College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD 20742, United States.
Curr Opin Neurobiol. 2011 Oct;21(5):774-81. doi: 10.1016/j.conb.2011.05.018.
Perception is about making sense, that is, understanding what events in the outside world caused the sensory observations. Consistent with this intuition, many aspects of human behavior confronting noise and ambiguity are well explained by principles of causal inference. Extending these insights, recent studies have applied the same powerful set of tools to perceptual processing at the neural level. According to these approaches, microscopic neural structures solve elementary probabilistic tasks and can be combined to construct hierarchical predictive models of the sensory input. This framework suggests that variability in neural responses reflects the inherent uncertainty associated with sensory interpretations and that sensory neurons are active predictors rather than passive filters of their inputs. Causal inference can account parsimoniously and quantitatively for non-linear dynamical properties in single synapses, single neurons and sensory receptive fields.
感知是关于理解意义的，也就是说，理解外界事件是什么引起了感官观察。与这种直觉一致的是，许多人类行为在面对噪声和模糊性时的表现都可以很好地用因果推理原则来解释。这些研究结果表明，微观神经结构可以解决基本的概率任务，并可以组合起来构建对感觉输入的分层预测模型。根据这些方法，神经反应的可变性反映了与感觉解释相关的固有不确定性，并且感觉神经元是其输入的主动预测器而不是被动滤波器。因果推理可以简洁地定量解释单突触、单神经元和感觉感受野中的非线性动力学特性。
