Struggling with doing my PhD in cog comp neuro. Here to record some thoughts
I'm currently a year 1 PhD student in cognitive computational neuroscience. By the end of the first year, we are working on our annual progress report, but I found it challenging to build a mature, logical storyline for my ongoing project.
Writing is hard for me, particularly since I'm not a native English speaker. It is also hard from another perspective, as I was not trained this way. I majored in computer science during undergrad, but the courses were all about engineering. I changed my major to cognitive neuroscience because I was interested in the human mind. It is interesting and a field in which I could leverage my previous coding skills. I had gained experience in a basic perceptual neuroscience project, yet it was not computational at all. Later, I completed an MRes in neuroscience and learned how to use computational models to understand our perceptions. My current work shifted from the perceptual field into the social sciences. I guess my supervisor sees my potential in conducting computational modelling to facilitate their work. However, doing such an interdisciplinary study is way more challenging than I thought.
Throughout this journey, I have two primary reflections. The first is that coding is not programming, especially since my previous courses did not provide me with any knowledge of cognitive modelling, Bayesian methods, or even neural networks (old-school lol). I learned a bit on my own from the internet, but I still find it insufficient for research. I guess students who trained this way may feel the same, as knowledge itself is evolving all the time. But given that the techniques are serving the research questions, so generally we actually do not need the most popular one to complete the study and the story behind. The second one is the complex storytelling about social cognitive computational neuroscience; it is way harder to knit together the social sciences-related background, the research question, and why compu, why how neuro, why neuro. In closing, my frustration is getting plain, and I deeply believe it is indeed fortunate to get into the field, even though the pain in doing it is so real and deep. The doubt of not being good enough, the pain of endless learning and negative feedback, and the frustration of uninterpretable results.
I think the next reflection is what science is, especially when working on the data-driven, project-based, and feedback-lacking journey. I was optimistic before because I saw it as a 3D observation of a toy in my hand. Different analyses are like changing views; looking at them, we will find a way to see the truth. But do we really have a truth within the current stuff in my hand?
Get back to my annual report writing, I still have another 5 days to improve it. Please give me any advice on how you got through the hardest time in doing your PhD. And if you have a similar background or struggles, maybe we can have more talks about how we see things similarly or differently, and how we could see things more scientifically as our supervisors :p