Sequential effects: Superstition or rational behavior?
Sequential effects: Superstition or rational behavior?
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发表时间：
2008-12
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通讯作者：
Angela J. Yu;J. Cohen
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作者：
Angela J. Yu;J. Cohen
在各种各样的行为任务中，受试者表现出一种自动的、明显的次优顺序效应：如果刺激强化了刺激历史中的局部模式，比如一连串的重复或改变，与违反这种模式相比，他们对刺激的反应更快、更准确。即使在随机设计的背景下，局部趋势偶然出现，刺激历史也没有真正的预测能力，情况往往也是如此。在这项工作中，我们使用一个规范的贝叶斯框架来检验这样的假设，即这些特质可能反映了对适应不断变化的环境至关重要的机制的无意参与。我们表明，在非平稳性的先验信念可以诱导实验观察到的顺序效应，否则贝叶斯最优算法。贝叶斯算法被证明可以很好地近似于对过去观测值进行线性指数滤波，这一特征在行为数据中也很明显。我们导出了精确贝叶斯算法的参数和计算量与近似线性指数滤波器的参数和计算量之间的显式关系。由于后者相当于泄漏集成过程，这是一种常用的神经元动力学模型，是感知决策和试验对试验依赖关系的基础，因此我们的模型提供了一个原则性的解释，说明为什么这种动态是有用的。我们还证明了泄漏积分过程的参数调整是可能的，使用随机梯度下降仅基于噪声二值输入。这证明了一个概念，即神经元不仅可以基于标准神经元动力学实现近乎最优的预测，而且它们还可以在不显式表示概率的情况下学习调整处理参数。
In a variety of behavioral tasks, subjects exhibit an automatic and apparently suboptimal sequential effect: they respond more rapidly and accurately to a stimulus if it reinforces a local pattern in stimulus history, such as a string of repetitions or alternations, compared to when it violates such a pattern. This is often the case even if the local trends arise by chance in the context of a randomized design, such that stimulus history has no real predictive power. In this work, we use a normative Bayesian framework to examine the hypothesis that such idiosyncrasies may reflect the inadvertent engagement of mechanisms critical for adapting to a changing environment. We show that prior belief in non-stationarity can induce experimentally observed sequential effects in an otherwise Bayes-optimal algorithm. The Bayesian algorithm is shown to be well approximated by linear-exponential filtering of past observations, a feature also apparent in the behavioral data. We derive an explicit relationship between the parameters and computations of the exact Bayesian algorithm and those of the approximate linear-exponential filter. Since the latter is equivalent to a leaky-integration process, a commonly used model of neuronal dynamics underlying perceptual decision-making and trial-to-trial dependencies, our model provides a principled account of why such dynamics are useful. We also show that parameter-tuning of the leaky-integration process is possible, using stochastic gradient descent based only on the noisy binary inputs. This is a proof of concept that not only can neurons implement near-optimal prediction based on standard neuronal dynamics, but that they can also learn to tune the processing parameters without explicitly representing probabilities.