Brain-tuning enhances brain alignment and downstream performance by fine-tuning speech language models with neural recordings. However, previous work relies primarily on fMRI, whose temporal resolution integrates neural activity over seconds, blending distinct processing stages into a single supervision signal and precluding temporally targeted training. We introduce ECoG-tuning, which leverages electrocorticography’s millisecond precision to train speech language models. We design temporally targeted windows—a speech window capturing acoustic-phonetic encoding and a language window capturing higher-order linguistic processing—grounded in neuroscientific findings about temporal encoding hierarchies. Evaluating three models on the Podcast ECoG dataset, we find that ECoG-tuning significantly improves brain alignment over pretrained and distillation baselines. Notably, full spatiotemporal dynamics yield 7–17% higher alignment than time-averaged supervision across models, and language-window tuning produces larger gains in higher-order language regions, indicating that temporal precision provides additional training value. Moreover, ECoG-tuned models consistently improve or maintain downstream performance. Overall, our work provides initial evidence that electrophysiology is a viable brain-tuning modality, demonstrating how neuroscientific insights into processing hierarchies can inform principled model training strategies. Code is available at [https://github.com/Mochizuki-BUPT/ECoG-Tuning-main](https://github.com/Mochizuki-BUPT/ECoG-Tuning-main).

Despite the remarkable generation capabilities demonstrated by large language models (LLMs), the issue of hallucination remains a critical challenge. This is largely attributed to the models’ tendency to fit spurious dependencies in pre-training data rather than underlying causal logic. To address this, from an information-theoretic perspective, this paper proposes a unified contrastive decoding framework based on dynamic pointwise mutual information (Dynamic PMI). Under this framework, we design three fine-grained input transformation strategies targeting context, syntax, and semantics to construct dynamic background distributions. These strategies systematically disentangle and suppress spurious dependencies induced by context priors, lexical co-occurrences, and syntactic structures, thereby guiding the model to prioritize underlying causal logic. Experiments on extensive discriminative and generative benchmarks demonstrate that our method significantly improves the model’s factuality and reasoning robustness. Notably, despite employing a single-model architecture, our framework surpasses state-of-the-art dual-model strategies while maintaining high computational efficiency. Furthermore, the framework exhibits strong cross-model generalizability and effectively alleviates the over-refusal tendency in open-ended generation.