Bullet Journal

PhD Timeline


Year One

October - December 2021

Defined two overarching research questions, which will motivate my research over the next 3+ years:

  1. Can playing AVGs improve cognition? Mixed findings so far, most positive findings come from one lab (Bavelier & Green) with small samples. Does expertise in AVGs differentially impact cognition? My MSc research provided correlational evidence regarding this, but further analysis of this data set and experimental research is necessary.

  2.  How does playing AVGs improve cognition? 'Learning to Learn' could be an explanation, but there is limited theoretical and experimental research on this topic. 

Used stricter filtering and re-analysed the MSc data set, which excluded participants from all levels of analysis who did not have full data for the three expertise measures - this resulted in a reduced sample of n = 273. Expertise clustering in the previous analysis was based on seven measures, this was reduced to three: total hours playtime, weekly hours playtime and self-rated expertise. Cluster group analysis suggested four groups would be the best fit, and allocation was as follows: novice = 105, casual = 107, intermediate = 28 and advanced = 33. These changes provided the following results.

Speed in single rule and repetition rule trials were fastest in the Advanced group. However, the casual group were fastest in switch rule trials, and also showed the lowest switching costs and mixing costs. Significant main effects of expertise group were observed for single rule trials only. 

These results suggest that processing speed, task switching and mixing abilities vary with expertise however, this relationship is non-linear. Experienced players, were fastest and most efficient in terms of their processing speed. However, contrary to previous findings, players with the second least CS: GO expertise (Casual group) performed most efficiently in mixing and switching conditions. These discrepancies may be explained by the larger sample, and range of expertise in the present sample compared to previous studies. Further examination of the literature is necessary to gain a proper understanding of our results.

Diffusion modelling is the next step of analysis, which maps the cognitive processes involved in fast decision-making tasks, and quantifies performance differences across participants. The EZ-diffusion model determines the most psychologically relevant parameters which are: quality of information or drift rate (ν), response conservativeness or boundary separation (α) and non-decision time (Τer). These parameters are estimated based on the inputs of three observed values from our data: mean response times (MRT), variance of response times (VRT) and proportion of correct decisions (Pc).

Data has been pre-processed and there are values for MRT, VRT and Pc for each participant however, further reading is necessary to develop my understanding of the EZ-diffusion model, before I can successfully complete the analysis.


Personal Development

October - December 2021

Biggest Success

Improving my coding skills. I have spent a lot of time tidying up my scripts, making them more efficient and easier to understand for the reader - which has improved how I write my scripts in the first instance. I am also getting faster at writing scripts and better recall the commands I use regularly. Whilst I still get lots of errors that need googling, I think that is just a part of coding.

Biggest Challenge

Diffusion modelling. The literature has felt inaccessible to me as a novice in diffusion modelling, and I was frustrated with myself for not understanding concepts from the first reading. There is a lot more work I need to do in terms of improving my understanding before I can conduct the EZ-analysis which I have had to come to terms with. Some things just take longer than you want them too but I am confident I will get there.


I achieved a Distinction on my MSc overall and on my dissertation which is something I am very proud of. Like most of the UK I also had COVID in December! It was certainly unpleasant but I am thankful to have recovered, other than my sense of smell and getting breathless on occasion.



November 2021


November 2021

Women in Cognitive Science (WiCSW)

November 2021

Women in Games Careers and Networking Expo

March 2022

Culture at Play

April 2022

Learning and Plasticity (LaP)