Causal evidence that language models use confidence to drive behaviour

Metacognition—assessing the quality of one’s own cognitive performance—guides adaptive behaviour across species. Confidence signals can be extracted from language model outputs, yet a fundamental question remains: do models actually use these signals to decide whether to answer or abstain? Here we developed a four-phase paradigm. Phase 1 elicited baseline confidence without an abstention option. Phase 2 showed that large language models apply an implicit threshold to internal confidence when abstaining, with confidence effect sizes roughly an order of magnitude larger than alternative mechanisms. Phase 3 provided causal evidence via activation steering: boosting or suppressing confidence correspondingly decreased or increased abstention, with mediation analysis confirming confidence redistribution as the primary mechanism. Phase 4 instructed models to abstain at different confidence levels and showed they adjusted their behaviour accordingly, indicating they read out and act on confidence to set abstention policies. Critically, verbal confidence—an explicit self-evaluation from a separate forward pass—independently predicted abstention across all models despite being less discriminatory of correctness, and activation decoding showed both measures (that is, token log probabilities and verbal confidence) are lossy read-outs of a richer internal representation. Together, these results suggest that abstention is captured by the joint operation of a multidimensional internal confidence representation and threshold-based policies—consistent with structured metacognitive control in large language models—a capacity of growing importance as models transition to autonomous agents that must recognize their own uncertainty. Kumaran et al. show that large language models making decisions on when to answer a question or abstain from answering can be influenced by boosting or suppressing confidence signals in the model.