Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation-猫眼课题宝

DOI：

10.1101/077719

发表时间：

2016-09

期刊：

bioRxiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

C. Ryali;Gautam Reddy;Angela J. Yu

了解人类和动物如何在稳定和不稳定的环境中学习统计规律，并利用这些规律做出预测和决策，是神经科学和心理学中的一个重要问题。使用贝叶斯建模框架，特别是动态信念模型(DBM)，以前已经表明，人类倾向于做出默认假设，即环境统计经历突然的、无信号的变化，即使环境统计实际上是稳定的。由于在这种情况下的准确贝叶斯推理(切换状态空间模型的例子)计算密集，已经提出了许多近似贝叶斯和启发式算法来解释大脑中的学习/预测。在这里，我们研究了一个神经上可信的算法，这是泄漏积分动力学的一个特例，我们将其表示为EXP(用于指数过滤)，该算法比之前建议的所有算法(除了增量学习规则)都要简单得多，并且在逼近贝叶斯预测性能方面远远优于增量规则。我们推导了DBM和EXP之间的理论关系，并表明EXP通过前述推理不确定性的表示(Delta规则也是如此)提高了计算效率，但它仍然获得了接近贝叶斯的性能，这是因为它能够结合DBM独有的、其他算法所没有的“持久先验”影响。此外，在视觉搜索任务中再现人类行为方面，EXP与DBM相当，但比所有其他模型更好，这表明人类的学习和预测也包含了持久先验的元素。更广泛地说，我们的工作表明，当观测信息贫乏时，检测变化或调节学习速率既困难，(因此)也没有必要进行贝叶斯最优预测。

Understanding how humans and animals learn about statistical regularities in stable and volatile environments, and utilize these regularities to make predictions and decisions, is an important problem in neuroscience and psychology. Using a Bayesian modeling framework, specifically the Dynamic Belief Model (DBM), it has previously been shown that humans tend to make the default assumption that environmental statistics undergo abrupt, unsignaled changes, even when environmental statistics are actually stable. Because exact Bayesian inference in this setting, an example of switching state space models, is computationally intensive, a number of approximately Bayesian and heuristic algorithms have been proposed to account for learning/prediction in the brain. Here, we examine a neurally plausible algorithm, a special case of leaky integration dynamics we denote as EXP (for exponential filtering), that is significantly simpler than all previously suggested algorithms except for the delta-learning rule, and which far outperforms the delta rule in approximating Bayesian prediction performance. We derive the theoretical relationship between DBM and EXP, and show that EXP gains computational efficiency by foregoing the representation of inferential uncertainty (as does the delta rule), but that it nevertheless achieves near-Bayesian performance due to its ability to incorporate a “persistent prior” influence unique to DBM and absent from the other algorithms. Furthermore, we show that EXP is comparable to DBM but better than all other models in reproducing human behavior in a visual search task, suggesting that human learning and prediction also incorporates an element of persistent prior. More broadly, our work demonstrates that when observations are information-poor, detecting changes or modulating the learning rate is both difficult and (thus) unnecessary for making Bayes-optimal predictions.

Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation

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