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Theorizing "Primal World Beliefs": A Relational-Developmental Account.

  • Published In: Human Development (0018716X), 2024, v. 68, n. 4. P. 159 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Mascolo, Michael F. 3 of 3

Abstract

Clifton and his colleagues have proposed a dimension-based conception of "primal world beliefs" – beliefs about "the overall character of the world." Citing the potential importance of "primal world beliefs," Lansford et al. (this volume) have called for research on their development. In this paper, I suggest that the concepts of "primal," "world," and "belief" rely on unarticulated everyday concepts that are psychologically undertheorized. The model of "primal world belief" is a kind of methodological artifact; that is, it arises as a dual product of everyday trait-like thinking and factor analytic methodology. Thus, while the authors are hopeful that "primal world beliefs" may provide an alternative way to explain the sources of stability that are typically attributed to "personality traits," Clifton and his colleagues have themselves produced a kind of "trait theory of beliefs" – one that is vulnerable to many of the same critiques directed toward trait models of personality. After a critical analysis of the theoretical framework that undergirds the concept of "primal world beliefs," I offer a relational-developmental approach to the empirical study of how children develop beliefs within and about our intersubjectively structured lifeworlds. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Human Development (0018716X). 2024/07, Vol. 68, Issue 4, p159
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:0018-716X
  • DOI:10.1159/000540595
  • Accession Number:180117683
  • Copyright Statement:Copyright of Human Development (0018716X) is the property of Karger AG and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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