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

Syracuse University, Syracuse NY

Investigators

Abstract

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.