Short Term Memory vs. Working Memory

Memory can be classified in different ways. In 1980, William James described two types of memory: primary and secondary. Primary memory refers to the “memory for events that have just happened” (Andrade & May, 2004, p. 59). Primary memory which is now referred to as short term memory (STM) is temporary and transient. On the other hand, secondary memory refers to the “memory that happened some time ago” (ibid.) secondary memory is permanent and long-lasting and it is now referred to as long-term memory (LTM) (Andrade & May, 2004)

The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke

Competing theories of short-term memory function make specific predictions about the functional anatomy of auditory short-term memory and its role in language comprehension. We analysed high-resolution structural magnetic resonance images from 210 stroke patients and employed a novel voxel based analysis to test the relationship between auditory short-term memory and speech comprehension. Using digit span as an index of auditory short-term memory capacity we found that the structural integrity of a posterior region of the superior temporal gyrus and sulcus predicted auditory short-term memory capacity, even when performance on a range of other measures was factored out. We show that the integrity of this region also predicts the ability to comprehend spoken sentences. Our results therefore support cognitive models that posit a shared substrate between auditory short-term memory capacity and speech comprehension ability. The method applied here will be particularly useful for modelling structure–function relationships within other complex cognitive domains

Insensitivity of visual short-term memory to irrelevant visual information

Several authors have hypothesised that visuo-spatial working memory is functionally analogous to verbal working memory. Irrelevant background speech impairs verbal short-term memory. We investigated whether irrelevant visual information has an analogous effect on visual short-term memory, using a dynamic visual noise (DVN) technique known to disrupt visual imagery (Quinn & McConnell, 1996a). Experiment 1 replicated the effect of DVN on pegword imagery. Experiments 2 and 3 showed no effect of DVN on recall of static matrix patterns, despite a significant effect of a concurrent spatial tapping task. Experiment 4 showed no effect of DVN on encoding or maintenance of arrays of matrix patterns, despite testing memory by a recognition procedure to encourage visual rather than spatial processing. Serial position curves showed a one-item recency effect typical of visual short-term memory. Experiment 5 showed no effect of DVN on short-term recognition of Chinese characters, despite effects of visual similarity and a concurrent colour memory task that confirmed visual processing of the characters. We conclude that irrelevant visual noise does not impair visual short-term memory. Visual working memory may not be functionally analogous to verbal working memory, and different cognitive processes may underlie visual short-term memory and visual imagery

Lipreading with Long Short-Term Memory

Lipreading, i.e. speech recognition from visual-only recordings of a speaker's face, can be achieved with a processing pipeline based solely on neural networks, yielding significantly better accuracy than conventional methods. Feed-forward and recurrent neural network layers (namely Long Short-Term Memory; LSTM) are stacked to form a single structure which is trained by back-propagating error gradients through all the layers. The performance of such a stacked network was experimentally evaluated and compared to a standard Support Vector Machine classifier using conventional computer vision features (Eigenlips and Histograms of Oriented Gradients). The evaluation was performed on data from 19 speakers of the publicly available GRID corpus. With 51 different words to classify, we report a best word accuracy on held-out evaluation speakers of 79.6% using the end-to-end neural network-based solution (11.6% improvement over the best feature-based solution evaluated).Comment: Accepted for publication at ICASSP 201

Serial order in short-term memory

How do we maintain a novel sequence of items in the correct order? For example, how do we remember the car number plate at the scene of a crime? Or how do we remember an unfamiliar telephone number during the few seconds between putting down the telephone directory and picking up the telephone? This immediate serial recall or ‘memory-span’ task has fascinated psychologists for decades; it has remained the dominant empirical tool behind contemporary theories of short-term memory, such as Alan Baddeley’s working-memory theory (Baddeley, 1986). However, like many questions in cognitive psychology, the apparent ease with which we perform such a simple task (providing the telephone number is not too long!) masks a rich and complex host of issues

Short-Term Memory in Orthogonal Neural Networks

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size.Comment: 4 pages, 4 figures, to be published in Phys. Rev. Let

Short-term memory as a working memory control process

Aben et al. (2012) take issue with the unthoughtful use of the terms “working memory” (WM) and “short-term memory” (STM) in the cognitive and neuroscientific literature. Whereas I agree that neuroscientists using the term WM to refer to sustained neural activation and cognitive psychologists using the terms interchangeably reflects that the field has lost control over its own dictionary, the recommendations to develop more tasks does not seem to get to the heart of the matter. Here, I argue in favor of a theoretical approach to the constructs of WM and STM, as the terms have become as impure as the tasks that purport to measure the constructs

Long Short-Term Memory Spatial Transformer Network

Spatial transformer network has been used in a layered form in conjunction with a convolutional network to enable the model to transform data spatially. In this paper, we propose a combined spatial transformer network (STN) and a Long Short-Term Memory network (LSTM) to classify digits in sequences formed by MINST elements. This LSTM-STN model has a top-down attention mechanism profit from LSTM layer, so that the STN layer can perform short-term independent elements for the statement in the process of spatial transformation, thus avoiding the distortion that may be caused when the entire sequence is spatially transformed. It also avoids the influence of this distortion on the subsequent classification process using convolutional neural networks and achieves a single digit error of 1.6\% compared with 2.2\% of Convolutional Neural Network with STN layer