Metacognition, the ability to assess one's own cognitive performance, is a fundamental capability documented across species where internal confidence estimates guide adaptive behavior. This research investigates whether Large Language Models (LLMs) actively utilize confidence signals to regulate their behavior through a four-phase abstention paradigm. The study first establishes internal confidence estimates without abstention options, then reveals that LLMs apply implicit thresholds to these estimates when deciding whether to answer or abstain. Findings demonstrate that confidence serves as the dominant predictor of behavior, with effect sizes an order of magnitude larger than knowledge retrieval accessibility or semantic features. Causal evidence is provided through activation steering experiments, where manipulating internal confidence signals correspondingly shifts abstention rates, demonstrating a direct causal relationship between confidence estimation and behavioral regulation.