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Cited by 287

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Evolution, the Five‐Factor Model, and Levels of Personality

MacDonald, Kovin·1995·Journal of Personality

Abstract

ABSTRACT This article interprets the five‐factor model as subsuming variation in normative, species‐typical systems with adaptive functions in the human environment of evolutionary adaptedness. It is argued that the evolutionary logic of personality systems is apparent in the patterning of mean sex differences in personality. Personality systems are conceptualized as evolved motivational systems with an affective core. The evolved motive dispositions at the core of personality anchor a hierarchy of levels of cognitive and behavioral functioning aimed at attaining or avoiding the affective states central to these personality systems. Personality systems are seen as often in dynamic conflict within individuals and as highly compartmentalized in their functioning between settings. While variation in personality consists of a range of viable strategies for humans, extremes on these systems tend to be maladaptive, although in at least some cases individuals who approach the maladaptive extremes of individual variation may be viewed as engaging in high‐risk evolutionary strategies. Within this wide range of viable strategies, personality variation functions as a resource environment for individuals in the sense that personality variation is evaluated according to the interests of the evaluator.

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Crossref · 10.1111/j.1467-6494.1995.tb00505.x

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Metadata

Authors
MacDonald, K.
Venue
Journal of Personality
Publisher
Wiley
Published
1995-09
Volume
63
Issue
3
Pages
525-567
ISSN
  • 0022-3506 (ISSN-L)
  • 1467-6494
OpenAlex
W2163238321
Cited by
287
References
15
FWCI
7.38
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.