Do language models show the same content effects humans do?
Do LLMs reproduce human reasoning biases—like believing conclusions based on familiarity rather than logic—across different logical tasks? This matters because converging patterns across independent tasks suggest a fundamental architectural property rather than a task-specific quirk.
Lampinen et al. evaluate three logical reasoning tasks — Natural Language Inference, syllogism validity judgment, and the Wason selection task — and find LMs reproduce the same content-sensitivity patterns humans show. NLI: accuracy depends on whether the believable completion matches the logically correct one. Syllogisms: judgments are biased by whether the conclusion is believable, reproducing Evans et al.'s belief-bias effect where humans endorse invalid syllogisms with believable conclusions roughly 90% of the time. Wason: accuracy improves when the conditional rule is instantiated as a familiar social rule rather than an abstract pattern. Three independent task structures with different logical demands all produce the same content-form entanglement.
The pattern matters because each individual task could be dismissed as a quirk. Three tasks converging on the same signature licenses calling content-form entanglement an architectural property rather than a benchmark artifact. The mechanistic vault notes establish why at circuit level: How do language models perform syllogistic reasoning internally? shows the formal-circuit + world-knowledge-contamination structure that produces belief-bias. This insight contributes the behavioral isomorphism — not just that the circuit produces some kind of contamination, but that the contamination's signature matches human error patterns item-for-item, including continuous response measures (LM token-probability distributions track human reaction times).
This converges with Do large language models reason symbolically or semantically? from the opposite direction. That note shows reasoning collapses when semantics are stripped; Lampinen shows reasoning improves with believable semantics and degrades with unbelievable semantics. Both findings point at the same property: the model is doing something like in-context semantic reasoning, where logical form is one input among others rather than the dominant computational frame. Calling this "reasoning" or "not reasoning" is the wrong question — the right question is what kind of reasoning, and the answer is reasoning that is constitutively content-sensitive, in humans and LMs alike, by the same item-level patterns.
Inquiring lines that read this note 47
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.
Can LLMs distinguish between linguistic form and semantic meaning?- How do LLM biases manifest differently across the three paradigms?
- How does tone sensitivity create systematic informational bias in model responses?
- How do minimal wording changes affect LLM moral reasoning consistency?
- Why do LLMs fall for and deploy logical fallacies with equal confidence?
- Do language models show the same truth bias as humans?
- Which knowledge types do LLMs handle better than humans in reasoning tasks?
- Do newer LLM generations create worse detector bias through increased linguistic divergence?
- What structural differences between human and LLM production create detectable signatures?
- How do different LLM integration paradigms affect inheritance of pretraining biases?
- Do LLMs rely on surface statistical patterns instead of causal structure?
- Can users experience the LLM Fallacy even when AI outputs are completely accurate?
- How does the LLM Fallacy differ from automation bias and cognitive offloading?
- How do LLM biases reflect social classification schemas rather than random errors?
- Do LLMs genuinely internalize human psychological structure or match surface patterns?
- How does rhetorical familiarity bias models toward their own arguments?
- How do LLMs access and draw on the same shared symbolic universe as humans?
- Do LLMs actually reason differently than humans about moral dilemmas?
- How do bimodal decision patterns in LLMs compare to human economic choice?
- How does truth bias in humans compare to face-saving in LLMs?
- Why do users attribute beliefs to LLMs despite uncertainty about their minds?
- Do language models exhibit the same causal biases that humans show?
- Why does hypothesis attestation bias exist separately from frequency bias in NLI?
- Why does optimism bias disappear when LLMs passively observe outcomes?
- How do LLMs default to surface-level strategies instead of genuine mental simulation?
- How does this motivational bias connect to LLMs' causal reasoning failures?
- Why do LLMs inherit causal biases from their training data?
- How might human-LLM teams reinforce each other's causal reasoning mistakes?
Related concepts in this collection 2
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Do large language models reason symbolically or semantically?
Can LLMs follow explicit logical rules when those rules contradict their training knowledge? Testing whether reasoning operates independently of semantic associations reveals what computational mechanisms actually drive LLM multi-step inference.
opposite-direction confirmation: stripping semantics breaks reasoning
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How do language models perform syllogistic reasoning internally?
Does formal symbolic reasoning exist as a distinct neural circuit in LLMs, or is it inevitably contaminated by world knowledge associations? Understanding the mechanism could reveal whether pure logical reasoning is separable from semantic inference.
mechanistic explanation for belief-bias signature
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Language models show human-like content effects on reasoning tasks
- Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey
- Premise Order Matters in Reasoning with Large Language Models
- Large Language Models Can Infer Psychological Dispositions of Social Media Users
- Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
- Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
- Semantic Structure in Large Language Model Embeddings
- Available but Unclaimed: An Empirical Study of Human-AI Synergy
Original note title
content effects in LLMs are behavioral confirmation that semantic content and logical form are not separable in transformer reasoning — across NLI syllogisms and Wason