ARTIFICIAL INTELLIGENCE AND COGNITIVE SCIENCE CONFERENCE


Artificial Intelligence and Cognitive Science Conference is one of the leading research topics in the international research conference domain. Artificial Intelligence and Cognitive Science is a conference track under the Psychology Conference which aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Psychology.

internationalconference.net provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of (Psychology).

Artificial Intelligence and Cognitive Science is not just a call for academic papers on the topic; it can also include a conference, event, symposium, scientific meeting, academic, or workshop.

You are welcome to SUBMIT your research paper or manuscript to Artificial Intelligence and Cognitive Science Conference Track will be held at “Psychology Conference in Barcelona, Spain in October 2021” - “Psychology Conference in San Francisco, United States in November 2021” - “Psychology Conference in Istanbul, Turkey in November 2021” - “Psychology Conference in Singapore, Singapore in November 2021” - “Psychology Conference in Bangkok, Thailand in December 2021” - “Psychology Conference in Paris, France in December 2021” .

Artificial Intelligence and Cognitive Science is also a leading research topic on Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, Elsevier, Scopus, Web of Science.

XXVIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

OCTOBER 21 - 22, 2021
BARCELONA, SPAIN

XXIX. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 02 - 03, 2021
SAN FRANCISCO, UNITED STATES

XXX. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 12 - 13, 2021
ISTANBUL, TURKEY

XXXI. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 19 - 20, 2021
SINGAPORE, SINGAPORE

XXXII. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 15 - 16, 2021
BANGKOK, THAILAND

XXXIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 28 - 29, 2021
PARIS, FRANCE

FINISHED

I. INTERNATIONAL PSYCHOLOGY CONFERENCE

MARCH 19 - 20, 2019
ISTANBUL, TURKEY

FINISHED

II. INTERNATIONAL PSYCHOLOGY CONFERENCE

JUNE 26 - 27, 2019
PARIS, FRANCE

FINISHED

III. INTERNATIONAL PSYCHOLOGY CONFERENCE

AUGUST 21 - 22, 2019
LONDON, UNITED KINGDOM

FINISHED

IV. INTERNATIONAL PSYCHOLOGY CONFERENCE

OCTOBER 08 - 09, 2019
NEW YORK, UNITED STATES

FINISHED

V. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 12 - 13, 2019
ROME, ITALY

FINISHED

VI. INTERNATIONAL PSYCHOLOGY CONFERENCE

FEBRUARY 13 - 14, 2020
LONDON, UNITED KINGDOM

FINISHED

VII. INTERNATIONAL PSYCHOLOGY CONFERENCE

APRIL 15 - 16, 2020
BARCELONA, SPAIN

FINISHED

VIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

MAY 11 - 12, 2020
ISTANBUL, TURKEY

FINISHED

IX. INTERNATIONAL PSYCHOLOGY CONFERENCE

JUNE 05 - 06, 2020
SAN FRANCISCO, UNITED STATES

FINISHED

X. INTERNATIONAL PSYCHOLOGY CONFERENCE

JULY 20 - 21, 2020
PARIS, FRANCE

FINISHED

XI. INTERNATIONAL PSYCHOLOGY CONFERENCE

AUGUST 10 - 11, 2020
NEW YORK, UNITED STATES

FINISHED

XII. INTERNATIONAL PSYCHOLOGY CONFERENCE

SEPTEMBER 10 - 11, 2020
TOKYO, JAPAN

FINISHED

XIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

SEPTEMBER 16 - 17, 2020
ZÜRICH, SWITZERLAND

FINISHED

XIV. INTERNATIONAL PSYCHOLOGY CONFERENCE

OCTOBER 21 - 22, 2020
BARCELONA, SPAIN

FINISHED

XV. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 02 - 03, 2020
SAN FRANCISCO, UNITED STATES

FINISHED

XVI. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 12 - 13, 2020
ISTANBUL, TURKEY

FINISHED

XVII. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 19 - 20, 2020
SINGAPORE, SINGAPORE

FINISHED

XVIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 15 - 16, 2020
BANGKOK, THAILAND

FINISHED

XIX. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 28 - 29, 2020
PARIS, FRANCE

FINISHED

XX. INTERNATIONAL PSYCHOLOGY CONFERENCE

FEBRUARY 13 - 14, 2021
LONDON, UNITED KINGDOM

FINISHED

XXI. INTERNATIONAL PSYCHOLOGY CONFERENCE

APRIL 15 - 16, 2021
BARCELONA, SPAIN

FINISHED

XXII. INTERNATIONAL PSYCHOLOGY CONFERENCE

MAY 11 - 12, 2021
ISTANBUL, TURKEY

FINISHED

XXIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

JUNE 05 - 06, 2021
SAN FRANCISCO, UNITED STATES

FINISHED

XXIV. INTERNATIONAL PSYCHOLOGY CONFERENCE

JULY 20 - 21, 2021
PARIS, FRANCE

FINISHED

XXV. INTERNATIONAL PSYCHOLOGY CONFERENCE

AUGUST 10 - 11, 2021
NEW YORK, UNITED STATES

FINISHED

XXVI. INTERNATIONAL PSYCHOLOGY CONFERENCE

SEPTEMBER 10 - 11, 2021
TOKYO, JAPAN

FINISHED

XXVII. INTERNATIONAL PSYCHOLOGY CONFERENCE

SEPTEMBER 16 - 17, 2021
ZÜRICH, SWITZERLAND

Psychology Conference Call For Papers are listed below:

Previously Published Papers on "Artificial Intelligence and Cognitive Science Conference"

  • Scholar Index for Research Performance Evaluation Using Multiple Criteria Decision Making Analysis
    Authors: C. Ardil, Keywords: Multiple Criteria Decision Making Analysis, MCDMA, Research Performance Evaluation, Scholar Index, h index, Science Citation Index, Science Efficiency, Cumulative Citation Index, Sciencemetrics DOI:10.5281/zenodo. Abstract: This paper aims to present an objective quantitative methodology on how to evaluate individual’s scholarly research output using multiple criteria decision analysis. A multiple criteria decision making analysis (MCDMA) methodological process is adopted to build a multiple criteria evaluation model. With the introduction of the scholar index, which gives significant information about a researcher's productivity and the scholarly impact of his or her publications in a single number (s is the number of publications with at least s citations); cumulative research citation index; the scholar index is included in the citation databases to cover the multidimensional complexity of scholarly research performance and to undertake objective evaluations with scholar index. The scholar index, one of publication activity indexes, is analyzed by considering it to be the most appropriate sciencemetric indicator which allows to smooth over many drawbacks of scholarly output assessment by mere calculation of the number of publications (quantity) and citations (quality). Hence, this study includes a set of indicators-based scholar index to be used for evaluating scholarly researchers. Google Scholar open science database was used to assess and discuss scholarly productivity and impact of researchers. Based on the experiment of computing the scholar index, and its derivative indexes for a set of researchers on open research database platform, quantitative methods of assessing scholarly research output were successfully considered to rank researchers. The proposed methodology considers the ranking, and the selection of data on which a scholarly research performance evaluation was based, the analysis of the data, and the presentation of the multiple criteria analysis results.
  • Attribute Analysis of Quick Response Code Payment Users Using Discriminant Non-negative Matrix Factorization
    Authors: Hironori Karachi, Haruka Yamashita, Keywords: Data science, non-negative matrix factorization, missing data, quality of services. DOI:10.5281/zenodo. Abstract: Recently, the system of quick response (QR) code is getting popular. Many companies introduce new QR code payment services and the services are competing with each other to increase the number of users. For increasing the number of users, we should grasp the difference of feature of the demographic information, usage information, and value of users between services. In this study, we conduct an analysis of real-world data provided by Nomura Research Institute including the demographic data of users and information of users’ usages of two services; LINE Pay, and PayPay. For analyzing such data and interpret the feature of them, Nonnegative Matrix Factorization (NMF) is widely used; however, in case of the target data, there is a problem of the missing data. EM-algorithm NMF (EMNMF) to complete unknown values for understanding the feature of the given data presented by matrix shape. Moreover, for comparing the result of the NMF analysis of two matrices, there is Discriminant NMF (DNMF) shows the difference of users features between two matrices. In this study, we combine EMNMF and DNMF and also analyze the target data. As the interpretation, we show the difference of the features of users between LINE Pay and Paypay.
  • Conceptualizing Thoughtful Intelligence for Sustainable Decision Making
    Authors: Musarrat Jabeen, Keywords: Thoughtful intelligence, Sustainable decision making, Thoughtful decision support system. DOI:10.5281/zenodo. Abstract: Thoughtful intelligence offers a sustainable position to enhance the influence of decision-makers. Thoughtful Intelligence implies the understanding to realize the impact of one’s thoughts, words and actions on the survival, dignity and development of the individuals, groups and nations. Thoughtful intelligence has received minimal consideration in the area of Decision Support Systems, with an end goal to evaluate the quantity of knowledge and its viability. This pattern degraded the imbibed contribution of thoughtful intelligence required for sustainable decision making. Given the concern, this paper concentrates on the question: How to present a model of Thoughtful Decision Support System (TDSS)? The aim of this paper is to appreciate the concepts of thoughtful intelligence and insinuate a Decision Support System based on thoughtful intelligence. Thoughtful intelligence includes three dynamic competencies: i) Realization about long term impacts of decisions that are made in a specific time and space, ii) A great sense of taking actions, iii) Intense interconnectivity with people and nature and; seven associate competencies, of Righteousness, Purposefulness, Understanding, Contemplation, Sincerity, Mindfulness, and Nurturing. The study utilizes two methods: Focused group discussion to count prevailing Decision Support Systems; 70% results of focus group discussions found six decision support systems and the positive inexistence of thoughtful intelligence among decision support systems regarding sustainable decision making. Delphi focused on defining thoughtful intelligence to model (TDSS). 65% results helped to conceptualize (definition and description) of thoughtful intelligence. TDSS is offered here as an addition in the decision making literature. The clients are top leaders.
  • A Constructivist Approach and Tool for Autonomous Agent Bottom-up Sequential Learning
    Authors: Jianyong Xue, Olivier L. Georgeon, Salima Hassas, Keywords: Cognitive development, constructivist learning, hierarchical sequential learning, self-adaptation. DOI:10.5281/zenodo. Abstract: During the initial phase of cognitive development, infants exhibit amazing abilities to generate novel behaviors in unfamiliar situations, and explore actively to learn the best while lacking extrinsic rewards from the environment. These abilities set them apart from even the most advanced autonomous robots. This work seeks to contribute to understand and replicate some of these abilities. We propose the Bottom-up hiErarchical sequential Learning algorithm with Constructivist pAradigm (BEL-CA) to design agents capable of learning autonomously and continuously through interactions. The algorithm implements no assumption about the semantics of input and output data. It does not rely upon a model of the world given a priori in the form of a set of states and transitions as well. Besides, we propose a toolkit to analyze the learning process at run time called GAIT (Generating and Analyzing Interaction Traces). We use GAIT to report and explain the detailed learning process and the structured behaviors that the agent has learned on each decision making. We report an experiment in which the agent learned to successfully interact with its environment and to avoid unfavorable interactions using regularities discovered through interaction.
  • The Mechanism Underlying Empathy-Related Helping Behavior: An Investigation of Empathy-Attitude- Action Model
    Authors: Wan-Ting Liao, Angela K. Tzeng, Keywords: Affective empathy, attitude, cognitive empathy, prosocial behavior, psychopathic traits. DOI:10.5281/zenodo. Abstract: Empathy has been an important issue in psychology, education, as well as cognitive neuroscience. Empathy has two major components: cognitive and emotional. Cognitive component refers to the ability to understand others’ perspectives, thoughts, and actions, whereas emotional component refers to understand how others feel. Empathy can be induced, attitude can then be changed, and with enough attitude change, helping behavior can occur. This finding leads us to two questions: is attitude change really necessary for prosocial behavior? And, what roles cognitive and affective empathy play? For the second question, participants with different psychopathic personality (PP) traits are critical because high PP people were found to suffer only affective empathy deficit. Their cognitive empathy shows no significant difference from the control group. 132 college students voluntarily participated in the current three-stage study. Stage 1 was to collect basic information including Interpersonal Reactivity Index (IRI), Psychopathic Personality Inventory-Revised (PPI-R), Attitude Scale, Visual Analogue Scale (VAS), and demographic data. Stage two was for empathy induction with three controversial scenarios, namely domestic violence, depression with a suicide attempt, and an ex-offender. Participants read all three stories and then rewrite the stories by one of two perspectives (empathetic vs. objective). They would then complete the VAS and Attitude Scale one more time for their post-attitude and emotional status. Three IVs were introduced for data analysis: PP (High vs. Low), Responsibility (whether or not the character is responsible for what happened), and Perspective-taking (Empathic vs. Objective). Stage 3 was for the action. Participants were instructed to freely use the 17 tokens they received as donations. They were debriefed and interviewed at the end of the experiment. The major findings were people with higher empathy tend to take more action in helping. Attitude change is not necessary for prosocial behavior. The controversy of the scenarios and how familiar participants are towards target groups play very important roles. Finally, people with high PP tend to show more public prosocial behavior due to their affective empathy deficit. Pre-existing value and belief as well as recent dramatic social events seem to have a big impact and possibly reduce the effect of the independent variables (IV) in our paradigm.
  • Language Policy as an Instrument for Nation Building and Minority Representation: Supporting Cases from South Asia
    Authors: Kevin You, Keywords: Language policy, South Asia, nation building, Artificial states. DOI:10.5281/zenodo. Abstract: Nation-building has been a key consideration in ethno-linguistically diverse post-colonial ‘artificial states’, where ethnic tensions, religious differences and the risk of persecution of minorities are common. Language policy can help with nation-building, but it can also hinder the process. An important challenge is in recognising which language policy to adopt. This article proposes that the designation of a widely used lingua franca as a national language (in an official capacity or otherwise) - in a culturally, ethnically and linguistically diverse post-colonial state - assists its nation-building efforts in the long run. To demonstrate, this paper looks at the cases of Sri Lanka, Indonesia and India: three young nations which together emerged out of the Second World War with comparable colonial experiences, but subsequently adopted different language policies to different effects. Insights presented underscore the significance of inclusive language policy in sustainable nation-building in states with comparable post-colonial experiences.
  • Machine Learning Techniques in Bank Credit Analysis
    Authors: Fernanda M. Assef, Maria Teresinha A. Steiner, Keywords: Artificial Neural Networks, ANNs, classifier algorithms, credit risk assessment, logistic regression, machine learning, support vector machines. DOI:10.5281/zenodo. Abstract: The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.
  • Personal Factors and Career Adaptability in a Call Centre Work Environment: The Mediating Effects of Professional Efficacy
    Authors: Nisha Harry, Keywords: Call centre, professional efficacy, career adaptability, emotional intelligence. DOI:10.5281/zenodo. Abstract: The study discussed in this article sought to assess whether a sense of professional efficacy mediates the relationship between personal factors and career adaptability. A quantitative cross-sectional survey approach was followed. A non–probability sample of (N = 409) of which predominantly early career and permanently employed black females in call centres in Africa participated in this study. In order to assess personal factors, the participants completed sense of meaningfulness and emotional intelligence measures. Measures of professional efficacy and career adaptability were also completed. The results of the mediational analysis revealed that professional efficacy significantly mediates the meaningfulness (sense of coherence) and career adaptability relationship, but not the emotional intelligence–career adaptability relationship. Call centre agents with professional efficacy are likely to be more work engaged as a result of their sense of meaningfulness and emotional intelligence.
  • Container Chaos: The Impact of a Casual Game on Learning and Behavior
    Authors: Lori L. Scarlatos, Ryan Courtney, Keywords: Behavior, carbon footprint, casual games, environmental impact, material sciences. DOI:10.5281/zenodo. Abstract: This paper explores the impact that playing a casual game can have on a player's learning and subsequent behavior. A casual mobile game, Container Chaos, was created to teach undergraduate students about the carbon footprint of various disposable beverage containers. Learning was tested with a short quiz, and behavior was tested by observing which beverage containers players choose when offered a drink and a snack. The game was tested multiple times, under a variety of different circumstances. Findings of these tests indicate that, with extended play over time, players can learn new information and sometimes even change their behavior as a result. This has implications for how other casual games can be used to teach concepts and possibly modify behavior.
  • On-Line Impulse Buying and Cognitive Dissonance: The Moderating Role of the Positive Affective State
    Authors: G. Mattia, A. Di Leo, L. Principato, Keywords: Cognitive dissonance, impulsive buying, online shopping, online consumer behavior. DOI:10.5281/zenodo. Abstract: The purchase impulsiveness is preceded by a lack of self-control: consequently, it is legitimate to believe that a consumer with a low level of self-control can result in a higher probability of cognitive dissonance. Moreover, the process of purchase is influenced by the pre-existing affective state in a considerable way. With reference to on-line purchases, digital behavior cannot be merely ascribed to the rational sphere, given the speed and ease of transactions and the hedonistic dimension of purchases. To our knowledge, this research is among the first cases of verification of the effect of moderation exerted by the positive affective state in the on-line impulse purchase of products with a high expressive value such as a smartphone on the occurrence of cognitive dissonance. To this aim, a moderation analysis was conducted on a sample of 212 impulsive millennials buyers. Three scales were adopted to measure the constructs of interest: IBTS for impulsivity, PANAS for the affective state, Sweeney for cognitive dissonance. The analysis revealed that positive affective state does not affect the onset of cognitive dissonance.