Some guesses about the state of the art of pupillometry
Pupillometry is the measurement of the pupil in response to something, such as a stimulus or a mental process. I'm interested in what this technique can reveal about hearing. I'd like to use it in my own research, but I don't know much about it yet.
Before reading the literature, I thought it would be fun to make a short list of things I imagine might be true about the state of the art of pupillometry. At some point in the future, I'll look back at this post to see how accurate my guesses were.
- Pupil size positively correlates with degree of cognitive effort (bigger pupil means thinking harder).
- Pupil responses to cognitive processes are non-specific (you can't distinguish between different kinds of processes).
- Regarding hardware, corporate solutions are very expensive and considered necessary if you want to do pupillometry right.
- In reality, inexpensive DIY rigs can work reasonably well.
- Pupil size estimation from raw data is difficult due to massive effects of ambient light, camera position, eye movements, and blinking.
- Someone has published a nice open Python package to do it all for you (fingers crossed).
- Once you've figured out how big a pupil is at any given moment, further data analysis, such as comparing average pupil size across different conditions in an experiment, is easy.
- Someone has published a very helpful tutorial or guide on pupillometric experimental design (fingers crossed again).
- Every idea for an experiment I come up with has already been done.