EVOLUTIONARY PSYCHIATRY CONFERENCE


Evolutionary Psychiatry Conference is one of the leading research topics in the international research conference domain. Evolutionary Psychiatry 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.

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I. INTERNATIONAL PSYCHOLOGY CONFERENCE

MARCH 19 - 20, 2019
ISTANBUL, TURKEY

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II. INTERNATIONAL PSYCHOLOGY CONFERENCE

JUNE 26 - 27, 2019
PARIS, FRANCE

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III. INTERNATIONAL PSYCHOLOGY CONFERENCE

AUGUST 21 - 22, 2019
LONDON, UNITED KINGDOM

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IV. INTERNATIONAL PSYCHOLOGY CONFERENCE

OCTOBER 08 - 09, 2019
NEW YORK, UNITED STATES

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V. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 12 - 13, 2019
ROME, ITALY

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VI. INTERNATIONAL PSYCHOLOGY CONFERENCE

FEBRUARY 13 - 14, 2020
LONDON, UNITED KINGDOM

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VII. INTERNATIONAL PSYCHOLOGY CONFERENCE

APRIL 15 - 16, 2020
BARCELONA, SPAIN

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VIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

MAY 11 - 12, 2020
ISTANBUL, TURKEY

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IX. INTERNATIONAL PSYCHOLOGY CONFERENCE

JUNE 05 - 06, 2020
SAN FRANCISCO, UNITED STATES

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X. INTERNATIONAL PSYCHOLOGY CONFERENCE

JULY 20 - 21, 2020
PARIS, FRANCE

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XI. INTERNATIONAL PSYCHOLOGY CONFERENCE

AUGUST 10 - 11, 2020
NEW YORK, UNITED STATES

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XII. INTERNATIONAL PSYCHOLOGY CONFERENCE

SEPTEMBER 10 - 11, 2020
TOKYO, JAPAN

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XIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

SEPTEMBER 16 - 17, 2020
ZÜRICH, SWITZERLAND

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XIV. INTERNATIONAL PSYCHOLOGY CONFERENCE

OCTOBER 21 - 22, 2020
BARCELONA, SPAIN

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XV. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 02 - 03, 2020
SAN FRANCISCO, UNITED STATES

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XVI. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 12 - 13, 2020
ISTANBUL, TURKEY

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XVII. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 19 - 20, 2020
SINGAPORE, SINGAPORE

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XVIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 15 - 16, 2020
BANGKOK, THAILAND

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XIX. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 28 - 29, 2020
PARIS, FRANCE

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XX. INTERNATIONAL PSYCHOLOGY CONFERENCE

FEBRUARY 13 - 14, 2021
LONDON, UNITED KINGDOM

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XXI. INTERNATIONAL PSYCHOLOGY CONFERENCE

APRIL 15 - 16, 2021
BARCELONA, SPAIN

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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

FINISHED

XXVIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

OCTOBER 21 - 22, 2021
BARCELONA, SPAIN

FINISHED

XXIX. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 02 - 03, 2021
SAN FRANCISCO, UNITED STATES

FINISHED

XXX. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 12 - 13, 2021
ISTANBUL, TURKEY

FINISHED

XXXI. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 19 - 20, 2021
SINGAPORE, SINGAPORE

FINISHED

XXXII. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 15 - 16, 2021
BANGKOK, THAILAND

FINISHED

XXXIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 28 - 29, 2021
PARIS, FRANCE

Psychology Conference Call For Papers are listed below:

Previously Published Papers on "Evolutionary Psychiatry Conference"

  • Understanding Evolutionary Algorithms through Interactive Graphical Applications
    Authors: Javier Barrachina, Piedad Garrido, Manuel Fogue, Julio A. Sanguesa, Francisco J. Martinez, Keywords: Education, evolutionary algorithms, evolution strategies, interactive learning applications. DOI:10.5281/zenodo.1126996 Abstract: It is very common to observe, especially in Computer Science studies that students have difficulties to correctly understand how some mechanisms based on Artificial Intelligence work. In addition, the scope and limitations of most of these mechanisms are usually presented by professors only in a theoretical way, which does not help students to understand them adequately. In this work, we focus on the problems found when teaching Evolutionary Algorithms (EAs), which imitate the principles of natural evolution, as a method to solve parameter optimization problems. Although this kind of algorithms can be very powerful to solve relatively complex problems, students often have difficulties to understand how they work, and how to apply them to solve problems in real cases. In this paper, we present two interactive graphical applications which have been specially designed with the aim of making Evolutionary Algorithms easy to be understood by students. Specifically, we present: (i) TSPS, an application able to solve the ”Traveling Salesman Problem”, and (ii) FotEvol, an application able to reconstruct a given image by using Evolution Strategies. The main objective is that students learn how these techniques can be implemented, and the great possibilities they offer.
  • Multi-Objective Evolutionary Computation Based Feature Selection Applied to Behaviour Assessment of Children
    Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco, Keywords: Feature selection, multi-objective evolutionary computation, unsupervised classification, behavior assessment system for children. DOI:10.5281/zenodo.1125385 Abstract: Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.
  • The Moderation Effect of Smart Phone Addiction in Relationship between Self-Leadership and Innovative Behavior
    Authors: Gi-Ryun Park, Gye-Wan Moon, Dong-Hoon Yang, Keywords: Innovative Behavior, Revolutionary Behavior, Self-leadership, Smartphone Addiction. DOI:10.5281/zenodo.1092359 Abstract: This study aims to explore the positive effects of self-leadership and innovative behavior that'd been proven in the existing researches proactively and understand the regulation effects of smartphone addiction which has recently become an issue in Korea. This study conducted a convenient sampling of college students attending the four colleges located at Daegu. A total of 210 questionnaires in 5-point Likert scale were distributed to college students. Among which, a total of 200 questionnaires were collected for our final analysis data. Both correlation analysis and regression analysis were carried out to verify those questionnaires through SPSS 20.0. As a result, college students' self-leadership had a significantly positive impact on innovative behavior (B= .210, P= .003). In addition, it is found that the relationship between self-leadership and innovative behavior can be adjusted depending on the degree of smartphone addiction in college students (B= .264, P= .000). This study could first understand the negative effects of smartphone addiction and find that if students' self-leadership is improved in terms of self-management and unnecessary use of smartphone is controlled properly, innovative behavior can be improved. In addition, this study is significant in that it attempts to identify a new impact of smartphone addiction with the recent environmental changes, unlike the existing researches that'd been carried out from the perspective of organizational behavior theory.
  • A Review of the Theoretical Context of the Role of Innovation in Economic Development
    Authors: Maria E. Eggink, Keywords: Economic development, evolutionary economics, innovation, Schumpeter. DOI:10.5281/zenodo.1335834 Abstract: It is the aim of this paper to place the role of innovation in economic development in its theoretical context through a literature review. The review compares classical economic theory and the neoclassical theories of “equilibrium in the markets” and “perfectly competitive markets” with the Schumpeterian theory. It was found that Schumpeter’s role in contributing towards economic development theories, and by creating awareness of the role of innovation in these theories is of immeasurable importance. His contribution led to a change in economic thinking, although this was only realized much later than when his theories were first published. The neo-Schumpeterian thinking expanded on the Schumpeterian theory by studying innovation within a system of interaction among different role players. Studies on innovation should be founded in the neo-Schumpeterian school of thought in order to accommodate the complexity of the innovation system concept.
  • Evolutionary Cobreeding of Cooperative and Competitive Subcultures
    Authors: Emilia Nercissians, Keywords: evolutionary stability, externalities, neofunctionalism, prisoners' dilemma. DOI:10.5281/zenodo.1078438 Abstract: Neoclassical and functionalist explanations of self organization in multiagent systems have been criticized on several accounts including unrealistic explication of overadapted agents and failure to resolve problems of externality. The paper outlines a more elaborate and dynamic model that is capable of resolving these dilemmas. An illustrative example where behavioral diversity is cobred in a repeated nonzero sum task via evolutionary computing is presented.
  • Evolved Disease Avoidance Mechanisms, Generalized Prejudice, Modern Attitudes towards Individuals with Intellectual Disability
    Authors: Campbell Townsend, David Hamilton, Keywords: avoidance, evolutionary psychology, intellectual disability, prejudice DOI:10.5281/zenodo.1077789 Abstract: Previous research has demonstrated that negative attitudes towards people with physical disabilities and obesity are predicted by a component of perceived vulnerability to disease; germ aversion. These findings have been suggested as illustrations of an evolved but over-active mechanism which promotes the avoidance of pathogen-carrying individuals. To date, this interpretation of attitude formation has not been explored with regard to people with intellectual disability, and no attempts have been made to examine possible mediating factors. This study examined attitudes in 333 adults and demonstrated that the moderate positive relationship between germ aversion and negative attitudes toward people with intellectual disability is fully mediated by social dominance orientation, a general preference for hierarchies and inequalities among social groups. These findings have implications for the design of programs which attempt to promote community acceptance and inclusion of people with disabilities.
  • Universal Method for Timetable Construction based on Evolutionary Approach
    Authors: Maciej Norberciak, Keywords: Evolutionary algorithms, tabu search, timetabling. DOI:10.5281/zenodo.1333937 Abstract: Timetabling problems are often hard and timeconsuming to solve. Most of the methods of solving them concern only one problem instance or class. This paper describes a universal method for solving large, highly constrained timetabling problems from different domains. The solution is based on evolutionary algorithm-s framework and operates on two levels – first-level evolutionary algorithm tries to find a solution basing on given set of operating parameters, second-level algorithm is used to establish those parameters. Tabu search is employed to speed up the solution finding process on first level. The method has been used to solve three different timetabling problems with promising results.
  • Linguistic, Pragmatic and Evolutionary Factors in Wason Selection Task
    Authors: Olimpia Matarazzo, Fabrizio Ferrara, Keywords: Deontic reasoning; Evolutionary, linguistic, logical, pragmatic factors; Wason selection task DOI:10.5281/zenodo.1059631 Abstract: In two studies we tested the hypothesis that the appropriate linguistic formulation of a deontic rule – i.e. the formulation which clarifies the monadic nature of deontic operators - should produce more correct responses than the conditional formulation in Wason selection task. We tested this assumption by presenting a prescription rule and a prohibition rule in conditional vs. proper deontic formulation. We contrasted this hypothesis with two other hypotheses derived from social contract theory and relevance theory. According to the first theory, a deontic rule expressed in terms of cost-benefit should elicit a cheater detection module, sensible to mental states attributions and thus able to discriminate intentional rule violations from accidental rule violations. We tested this prevision by distinguishing the two types of violations. According to relevance theory, performance in selection task should improve by increasing cognitive effect and decreasing cognitive effort. We tested this prevision by focusing experimental instructions on the rule vs. the action covered by the rule. In study 1, in which 480 undergraduates participated, we tested these predictions through a 2 x 2 x 2 x 2 (type of the rule x rule formulation x type of violation x experimental instructions) between-subjects design. In study 2 – carried out by means of a 2 x 2 (rule formulation x type of violation) between-subjects design - we retested the hypothesis of rule formulation vs. the cheaterdetection hypothesis through a new version of selection task in which intentional vs. accidental rule violations were better discriminated. 240 undergraduates participated in this study. Results corroborate our hypothesis and challenge the contrasting assumptions. However, they show that the conditional formulation of deontic rules produces a lower performance than what is reported in literature.
  • Eco-Innovation as a New Sustainable Development Strategy: Case Studies
    Authors: Orhan Çoban, Nuryağdı Rozıyev, Fehmi Karasioğlu, Keywords: Sustainable Development, Innovation, Ecoinnovation, Neoclassical Approach, Evolutionary Approach, Case Studies DOI:10.5281/zenodo.1057159 Abstract: Sustainable development is one of the most debated issues, recently. In terms of providing more livable Earth continuity, while Production activities are going on, on the other hand protecting the environment has importance. As a strategy for sustainable development, eco-innovation is the application of innovations to reduce environmental burdens. Endeavors to understand ecoinnovation processes have been affected from environmental economics and innovation economics from neoclassical economics, and evolutionary economics other than neoclassical economics. In the light of case study analyses, this study aims to display activities in this field through case studies after explaining the theoretical framework of eco-innovations. This study consists of five sections including introduction and conclusion. In the second part of the study identifications of the concepts related with eco-innovation are described and eco-innovations are classified. Third section considers neoclassical and evolutionary approaches from neoclassical economics and evolutionary economics, respectively. Fourth section gives the case studies of successful eco-innovations. Last section is the conclusion part and offers suggestions for future eco-innovation research according to the theoretical framework and the case studies.

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