Parr Thomas, Benrimoh David A, Vincent Peter, Friston Karl J
Institute of Neurology, Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.
Front Integr Neurosci. 2018 Sep 20;12:39. doi: 10.3389/fnint.2018.00039. eCollection 2018.
Accurate perceptual inference fundamentally depends upon accurate beliefs about the reliability of sensory data. In this paper, we describe a Bayes optimal and biologically plausible scheme that refines these beliefs through a gradient descent on variational free energy. To illustrate this, we simulate belief updating during visual foraging and show that changes in estimated sensory precision (i.e., confidence in visual data) are highly sensitive to prior beliefs about the contents of a visual scene. In brief, confident prior beliefs induce an increase in estimated precision when consistent with sensory evidence, but a decrease when they conflict. Prior beliefs held with low confidence are rapidly updated to posterior beliefs, determined by sensory data. These induce much smaller changes in beliefs about sensory precision. We argue that pathologies of scene construction may be due to abnormal priors, and show that these can induce a reduction in estimated sensory precision. Having previously associated this precision with cholinergic signaling, we note that several neurodegenerative conditions are associated with visual disturbances and cholinergic deficits; notably, the synucleinopathies. On relating the message passing in our model to the functional anatomy of the ventral visual stream, we find that simulated neuronal loss in temporal lobe regions induces confident, inaccurate, empirical prior beliefs at lower levels in the visual hierarchy. This provides a plausible, if speculative, computational mechanism for the loss of cholinergic signaling and the visual disturbances associated with temporal lobe Lewy body pathology. This may be seen as an illustration of the sorts of hypotheses that may be expressed within this computational framework.
准确的感知推理从根本上依赖于关于感官数据可靠性的准确信念。在本文中，我们描述了一种贝叶斯最优且符合生物学原理的方案，该方案通过对变分自由能进行梯度下降来完善这些信念。为了说明这一点，我们模拟了视觉觅食过程中的信念更新，并表明估计的感官精度变化（即对视觉数据的信心）对关于视觉场景内容的先验信念高度敏感。简而言之，自信的先验信念在与感官证据一致时会导致估计精度增加，但在它们冲突时会导致降低。低置信度持有的先验信念会迅速更新为后验信念，由感官数据决定。这些信念在关于感官精度的信念上引起的变化要小得多。我们认为场景构建的病理可能是由于异常的先验信念，并表明这些信念会导致估计的感官精度降低。我们之前已将这种精度与胆碱能信号联系起来，我们注意到几种神经退行性疾病与视觉障碍和胆碱能缺陷有关；特别是突触核蛋白病。在将我们模型中的信息传递与腹侧视觉流的功能解剖学联系起来时，我们发现颞叶区域模拟的神经元丢失会在视觉层次结构的较低水平上诱导出自信但不准确的经验先验信念。这为胆碱能信号丧失以及与颞叶路易体病理相关的视觉障碍提供了一种合理的（如果是推测性的）计算机制。这可以被视为在这个计算框架内可能表达的各种假设的一个例证。
