Dimensions of Normal Personality as Networks in Search of Equilibrium: You Can't Like Parties if You Don't Like People

Angélique O. J. Cramer, Sophie van der Sluis, Arjen Noordhof, Marieke Wichers, Nicole Geschwind, Steven H. Aggen, Kenneth S. Kendler, Denny Borsboom
2012 European Journal of Personality  
In one currently dominant view on personality, personality dimensions (e.g. extraversion) are causes of human behaviour, and personality inventory items (e.g. 'I like to go to parties' and 'I like people') are measurements of these dimensions. In this view, responses to extraversion items correlate because they measure the same latent dimension. In this paper, we challenge this way of thinking and offer an alternative perspective on personality as a system of connected affective, cognitive and
more » ... ehavioural components. We hypothesize that these components do not hang together because they measure the same underlying dimension; they do so because they depend on one another directly for causal, homeostatic or logical reasons (e.g. if one does not like people and it is harder to enjoy parties). From this 'network perspective', personality dimensions emerge out of the connectivity structure that exists between the various components of personality. After outlining the network theory, we illustrate how it applies to personality research in four domains: (i) the overall organization of personality components; (ii) the distinction between state and trait; (iii) the genetic architecture of personality; and (iv) the relation between personality and psychopathology. 2 Please note that for this graph, and the other networks that are presented in this paper, the positions of the nodes in the graph are not identified. That is, by using a force-embedded algorithm, the graphs are a two-dimensional representation of networks that are multi-dimensional. In this representation, the position of a node is defined relative to other nodes in the network. The resulting distance in two dimensions between two nodes does not represent the correlation but, rather, is an approximation of the distances in the multi-dimensional network. 418 A. O. J. Cramer et al. were obtained from the GAIN Database found at http://view. ncbi.nlm.nih.gov/dbgap, controlled through dbGAP accession number phs000020.v2.p1. Samples and associated phenotype data were provided by the Netherlands Study of Depression and Anxiety (NESDA) and the Netherlands Twin Register (NTR).
doi:10.1002/per.1866 fatcat:fih424kojjhelns3wwhnqhxrne