Every morning, I’ll walk into the LSRC and I’ll see stairs—three long, tall flights of stairs I have to walk up in order to get to lab. After making my way to the third floor, I’ll round the corner into the Adcock Lab, set down my things onto my desk, and catch up on the messages in the lab’s Slack page. My mentor Abby will often stop by and we’ll set up a time to meet that day. This meeting will usually regard problems that need trouble-shooting or tasks that need to be done that day, and we’ll discuss what kind of work I’ll need to do for the next steps of the experiment. The daily work I do differs based on the stage the project is at. We first began by creating the experiment itself, a computer-based task. My mentor developed and coded the first task of immediate curiosity on conditional engagement, and I prepared the second task, a memory test. Since the experiment is centered on videos of art and drawings, our memory test essentially consists of comparing screenshots of old videos with new ones—command-shift-4 has now become one of my best friends! I also compiled relevant questionnaires and inputted each question into the task. Since finalizing these parts, we’ve sent out our pilot studies and gathered our first data points—which brings me to the work I do today!
Since we send out our study in batches, I’ll spend my time cleaning the data received from the latest cohort, such as removing data from participants who didn’t complete their tasks. Our experiment has two-parts, so it’s also important to confirm that the subjects in each pool match each other. I’ll cross-check subject IDs from part one with part two, allowing us to make sure that the subjects who completed both tasks receive their full compensation and eliminating those who have not.
I’ll then do some preliminary data analysis—mostly just some basic data science manipulations using Python. I’ll first reformat the data sets to look more organized and show only relevant information. I’ll then usually try to map trends through graphical analysis, as well as examine individual outliers, errors, and uncertainties. This way, I can practice my techie skills but also still gain knowledge from the cohort’s data! This is one of my favorite parts of my work, as having more data points strengthens and clarifies trends and brings you one step closer to a completed puzzle. It’s also an incredible feeling to realize that your work is real—the data is tangible, concrete—real!! Some days are slower than others, but that’s research. Research is for discovery, and as long as my little efforts contribute in some way to that truth or that discovery—I’ll be happy.
