6
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Testing the bipolar assumption of Singer-Loomis Type Deployment Inventory for Korean adults using classification and multidimensional scaling

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In this study, we explored whether the Korean version of Singer Loomis Type Deployment Inventory II (K-SLTDI) captures the opposing tendencies of Jung’s theory of psychological type. The types are Extroverted Sensing, Extroverted Intuition, Extroverted Feeling, Extroverted Thinking, Introverted Sensing, Introverted Intuition, Introverted Feeling, and Introverted Thinking. A nationwide online survey was conducted in South Korea. We performed multidimensional scaling and classification analyses based on 521 Korean adult profiles with eight psychological types to test the bipolarity assumption. The results showed that the Procrustes-rotated four-dimensional space successfully represented four types of opposing tendencies. Moreover, the bipolarity assumption in the four dimensions of Jungian typology was tested and compared between lower and higher psychological distress populations via cluster analysis. Lastly, we explored patterns of responses in lower and higher psychological distress populations using intersubject correlation. Both similarity analyses and classification results consistently support the theoretical considerations on the conceptualization of Jung’s type in independent order that the types could be derived without bipolar assumption as Singer and Loomis expected in their Type Development Inventory. Limitations in our study include the sample being randomly selected internet users during the COVID−19 pandemic, despite excellence in the use of the internet in the general Korean population.

          Related collections

          Most cited references111

          • Record: found
          • Abstract: not found
          • Article: not found

          Big Data and Machine Learning in Health Care

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Machine Learning for Medical Imaging.

            Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Beyond mind-reading: multi-voxel pattern analysis of fMRI data.

              A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                31 January 2024
                2023
                : 14
                : 1249185
                Affiliations
                Psychology Department, Jeonbuk National University , Jeonju, Republic of Korea
                Author notes

                Edited by: Gerald Matthews, George Mason University, United States

                Reviewed by: James Houran, Integrated Knowledge Systems, United States

                Franca Crippa, University of Milano-Bicocca, Italy

                *Correspondence: Jongwan Kim, jongwankim80@ 123456jbnu.ac.kr
                Article
                10.3389/fpsyg.2023.1249185
                10864660
                38356992
                90cbefea-208c-4965-838d-25ce71c3c5f7
                Copyright © 2024 Lee and Kim.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 June 2023
                : 26 December 2023
                Page count
                Figures: 4, Tables: 2, Equations: 0, References: 111, Pages: 10, Words: 8874
                Funding
                The research received funding from the Brain Korea 21 fourth project of the Korea Research Foundation (Jeonbuk National University, Psychology Department no. 4199990714213).
                Categories
                Psychology
                Original Research
                Custom metadata
                Personality and Social Psychology

                Clinical Psychology & Psychiatry
                singer-loomis type deployment inventory,jungian personality,bipolar assumption,multidimensional scaling,classification,intersubject correlation

                Comments

                Comment on this article