» Articles » PMID: 35773737

How Selves Differ Within and Across Cognitive Domains: Self-prioritisation, Self-concept, and Psychiatric Traits

Overview
Journal BMC Psychol
Publisher Biomed Central
Specialty Psychology
Date 2022 Jun 30
PMID 35773737
Authors
Affiliations
Soon will be listed here.
Abstract

Background: How we build and maintain representations of ourselves involves both explicit features which are consciously accessible on reflection and implicit processes which are not, such as attentional biases. Understanding relations between different ways of measuring self-cognition both within and across such cognitive domains is important for understanding how selves may differ from one another, and whether self-cognition is best understood as largely uni-dimensional or more multi-dimensional. Further, uncovering this structure should inform research around how self-cognition relates to psychiatric and psychological conditions. This study explores the relations between different constructs of self-cognition and how variability within them relates to psychiatric traits.

Methods: Our final dataset includes within-subject (n = 288, general population) measures of explicit self-concept (using both the Self Concept Clarity Scale and Self Concept and Identity Measure), implicit self-prioritisation in a shape-label matching task (for both reaction time and sensitivity) and measurement of traits for five psychiatric conditions (autism, borderline personality disorder, schizophrenia, depression and anxiety). We first test whether self-cognitive measures within and across domains are correlated within individuals. We then test whether these dimensions of self-cognition support a binary distinction between psychiatric conditions that either are or are not characterised in terms of self, or whether they support self-cognition as transdiagnostically predictive of the traits associated with psychiatric conditions. To do this we run a series of planned correlations, regressions, and direct correlation comparison statistics.

Results: Results show that implicit self-prioritisation measures were not correlated with the explicit self-concept measures nor the psychiatric trait measures. In contrast, all the psychiatric traits scores were predicted, to varying degrees, by poorer explicit self-concept quality. Specifically, borderline personality disorder traits were significantly more strongly associated with composite explicit self-concept measures than any of depression, anxiety, or autism traits scores were.

Conclusions: Our results suggest that selves can differ considerably, along different cognitive dimensions. Further, our results show that self-cognition may be a promising feature to include in future dimensional characterisations of psychiatric conditions, but care should be taken to choose relevant self-cognitive domains.

Citing Articles

Self-Bias and Self-Related Mentalizing are Unaltered in Adolescents with Autism.

Amodeo L, Nijhof A, Williams D, Wiersema J J Autism Dev Disord. 2025; .

PMID: 39821723 DOI: 10.1007/s10803-024-06705-8.


Agency in schizophrenia and autism: a systematic review.

Tan D, Carter O, Marshall D, Perrykkad K Front Psychol. 2024; 14:1280622.

PMID: 38187412 PMC: 10768057. DOI: 10.3389/fpsyg.2023.1280622.

References
1.
Osterling J, Dawson G . Early recognition of children with autism: a study of first birthday home videotapes. J Autism Dev Disord. 1994; 24(3):247-57. DOI: 10.1007/BF02172225. View

2.
Mars A, Mauk J, Dowrick P . Symptoms of pervasive developmental disorders as observed in prediagnostic home videos of infants and toddlers. J Pediatr. 1998; 132(3 Pt 1):500-4. DOI: 10.1016/s0022-3476(98)70027-7. View

3.
Sugranyes G, Kyriakopoulos M, Corrigall R, Taylor E, Frangou S . Autism spectrum disorders and schizophrenia: meta-analysis of the neural correlates of social cognition. PLoS One. 2011; 6(10):e25322. PMC: 3187762. DOI: 10.1371/journal.pone.0025322. View

4.
Burbach J, Van der Zwaag B . Contact in the genetics of autism and schizophrenia. Trends Neurosci. 2009; 32(2):69-72. DOI: 10.1016/j.tins.2008.11.002. View

5.
Litman L, Robinson J, Abberbock T . TurkPrime.com: A versatile crowdsourcing data acquisition platform for the behavioral sciences. Behav Res Methods. 2016; 49(2):433-442. PMC: 5405057. DOI: 10.3758/s13428-016-0727-z. View