Models of memory in cognitive science and philosophy have traditionally explained human remembering in terms of storage and retrieval. This tendency has been entrenched by reliance on computationalist explanations over the course of the twentieth century; even research programs that eschew computationalism in name, or attempt the revision of traditional models, demonstrate tacit commitment to computationalist assumptions. It is assumed that memory must be stored by means of an isomorphic trace, that memory processes must divide into conceptually distinct systems and (...) phases, and that human remembering consists in inner, cognitive processes that are implemented by distinct neural processes. This dissertation draws on recent empirical work, and on philosophical arguments from Ludwig Wittgenstein and others, to demonstrate that this latent computationalism in the study of memory is problematic, and that it can and should be eliminated. Cognitive psychologists studying memory have encountered numerous data in recent decades that belie archival models. In cognitive neuroscience, establishing the neural basis of storage and retrieval processes has proven elusive. A number of revised models on offer in memory science, that have taken these issues into account, fail to sufficiently extricate the archival framework. Several impasses in memory science are products of these underlying computationalist assumptions. Wittgenstein and other philosophers offer a number of arguments against the need for, and the efficacy of, the storage and retrieval of traces in human remembering. A study of these arguments clarifies the ways that these computationalist assumptions are presently impeding the science of memory, and provides ways forward in removing them. We can and should characterize and model human memory without invoking the storage and retrieval of traces. A range of work in connectionism, dynamical systems theory, and recent philosophical accounts of memory demonstrate how the science of memory can proceed without these assumptions, toward non-archival models of remembering. (shrink)

Education has recently been shaped by the cognitive science of memory. In turn, the science of memory has been infused by revolutionary ideas found in Wittgenstein’s works. However, the memory science presently applied to education draws mainly on traditional models that are quickly becoming outmoded; Wittgenstein’s insights have yet to be fruitfully applied, though they have helped to develop the science of memory. In this chapter, I examine three Wittgensteinian reforms in memory science as they pertain to education . First, (...) Wittgenstein has inspired a particular strain of enactive models of memory and cognition, with important implications for theories of situated learning in education. Second, researchers have begun modeling memory as public practice , which deeply informs, inter alia, fraught theoretical discussions of assessment. Third, a number of memory researchers have rejected models based on a stored trace, a fundamental, Wittgensteinian revision with broad implications for characterizations of learning. (shrink)

Given the importance of cross-disciplinary research, facilitating CDR effectiveness is a priority for many institutions and funding agencies. There are a number of CDR types, however, and the effectiveness of facilitation efforts will require sensitivity to that diversity. This article presents a method characterizing a spectrum of CDR designed to inform facilitation efforts that relies on bibliometric techniques and citation data. We illustrate its use by the Toolbox Project, an ongoing effort to enhance cross-disciplinary communication in CDR teams through structured, (...) philosophical dialogue about research assumptions in a workshop setting. Toolbox Project workshops have been conducted with more than 85 research teams, but the project's extensibility to an objectively characterized range of CDR collaborations has not been examined. To guide wider application of the Toolbox Project, we have developed a method that uses multivariate statistical analyses of transformed citation proportions from published manuscripts to identify candidate areas of CDR, and then overlays information from previous Toolbox participant groups on these areas to determine candidate areas for future application. The approach supplies 3 results of general interest: A way to employ small data sets and familiar statistical techniques to characterize CDR spectra as a guide to scholarship on CDR patterns and trends. A model for using bibliometric techniques to guide broadly applicable interventions similar to the Toolbox. A method for identifying the location of collaborative CDR teams on a map of scientific activity, of use to research administrators, research teams, and other efforts to enhance CDR projects. (shrink)