Encoding (memory)

About: Encoding (memory) is a research topic. Over the lifetime, 7547 publications have been published within this topic receiving 120214 citations. The topic is also known as: memory encoding & encoding of memories.

Memory and Aging

Abstract: Publisher Summary This chapter examines four accounts of the nature of memory impairment in old age. These range from the view that poorer memory in old age arises from inefficient encoding and retrieval strategies that are subject to remediation by appropriate interventions to less optimistic views that declining memory is the result of irreversible age-related changes in basic mechanisms underlying cognition, such as reductions in working memory capacity, reduced processing speed, and impaired inhibition. The four classes of hypotheses considered are that age-related decrements in memory are attributable to (1) failures of strategic processing, (2) deficits in semantic processing, (3) problems in the utilization of context, and (4) changes in basic mechanisms underlying all aspects of cognition. Processing-resource approaches are appealing because they seek to identify deficits in basic mechanisms underlying not only memory but also other aspects of cognition. The findings relevant to the hypotheses that memory changes in old age are due to reduced attentional capacity, smaller working-memory capacity, defective inhibitory processing, or general slowing have been reviewed.

Dynamically adaptive MRI with encoding by singular value decomposition

Abstract: A new MRI spatial encoding method based upon the singular value decomposition (SVD) and using spatially selective RF excitation is described. This encoding technique is particularly applicable to dynamic adaptive MRI, because it provides a near minimal set of spatial encoding profiles computed using an image estimate that is determined from a previously obtained image. Experimental results are presented for two cases, which exemplify its potential use in different dynamic imaging tasks. SVD-encoded MRI has demonstrated to be a highly efficient encoding scheme.

Multi-label Classification via Feature-aware Implicit Label Space Encoding

Abstract: To tackle a multi-label classification problem with many classes, recently label space dimension reduction (LSDR) is proposed It encodes the original label space to a low-dimensional latent space and uses a decoding process for recovery In this paper, we propose a novel method termed FaIE to perform LSDR via Feature-aware Implicit label space Encoding Unlike most previous work, the proposed FaIE makes no assumptions about the encoding process and directly learns a code matrix, ie the encoding result of some implicit encoding function, and a linear decoding matrix To learn both matrices, FaIE jointly maximizes the recoverability of the original label space from the latent space, and the predictability of the latent space from the feature space, thus making itself feature-aware FaIE can also be specified to learn an explicit encoding function, and extended with kernel tricks to handle non-linear correlations between the feature space and the latent space Extensive experiments conducted on benchmark datasets well demonstrate its effectiveness

Memory for faces: encoding and retrieval operations.

Abstract: The reliable finding that trait judgments of faces yield better recognition memory than do feature judgments of faces is conceptualized as an encoding-specificity effect. Specifically, both trait-judgment encodings of faces and face-recognition tests are argued to be holistic, involving topographical information with between-feature processing. Consistent with the concept that encoding and retrieval operations interact to produce retrieval success, it was expected that a memory-for-face test using the Identi-kit (which requires reconstructions of the face at a feature level of analysis) would show trait-encoding tasks to be inferior to feature-encoding tasks. Eighty subjects were assigned randomly to judge a face on 10 trait dimensions (e.g., honesty-dishonesty) or on 10 feature dimensions (e.g., narrow nose-wide nose) and subsequently attempted to recognize the target among five distractors or to reconstruct the face from an Identi-kit. The significant interaction between encoding and retrieval operations indicated that the face was best identified under trait-encoding conditions but best reconstructed under feature-encoding conditions. The match between trait encoding and recognition in yielding high memory performance suggests strongly that trait judgments foster holistic processing of faces (i.e., interfeature topographical information is p-art of the context) and that the recognition of faces also is holistic. Finally, the utility of the feature- vs. holistic-processing distinction is questioned, and an alternative is proposed.