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Title: Hormones and behaviour : the importance of the derivative
Author: Moakes, Kaylee J.
ISNI:       0000 0004 5347 8454
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2015
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Although anecdotally sex differences and the impact they may have on cognition is a hot topic in the media, and indeed in research, very little consensus has been found for hormone-related changes in behaviour. Previous work in this area tends to use a two-time repeated measures method to investigate the impact of hormones across the menstrual cycle. However this method has been shown to be relatively ineffective. The female menstrual cycle is roughly 28 days long with very a dynamic, and rapidly changing, mixture of hormone concentrations across the cycle. To capture this fully we suggest it is necessary to collect data at a significantly greater number of time points with more rigorous methods and suitably sensitive analysis. Four studies are presented in this thesis which focus on capturing more accurately the effect of hormones on cognition across the menstrual cycle. Studies One – Three present data from 13 participants using the standard two-time repeated measure method to investigate categorisation behaviour. This is a completely novel area of research in terms of cognition and hormones. Previous research has focused on various aspects of cognition, yet despite categorisation behaviour being a clearly distinct area of cognition, research into the impact of hormone changes has neglected to look for differences here. The focus on categorisation behaviour provides us with a more holistic picture of the impact of hormones on performances and cognition. Study Four provided a novel approach to the way in which studies are conducted in the area. Here we present a multiple repeated measures study with data from 22 participants using a figural comparison task with measurements at twelve time points across their cycles. A previous study by Hausmann et al,. (2002) using a figural comparison task showed clear impact of the menstrual cycle on cognitive performance using 15 time-point measurements in a sample of 12 participants. In addition to an increase in measurement sensitivity using more time points, we also developed a novel mathematical approach to model the performance change over the menstrual cycle. From Studies One to Three we determined that categorisation performance does not appear to be influenced by changes in cyclical hormone changes. However we did find an influence of hormonal changes on performance in a 1-dimensional categorisation task which demonstrates that hormones may have an impact upon Rule-based categorisation. From Study 4 we were unable to replicate Hausmann and colleagues findings. However we were able to successfully develop a novel modelling method that could accurately predict participant performance on a figural comparison task across the menstrual cycle. Overall this thesis presents a comprehensive investigation into hormone related changes in cognition across the menstrual cycle. We looked into a novel area of behavioural categorisation to determine the impact of hormone related changes in performance on such a task. Through which we demonstrated that in this one area there is little impact despite most other areas of cognition being influenced by cyclical hormonal changes. We then investigated the methodology used in the field in an attempt to improve and develop more accurate and sensitive measures. We were unable to replicate a previous study using multiple time points, however the success of developing a model to predict performance on the figural comparison task provides a useful tool for researchers in the area in the future. The thesis clearly demonstrates that this is an area in the field of psychology and neurobiology that is still in need of further investigation and that we still have much to understand in terms of the ways in which our hormones can impact our behaviour.
Supervisor: McNamara, A.; Sterr, Annette Sponsor: ESRC ; MRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available