CAREER: Recognition-Memory Modeling: Testing Foundations and Extending Boundaries

职业：识别记忆建模：测试基础和扩展边界

• 批准号: 2145308
• 负责人: David Kellen
• 金额: $ 50.34万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145308&HistoricalAwards=false
• 关键词: CAREER; Recognition; Memory; Modeling; Testing

In recent decades, psychologists have used formal, computational models to make considerable advances in understanding the cognitive processes underlying people’s recognition memory judgements (e.g., “I remember seeing this person before”) and their associated characteristics (e.g., their accuracy, the subjective confidence ascribed to the memory). These formal models provide researchers with ways to measure the relative contribution of different cognitive processes (e.g., “familiarity” versus “episodic recollection”), ways to answer questions regarding their development across the lifespan (e.g., children vs. young adults vs. older adults), and allow for comparison across different clinical populations (e.g., Alzheimer patients). Formal models of recognition memory also enable researchers to confidently tackle socially-relevant issues, such as critically evaluating different eyewitness identification procedures used by police departments. The proposed research addresses the present problem that multiple candidate models may offer alternative characterizations of the same data (e.g., some candidate models postulate that recognition judgments are driven by a single mnemonic process whereas others postulate two or more). While unresolved, this issue stands in the way of researchers and practitioners having properly validated tools for characterizing people’s mnemonic processes in detail. A key aspect of the present work is the novelty of the methods applied: the team closely coordinates experimental designs and mathematical proofs in order to reveal “behavioral signature patterns” predicted by the different models. The empirical results from these studies allow the team to directly test models by addressing questions regarding: 1) the relationship between mnemonic information, confidence judgments, and response bias, 2) the way mnemonic information is represented (in absolute versus relative terms), 3) how exactly mnemonic information supporting a previous encounter (“did I see X before?”) relates to contextual mnemonic information (“where and how did I see X before?”), and 4) how many distinct retrieval processes are necessary to adequately characterize recognition judgments (e.g., do we need to postulate separate “episodic recollection” processes?). The overarching result is a drastic reduction in the number of viable candidate models and a convergence towards a single validated account of recognition memory that brings the formal modeling of recognition to its full potential in both research and applied settings. In turn, the educational component of this work establishes as its goals the development of much-needed coursework on the foundations of psychological science as well as an undergraduate training program on formal modeling that is explicitly targeted at increasing the graduate-level representation of members of historically underrepresented groups and US nationals more broadly. Together, the work resolves a number of open research problems and increases the number of US college students equipped to take on the scientific challenges of tomorrow using state-of-the-art formal methods.Different models of recognition memory are currently being used in research and applied settings to characterize the cognitive processes behind people’s memory judgments. These models – couched in Signal Detection or High-Threshold frameworks – differ in terms of the nature and number of cognitive processes that they postulate. The existence of multiple candidate models creates a problem in that the same data can be interpreted in multiple, incompatible ways. The present research critically compares these different models with goal of obtaining a single validated account. This goal is achieved by articulating experimental designs collecting forced-choice and ranking judgments along with formal results that speak to all candidate models (e.g., Block-Marschak and Tversky-Sattath inequalities) in order to obtain privileged testing grounds that require minimal auxiliary assumptions. In practice, this means that it is possible to set aside the generic model-comparison methods (model fit + complexity penalty) traditionally used by researchers up to this point and re-focus efforts on specific model predictions that can be directly tested at the level of the data using order-constrained inferential methods. The test results produced here are able to address a number of key questions, such as: 1) are confidence ratings direct, noiseless mappings of latent strengths? 2) are latent-strength values represented in terms of likelihood ratios? 3) are “episodic recollection” processes necessary (in addition to latent strengths) to adequately describe the retrieval of contextual information or the discrimination of studied items from highly similar foils? These answers can drastically reduce the set of candidate models of recognition memory, and will lead to the development of strongly-validated cognitive psychometric tools for experimental procedures, such as the Memory Similarity Task, which is widely used in developmental and neurocognitive research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近几十年来，心理学家使用正式的计算模型，在理解人们识别记忆判断（例如，“我记得以前见过这个人”）及其相关特征（例如，准确性，归因于记忆的主观自信）的认知过程方面取得了相当大的进展。这些正式的模型为研究人员提供了测量不同认知过程的相对贡献的方法（例如，“熟悉”与“情景回忆”），回答有关其整个生命周期发展的问题的方法（例如，儿童与年轻人与老年人），并允许在不同的临床人群中进行比较（例如，阿尔茨海默病患者）。识别记忆的正式模型也使研究人员能够自信地解决与社会相关的问题，例如批判性地评估警察部门使用的不同目击者识别程序。提出的研究解决了目前的问题，即多个候选模型可能提供相同数据的不同特征（例如，一些候选模型假设识别判断是由单个助记过程驱动的，而另一些则假设两个或更多）。虽然尚未解决，但这个问题阻碍了研究人员和实践者找到适当的验证工具来详细描述人们的记忆过程。当前工作的一个关键方面是应用方法的新颖性：该团队密切协调实验设计和数学证明，以揭示由不同模型预测的“行为特征模式”。这些研究的实证结果允许团队通过解决以下问题来直接测试模型：1)助记信息、信心判断和反应偏差之间的关系，2)助记信息的表示方式（绝对与相对），3)助记信息如何准确地支持以前的相遇（“我以前见过X吗？”）与上下文助记信息相关（“我之前在哪里以及如何看到X ?”）4)需要多少不同的检索过程才能充分表征识别判断（例如，我们是否需要假设单独的“情景回忆”过程？）。总体结果是可行的候选模型数量急剧减少，并向识别记忆的单一验证账户趋同，从而使识别的正式建模在研究和应用设置中发挥其全部潜力。反过来，这项工作的教育部分建立了其目标，即开发急需的心理科学基础课程，以及一个正式建模的本科培训项目，该项目明确针对的是增加历史上代表性不足的群体和美国国民的研究生水平的代表性。总之，这项工作解决了许多开放的研究问题，并增加了美国大学生的数量，他们有能力使用最先进的正式方法来应对未来的科学挑战。目前，不同的识别记忆模型被用于研究和应用环境中，以表征人们记忆判断背后的认知过程。这些模型——用信号检测或高阈值框架来表述——在它们所假定的认知过程的性质和数量上有所不同。多个候选模型的存在产生了一个问题，即相同的数据可以用多种不兼容的方式解释。目前的研究对这些不同的模型进行了批判性的比较，目的是获得一个单一的有效账户。这一目标是通过阐明实验设计，收集强制选择和排序判断，以及与所有候选模型（例如，Block-Marschak和tversky - satath不等式）对话的正式结果来实现的，以便获得需要最小辅助假设的特权测试场地。在实践中，这意味着有可能将研究人员迄今为止传统上使用的通用模型比较方法（模型拟合+复杂性惩罚）放在一边，并将精力重新集中在可以使用顺序约束推理方法在数据层面直接测试的特定模型预测上。这里产生的测试结果能够解决一些关键问题，例如：1)信心评级是潜在优势的直接、无噪声映射吗？2)潜在强度值是否用似然比表示？3)“情景回忆”过程是否必要（除了潜在的优势）来充分描述上下文信息的检索或从高度相似的箔中区分研究项目？这些答案可以大大减少识别记忆的候选模型集，并将导致实验过程中强有力的验证认知心理测量工具的发展，例如记忆相似性任务，它被广泛用于发展和神经认知研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Bayes Factors for Mixed Models: a Discussion

混合模型的贝叶斯因子：讨论

• DOI: 10.1007/s42113-022-00160-3
• 发表时间: 2023
• 期刊: Computational Brain & Behavior
• 作者: van Doorn, Johnny;Haaf, Julia M.;Stefan, Angelika M.;Wagenmakers, Eric-Jan;Cox, Gregory Edward;Davis-Stober, Clintin P.;Heathcote, Andrew;Heck, Daniel W.;Kalish, Michael;Kellen, David
• 通讯作者: Kellen, David

Statistics in the Service of Science: Don’t Let the Tail Wag the Dog

统计为科学服务：不要让尾巴摇狗

• DOI: 10.1007/s42113-022-00129-2
• 发表时间: 2023
• 期刊: Computational Brain & Behavior
• 作者: Singmann, Henrik;Kellen, David;Cox, Gregory E.;Chandramouli, Suyog H.;Davis-Stober, Clintin P.;Dunn, John C.;Gronau, Quentin F.;Kalish, Michael L.;McMullin, Sara D.;Navarro, Danielle J.
• 通讯作者: Navarro, Danielle J.