Hormones and behaviour : the importance of the derivative.

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

The Importance of the Derivative in Sex-Hormone Cycles: A Reason Why Behavioural Measures in Sex-Hormone Studies Are So Mercurial

<div><p>To study the dynamic changes in cognition across the human menstrual cycle, twenty, healthy, naturally-cycling women undertook a lateralized spatial figural comparison task on twelve occasions at approximately 3–4 day intervals. Each session was conducted in laboratory conditions with response times, accuracy rates, eye movements, salivary estrogen and progesterone concentrations and Profile of Mood states questionnaire data collected on each occasion. The first two sessions of twelve for the response variables were discarded to avoid early effects of learning thereby providing 10 sessions spread across each participant's complete menstrual cycle. Salivary progesterone data for each participant was utilized to normalize each participant's data to a standard 28 day cycle. Data was analysed categorically by comparing peak progesterone (luteal phase) to low progesterone (follicular phase) to emulate two-session repeated measures typical studies. Neither a significant difference in reaction times or accuracy rates was found. Moreover no significant effect of lateral presentation was observed upon reaction times or accuracy rates although inter and intra individual variance was sizeable. We demonstrate that hormone concentrations alone cannot be used to predict the response times or accuracy rates. In contrast, we constructed a standard linear model using salivary estrogen, salivary progesterone and their respective derivative values and found these inputs to be very accurate for predicting variance observed in the reaction times for all stimuli and accuracy rates for right visual field stimuli but not left visual field stimuli. The identification of sex-hormone derivatives as predictors of cognitive behaviours is of importance. The finding suggests that there is a fundamental difference between the up-surge and decline of hormonal concentrations where previous studies typically assume all points near the peak of a hormonal surge are the same. How contradictory findings in sex-hormone research may have come about are discussed.</p></div

Results of Wilcoxon signed rank tests between conditions, prog = progesterone.

<p>The values of <i>Z</i>, <i>P</i> and the effect size <i>r</i>, are shown for each test. Data is shown graphically in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111891#pone-0111891-g003" target="_blank">Figure 3d</a>.</p><p>Results of Wilcoxon signed rank tests between conditions, prog = progesterone.</p

A cartoon showing the figural comparison, match-to-sample task.

<p>A trial commences with an inter-trial interval (2 sec) during which a white central fixation cross is shown on a black background. A centrally presented black polygon is presented for 200 ms followed again by white central fixation cross on a black background (2 sec). Subsequently two white squares are presented to the left and right of the crosshair. In one of the two squares a polygon is presented. The participant must indicate whether the laterally presented stimulus was the ‘same’ or a ‘different’ polygon to that presented previously.</p

Phase plane plots of data averaged across participants.

<p>Numbers next to the line on the plot indicated the approximate day of a normalized 28 day cycle. Following a single plot allows one to follow data through a 28 day cycle. a) Phase plane plot of group's normalized (to 28 day) estrogen and progesterone saliva concentrations across the menstrual cycle. Colour indicates group's average Left Visual Field Accuracy (LVF-ACC) as percentage of trials where a correct response was given. The arrow indicates point where estrogen and progesterone values are equal but LVF-ACC is different. b) Group average LVF-ACC and RVF-ACC plotted against group average changes progesterone in a normalized 28 day cycle. c) Group average LVF-RT and RVF-RT plotted against group average changes progesterone in a normalized 28 day cycle.</p

Result of modelling accuracy rates & reaction times with both hormonal and psychological variables.

<p>a) Impact of including different combinations of the salivary concentrations estrogen (<i>E</i>), progesterone (<i>P</i>) and their derivatives (<i>Ed</i> and <i>Pd</i>) upon prediction of behavioural outputs. Behavioural outputs include the ‘accuracy’ rates and ‘response times’ for the left and right visual. Fit measured as adjusted R². Combinations plotted in ascending order of best mean fit (horizontal thick line) across the four output measures. b) A linear model was implemented using salivary concentrations of progesterone, estrogen and their derivatives in an attempt to explain the four behavioural outcomes. Each panel shows the group averaged data (unbroken line), the output of the model used to predict the data (dotted line) and the adjusted R² value indicating the fit of the model to the data. i) Fit for LVF-ACC. ii) Fit for RVF-ACC. iii) Fit for LVF-RT. iv) Fit for RVF-RT. c) Impact of including different psychological variables of mood to the model. Behavioural outputs include the ‘accuracy’ rates and ‘response times’ for the left and right visual field. Fit measured as adjusted R². Plotted in ascending order of, best mean fit across the four output measures.</p