What I learned in grad school 🪴

About 2 years ago around this time, my goal was to start 2022 in grad school. If you had asked my 22 year old self, a fresh lady with a bachelor’s degree (spinsters degree), I would have laughed in your face. Why I would want to waste so much money doing that?? The era of COVID definitely shifted my mindset on that…

As I had my last class of my graduate program this week, I thought it might be helpful to go through my experience and compare expectations versus reality. Feeling lots of feels lately so cue sappy graduation music.

I found my applications to different schools. All of them showcased my “objective”:

To leverage an understanding of cognitive science, people, and technology in order to build truly human-centric, ethics based systems.

A lofty goal indeed. Context appropriate, considering I was nostalgic for the student whose mind was being blown in her psychology of mind class discussing the Chinese Room Thought Experiment. I was also ripe in the world of tech ethics at that point and baffled by how many smart, inspiring people were referencing my cognitive science topics in their debates over ethical technology practices. I wanted to be one of those smart people.

“To do this, I needed to expand my experience with graduate training in developing studies, analyzing results, and translating research insights to design systems compatible with human capabilities and limitations.”

I wrote that in one of my applications as my rationale for attending grad school. It was true. I felt comfortable enough with cognitive science lingo and concepts but if I was going to be one of the smart people actually applying the research to the design of technical systems, I definitely had to upgrade my research skillset. I needed to put cognitive science in the context of UX research and revisit, at a deeper level, how to conduct official research studies.

Spring 2022 🌷

• Computational Modeling
  • I enjoyed seeing the relationship between the human mind and the foundations in machine learning. One of the nerdiest moments I had was hearing how experts from mathematical psychology, artificial intelligence, and statistics arrived at the same theorem! The comparisons between how information is represented in our brains and distributed feature representations still sticks with me. The lab portion helped me gain experience with Julia’s machine learning framework. We were tasked to worked in a group to build a machine learning model and apply the techniques discussed in lecture. I really enjoyed playing the role of engineer as I trained the model and figured out how to tune the hyperparameters to arrive at the “best” model. This class definitely left me feeling inspired by what I could do with ML :)
• Memory
  • This course was amazing!! While I was aware of the distinctions between different memory systems (like episodic, implicit, vs working memory), this course taught me granular details of the systems and how experts study memory. One of my favorite topics was how concepts are represented in our minds and how creatively researchers study that. Not only did this course go into the ins and outs of different memory systems but it was the perfect way to get my head in the research zone. It also had me look closer at the neural structures. One of the things that has stuck with me is how consciousness is not a requirement for memory. I’m especially thinking about this with regards to the ideas like building a second brain.
• Applied HCI
  • I had high hopes for this class. However, I was VERY disappointed by how after midterms our professor ghosted our entire class. I could forgive the dated content and lack of feedback… But to pay that much money for an education to help my career path was absolutely unacceptable. At the very least I gained familiarity with concepts like heuristic evaluations and an introduction to Figma. The best thing I learned was standing up for my education and trying to work with my program head and classmates to advocate for a better experience for future students.

Fall 2022 🎃

• Neural Net Math
  • As someone whose furthest math class was college level calculus, I was very nervous for this class. I tried my best to get familiar with linear algebra over the summer but only got so far. While I really struggled to grasp all the concepts in this class, it was a wonderful feeling when I understood what was under the hood of the machine learning models. Learning the underlying math made me feel less like machine learning was magic and more grateful for computing power to be able to carry out these math & stat techniques. We had another group project in this class where we had to build our own models without using pre-existing software. I felt very smart knowing the basic architecture of machine learning models and how the training process worked!
• Research Methods I
  • My last class using SPSS for psychology research was either my freshman or sophomore year in undergrad. This class was a good reminder on using the tool to run ANOVAs, regression, correlation analyses. However, my favorite part of this class was learning how to preprocess data and analyze it PRIOR to building a model. I think the techniques we learned to check the data for assumptions is one of the most important lessons I’ll carry through my career.
• Cognitive Psychology
  • This class was a great overview of all my favorite topics in cognition from sensation, perception, attention, memory, decision making! I loved the way the teacher structured the class with readings and discussion boards. The class helped me feel more comfortable reading and interpreting academic research papers. I feel like I gained a voice after commenting and having involved discussions on the content!!

Spring 2023 🌸

• Statistical ML
  • This was similar to Neural Net but covered other mathematical underpinnings of machine learning models.
• Research Methods II
  • I left this class with a deeper understanding of ANOVAs, linear regression, and correlation analyses. Our professor decrypted the models by tasking us to work through these problems using a basic calculator, statistical tables, and formulas. I gained an intuition for the calculations behind the models and felt less like I was just blatantly trusting the software when working through these problems. A side effect of this class was that I started to informally apply this way of thinking to my personal problems, mostly in a goofy way.
• Art & Web
  • I had no idea what to expect for this class but man was I happy I took it!! I revisited my dusty web design skills and was able to do what I had been wanting to for awhile now - start my own website from scratch! I loved learning about the creative web design processes and how different ways artists have utilized web art. I was lucky enough to be struck with this idea that I feel so excited about, Mind Blend Cafe. With the skills I learned from this class, I created a prototype and started a whole backlog of ideas. I have received good feedback and more importantly I am so in love with it. It makes me really happy to create something that is a manifestation of my ideas. I feel beyond happy that the class gave me the tools to actually make the idea come true.

Summer 2023 🌻

• Usability Engineering
  • This class gave me a lot to think about. It was nice to be in a group and apply what different experts in the field of behavioral science and UX advocate for. I kept thinking about the The Social Dilemma and how many of the big tech companies took advantage of neuroscience research in their product/service/app design and the negative psychological impact it has had. A question on my mind was how to identify when it becomes manipulation when using cognitive & behavioral science principles to nudge use of a product/app/service. I will continue pondering the concepts covered in the class to see how one might approach design and arguably manipulative practices in an ethical manner AND business friendly manner.
• Research Lab
  • I informally joined a lab on Memory & Depression late my spring semester and formally continued into the summer session. I had to renew my IRB training to participate in the lab which I enjoyed more than I thought I was going to. I feel like there are a lot of practices that could be recycled from the ethics of psychological study design to technology development, especially as we learn how tech affects the psyche. I also learned a lot about the practicalities of being in a lab through this experience like scheduling participants, organizing related materials, and figuring out how to handle unexpected factors. Most of the work I did pertained to running participants through memory tasks. It was helpful to see how far theory can go and what complications arise when a study is done in practice. I’m looking forward to the next part of this lab which is to clean/prep the data and use R for analysis.