核心要点
- 问题/背景
- 这篇 Nature 论文把历史依赖决策从行为偏差推进到全脑细胞分辨率机制层面：动物当前选择并不只由当前刺激决定，近期历史会通过可持续的网络状态改变后续判断。
- 方法/机制
- 作者利用全脑细胞级成像识别出丘脑-脑干层级 attractor network，该网络编码近期历史并驱动 history-biased decisions。它把决策中的短期记忆、状态吸引子和行为偏置放进同一条可观测回路。
- 结果/证据
- 正式收录价值在于它提供了一个清晰的决策记忆机制：历史信息不是松散残留，而是由特定层级网络维持并影响后续动作选择。对仓库的认知科学主线，它补强的是 decision-making、state memory 和 brain-wide coordination 的机制证据。
- 收录价值
- 它不是更高一级，因为证据来自斑马鱼模型，跨物种外推到人类复杂认知仍需谨慎；但作为历史依赖决策的全脑 attractor 机制论文，已达到突破性收录标准。
原始摘要与中文对照
中文对照翻译
自然环境通常是逐渐变化的，因此根据近期过去来偏倚决策是一种适应性行为——这种现象被称为序列依赖。行为期间的大规模记录已发现序列依赖是决策的常见模式，过去经验的神经表征遍布整个大脑。然而，这种偏倚是否源于具有历史特异性计算的专用神经回路仍不清楚。我们利用斑马鱼在执行记忆引导的规避机动时的全脑细胞分辨率成像，识别出一个分层回路，该回路维持过去信息并偏倚未来的选择。背侧丘脑中的离散吸引子编码了最近障碍物的位置，通过持续10-20秒的持续活动维持了分类记忆。对背侧丘脑进行光遗传学操控可以消除或施加序列偏倚。一个下游后脑整合器接收来自丘脑的输入，并将其与当前感觉线索结合，产生反映多试验历史的渐变响应。借鉴斑马鱼的综合脑图谱，我们构建了一个全脑计算模型，该模型再现了行为，并预测了异质性抑制性亚型在实现灵活状态转换中的关键作用。这种吸引子-整合器架构揭示了一种分层和模块化的计算，它将鲁棒的记忆保持与灵活的感觉整合统一起来，为历史偏倚决策提供了一个通用原则。
原始摘要
Natural environments often change gradually, making it adaptive to bias decisions on the basis of the recent past — a phenomenon known as serial dependence . Large-scale recordings during behaviour have identified that serial dependence is a common motif for decision-making, with neural representations of past experiences found throughout the brain . However, it remains unclear whether this bias arises from dedicated neural circuits with history-specific computations. Using whole-brain, cellular-resolution imaging in zebrafish performing memory-guided evasive manoeuvres , we identified a hierarchical circuit that maintains past information and biases future choices. Discrete attractors in the dorsal thalamus encoded the position of the most recent obstacle, maintaining a categorical memory via persistent activity lasting 10–20 s. Optogenetic manipulation of the dorsal thalamus abolished or imposed serial bias. A downstream hindbrain integrator received input from the thalamus and combined it with current sensory cues to produce graded responses reflecting multi-trial history. Leveraging a comprehensive brain atlas in zebrafish , we constructed a whole-brain computational model that recapitulated behaviour and also predicted a key role for heterogeneous inhibitory subtypes in enabling flexible state transitions. This attractor–integrator architecture reveals a hierarchical and modular computation that unifies robust memory retention with flexible sensory integration, providing a general principle for history-biased decisions.