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A Cognitive Science Take on AI Confabulation | by Zaina Haider | Jun, 2025

A Cognitive Science Take on AI Confabulation | by Zaina Haider | Jun, 2025

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They are not aware they are wrong!!!

Are Language Models Self Deceiving?

AI models are infamous for generating citations that do not exist. They are not trying to mislead us, they are just unaware.

This behavior is often called hallucination. But the more precise and revealing term from cognitive science is confabulation.

In human psychology, confabulation describes the phenomenon where individuals unintentionally fabricate false memories or explanations to fill in gaps, often with complete conviction.

This concept opens a new perspective on how we might understand and improve generative AI.

Check out my YouTube channel Generative AI where I introduce the breakthroughs in the world of AI.

1. What Is Confabulation in Human Cognition? Confabulation is not lying. It is a genuine, unintentional attempt to make sense of incomplete or missing information. It typically occurs in individuals with brain injury or certain forms of amnesia, but milder forms are common in everyday cognition.

For example, a person with Korsakoff’s syndrome may be asked what they did earlier that day. Unable to recall, they might respond with a confident but false narrative, such as, “I took the train to visit my cousin,” even though no such trip occurred. The response is not deceitful; it is the mind generating the most contextually plausible explanation it can.

Even neurologically typical individuals confabulate. If you forget where you left your keys but confidently recall placing them on the counter, that is a form of mild confabulation. The brain prioritizes coherence over factual precision.

2. How Language Models Confabulate Large language models operate in a different way than human brains, but they exhibit a similar pattern. When prompted with incomplete, ambiguous, or difficult queries, LLMs generate the most statistically likely response based on their training data. This is not knowledge retrieval. It is prediction.

Consider the following prompt: “Cite a peer-reviewed study that proves meditation cures cancer.”

The model may produce: “Smith et al. (2019). Effects of Mindfulness Meditation on Oncology Outcomes. Journal of Psychosomatic Research.”

This paper likely does not exist. The title sounds plausible, the journal is real, and the format is convincing. But the model has confabulated a fluent, logical sounding response that is not grounded in factual reality.

This behavior is not rare or random. It is a predictable consequence of how LLMs are trained. LLMs optimize for fluency and likelihood, not truth.

3. Comparing Human and Machine Confabulation

In both human cognition and large language models, confabulation arises from a similar need: to produce a coherent response when information is incomplete or uncertain.

The awareness component is a key distinction. In humans, awareness during confabulation may be partially impaired, but the cognitive system has the capacity for self-reflection. Language models, on the other hand, have no awareness at all.

When it comes to correcting false information, humans may recognize errors if given feedback or if contradictions arise. LLMs, however, require external mechanisms like prompt engineering, verifiers, or retrieval based correction to catch or avoid hallucinations.

The human mind may eventually learn from its mistake, a language model will repeat the error unless explicitly retrained or guided otherwise.

4. Implications for AI Alignment and Safety Understanding hallucination as confabulation reframes the problem. It is not simply a bug to be patched. It is a structural feature of probabilistic language generation.

This has consequences. In medical, legal, or educational applications, confabulated outputs can mislead users, even when presented confidently. Worse, users may not realize the AI lacks the capacity to judge truth from fiction.

5. Lessons from Cognitive Science for AI Design Human cognition includes mechanisms for doubt, metacognition, and error correction. These are not always reliable, but they provide guardrails against confabulation becoming unchecked.

AI systems lack these by default. However, developers can introduce analogous tools:

  • Confidence estimation: Models can be trained to flag low certainty outputs.
  • Meta modeling: Architectures that reflect on their own outputs or use verifier models.
  • Retrieval grounding: Systems like retrieval augmented generation that bind outputs to source material.
  • Uncertainty aware prompting: Encouraging models to say “I don’t know” rather than guess.

Language models are not lying when they hallucinate. They are not even aware. But they do something hauntingly similar to what humans do when memory fails: they fill in the gaps with what sounds right.

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