1); Representations as Usable (Level 2); and Representations as Used (Level 3). Our account is meant to give readers an appreciation for the diversity of notions of "neural representation," help them navigate the vast and multi-disciplinary literature on the topic, and help them clarify the appropriate notion of representation for their own investigations.
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Core Aspects of AI Utility
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Levels of Representation Clarity
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Deep Analysis & Enterprise Applications
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Level 1: Representations as Information
At the foundational Level 1, representations are defined as any internal state that simply carries information about something, regardless of its usefulness, usability, or whether it's actively used downstream. This broad definition allows for the study of mathematical properties of information, statistical dependencies, and the basic grounding of internal states in sensory data. It is widely used in machine learning for basic feature extraction and in neuroscience for identifying neural correlates of stimuli. While permissive, it forms the bedrock for more complex notions of representation, providing insights into raw information content.
Enterprise Relevance: Useful for initial data mapping, understanding raw correlations in AI models (e.g., what features a deep learning layer responds to), and basic anomaly detection without immediate concern for practical application or causal impact.
Level 2: Representations as Usable & Useful
Level 2 advances beyond mere information by introducing the crucial concepts of usefulness and usable format. A representation at this level not only carries information but that information is relevant to the agent's goals and structured in a way that can be feasibly processed by downstream systems (e.g., linearly decodable). This level supports "how possibly" explanations, allowing researchers to explore how systems *could* function to achieve goals. It enables the identification of misrepresentations based on task-relevance or format limitations and is key for designing AI systems with interpretable and actionable internal states.
Enterprise Relevance: Critical for designing AI models whose outputs are directly relevant to specific business tasks (e.g., fraud detection, predictive maintenance) and are formatted for easy integration and consumption by existing enterprise systems or human decision-makers. Focuses on potential, not necessarily actual, use.
Level 3: Representations as Actually Used
The most stringent level, Level 3, defines representations by their actual causal role in influencing downstream functions or behavior. This means an internal state qualifies as a representation only if it demonstrably contributes to the system's actions. This level is essential for "how actually" explanations, moving beyond potential to validated causal impact. It allows for a nuanced understanding of misrepresentation as a causal failure or misuse, and helps in evaluating how representations evolve and are repurposed over time within a system.
Enterprise Relevance: Paramount for deploying and validating mission-critical AI systems, ensuring that model predictions and internal states are not just correlated or useful in theory, but *actually* drive desired operational behaviors and measurable business outcomes. Crucial for compliance, accountability, and real-world performance.
Key Insight: Four Foundational Aspects
4 Core Aspects of RepresentationThe paper identifies four fundamental aspects: representations generally carry information; that information may or may not be useful and it may or may not be encoded in a usable format; and the representations may or may not be used downstream. These underpin the varying definitions of "representation" across fields.
Enterprise Process Flow
Level 1: Information
→
Level 2: Usable Format & Usefulness
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Level 3: Actual Use
The framework organizes existing perspectives on neural representations into three levels, moving from basic information-carrying to actively used systems, offering a clear progression for evaluating AI model utility.
| Evaluation Criteria | Level 1: Information | Level 2: Usable & Useful | Level 3: Actually Used |
|---|---|---|---|
| Primary Focus | Correlation with external states | Potential for goal-relevant action | Causal impact on system behavior |
| Misrepresentation Detection | Not applicable (only information content) | Detects errors based on task relevance or format limitations | Identifies errors due to causal misuse or failure in behavior |
| Enterprise Application | Basic data mapping & feature extraction | Designing models for specific tasks & decodability | Validating models' direct impact on business outcomes |
| Key Question Answered | What information is present? | How could this information be leveraged? | How is this information driving results? |
Collaborative Intelligence in AI Research
This paper itself is a testament to the power of interdisciplinary collaboration, born from a Generative Adversarial Collaboration (GAC) at CCN 2021. By bringing together perspectives from philosophy, neuroscience, and computer science, the authors successfully explicitized tacit assumptions and clarified complex conceptual differences regarding 'representation.' This model of collaboration is highly relevant for enterprises tackling complex AI challenges, where diverse expertises are crucial for comprehensive problem-solving and innovation.
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Your AI Transformation Roadmap
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Phase 1: Conceptual Alignment & Assessment
Conduct a deep dive into your existing AI landscape, aligning current models with the proposed Level 1, 2, and 3 framework. Identify opportunities for enhanced usefulness and usability.
Phase 2: Framework Integration & Pilot
Implement our recommended strategies for designing AI representations that prioritize task-relevance, usable formats, and clear causal roles. Pilot these strategies in a targeted business unit.
Phase 3: Validation, Scaling & Governance
Rigorously validate the causal impact (Level 3) of AI representations on key business outcomes. Establish governance models for continuous improvement and responsible AI deployment.
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