A Field Study on the Impact of Variations in Short-Term Memory Demands on Drivers’ Visual Attention and Driving Performance Across Three Age Groups

AbstractFor many EFL learners, listening poses a grave challenge. The difficulty in segmenting a stream of speech and limited capacity in short-term memory are common weaknesses for language learners. Specifically, reduced forms, which frequently appear in authentic informal conversations, compound the challenges in listening comprehension. Numerous interventions have been implemented to assist EFL language learners, and of these, the application of captions has been found highly effective in promoting learning. Few studies have examined how different modes of captions may enhance listening comprehension. This study proposes three modes of captions: full, keyword-only, and annotated keyword captions and investigates their contribution to the learning of reduced forms and overall listening comprehension. Forty-four EFL university students participated in the study and were randomly assigned to one of the three groups. The results revealed that all three groups exhibited improvement on the pre-test while the annotated keyword caption group exhibited the best performance with the highest mean score. Comparing performances between groups, the annotated keyword caption group also emulated both the full caption and the keyword-only caption groups, particularly in the ability to recognize reduced forms. The study sheds light on the potential of annotated keyword captions in enhancing reduced forms learning and overall listening comprehension.

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The impact of aging and hearing status on verbal short-term memory

Aging Neuropsychology and Cognition ◽

10.1080/13825585.2013.832725 ◽

2013 ◽

Vol 21 (4) ◽

pp. 464-482 ◽

Cited By ~ 10

Author(s):

Clémence Verhaegen ◽

Fabienne Collette ◽

Steve Majerus

Keyword(s):

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Hearing Status ◽

The Impact

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Visual Attention and Visual Short-Term Memory in Alzheimer's Disease

Interdisciplinary Topics in Gerontology - Vision in Alzheimer's Disease ◽

10.1159/000080012 ◽

2004 ◽

pp. 248-270 ◽

Cited By ~ 3

Author(s):

Shaun P. Vecera ◽

Matthew Rizzo

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Visual Attention ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Visual Short Term Memory

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Visual short-term memory guides infants’ visual attention

Cognition ◽

10.1016/j.cognition.2018.04.016 ◽

2018 ◽

Vol 177 ◽

pp. 189-197 ◽

Cited By ~ 1

Author(s):

Samantha G. Mitsven ◽

Lisa M. Cantrell ◽

Steven J. Luck ◽

Lisa M. Oakes

Keyword(s):

Visual Attention ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Visual Short Term Memory

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Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

SPE Reservoir Evaluation & Engineering ◽

10.2118/208596-pa ◽

2021 ◽

pp. 1-17

Author(s):

Enda Du ◽

Yuetian Liu ◽

Ziyan Cheng ◽

Liang Xue ◽

Jing Ma ◽

...

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Production Forecasting ◽

Temporal Correlations ◽

Proposed Model ◽

The Mean ◽

Long Short Term Memory ◽

The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

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Analysis of Groundwater Level Variations Caused by the Changes in Groundwater Withdrawals Using Long Short-Term Memory Network

Hydrology ◽

10.3390/hydrology7030064 ◽

2020 ◽

Vol 7 (3) ◽

pp. 64 ◽

Cited By ~ 1

Author(s):

Mun-Ju Shin ◽

Soo-Hyoung Moon ◽

Kyung Goo Kang ◽

Duk-Chul Moon ◽

Hyuk-Joon Koh

Keyword(s):

Groundwater Level ◽

Short Term Memory ◽

Groundwater Resources ◽

Prediction Performance ◽

Groundwater Withdrawal ◽

Short Term ◽

Maximum Increase ◽

Term Memory ◽

Long Short Term Memory ◽

The Impact

To properly manage the groundwater resources, it is necessary to analyze the impact of groundwater withdrawal on the groundwater level. In this study, a Long Short-Term Memory (LSTM) network was used to evaluate the groundwater level prediction performance and analyze the impact of the change in the amount of groundwater withdrawal from the pumping wells on the change in the groundwater level in the nearby monitoring wells located in Jeju Island, Korea. The Nash–Sutcliffe efficiency between the observed and simulated groundwater level was over 0.97. Therefore, the groundwater prediction performance of LSTM was remarkably high. If the groundwater level is simulated on the assumption that the future withdrawal amount is reduced by 1/3 of the current groundwater withdrawal, the range of the maximum rise of the groundwater level would be 0.06–0.13 m compared to the current condition. In addition, assuming that no groundwater is taken, the range of the maximum increase in the groundwater level would be 0.11–0.38 m more than the current condition. Therefore, the effect of groundwater withdrawal on the groundwater level in this area was exceedingly small. The method and results can be used to develop new groundwater withdrawal sources for the redistribution of groundwater withdrawals.

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Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

Complexity ◽

10.1155/2020/1846926 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Daniel Štifanić ◽

Jelena Musulin ◽

Adrijana Miočević ◽

Sandi Baressi Šegota ◽

Roman Šubić ◽

...

Keyword(s):

Crude Oil ◽

Stock Prices ◽

Short Term Memory ◽

Oil Price ◽

Stationary Wavelet Transform ◽

Crude Oil Price ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

The Impact

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

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Using long short-term memory networks for river flow prediction

Hydrology Research ◽

10.2166/nh.2020.026 ◽

2020 ◽

Vol 51 (6) ◽

pp. 1358-1376

Author(s):

Wei Xu ◽

Yanan Jiang ◽

Xiaoli Zhang ◽

Yi Li ◽

Run Zhang ◽

...

Keyword(s):

River Flow ◽

Short Term Memory ◽

River Basins ◽

Short Term ◽

Term Memory ◽

Learning Efficiency ◽

Flow Prediction ◽

Non Linear ◽

Long Short Term Memory ◽

The Impact

Abstract Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow. The batch size and the number of LSTM cells are sensitive parameters and should be carefully tuned to achieve a balance between learning efficiency and stability. Compared with several hydrological models, the LSTM network achieves good performance in terms of three evaluation criteria, i.e., coefficient of determination, Nash–Sutcliffe Efficiency and relative error, which demonstrates its powerful capacity in learning non-linear and complex processes in hydrological modelling.

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Do elite breath-hold divers suffer from mild short-term memory impairments?

Applied Physiology Nutrition and Metabolism ◽

10.1139/apnm-2017-0245 ◽

2018 ◽

Vol 43 (3) ◽

pp. 247-251 ◽

Cited By ~ 3

Author(s):

François Billaut ◽

Patrice Gueit ◽

Sylvane Faure ◽

Guillaume Costalat ◽

Frédéric Lemaître

Keyword(s):

Short Term Memory ◽

Breath Hold ◽

Short Term ◽

Attentional Processing ◽

Term Memory ◽

Memory Impairments ◽

Neurocognitive Functions ◽

Neuropsychological Tasks ◽

The Impact

Repeated apneas are associated with severe hypoxemia that may ultimately lead to loss of consciousness in some breath-hold divers. Despite increasing number of practitioners, the relationship between apnea-induced hypoxia and neurocognitive functions is still poorly understood in the sport of free diving. To shed light onto this phenomenon, we examined the impact of long-term breath-hold diving training on attentional processing, short-term memory, and long-term mnesic and executive functions. Thirty-six men matched for age, height, and weight were separated into the following 3 groups: (i) 12 elite breath-hold divers (EBHD), mean static apnea best time 371 s, 105 months mean apnea experience; (ii) 12 novice breath-hold divers, mean best time 243 s, 8.75 months mean apnea experience; and (iii) 12 physical education students with no breath-hold diving experience; all of these participants performed varied written and computerized neuropsychological tasks. Compared with the 2 other groups, the EBHD group was slower to complete the interference card during a Stroop test (F[1,33] = 4.70, p < 0.05), and presented more errors on the interference card (F[1,33] = 2.96, p < 0.05) and a lower total interference score (F[1,33] = 5.64, p < 0.05). The time to complete the interference card test was positively correlated with maximal static apnea duration (r = 0.73, p < 0.05) and the number of years of breath-hold diving training (r = 0.79, p < 0.001). These findings suggest that breath-hold diving training over several years may cause mild, but persistent, short-term memory impairments.

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