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2 results

  • Chapter 5 - Wisdom

  • from Part I - Introduction to Wisdom Theory and Research
  • Edited by Robert J. Sternberg, Cornell University, New York, Judith Glück, Universität Klagenfurt, Austria
  • Book:
    The Psychology of Wisdom
    Published online:
    26 May 2022
    Print publication:
    09 June 2022, pp 70-88

  • Summary

    Some people think wisdom is a stable and invariable individual disposition. Others view wisdom as deeply embedded in culture, experiences, and situations, and treat these features as mutually making up wisdom. What are the implications of each view for measurement, training, and the fundamental nature of wisdom itself? This chapter reviews evidence concerning the dispositional versus situational approaches to study wisdom. Even though main features of wisdom show some stability, there is also a profound and systematic variability in response to situational demands. By conceptualizing dispositions as a distribution of situation-specific responses, one can integrate dispositional and situational approaches to wisdom. Building on these insights, it is recommended to pay attention to contextual factors in measurement. Insight about contextual factors can also shed light on how to develop interventions for training wisdom.

  • The trait and state negative affect can be separately predicted by stable and variable resting-state functional connectivity

  • Yu Li, Kaixiang Zhuang, Zili Yi, Dongtao Wei, Jiangzhou Sun, Jiang Qiu
  • Journal:
    Psychological Medicine / Volume 52 / Issue 5 / April 2022
    Published online by Cambridge University Press:
    13 July 2020, pp. 813-823
  • Background

    Many emotional experiences such as anxiety and depression are influenced by negative affect (NA). NA has both trait and state features, which play different roles in physiological and mental health. Attending to NA common to various emotional experiences and their trait-state features might help deepen the understanding of the shared foundation of related emotional disorders.

    Methods

    The principal component of five measures was calculated to indicate individuals' NA level. Applying the connectivity-based correlation analysis, we first identified resting-state functional connectives (FCs) relating to NA in sample 1 (n = 367), which were validated through an independent sample (n = 232; sample 2). Next, based on the variability of FCs across large timescale, we further divided the NA-related FCs into high- and low-variability groups. Finally, FCs in different variability groups were separately applied to predict individuals' neuroticism level (which is assumed to be the core trait-related factor underlying NA), and the change of NA level (which represents the state-related fluctuation of NA).

    Results

    The low-variability FCs were primarily within the default mode network (DMN) and between the DMN and dorsal attention network/sensory system and significantly predicted trait rather than state NA. The high-variability FCs were primarily between the DMN and ventral attention network, the fronto-parietal network and DMN/sensory system, and significantly predicted the change of NA level.

    Conclusions

    The trait and state NA can be separately predicted by stable and variable spontaneous FCs with different attentional processes and emotion regulatory mechanisms, which could deepen our understanding of NA.