Making decisions with unknown sensory reliability.
Making decisions with unknown sensory reliability.
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DOI：
10.3389/fnins.2012.00075
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发表时间：
2012
影响因子：
4.3
通讯作者：
Deneve S
中科院分区：
文献类型：
--
作者：
Deneve S
为了做出快速而准确的行为选择，我们需要整合嘈杂的感官输入，考虑先验知识，调整我们的决策标准。前面已经证明，在两种选择任务中，最优决策可以在序列概率比检验的框架中形式化，然后等效于扩散模型。然而，这个类比隐藏了一个“先有鸡还是先有蛋”的问题：要知道我们应该多快地整合感官输入并设置最佳决策阈值，感官观察的可靠性必须提前知道。大多数情况下，如果不首先观察决策结果，我们无法知道这种可靠性。我们在这里考虑一个贝叶斯决策模型，它同时推断出两种不同选择的概率，同时估计这种选择所依据的感官信息的可靠性。我们表明，这可以在一次试验中实现，基于感觉尖峰神经元的噪声反应。所得到的模型是一个非线性扩散边界，其中感官输入的权重和决策阈值都随时间动态变化。在困难的决策试验中，早期的感官输入对决策的影响更大，阈值会崩溃，从而更快地做出选择，但准确性较低。在简单的试验中，情况正好相反：感官权重和阈值随着时间的推移而增加，导致决策速度变慢，但准确性更高。与标准扩散模型相比，自适应感觉权重构建了每个选择概率的准确表示。然后，这些信息可以与其他不可靠的线索（如先验）适当地结合起来。我们表明，该模型可以解释运动识别任务中的最新发现，并且可以在使用快速Hebbian学习的神经结构中实现。
To make fast and accurate behavioral choices, we need to integrate noisy sensory input, take prior knowledge into account, and adjust our decision criteria. It was shown previously that in two-alternative-forced-choice tasks, optimal decision making can be formalized in the framework of a sequential probability ratio test and is then equivalent to a diffusion model. However, this analogy hides a “chicken and egg” problem: to know how quickly we should integrate the sensory input and set the optimal decision threshold, the reliability of the sensory observations must be known in advance. Most of the time, we cannot know this reliability without first observing the decision outcome. We consider here a Bayesian decision model that simultaneously infers the probability of two different choices and at the same time estimates the reliability of the sensory information on which this choice is based. We show that this can be achieved within a single trial, based on the noisy responses of sensory spiking neurons. The resulting model is a non-linear diffusion to bound where the weight of the sensory inputs and the decision threshold are both dynamically changing over time. In difficult decision trials, early sensory inputs have a stronger impact on the decision, and the threshold collapses such that choices are made faster but with low accuracy. The reverse is true in easy trials: the sensory weight and the threshold increase over time, leading to slower decisions but at much higher accuracy. In contrast to standard diffusion models, adaptive sensory weights construct an accurate representation for the probability of each choice. This information can then be combined appropriately with other unreliable cues, such as priors. We show that this model can account for recent findings in a motion discrimination task, and can be implemented in a neural architecture using fast Hebbian learning.
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