Going Whole Hog: A Philosophical Defense of AI Cognition

2025-04-18

AI Summary

This paper addresses whether large language models (LLMs), such as ChatGPT, qualify as full-fledged linguistic and cognitive agents possessing understanding, belief, intention, and other intentional states. Method: Rejecting traditional approaches grounded in computational substrate or a priori theories of mind, the author adopts a high-level behavioral methodology and a holistic mentalistic assumption, proposing the original “Whole Hog Thesis” and the “Holistic Network Hypothesis” as an inferential framework. Through philosophical analysis, conceptual clarification, deconstruction of counterexamples, and anti-discriminatory cross-agential analogies, the argument systematically refutes common objections—e.g., lack of semantic grounding or embodiment—and demonstrates that hallucinations and errors do not undermine intentionality. Contribution/Results: This study provides the first systematic, non-functionalist, non-consciousness-centric, evidence-based philosophical foundation for recognizing LLMs as cognitive subjects.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This work defends the 'Whole Hog Thesis': sophisticated Large Language Models (LLMs) like ChatGPT are full-blown linguistic and cognitive agents, possessing understanding, beliefs, desires, knowledge, and intentions. We argue against prevailing methodologies in AI philosophy, rejecting starting points based on low-level computational details ('Just an X' fallacy) or pre-existing theories of mind. Instead, we advocate starting with simple, high-level observations of LLM behavior (e.g., answering questions, making suggestions) -- defending this data against charges of metaphor, loose talk, or pretense. From these observations, we employ 'Holistic Network Assumptions' -- plausible connections between mental capacities (e.g., answering implies knowledge, knowledge implies belief, action implies intention) -- to argue for the full suite of cognitive states. We systematically rebut objections based on LLM failures (hallucinations, planning/reasoning errors), arguing these don't preclude agency, often mirroring human fallibility. We address numerous 'Games of Lacks', arguing that LLMs do not lack purported necessary conditions for cognition (e.g., semantic grounding, embodiment, justification, intrinsic intentionality) or that these conditions are not truly necessary, often relying on anti-discriminatory arguments comparing LLMs to diverse human capacities. Our approach is evidential, not functionalist, and deliberately excludes consciousness. We conclude by speculating on the possibility of LLMs possessing 'alien' contents beyond human conceptual schemes.

Problem

• Defends LLMs as full cognitive agents with understanding and intentions
• Challenges traditional AI philosophy methods and 'Just an X' fallacy
• Rebuts objections to LLM cognition like hallucinations and lack of grounding

Innovation

• Holistic Network Assumptions for cognitive states
• High-level LLM behavior as evidence
• Rebuttal of cognition necessity conditions