Valérie Camos

Bio: Valérie Camos is an academic researcher from University of Fribourg. The author has contributed to research in topics: Working memory & Short-term memory. The author has an hindex of 31, co-authored 105 publications receiving 4394 citations. Previous affiliations of Valérie Camos include University of Burgundy & Institut Universitaire de France.

Time constraints and resource sharing in adults' working memory spans.

Abstract: This article presents a new model that accounts for working memory spans in adults, the time-based resource-sharing model. The model assumes that both components (i.e., processing and maintenance) of the main working memory tasks require attention and that memory traces decay as soon as attention is switched away. Because memory retrievals are constrained by a central bottleneck and thus totally capture attention, it was predicted that the maintenance of the items to be recalled depends on both the number of memory retrievals required by the intervening treatment and the time allowed to perform them. This number of retrievals:time ratio determines the cognitive load of the processing component. The authors show in 7 experiments that working memory spans vary as a function of this cognitive load.

Time and cognitive load in working memory.

Abstract: According to the time-based resource-sharing model (P. Barrouillet, S. Bernardin, & V. Camos, 2004), the cognitive load a given task involves is a function of the proportion of time during which it captures attention, thus impeding other attention-demanding processes. Accordingly, the present study demonstrates that the disruptive effect on concurrent maintenance of memory retrievals and response selections increases with their duration. Moreover, the effect on recall performance of concurrent activities does not go beyond their duration insofar as the processes are attention demanding. Finally, these effects are not modality specific, as spatial processing was found to disrupt verbal maintenance. These results suggest a sequential and time-based function of working memory in which processing and storage rely on a single and general purpose attentional resource needed to run executive processes devoted to constructing, maintaining, and modifying ephemeral representations.

On the law relating processing to storage in working memory.

Abstract: Working memory is usually defined in cognitive psychology as a system devoted to the simultaneous processing and maintenance of information. However, although many models of working memory have been put forward during the last decades, they often leave underspecified the dynamic interplay between processing and storage. Moreover, the account of their interaction proposed by the most popular A. D. Baddeley and G. Hitch's (1974) multiple-component model is contradicted by facts, leaving unresolved one of the main issues of cognitive functioning. In this article, the author derive from the time-based resource-sharing model of working memory a mathematical function relating the cognitive load involved by concurrent processing to the amount of information that can be simultaneously maintained active in working memory. A meta-analysis from several experiments testing the effects of processing on storage corroborates the parameters of the predicted function, suggesting that it properly reflects the law relating the 2 functions of working memory.

Working memory span development: a time-based resource-sharing model account.

Abstract: The time-based resource-sharing model (P. Barrouillet, S. Bernardin, & V. Camos, 2004) assumes that during complex working memory span tasks, attention is frequently and surreptitiously switched from processing to reactivate decaying memory traces before their complete loss. Three experiments involving children from 5 to 14 years of age investigated the role of this reactivation process in developmental differences in working memory spans. Though preschoolers seem to adopt a serial control without any attempt to refresh stored items when engaged in processing, the reactivation process is efficient from age 7 onward and increases in efficiency until late adolescence, underpinning a sizable part of developmental differences.

Sources of Method Bias in Social Science Research and Recommendations on How to Control It

Abstract: Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.

Working Memory: Theories, Models, and Controversies

Abstract: I present an account of the origins and development of the multicomponent approach to working memory, making a distinction between the overall theoretical framework, which has remained relatively stable, and the attempts to build more specific models within this framework. I follow this with a brief discussion of alternative models and their relationship to the framework. I conclude with speculations on further developments and a comment on the value of attempting to apply models and theories beyond the laboratory studies on which they are typically based.

Neural Turing Machines

Abstract: We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-toend, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.

Neural Turing Machines

Abstract: We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.