A Bayesian inference model for metamemory.
A Bayesian inference model for metamemory.
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元记忆的贝叶斯推理模型
DOI：
10.1037/rev0000270
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发表时间：
2021-10
影响因子：
5.4
通讯作者：
Luo, Liang
中科院分区：
文献类型：
--
作者：
Hu, Xiao;Zheng, Jun;Su, Ningxin;Fan, Tian;Yang, Chunliang;Yin, Yue;Fleming, Stephen M.;Luo, Liang
元记忆的双重基础理论认为，人们评价自己的记忆表现既基于记忆过程中的加工经验，也基于他们对整体记忆能力的先验信念。然而，很少有研究提出一个正式的计算模型来定量表征加工经验和先验信念在元记忆监测过程中的整合。在这里，我们介绍了一个元内存的贝叶斯推理模型（BIM），它为元内存监控过程提供了一个理论和计算框架。BIM假设当人们评估自己的记忆表现时，他们通过贝叶斯推理整合了处理经验和先验信念。我们表明，BIM可以适用于连续或离散规模的置信度评级的召回或识别任务。数据仿真结果表明，BIM可以成功地恢复大部分生成参数值，并证明了BIM中的参数与之前的元认知计算模型（如随机检测和检索模型（SDRM）和元d '模型）之间存在系统关系。我们还展示了将BIM与几个实验的经验数据集相匹配的例子，这表明BIM的预测与先前关于元记忆的研究一致。此外，与SDRM相比，BIM可以更简洁地解释回忆任务中学习判断（JOLs）和记忆表现的数据。最后，我们讨论了BIM的扩展，它说明了信念更新，并以BIM如何有益于元记忆研究的讨论结束。
The dual-basis theory of metamemory suggests that people evaluate their memory performance based on both processing experience during the memory process and their prior beliefs about overall memory ability. However, few studies have proposed a formal computational model to quantitatively characterize how processing experience and prior beliefs are integrated during metamemory monitoring. Here, we introduce a Bayesian inference model for metamemory (BIM) which provides a theoretical and computational framework for the metamemory monitoring process. BIM assumes that when people evaluate their memory performance, they integrate processing experience and prior beliefs via Bayesian inference. We show that BIM can be fitted to recall or recognition tasks with confidence ratings on either a continuous or discrete scale. Results from data simulation indicate that BIM can successfully recover a majority of generative parameter values, and demonstrate a systematic relationship between parameters in BIM and previous computational models of metacognition such as the stochastic detection and retrieval model (SDRM) and the meta-d′ model. We also show examples of fitting BIM to empirical data sets from several experiments, which suggest that the predictions of BIM are consistent with previous studies on metamemory. In addition, when compared with SDRM, BIM could more parsimoniously account for the data of judgments of learning (JOLs) and memory performance from recall tasks. Finally, we discuss an extension of BIM which accounts for belief updating, and conclude with a discussion of how BIM may benefit metamemory research.
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