Abstract
An ongoing question in social and cognitive sciences is whether and how understanding the self differs from understanding others. One obstacle to answering this question may be a lack of conceptual clarity about what is meant by "self" and "other" in mentalizing research. In this chapter, we delineate three constructs that provide a more nuanced understanding of different ways to define self versus other processes. These constructs include identifying the target of mentalizing, the source of mentalizing representations, and the visual perspective used in mentalizing-relevant imagery. For each construct, we first describe its key components, including similarities and differences in its manifestations in self-versus other-related processes. We next describe neural correlates underlying each construct, highlighting the role of subsystems of the brain's default mode network across these constructs. We then examine how over-emphasis on the self in each construct may contribute to social anxiety disorder and psychopathology more broadly. A complete understanding of mentalizing will likely require identifying the similarities and differences among these constructs. By shedding light on the interdependence of different self-and other-related processes in mentalizing, we hope to inform our understanding of both functional and dysfunctional mentalizing, identify potential targets for therapeutic intervention, and suggest exciting areas for future research.
Original language | English (US) |
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Title of host publication | The Neural Basis of Mentalizing |
Publisher | Springer |
Pages | 629-658 |
Number of pages | 30 |
ISBN (Electronic) | 9783030518905 |
ISBN (Print) | 9783030518899 |
DOIs | |
State | Published - May 11 2021 |
Externally published | Yes |
Keywords
- Default mode network
- Egocentricity
- Mental imagery
- Mentalizing
- Prefrontal cortex
- Psychopathology
- Self-focus
- Social anxiety
- Social cognition
- Visual perspective
ASJC Scopus subject areas
- General Psychology
- General Medicine
- General Neuroscience