An embedded computational framework of memory: The critical role of representations in veridical and false recall predictions

Abstract

Human memory is reconstructive and thus fundamentally imperfect. One of its critical flaws is false recall-the erroneous recollection of unstudied items. Despite its significant implications, false recall poses a challenge for existing computational models of serial recall, which struggle to provide item-specific predictions. Across six experiments, each involving 100 young adults, we address this issue using the Embedded Computational Framework of Memory (eCFM) that integrates existing accounts of semantic and episodic memory. While the framework provides a comprehensive account of memory processing, its innovation lies in the inclusion of a comprehensive lexicon of word knowledge derived from distributional semantic models. By integrating a lexicon that captures orthographic, phonological, and semantic relationships within an episodic memory model, the eCFM successfully accounts for patterns of veridical serial recall (e.g., proportion correct, intralist errors, omissions) while also capturing false recall (e.g., extralist errors including both critical lures and non-critical lures). We demonstrate the model's capabilities through simulations applied to six experiments, with lists of words (Experiments 1A, 1B, 2A, and 2B) and non-words (Experiments 3A and 3B) that are either related or unrelated semantically (Experiments 1A and 1B), phonologically (Experiments 2A and 2B), or orthographically (Experiments 3A and 3B). This approach fills a computational gap in modelling serial recall and underscores the importance of integrating traditionally separate areas of semantic and episodic memory to provide more precise predictions and holistic memory models.

Keywords: Computational model; Distributional semantic models; False recall; Serial recall.