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Videogame Correlates of Real Life Traits and Characteristics.

Kokkinakis, AV (2018) Videogame Correlates of Real Life Traits and Characteristics. PhD thesis, University of York.

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Abstract

This thesis attempts to link real life cognitive traits and demographics with in-game player created data. The first experiment focuses on the nicknames chosen by players and the information one can extract from them. Players with nicknames with negative valence (highly racist or vulgar) tend to report others more and receive more reports from other players themselves when compared to their peers. Additionally, many individuals tend to have their real birthdate appended to their nickname (“Jim1986”). This is the first study that has successfully shown this phenomenon. Additionally, by using the extracted dates I showed that negative interactions tend to diminish as one ages. In my second experiment I linked age with in-game performance, as indicated by rank for the videogame League of Legends (LoL). More specifically, performance in LoL tends to peak in one’s mid to late twenties while performance in first person shooters tends to follow a different pattern with an earlier peak; both seem to have a drop after 28. Moreover, I showed that fluid intelligence, as measured by the Wechsler Abbreviated Scale of Intelligence, as well as rotational working memory, which is an overlapping construct, are positively correlated with in-game rank suggesting they are the driving force between the age-rank findings. Finally, I examined personality through the HEXACO framework. One of the most consistent findings in personality research is the existence of Neuroticism or Emotionality which is why it was my choice of focus. Individuals scoring highly on Emotionality, which is a trait linked to anxiety and sentimentality, tend to underperform in the competitive ladder. This was replicated with two videogames of different genres: Hearthstone and LoL. This thesis suggests that we can successfully extract meaningful information at a mass level through commercial videogames.

Item Type: Thesis (PhD)
Related URLs:
Academic Units: The University of York > Computer Science (York)
Identification Number/EthosID: uk.bl.ethos.778910
Depositing User: Mr AV Kokkinakis
Date Deposited: 04 Jun 2019 13:40
Last Modified: 19 Feb 2020 13:08
URI: http://etheses.whiterose.ac.uk/id/eprint/24105

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