Human discourse comprehension includes graded expectations about whether a speaker is likely to lie. If language models capture human-like discourse expectations, they should be sensitive not only to factual consistency but also to lie expectancy as a contextual probability from complex pragmatic cues. We test this idea using discourse scenarios with varying incentives to deceive. Human lie probability is estimated from free continuations, and model lie expectancy is derived from the probability mass assigned to human-produced lie versus truth continuations. Across Qwen3 models, likelihood-derived lie mass aligns strongly with human lie expectancy. The best performance comes from the base checkpoints. By contrast, post-trained and mode-specialized variants show weaker alignment. Qualitative analysis suggests a structured error pattern: models tend to overpredict lies when a response directly conflicts with known facts, but underpredict them when lie expectancy depends more on contextual pressures such as politeness, self-protection, or strategic gain. These results suggest that graded lie expectancy is recoverable from model continuation probabilities and can be learned, at least in part, through the ordinary next-token prediction objective.

When the speaker has a motive to lie — profit, face-saving, fear of punishment — an informed reader expects the lie. Forming this expectation requires three distinct abilities: representing what is actually true in the described situation, aligning linguistic expectations with the speaker’s incentives to be truthful, and adapting those expectations across situations. We study whether twelve open Qwen3 and OLMo2 models possess these abilities, asking how each is organized, whether they are separable, and when each is acquired. Within each model, fact information is linearly decodable at mid depth but becomes directly expressed only in the final third of the network; motive recognition emerges slightly later, and both grow with scale. Two-layer ablations dissociate them: removing a mid-depth band of layers weakens fact expression and motive recognition together. Ablating a deeper pair of layers, in contrast, leaves both essentially intact while eliminating human alignment: being sensitive to motives and being sensitive in the way humans are turn out to be separable abilities. Across 19 public checkpoints of OLMo2-7B, fact representation and motive recognition become detectable at around 0.3T tokens and grow steadily throughout pretraining, while human alignment stays non-significant until mid-training — a brief continued training on curated data. Altogether, representing what is true and recognizing whether a speaker is motivated to lie are closely coordinated; matching human judgments about when lying is likely is a later and partially separable achievement.