Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation.
Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation.
复制标题
为什么更高的工作记忆容量可以帮助您学习：采样、搜索和近似度。
DOI：
10.1111/cogs.12805
复制
发表时间：
2019
影响因子：
2.5
通讯作者：
Lloyd K
中科院分区：
文献类型：
--
作者：
Lloyd K
用于近似贝叶斯推断的算法，诸如基于采样的算法（即，蒙特卡罗方法），提供了一个自然的来源模型，人们如何处理有限的认知资源的不确定性。在这里，我们考虑的想法，工作记忆容量（WMC）的个体差异，可以有效地建模的样本数量，或“粒子”，可用于执行推理。为了验证这个想法，我们专注于最近的两个实验，报告WMC和两个不同方面的分类性能之间的积极关联：学习新类别的能力，以及在不同的分类策略（“知识重组”）之间切换的能力。为了支持将WMC建模为多个粒子的想法，我们证明了单个模型可以通过改变粒子的数量来重现两个实验结果-增加粒子的数量可以加快类别学习和改善策略切换。此外，当我们将模型拟合到个体参与者时，我们发现WMC与策略切换的最佳拟合粒子数之间存在正相关。然而，在类别学习中，WMC和最佳拟合粒子数之间没有关联。这些结果进行了讨论的背景下，解开不同的潜在来源的行为变异的贡献的一般挑战。
Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.