@jonny Hey Jonny! Would you be willing to share some advice on a very basic experimental design, for a student project? We wish to explore if energy/high-sugar/carbonated drinks impact/reduce student reaction times (measured with Backyard Brains Muscle Spiker + Reaction Timer). The project is aimed to be an exercise in experimental design (neurophysiology is just the research context). The general idea is to measure the reaction times of volunteers before and after consuming the drink. 1/2
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@jonny We can play with drink selection - Red Bull, sugar-free Red Bull, a local energy drink ("Guarana"), maybe also with a sugar-free variety, Coca Cola, Coca Cola Zero, and local soda without caffeine ("Kokta" + sugar-free "Kokta"). I was thinking in lieu of factorial design and repeated measures ANOVA. We need to work out the details: do we test each volunteer for each drink? Are we bound to just one test a day, per volunteer? Do we measure baseline reaction times only once per volunteer?
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@rubinjoni first of all incredible question
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@rubinjoni (boosted so hopefully some other neuroscientists wander by)
I'm assuming we're talking high school/college students from backyard brains & teaching experimental design? I really like what you've already gotten here, thinking about recovery time/getting back to 'baseline' after a dose is great, especially coupled with how to design the experiment as a within-subjects vs. between subjects lesson
some ideas that come to mind for the lesson: you probably would start with something like variable isolation, caffeine vs. sugar is a good one - which sets of drinks vary sugar while keeping caffeine stable and vice versa.
then the other side - what is the spikerbox measuring? what are the possible things we could measure given the tool at hand, electrodes at two positions, where on the body? why?
that's a way to back into framing the hypothesis - what is it that we believe will happen here, and how do we think through "caffeine + sugar = subjectively fast" from what we understand about neurophysiological signals. does that help us design what we measure, or how does that influence how we want to design the experiment as exploratory across a lot of factors, or many repetitions of a single design (to pose a dichotomy). what is the importance of theory? how do we go from correlating observations to feeling like we understand something? how does a hypothesis, as a prediction based on some principled belief about a system, affect how we design experiments?
depending on how many are in your class, you could split them off into groups to do cognitive reaction times (there are some web platforms, i think psychopy's web tool will be able to this, lmk if you'd like help setting this up for your students) vs. electrophysiological reaction times, so then you have a 2x2 control vs. experimental, cognitive vs. ephys comparison that you can rotate them through and so they learn about a few techniques at once. what kind of evidence lets us answer what questions? which are important to our hypothesis?
one thing that i would want to do if this was a younger/gen-ed class (rather than a stats-focused 300/400 level college class where i'd want to introduce it more head on), but might be a stretch and might be distracting depending on what level you're teaching is slipping in a little 'why are we asking this question' lesson - you present them the scientific lesson as the instructor sure, but you could make a big deal about some chemical they advertise (eg. pick something like guarana or taurine, or sugar would also work actually) and then in the second or third lesson start trying to steer them back to that question after they probably discard it in the first day while deciding the independent variables. get them thinking about 'why are we asking this question at all,' who's funding this, whose interest does this question serve. especially with sugary drinks that would be a way to get them thinking about the way that even in picking which questions you choose to answer you're making a choice about what's important to know.
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@rubinjoni oh wait rereading your post, are you one of the students or the teacher~?!?! sorry i assumed just talking to profs all day.
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@rubinjoni i'm about to go to bed but hopefully some of that is useful, but like i said i like what you've got already there! will return tomorrow
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@jonny Thanks!
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@jonny I'm the teacher, but I would like to approach the project with student mindset (discuss the issues and ask for advice, not just micromanage my students and tell them "the right way" to do stuff).
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@rubinjoni i love this!!!!!!!!!!!!! ok yes i need to sleep but this is a great way of approaching a lab class. you've got a great set of ideas that give flexibility and allow the students to pick their course but there are clear questions so they can't get too lost. one more choice to think about for now is also how you want to group them up - are they deciding on experiments as a class, as a small number like 2-4 groups, or as lab partners? you can do multiple across sessions obvi, but that will also help with narrowing down lesson design <3
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@jonny I just took a quick look at psychopy.org and pavlovia.org - we could ditch the spiker/reaction timer and just do the online experiments. This would be a huge logistical improvement. I'll discuss it with the students, thanks!
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@rubinjoni Not a pro, but since neurostimulants are chemicals that persist in the system for a while, I think your students should be able to work out a reasonable limitation on how often a person can be tested without cross-contamination.
Other potential calibration questions : Getting used to the experiment.
Is the experiment more or less reliable when it feels novel to the subject?
If subjects do get used to the experiment, would it be expected to override the potential impact of the stimulants?
Is it interesting to see if people get used to the impact of the stimulant also?
And for the theory part: How long should be expected to pass between ingestion and measurable impact?
@jonny
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@androcat @jonny Training is, of course, a factor. Habituation to energy drinks is another. Lots of things to consider. I'm thinking of just randomly assigning students to treatment groups, and doing a simple one way ANOVA to see if any treatment had significant effect...
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