Can cognitive science methods unlock how LLMs actually work?
Does Marr's three-level framework—developed to understand biological minds—offer interpretability researchers the structured methodology they need to decode opaque language models?
David Marr's framework — the computational level (what abstract problem is the system solving), the algorithmic level (what representations and processes does it use), and the implementation level (what physical mechanisms realize the computations) — has been the backbone of cognitive science for decades. The argument in Levels of Analysis for Large Language Models is that this framework now imports usefully into LLM interpretability, because the field's problem is structurally the same problem cognitive science has had for 70 years: opaque systems whose behavior is interesting and whose internals resist direct inspection.
The historical asymmetry was that cognitive science had a methodology and few systems to study, while AI had many systems and no methodology for understanding them. The asymmetry inverts now. Cognitive science's accumulated toolkit — behavioral probes, implicit association tests, double-dissociation paradigms, representational similarity analysis, causal interventions — was developed for one kind of mind and can be redeployed for another. The methodology was always more general than its initial object.
The Marr framework does specific work in this redeployment. The computational level reframes interpretability questions around the abstract problem the LLM is solving (next-token prediction with learned objectives), independent of how. The algorithmic level surfaces the representations and processes — circuits, features, attention patterns — and the cognitive-architecture question (Newell, Anderson) of which level the algorithms run on. The implementation level connects representations to the artificial neurons that realize them.
Beyond the framework, the deeper claim is that interpretability needs layered analysis rather than monolithic explanation. A complete account of why an LLM does what it does requires all three levels, and the disciplines that have learned to do this work for biological minds are the natural source of the methods.
Inquiring lines that read this note 24
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
What safeguards enable trustworthy AI-assisted scientific peer review at scale? How should designers communicate what AI systems truly are and can do?- What distinguishes genuine cultural understanding from exploited surface-level elimination strategies?
- How does methodological convenience in AI research become implicit ontology?
- How does treating cognition as computation reshape education and work?
- Can mechanistic interpretability reveal how ideologies decompose into simpler features?
- How do mechanistic features compare to natural language for interpretability?
- How do mechanistic interpretability tools help distinguish truthfulness from honesty?
- How do probe-based interventions in activation space compare to mechanistic interpretability approaches?
- Can mechanistic interpretability findings guide practical interventions in model design?
- Do LLMs genuinely internalize human psychological structure or match surface patterns?
- What structural framework prevents LLM explanations from becoming just plausible fiction?
- What cognitive capacities do LLMs actually lack that commentary assumes they have?
- Can quasi-interpretivism bridge functional description to moral status?
- Why do both deflationary and anthropomorphic framings of LLMs persist in research?
Related concepts in this collection 4
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Can we predict where language models will fail?
Does characterizing the abstract computational problem an LLM solves—as a probability machine over sequences—let us predict which tasks it will struggle with systematically, before running experiments?
same paper, the computational-level example
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Can psychology methods reveal what alignment training conceals?
Do indirect cognitive psychology techniques like the IAT expose LLM associations that direct questioning misses because alignment training teaches models to filter verbal responses? This matters for evaluating whether models truly lack biases or simply hide them.
same paper, the algorithmic-level methodology
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Can LLM understanding rely on just representation or causation alone?
Explores whether mechanistic interpretability of language models requires both mapping what is encoded (representational analysis) and testing if that encoding drives behavior (causal analysis), or whether either method suffices alone.
same paper, the implementation-level methodology
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Can computation arise without a conscious mapmaker?
Explores whether algorithms can generate the conscious agent needed to convert continuous physics into discrete symbols, or whether that agent must exist prior to computation itself.
adjacent (tension): challenges substrate-independence assumption underlying Marr's framework
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Levels of Analysis for Large Language Models
- Mechanistic Indicators of Understanding in Large Language Models
- Language Models’ Hall of Mirrors Problem: Why AI Alignment Requires Peircean Semiosis
- Mapping the Emerging Social Science of Large Language Models
- Computational structuralism: Toward a formal theory of meaning in the age of digital intelligence
- Probing Structured Semantics Understanding and Generation of Language Models via Question Answering
- Explain-Query-Test: Self-Evaluating LLMs Via Explanation and Comprehension Discrepancy
- Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning
Original note title
Marr's three levels of analysis provide a structured toolkit for making LLMs interpretable