感知
认知科学
计算机科学
等级制度
动力学(音乐)
人口
人工智能
认知心理学
任务(项目管理)
知觉
计算模型
过程(计算)
优雅
同种类的
自然(考古学)
认知
决策过程
机器学习
心理学
分类
决策论
因果模型
钥匙(锁)
人机交互
感觉系统
管理科学
概率逻辑
人工神经网络
前馈
可解释性
统计模型
计算
抽象
运动(物理)
口译(哲学)
点(几何)
作者
Chandramouli Chandrasekaran,Diksha Gupta,Mitra Javadzadeh,Tian Wang,Miguel Vivar Lazo,Tatiana Engel,Paul Cisek,Christopher R. Fetsch
标识
DOI:10.1523/jneurosci.1633-25.2025
摘要
Perceptual decision-making is the process by which sensory evidence is combined with prior knowledge and transformed into possible movement plans according to a rule or policy. Classic studies suggested that perceptual decisions emerge from a feedforward hierarchy of brain areas with distinct functions and fairly homogeneous neural representations. However, more recent findings argue that decisions emerge from distributed, recurrent computations across many brain areas (a “heterarchy”) with complex, heterogeneous representations. How can we make sense of these findings in a way that preserves the computational elegance of the conventional view? In this review, we describe how a new generation of studies is leveraging high-density electrophysiology, incisive task designs, causal manipulations (e.g., optogenetics) and statistical approaches for probing inter-area communication, and theoretical methods that connect population dynamics with representational geometry to build a modern framework for understanding perceptual decisions.
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