scholarly journals Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity

2020 ◽  
Vol 31 (10) ◽  
pp. 1245-1260
Author(s):  
Domicele Jonauskaite ◽  
Ahmad Abu-Akel ◽  
Nele Dael ◽  
Daniel Oberfeld ◽  
Ahmed M. Abdel-Khalek ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Cognitive Architecture ◽  
Well Being ◽  
Similarity Coefficient ◽  
Geographic Proximity ◽  
Native Languages ◽  
Pattern Similarity ◽  
Average Similarity ◽  
Color Emotion ◽  
Emotion Concepts

Many of us “see red,” “feel blue,” or “turn green with envy.” Are such color-emotion associations fundamental to our shared cognitive architecture, or are they cultural creations learned through our languages and traditions? To answer these questions, we tested emotional associations of colors in 4,598 participants from 30 nations speaking 22 native languages. Participants associated 20 emotion concepts with 12 color terms. Pattern-similarity analyses revealed universal color-emotion associations (average similarity coefficient r = .88). However, local differences were also apparent. A machine-learning algorithm revealed that nation predicted color-emotion associations above and beyond those observed universally. Similarity was greater when nations were linguistically or geographically close. This study highlights robust universal color-emotion associations, further modulated by linguistic and geographic factors. These results pose further theoretical and empirical questions about the affective properties of color and may inform practice in applied domains, such as well-being and design.

Are Emotions Abstract or Concrete?

2017 ◽  
Vol 64 (5) ◽  
pp. 315-324 ◽  
Author(s):  
Xu Xu ◽  
Chunyan Kang ◽  
Kaia Sword ◽  
Taomei Guo
Keyword(s):  
Lexical Decision ◽  
Lexical Decision Task ◽  
Well Being ◽  
Event Related Potential ◽  
Decision Task ◽  
N400 Amplitude ◽  
Abstract Nouns ◽  
Concrete Nouns ◽  
Emotion Concepts ◽  
Healthy Participants

Abstract. The ability to identify and communicate emotions is essential to psychological well-being. Yet research focusing exclusively on emotion concepts has been limited. This study examined nouns that represent emotions (e.g., pleasure, guilt) in comparison to nouns that represent abstract (e.g., wisdom, failure) and concrete entities (e.g., flower, coffin). Twenty-five healthy participants completed a lexical decision task. Event-related potential (ERP) data showed that emotion nouns elicited less pronounced N400 than both abstract and concrete nouns. Further, N400 amplitude differences between emotion and concrete nouns were evident in both hemispheres, whereas the differences between emotion and abstract nouns had a left-lateralized distribution. These findings suggest representational distinctions, possibly in both verbal and imagery systems, between emotion concepts versus other concepts, implications of which for theories of affect representations and for research on affect disorders merit further investigation.

Social Support and Well-Being: Preliminary Indicators Based on Two Samples of the Elderly

1986 ◽  
Vol 21 (1) ◽  
pp. 61-77 ◽  
Author(s):  
Mary J. Levitt ◽  
Toni C. Antonucci ◽  
M. Cherie Clark ◽  
James Rotton ◽  
Gordon E. Finley
Keyword(s):  
Social Support ◽  
Life Satisfaction ◽  
Well Being ◽  
The Elderly ◽  
National Sample ◽  
Network Size ◽  
Support Network ◽  
Geographic Proximity ◽  
Supportive Relationships ◽  
Two Samples

The structure of social support and its relation to health, affect, and life satisfaction are compared for two samples of the elderly. The first is a national representative sample; the second is a distressed sample from South Miami Beach. Although there are similarities in the structure of social support across the two groups, those in the Miami Beach sample report fewer support figures, and far fewer within geographic proximity, than do those in the national sample. This comparative network impoverishment is particularly marked for male respondents and is accentuated by a high number of isolates in this group. In addition, stronger relationships are found between support network size and affect, and among affect, life satisfaction, and health in the South Miami Beach sample. Older men in poor health and without supportive relationships are targeted as a particularly high risk subgroup. The discussion includes a focus on personal, situational, and life span differences related to variations in support and well-being and a consideration of implications for more recent waves of elderly sun-belt migrants.

scholarly journals Implementation of modified Q learning technique in EMCAP control architecture

2017 ◽  
Vol 7 (1.5) ◽  
pp. 269
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Learning Algorithm ◽  
Control Strategies ◽  
Cognitive Architecture ◽  
Dynamic Environment ◽  
Learning System ◽  
Selection Strategy ◽  
Q Learning ◽  
Learning Techniques ◽  
Markov Decision ◽  
Environment Experiment

This research introduces a self learning modified (Q-Learning) techniques in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). Q-learning is a modelless reinforcement learning (RL) methodology technique. In Specific, Q-learning can be applied to establish an optimal action-selection strategy for any respective Markov decision process. In this research introduces the modified Q-learning in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). EMCAP architecture [1] enables and presents various agent control strategies for static and dynamic environment.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance.his modified q learning algorithm can be more suitable in EMCAP architecture.  The experiments are conducted the modified Q-Learning system gets more rewards compare to existing Q-learning.

scholarly journals AI-powered Posture Training: Application of Machine Learning in Sitting Posture Recognition Using the LifeChair Smart Cushion

2019 ◽  
Author(s):  
Katia Bourahmoune ◽  
Toshiyuki Amagasa
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Well Being ◽  
Recognition System ◽  
Training System ◽  
Sensor Data ◽  
Physical And Mental Health ◽  
Sitting Posture ◽  
Posture Monitoring ◽  
Posture Recognition

Humans spend on average more than half of their day sitting down. The ill-effects of poor sitting posture and prolonged sitting on physical and mental health have been extensively studied, and solutions for curbing this sedentary epidemic have received special attention in recent years. With the recent advances in sensing technologies and Artificial Intelligence (AI), sitting posture monitoring and correction is one of the key problems to address for enhancing human well-being using AI. We present the application of a sitting posture training smart cushion called LifeChair that combines a novel pressure sensing technology, a smartphone app interface and machine learning (ML) for real-time sitting posture recognition and seated stretching guidance. We present our experimental design for sitting posture and stretch pose data collection using our posture training system. We achieved an accuracy of 98.93% in detecting more than 13 different sitting postures using a fast and robust supervised learning algorithm. We also establish the importance of taking into account the divergence in user body mass index in posture monitoring. Additionally, we present the first ML-based human stretch pose recognition system for pressure sensor data and show its performance in classifying six common chair-bound stretches.

Is Infidelity Predictable? Using Interpretable Machine Learning to Identify the Most Important Predictors of Infidelity

2020 ◽  
Author(s):  
Laura Marika Vowels ◽  
Matthew J Vowels ◽  
Kristen P Mark
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Large Body ◽  
Predictor Variable ◽  
Well Being ◽  
Relationship Length ◽  
Three Samples ◽  
Online Infidelity ◽  
Shapley Values ◽  
Game Theoretic

Infidelity is a common occurrence in relationships and can have a devastating impact on both partners’ well-being. A large body of literature have attempted to factors that can explain or predict infidelity but have been unable to estimate the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict in-person and online infidelity and intentions toward future infidelity across three samples (two dyadic samples; N = 1846). We also used a game theoretic explanation technique, Shapley values, which allowed us to estimate the effect size of each predictor variable on infidelity. The present study showed that infidelity was somewhat predictable overall with interpersonal factors (relationship satisfaction, love, desire, relationship length) being the most predictive. The results suggest that addressing relationship difficulties early in the relationship can help prevent future infidelity.

Geographic Proximity of Adult Children and the Well-Being of Older Persons

2014 ◽  
Vol 37 (5) ◽  
pp. 524-551 ◽  
Author(s):  
Marieke van der Pers ◽  
Clara H. Mulder ◽  
Nardi Steverink
Keyword(s):  
Adult Children ◽  
Older Persons ◽  
Well Being ◽  
Geographic Proximity

scholarly journals Age-based facial recognition using convoluted neural network deep learning algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 424
Author(s):  
Julius Yong Wu Jien ◽  
Aslina Baharum ◽  
Shaliza Hayati A. Wahab ◽  
Nordin Saad ◽  
Muhammad Omar ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Facial Recognition ◽  
Well Being ◽  
Deep Learning Algorithm ◽  
Business Applications ◽  
Image Dataset ◽  
The Face ◽  
The Individual

Face recognition is the use of biometric innovations that can see or validate a person by seeing and investigating designs depending on the shape of the individual. Face recognition is used largely for the purpose of well-being, despite the fact that passion for different areas of use is growing. Overall, face recognition innovations are worth considering because they have the potential for broad legal jurisdiction and different business applications. It is widely used in many spaces. How it works is a product of facial recognition processing facial geometry. The hole between the ear and the good way from the front to the jaw are the main variables. This code distinguishes the highlight of the face that is important for your facial separation and creates your facial expression. Therefore, this study gives an overview of age detection using a different combination of machine learning and image processing methods on the image dataset.

scholarly journals IDENTIFIKASI KERAGAMAN GENETIK KENTANG SUPEJOHN TRANSGENIK (Solanum tuberosum L. var supejohn)

EUGENIA ◽  
2012 ◽  
Vol 18 (3) ◽  
Author(s):  
Christine L.W. Lengkong ◽  
Jeany Polii-Mandang ◽  
Edy F. Lengkong
Keyword(s):  
Genetic Diversity ◽  
Dna Isolation ◽  
Rapd Analysis ◽  
Solanum Tuberosum L ◽  
Similarity Coefficient ◽  
Agarose Gel ◽  
Buffer Solution ◽  
Random Primers ◽  
Average Similarity ◽  
Polymorphic Bands

ABSTRACT   The aim of this study was to determine the genetic diversity of potato Supejohn transgenic that had been twenty-five times subcultured with exploration  method. The genetic variability was calculated using molecular marker Polimorphic Random Amplified DNA (RAPD) analysis of ten samples from four weeks planlets of Supejohn transgenic plants using ten random primers. DNA isolation of 10 samples using CTAB buffer  then measurement of the DNA qualification and quantification,  afterwards amplification of DNA by PCR using 10 random primers followed by electrophoresis on a 1% agarose gel with TAE 1x electrode buffer solution. Visualisation  the results used UV Transluminator to see DNA bands and the data of  polymorphic bands had been analized used the NTSYS-pc version of 1.07 program to obtain the similarity coefficient  and the dendrogram.The results showed that five random primers produced polymorphic DNA bands with 0.39 - 0.88 similarity coefficient  and the average similarity coefficient is 0.63 (63%) or the genetic diversity of the samples as many as 37%. Dendrogram formed eight distinct clusters corresponding similarity coefficient, the formation of clusters means that there is genetic diversity among the DNA samples. Keywords: potato Supejohn transgenic, RAPD, genetic diversity ABSTRAK Penelitian ini bertujuan untuk  menentukan keragaman genetik kentang Supejohn transgenik yang telah dua puluh lima kali disubkultur dengan menggunakan metode penelitian eksplorasi dengan penanda molekuler  Random Amplified Polimorphic DNA (RAPD) terhadap 10 sampel tanaman Supejohn transgenik, berumur empat minggu dan  menggunakan  10 primer random.  Isolasi DNA dari 10 sampel menggunakan buffer CTAB sesudah itu dilakukan pengukuran kualitas dan kuantitas  DNA, kemudian dilanjutkan dengan amplifikasi DNA secara PCR menggunakan 10 primer random diikuti dengan  elektroforesis pada gel agarose 1% dengan larutan penyanggah elektroda TAE 1x. Visualisasi hasil elektroforesis  menggunakan UV Transluminator untuk melihat pita DNA  dan analisis  data pita polimorfik  menggunakan program NTSYS-pc versi 1.07 sehingga didapatkan koefisien kesamaan dan dendrogram. Hasil penelitian menunjukkan bahwa lima primer random menghasilkan pita DNA polimorfik dengan koefisien kesamaan 0.39 – 0.88 dan rata-rata koefisien kesamaan yaitu 0.63 (63%) atau  keragaman  genetik sampel sebesar 37%.  Dendrogram  membentuk delapan cluster yang berbeda sesuai koefisien kesamaan,terbentuknya clusters mengartikan bahwa ada keragaman genetik antar sampel DNA. Eugenia Volume 18 No. 3  Desember 2012 Kata kunci : kentang Supejohn transgenik , RAPD, keragaman genetik

scholarly journals A diagram for evaluating multiple aspects of model performance in simulating vector fields

2016 ◽  
Vol 9 (12) ◽  
pp. 4365-4380 ◽  
Author(s):  
Zhongfeng Xu ◽  
Zhaolu Hou ◽  
Ying Han ◽  
Weidong Guo
Keyword(s):  
Vector Fields ◽  
Climate Models ◽  
Regional Climate ◽  
Model Performance ◽  
Systematic Difference ◽  
Similarity Coefficient ◽  
Mean Square ◽  
Vector Length ◽  
Taylor Diagram ◽  
Pattern Similarity

Abstract. Vector quantities, e.g., vector winds, play an extremely important role in climate systems. The energy and water exchanges between different regions are strongly dominated by wind, which in turn shapes the regional climate. Thus, how well climate models can simulate vector fields directly affects model performance in reproducing the nature of a regional climate. This paper devises a new diagram, termed the vector field evaluation (VFE) diagram, which is a generalized Taylor diagram and able to provide a concise evaluation of model performance in simulating vector fields. The diagram can measure how well two vector fields match each other in terms of three statistical variables, i.e., the vector similarity coefficient, root mean square length (RMSL), and root mean square vector difference (RMSVD). Similar to the Taylor diagram, the VFE diagram is especially useful for evaluating climate models. The pattern similarity of two vector fields is measured by a vector similarity coefficient (VSC) that is defined by the arithmetic mean of the inner product of normalized vector pairs. Examples are provided, showing that VSC can identify how close one vector field resembles another. Note that VSC can only describe the pattern similarity, and it does not reflect the systematic difference in the mean vector length between two vector fields. To measure the vector length, RMSL is included in the diagram. The third variable, RMSVD, is used to identify the magnitude of the overall difference between two vector fields. Examples show that the VFE diagram can clearly illustrate the extent to which the overall RMSVD is attributed to the systematic difference in RMSL and how much is due to the poor pattern similarity.

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