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.

The effects of prior knowledge on the encoding of episodic contextual details

Abstract: A positive relationship between prior knowledge and item memory is a consistent finding in the literature. In the present study, we sought to determine whether this relationship extends to episodic details that are present at the time of encoding, namely source memory. Using a novel experimental design, we were able to show both between- and within-subjects effects of prior knowledge on source memory. Specifically, the results revealed that the degree of prior knowledge positively predicted memory for source specifying contextual details. In addition, by including two conditions in which attention was divided either at encoding or retrieval, we were able to show that prior knowledge influences memory by affecting encoding processes. Overall, the data suggest that a priori knowledge within a specific domain allows attentional resources to be allocated toward the encoding of contextual details.

Low-Power Neuromorphic Hardware for Signal Processing Applications.

Abstract: Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even super-human performance, their energy consumption has often proved to be prohibitive in the absence of costly super-computers. Most state-of-the-art machine learning solutions are based on memory-less models of neurons. This is unlike the neurons in the human brain, which encode and process information using temporal information in spike events. The different computing principles underlying biological neurons and how they combine together to efficiently process information is believed to be a key factor behind their superior efficiency compared to current machine learning systems. Inspired by the time-encoding mechanism used by the brain, third generation spiking neural networks (SNNs) are being studied for building a new class of information processing engines. Modern computing systems based on the von Neumann architecture, however, are ill-suited for efficiently implementing SNNs, since their performance is limited by the need to constantly shuttle data between physically separated logic and memory units. Hence, novel computational architectures that address the von Neumann bottleneck are necessary in order to build systems that can implement SNNs with low energy budgets. In this paper, we review some of the architectural and system level design aspects involved in developing a new class of brain-inspired information processing engines that mimic the time-based information encoding and processing aspects of the brain.

Benefit of enactment over oral repetition of verbal instruction does not require additional working memory during encoding

Abstract: For this research, we used a dual-task approach to investigate the involvement of working memory in following written instructions. In two experiments, participants read instructions to perform a series of actions on objects and then recalled the instructions either by spoken repetition or performance of the action sequence. Participants engaged in concurrent articulatory suppression, backward-counting, and spatial-tapping tasks during the presentation of the instructions, in order to disrupt the phonological-loop, central-executive, and visuospatial-sketchpad components of working memory, respectively. Recall accuracy was substantially disrupted by all three concurrent tasks, indicating that encoding and retaining verbal instructions depends on multiple components of working memory. The accuracy of recalling the instructions was greater when the actions were performed than when the instructions were repeated, and this advantage was unaffected by the concurrent tasks, suggesting that the benefit of enactment over oral repetition does not cost additional working memory resources.

Spatio-Temporal VLAD Encoding for Human Action Recognition in Videos

Abstract: Encoding is one of the key factors for building an effective video representation. In the recent works, super vector-based encoding approaches are highlighted as one of the most powerful representation generators. Vector of Locally Aggregated Descriptors (VLAD) is one of the most widely used super vector methods. However, one of the limitations of VLAD encoding is the lack of spatial information captured from the data. This is critical, especially when dealing with video information. In this work, we propose Spatio-temporal VLAD (ST-VLAD), an extended encoding method which incorporates spatio-temporal information within the encoding process. This is carried out by proposing a video division and extracting specific information over the feature group of each video split. Experimental validation is performed using both hand-crafted and deep features. Our pipeline for action recognition with the proposed encoding method obtains state-of-the-art performance over three challenging datasets: HMDB51 (67.6%), UCF50 (97.8%) and UCF101 (91.5%).