scholarly journals Effects of Audiovisual Memory Cues on Working Memory Recall

Vision ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Hilary C. Pearson ◽  
Jonathan M. P. Wilbiks
Keyword(s):  
Memory Recall ◽  
Accuracy Score ◽  
Multimodal Integration ◽  
Multimodal Learning ◽  
Object Discrimination ◽  
Learning Techniques ◽  
Main Effect ◽  
Efficient Learning ◽  
With Memory ◽  
Self Reference

Previous studies have focused on topics such as multimodal integration and object discrimination, but there is limited research on the effect of multimodal learning in memory. Perceptual studies have shown facilitative effects of multimodal stimuli for learning; the current study aims to determine whether this effect persists with memory cues. The purpose of this study was to investigate the effect that audiovisual memory cues have on memory recall, as well as whether the use of multiple memory cues leads to higher recall. The goal was to orthogonally evaluate the effect of the number of self-generated memory cues (one or three), and the modality of the self-generated memory-cue (visual: written words, auditory: spoken words, or audiovisual). A recall task was administered where participants were presented with their self-generated memory cues and asked to determine the target word. There was a significant main effect for number of cues, but no main effect for modality. A secondary goal of this study was to determine which types of memory cues result in the highest recall. Self-reference cues resulted in the highest accuracy score. This study has applications to improving academic performance by using the most efficient learning techniques.

scholarly journals Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4850 ◽  
Author(s):  
Carlos S. Pereira ◽  
Raul Morais ◽  
Manuel J. C. S. Reis
Keyword(s):  
Transfer Learning ◽  
Climatic Conditions ◽  
Fine Tuning ◽  
Variety Identification ◽  
Test Accuracy ◽  
Accuracy Score ◽  
Learning Techniques ◽  
Four Corners ◽  
Integrated Software ◽  
Grape Varieties

Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.

scholarly journals Four Minutes of Sprint Interval Training Had No Acute Effect on Improving Alertness, Mood, and Memory of Female Primary School Children and Secondary School Adolescents: A Randomized Controlled Trial

2020 ◽  
Vol 5 (4) ◽  
pp. 92
Author(s):  
Terence Chua ◽  
Abdul Rashid Aziz ◽  
Michael Chia
Keyword(s):  
Controlled Trial ◽  
Exercise Adherence ◽  
Interval Training ◽  
Acute Effect ◽  
Primary School Children ◽  
Memory Recall ◽  
Sprint Interval Training ◽  
Single Item ◽  
Main Effect ◽  
Hedonic Scale

We investigated whether a 4-min sprint interval training (SIT) protocol had an acute effect (15 min after) on improving alertness, mood, and memory recall in female students. Sixty-three children and 131 adolescents were randomly assigned to either a SIT or control (CON) group by the class Physical Education (PE) teachers. The SIT intervention was delivered twice a week for 3 weeks. SIT participants performed three, 20-s ‘all-out’ effort sprints interspersed with 60-s intervals of walking while CON group sat down and rested. PE lessons were arranged such that the first two sessions were to familiarise participants with the SIT protocol leading to acute assessments conducted on the third session. On that occasion, both groups rated their alertness and mood on a single-item hedonic scale and underwent an adapted memory recall test. The same assessments were administered to both groups fifteen minutes after delivery of SIT intervention. A 4-min SIT involving three, 20 s ‘all-out’ effort intensity sprints did not have an acute main effect on improving alertness, mood and, memory recall in female children (ηp2 = 0.009) and adolescents (ηp2 = 0.012). Students’ exercise adherence and feedback from PE teachers are indicatives of the potential scalability of incorporating SIT into PE programmes. Different work-to-rest ratios could be used in future studies.

Collaborative Bayesian Image Annotation and Retrieval

2011 ◽  
pp. 146-169
Author(s):  
Rui Zhang ◽  
Ling Guan
Keyword(s):  
Image Retrieval ◽  
Life Experience ◽  
Image Annotation ◽  
Contextual Information ◽  
Experimental Studies ◽  
Real Life ◽  
Learning Techniques ◽  
Semantic Concepts ◽  
Efficient Learning ◽  
Classification Procedures

With nearly twenty years of intensive study on the content-based image retrieval and annotation, the topic still remains difficult. By and large, the essential challenge lies in the limitation of using low-level visual features to characterize the semantic information of images, commonly known as the semantic gap. To bridge this gap, various approaches have been proposed based on the incorporation of human knowledge and textual information as well as the learning techniques utilizing the information of different modalities. At the same time, contextual information which represents the relationship between different real world/conceptual entities has shown its significance with respect to recognition tasks not only through real life experience but also scientific studies. In this chapter, the authors first review the state of the art of the existing works on image annotation and retrieval. Moreover, a general Bayesian framework which integrates content and contextual information and its application to both image annotation and retrieval are elaborated. The contextual information is considered as the statistical relationship between different images and different semantic concepts for image retrieval and annotation, respectively. The framework has efficient learning and classification procedures and the effectiveness is evaluated based on experimental studies, which demonstrate its advantage over both content-based and context-based approaches.

scholarly journals Analysis of Mutants Suggests Kamin Blocking in C. elegans is Due to Interference with Memory Recall Rather than Storage

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Daniel M. Merritt ◽  
Justina G. Melkis ◽  
Belinda Kwok ◽  
Celina Tran ◽  
Derek van der Kooy
Keyword(s):  
Memory Recall ◽  
C Elegans ◽  
With Memory

Learning Techniques in Economics at the Principles Level

1997 ◽  
Vol 41 (2) ◽  
pp. 54-61 ◽  
Author(s):  
Munir Quddus ◽  
Marie Bussing-Burks
Keyword(s):  
Social Sciences ◽  
Economic Education ◽  
Economic Literature ◽  
Elasticity Of Demand ◽  
Learning Techniques ◽  
The Subject ◽  
Study Techniques ◽  
Efficient Learning ◽  
Introductory Economics ◽  
Specific Learning

When a student in introductory economics approaches us for study advice, what study methods should we recommend? Economists have been vocal about their belief that their discipline is more analytical and rigorous than other social sciences and, therefore, needs special study techniques for efficient learning. However, the literature on economic education is silent on what these techniques are. This paper attempts to fill the gap in the economic literature on discipline-specific learning techniques. We discuss, in the context of an illustrative lecture on price elasticity of demand, several study tips for efficient learning of economics. We believe these study techniques, though valuable for other disciplines, are uniquely suited for economics and would enhance the chances of students in introductory economics learning the subject well.

Accessible Interface for Context Awareness in Mobile Devices for Users With Memory Impairment

2019 ◽  
Vol 8 (2) ◽  
pp. 1-30
Author(s):  
Iyad Abu Abu Doush ◽  
Sanaa Jarrah
Keyword(s):  
Mobile Application ◽  
Memory Impairment ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Memory Problems ◽  
User Context ◽  
Classification Technique ◽  
Learning Techniques ◽  
Context Detection ◽  
With Memory

Memory problems usually appear because of aging or may happen because of a brain injury. Such problems prevent the person from performing daily activities. In this paper, the authors propose a framework to develop a smartphone solution to detect and recognize the user context. In order to build the context detection framework, the authors compare three different machine learning techniques (C.4.5, random, and BFTree) in terms of context detection accuracy. Then, the authors use the classification technique with the highest accuracy in a mobile application to help users by detecting their context. The authors develop two interfaces based on the suggested accessibility features for users with memory impairment. Two scenarios are used to evaluate the user interface, and the results prove the applicability and the usability of the proposed context detection framework.

scholarly journals Supervised Machine Learning-Based Cardiovascular Disease Analysis and Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
M. D. Amzad Hossen ◽  
Tahia Tazin ◽  
Sumiaya Khan ◽  
Evan Alam ◽  
Hossain Ahmed Sojib ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Heart Disease ◽  
Supervised Machine Learning ◽  
Healthcare Sector ◽  
Accuracy Score ◽  
Learning Techniques ◽  
Cardiac Illness ◽  
The Moment ◽  
Cardiovascular Illness

Cardiovascular illness, often commonly known as heart disease, encompasses a variety of diseases that affect the heart and has been the leading cause of mortality globally in recent decades. It is associated with numerous risks for heart disease and a requirement of the moment to get accurate, trustworthy, and reasonable methods to establish an early diagnosis in order to accomplish early disease treatment. In the healthcare sector, data analysis is a widely utilized method for processing massive amounts of data. Researchers use a variety of statistical and machine learning methods to evaluate massive amounts of complicated medical data, assisting healthcare practitioners in predicting cardiac disease. This study covers many aspects of cardiac illness, as well as a model based on supervised learning techniques such as Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). It makes use of an existing dataset from the UCI Cleveland database of heart disease patients. There are 303 occurrences and 76 characteristics in the collection. Only 14 of these 76 characteristics are evaluated for testing, which is necessary to validate the performance of various methods. The purpose of this study is to forecast the likelihood of individuals getting heart disease. The findings show that logistic regression achieves the best accuracy score (92.10%).

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