A Systematic Exploration of when Elements of Episodic Memory Begin to Emerge in Young Children

几十年来，人们一直在研究情景记忆的潜在机制，包括它的出现和个体发育。许多早期关于情景记忆出现的研究声称，它直到4岁左右才出现(例如，Tulving，2005)。然而，最近的研究使用对更小的儿童的EM的改编测量，发现了3岁(例如，Bauer等人，2013)甚至到婴儿2岁(Bauer&Levon，2013)存在情景记忆的证据。对这些发现的表面水平的解释只意味着以前的研究说明了关于EM出现的不确定性。然而，这些发现不应该被放在一起考虑，因为它们采用了不同的任务(例如，图片序列记忆任务和引出的模仿)，虽然这些都是EM的测量，但我们必须小心地假设它们是测量相同的心理过程。此外，情节记忆可能直到4岁才完全出现，但这可能是由于构成完整情节记忆的组成部分认知能力的逐渐出现和发展。这样的发现只会让这样一种观念永久化，即不应该将情节记忆作为一个单一的结构来对待，而应该将其作为一个多方面的过程来对待。系统地实施和使用对情节记忆的多重评估可以让我们更好地理解这些不同的过程在早期几年开始出现的时间和发展轨迹。这种分而治之的评估EM元素的设计(Cystore&Suddendorf，2019)在与前语言或早期语言参与者合作时尤其重要，因为试图创建一个单一、统一的整体EM测量可能需要复杂水平的认知和语言能力，而这些能力在这个阶段还没有完全发展起来。如果这样的电池能够有效地为非常年幼的儿童开发，这些评估就可以用来测试人工智能代理。最近的一系列研究，动物-人工智能试验床(Crosby等人，2020)采用了比较认知设计，将人工智能代理放置在3D虚拟环境中，在那里他们可以在一系列认知任务中进行测试，这些任务以前已经由动物成功完成。然而，值得注意的是，仍然没有开发出对人工智能代理中EM所需的认知过程的评估，这可以为围绕人工智能融入社会和专业领域而迅速发展的工作提供宝贵的洞察力。这份现有文献的简要概述揭示了我在这个项目中旨在克服的两个主要缺陷：婴幼儿/早期儿童情节记忆的系统个体发生，以及为测试人工智能代理中的情景记忆元素而开发的稳健和有效的评估。因此，研究的目的如下：1)实施现有的并开发新的EM测试，最终形成一系列针对幼儿的评估2)使用关于任务绩效的数据来开发针对言语前婴儿和人工智能代理人的EM额外测试本研究将有助于建立一个关于认知如何发展到最终形成完整的、重建的情景记忆的综合时间表。对幼儿情景样认知的这种演变的时间表将为EM出现的知识基础增加新的洞察力。开发一种在人工智能中测试情节记忆的媒介，也将使我们能够在这一指标上进行首次人类与人工智能的直接比较。

There have been decades of research into the underlying mechanisms of episodic memory including its' emergence and ontogeny. Much of the early research into the emergence of episodic memory claims that it does not appear until around the age of 4 (e.g., Tulving, 2005). However, more recent research, using adapted measures of EM for even younger children, have found evidence for episodic memory present at 3 years (e.g., Bauer et al, 2013) and even by the 2nd year of infancy (Bauer & Leventon, 2013).A surface-level interpretation of these findings would merely imply that previous research illustrates uncertainties regarding the emergence of EM. However, these findings should not be considered in parallel with one another as they adopt the use of different tasks (e.g., picture sequence memory task and elicited imitation) and whilst these are known to be measures of EM, we must be careful to assume that they are measuring identical psychological processes. Furthermore, it may be the case that episodic memory does not fully emerge until the age of 4, however this is likely due to the gradual emergence and development of constituent cognitive abilities that contribute to complete episodic memory. Such discoveries only perpetuate the notion that episodic memory should be approached not as a unitary construct, but rather a multifaceted process. Implementing and using multiple assessments of episodic memory in a systematic manner can allow us to gain a greater understanding of when these different processes begin to emerge in earlier years and the trajectory at which they develop. This 'divide-and-conquer' design for assessing elements of EM (Cystal & Suddendorf, 2019) is particularly essential when working with pre- or early-verbal participants, as attempting to create a single, unified measure of wholistic EM may require complex levels of cognition and language abilities that have not yet been fully developed at this stage.If such a battery can be effectively developed for very young children, these assessments could then be adapted to test AI agents. A recent line of research, the Animal-AI testbed (Crosby et al., 2020), adopts a comparative cognition design, placing an AI agent in a 3D virtual environment where they can be tested on a range of cognitive tasks that have been previously successfully completed by animals. Notably, however, there remains to have been developed an assessment of the cognitive processes required for EM in AI agents, which could provide invaluable insight into the rapidly evolving work surrounding the integration of Artificial Intelligence into social and professional spheres.This brief outline of existing literature reveals two overarching lacunae that I aim to overcome in this project: a systematic ontogeny of episodic memory in infancy/early childhood, and the development of robust and valid assessments for testing elements of episodic memory in AI agents. As such, the research aims are as follows: 1) To implement existing, and develop novel, tests of EM culminating in a battery of assessments for very young children 2) To use the data on task performance to develop additional tests of EM for preverbal infants and AI agents This research will contribute to a comprehensive timeline of how cognition develops to culminate in full, reconstructive, episodic memory. A timeline of this evolvement of episodic-like cognition in young children would add novel insight to the knowledge base of EM emergence. The development of a medium through which to test episodic memory in artificial intelligence would also allow us to conduct the first direct human-AI comparison on this measure.