Kevin Murnane

Bio: Kevin Murnane is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Context (language use) & Empirical measure. The author has an hindex of 12, co-authored 13 publications receiving 1287 citations. Previous affiliations of Kevin Murnane include Indiana University & University of Maryland, College Park.

Context-dependent recognition memory: The ICE theory

Abstract: A solution to the problem of context-dependent recognition memory is presented in terms of the item, associated context, and ensemble (ICE) theory. It is argued that different types of context effects depend on how context information is encoded at both learning and retrieval. Matching associated context in memory and a retrieval cue produces increases in both hit and false alarm rates and may not be accompanied by a change in discrimination. Integrating item and context information in an ensemble and matching ensemble information in memory and a retrieval cue produces context-dependent discrimination. Empirical support for these predictions is presented.

An evaluation of empirical measures of source identification.

Abstract: Source identification refers to memory for the origin of information. A consistent nomenclature is introduced for empirical measures of source identification which are then mathematically analyzed and evaluated. The ability of the measures to assess source identification independently of identification of an item as old or new depends on assumptions made about how inconsistencies between the item and source components of a source-monitoring task may be resolved. In most circumstances, the empirical measure that is used most often when source identification is measured by collapsing across pairs of sources (sometimes called “the identification-of-origin score”) confounds item identification with source identification. Alternative empirical measures are identified that do not confound item and source identification in specified circumstances. None of the empirical measures examined provides a valid measure of source identification in all circumstances.

A global activation approach to the effect of changes in environmental context on recognition.

Abstract: A number of prior studies have not found declines in recognition performance when testing occurs in an environmental context that is different from the learning context. These findings raise serious problems for global activation theories of recognition which predict that hit and false alarm rates will decline when the test context does not match the learning context. Environmental context was manipulated as a unique combination of foreground color, background color, and location on a computer screen in three experiments using intact-rearranged recognition testing and two experiments using single-item testing. Changes in context resulted in reduced hit and false alarm rates as predicted by global activation theories in all five experiments. Mental reinstatement of the learning context was also examined

Interference and the representation of events in memory.

Abstract: Most current models of memory predict that the presence of increasingly well-learned, or strong, items in memory will cause increasing interference. This phenomenon, the list-strength effect, occurs as predicted when memory is tested by free recall but not when a recognition test is used. Four experiments use end-of-session testing to demonstrate that redistribution of storage time or effort from strong to weak items on mixed lists does not occur and therefore cannot be masking interference by strong items. Delay between study and test is found to cause memory loss independent of the basic list-strength findings. It is concluded that the presence of strong items in memory does not interfere with recognition performance and that interference is due to failures of retrieval rather than to composition or other forms of destructive interaction during storage. Many types of interference phenomena have been linked to memory loss. Does interference occur during storage or retrieval? McGeoch (1942) viewed interference primarily as a form of retrieval failure caused by response competition at the time of retrieval. In contrast, Melton and Irwin (1940) saw one component of interference as a storage phenomenon stemming from the unlearning of prior events when later, highly related events are learned. Barnes and Underwood (1959) provided an empirical argument that both storage- and retrieval-based forms of interference take place. Starting in the 1960s, interest in this issue waned, partly because new and more complex models seemed to show that a model taking the retrieval view would be able to handle the results (e.g., Anderson & Bower, 1972; Atkinson & Shiffrin, 1968; Crowder, 1976; Mensink & Raaijmakers, 1988; Raaijmakers & Shiffrin, 1980, 1981).

Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach.

Abstract: The authors present a computational neural-network model of how the hippocampus and medial temporal lobe cortex (MTLC) contribute to recognition memory. The hippocampal component contributes by recalling studied details. The MTLC component cannot support recall, but one can extract a scalar familiarity signal from MTLC that tracks how well a test item matches studied items. The authors present simulations that establish key differences in the operating characteristics of the hippocampal-recall and MTLC-familiarity signals and identify several manipulations (e.g., target–lure similarity, interference) that differentially affect the 2 signals. They also use the model to address the stochastic relationship between recall and familiarity and the effects of partial versus complete hippocampal lesions on recognition. Memory can be subdivided according to functional categories (e.g., declarative vs. procedural memory; Cohen & Eichenbaum, 1993; Squire, 1992b) and according to neural structures (e.g., hippocampally dependent vs. nonhippocampally dependent forms of memory). Various attempts have been made to align these functional and neural levels of analysis; for example, Squire (1992b) and others have argued that declarative memory depends on the medial temporal lobe whereas procedural memory depends on other cortical and subcortical structures. Recently, we and our colleagues have set forth a computationally explicit theory of how hippocampus and neocortex contribute to learning and memory (the complementary-learning-systems model; McClelland, McNaughton, & O’Reilly, 1995; O’Reilly & Rudy, 2001). In this article, we advance the complementary-learning-systems model by using it to provide a comprehensive treatment of recognitionmemory performance. In this introductory section, we describe two questions that have proved challenging for math-modeling and cognitive-neuroscience approaches to recognition, respectively: In the math-modeling literature, there has been considerable controversy regarding how to characterize the contribution of recall (vs. familiarity) to recognition memory; in the cognitive-neuroscience literature, researchers have debated how the hippocampus (vs. surrounding cortical regions) contributes to recognition. Then, we show how our modeling approach, which is jointly constrained by behavioral and neuroscientific data, can help resolve these controversies.

A model for recognition memory: REM—retrieving effectively from memory

Abstract: A new model of recognition memory is reported. This model is placed within, and introduces, a more elaborate theory that is being developed to predict the phenomena of explicit and implicit, and episodic and generic, memory. The recognition model is applied to basic findings, including phenomena that pose problems for extant models: the list-strength effect (e.g., Ratcliff, Clark, & Shiffrin, 1990), the mirror effect (e.g., Glanzer & Adams, 1990), and the normal-ROC slope effect (e.g., Ratcliff, McKoon, & Tindall, 1994). The model assumes storage of separate episodic images for different words, each image consisting of a vector of feature values. Each image is an incomplete and error prone copy of the studied vector. For the simplest case, it is possible to calculate the probability that a test item is “old,” and it is assumed that a default “old” response is given if this probability is greater than .5. It is demonstrated that this model and its more complete and realistic versions produce excellent qualitative predictions.

Environmental context-dependent memory: a review and meta-analysis.

Abstract: To address questions about human memory’s dependence on the coincidental environmental contexts in which events occur, we review studies of incidental environmental context-dependent memory in humans and report a meta-analysis. Our theoretical approach to the issue stems from Glenberg’s (1997) contention that introspective thought (e.g., remembering, conceptualizing) requires cognitive resources normally used to represent the immediate environment. We propose that if tasks encourage processing of noncontextual information (i.e., introspective thought) at input and/or at test, then both learning and memory will be less dependent on the ambient environmental contexts in which those activities occur. The meta-analysis showed that across all studies, environmental context effects were reliable, and furthermore, that the use of noncontextual cues during learning (overshadowing) and at test (outshining), as well as mental reinstatement of appropriate context cues at test, all reduce the effect of environmental manipulations. We conclude that environmental context-dependent memory effects are less likely to occur under conditions in which the immediate environment is likely to be suppressed.

Oscillator-based memory for serial order

Abstract: A computational model of human memory for serial order is described (OSCillator-based Associative Recall [OSCAR]). In the model, successive list items become associated to successive states of a dynamic learning-context signal. Retrieval involves reinstatement of the learning context, successive states of which cue successive recalls. The model provides an integrated account of both item memory and order memory and allows the hierarchical representation of temporal order information. The model accounts for a wide range of serial order memory data, including differential item and order memory, transposition gradients, item similarity effects, the effects of item lag and separation in judgments of relative and absolute recency, probed serial recall data, distinctiveness effects, grouping effects at various temporal resolutions, longer term memory for serial order, list length effects, and the effects of vocabulary size on serial recall.