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      The Converging Triangle of Cultural Content, Cognitive Science, and Behavioral Economics

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          Abstract

          How online cultural content is chosen based on conscious or subconscious criteria is an central question across a broad spectrum of sciences and for the entertainment industry, including content providers and distributors. To this end, a number of tailored analytics forming the backbone of recommendation engines specialized for retrieving cultural content are proposed. Their strength derives directly from well-established principles of cognitive science and behavioral economics, both scientific fields exploring aspects of human decision making. Another novel contribution of this conference paper is that these analytics are implemented in Neo4j expressed as Cypher queries. Various aspects of the cultural content and digital consumers can be naturally represented by appropriately configured vertices, whereas edges represent various connections indicating content delivery preferences. Early experiments conducted over a synthetic dataset mimicking the distributions of preferences and ratings of well-known movie datasets are encouraging as the proposed analytics outperformed the baseline of a multilayer feedforward neural network of various configurations. The synthetic dataset contains enriched preferences of mobile digital consumers of cultural content regarding literature of the Greek region of Ionian Islands.

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          Most cited references16

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          Tensor Decompositions and Applications

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            Processing and visualization for diffusion tensor MRI.

            This paper presents processing and visualization techniques for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). In DT-MRI, each voxel is assigned a tensor that describes local water diffusion. The geometric nature of diffusion tensors enables us to quantitatively characterize the local structure in tissues such as bone, muscle, and white matter of the brain. This makes DT-MRI an interesting modality for image analysis. In this paper we present a novel analytical solution to the Stejskal-Tanner diffusion equation system whereby a dual tensor basis, derived from the diffusion sensitizing gradient configuration, eliminates the need to solve this equation for each voxel. We further describe decomposition of the diffusion tensor based on its symmetrical properties, which in turn describe the geometry of the diffusion ellipsoid. A simple anisotropy measure follows naturally from this analysis. We describe how the geometry or shape of the tensor can be visualized using a coloring scheme based on the derived shape measures. In addition, we demonstrate that human brain tensor data when filtered can effectively describe macrostructural diffusion, which is important in the assessment of fiber-tract organization. We also describe how white matter pathways can be monitored with the methods introduced in this paper. DT-MRI tractography is useful for demonstrating neural connectivity (in vivo) in healthy and diseased brain tissue.
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              Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits.

              Findings from a complex decision-making task (the Iowa gambling task) show that individuals with neuropsychological disorders are characterized by decision-making deficits that lead to maladaptive risk-taking behavior. This article describes a cognitive model that distills performance in this task into three different underlying psychological components: the relative impact of rewards and punishments on evaluations of options, the rate that the contingent payoffs are learned, and the consistency between learning and responding. Findings from 10 studies are organized by distilling the observed decision deficits into the three basic components and locating the neuropsychological disorders in this component space. The results reveal a cluster of populations characterized by making risky choices despite high attention to losses, perhaps because of difficulties in creating emotive representations. These findings demonstrate the potential contribution of cognitive models in building bridges between neuroscience and behavior.
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                Author and article information

                Contributors
                imaglo@unipi.gr
                liliadis@civil.duth.gr
                elias.pimenidis@uwe.ac.uk
                c16drak@ionio.gr
                igian@upatras.gr
                fmylonas@ionio.gr
                sioutas@ceid.upatras.gr
                Journal
                978-3-030-49190-1
                10.1007/978-3-030-49190-1
                Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops
                Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops
                MHDW 2020 and 5G-PINE 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings
                978-3-030-49189-5
                978-3-030-49190-1
                04 May 2020
                2020
                : 585
                : 200-212
                Affiliations
                [15 ]GRID grid.4463.5, ISNI 0000 0001 0558 8585, Department of Digital Systems, , University of Piraeus, ; Piraeus, Greece
                [16 ]GRID grid.12284.3d, ISNI 0000 0001 2170 8022, Department of Civil Engineering, Lab of Mathematics and Informatics (ISCE), , Democritus University of Thrace, ; Xanthi, Greece
                [17 ]GRID grid.6518.a, ISNI 0000 0001 2034 5266, Department of Computer Science and Creative Technologies, , University of the West of England, ; Bristol, UK
                [18 ]GRID grid.449127.d, ISNI 0000 0001 1412 7238, Department of Informatics, , Ionian University, ; Kerkyra, Hellas
                [19 ]GRID grid.11047.33, ISNI 0000 0004 0576 5395, CEID, University of Patras, ; Patras, Achaia Hellas
                [20 ]GRID grid.11047.33, ISNI 0000 0004 0576 5395, Department of Management Science and Technology, , University of Patras, ; Achaia, Hellas
                Author information
                http://orcid.org/0000-0002-0975-1877
                http://orcid.org/0000-0003-2112-6430
                http://orcid.org/0000-0002-6916-3129
                Article
                18
                10.1007/978-3-030-49190-1_18
                7256426
                a16bc9f4-6b3c-4fdb-a7ee-37acab6ed36e
                © IFIP International Federation for Information Processing 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

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                © IFIP International Federation for Information Processing 2020

                cognitive science,behavioral economics,cultural content,content delivery,graph recommendation,graph databases,graph analytics,neo4j,cypher,humanistic data

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