Dr. Macarena Suarez-Pellicioni
Principal Investigator of the Brain, Learning, & Education Lab
Please describe your research:
I study how the brain supports math learning and math performance in children and adults and the role of other skills (such as language) and affective factors (such as math attitudes or math anxiety) in explaining these individual differences in learning and performance.
What draws you to your work?
The curiosity about the human brain in general, and specifically, about how the brain explains cognition and behavior.
If you weren't doing this, what would you be doing?
I’d probably be studying the brain from another perspective. Something I’m fascinated with is the behavioral and cognitive consequences of brain lesions. This is why I did my internship in a hospital, where we administered neuropsychological tests to patients suffering from different neurological conditions (stroke, tumors, etc). I guess this is something I could be doing if I weren’t doing what I’m doing.
If you could share one piece of advice
with students, what would it be?
You can achieve pretty much anything with hard work and perseverance. Think about where you want to be in a few years and focus on doing what you need to do to get there. Make sure you give 100% of yourself towards achieving your goal. Compete with yourself, not with others. Don’t be over-critical with yourself. (Try to) trust your instinct. Don’t let your curiosity die. Be humble.
How would you explain what you do to
someone unfamiliar with your work and field?
I use different neuroimaging techniques to study math learning and performance in children and adults. Some of my studies used event-related potentials (ERPs), which measures brain electrical activity, to understand differences in math processing between adult students who are anxious about math and those who are not, particularly trying to understand the detrimental effects that anxiety can have on performance, even for high-skill people.
My most recent research uses functional magnetic resonance imaging, which measures which brain regions are activated when participants solve a task inside the scanner, to try to understand which brain regions are associated with being good at math, the different brain regions explaining performance on different operations (i.e. subtractions and multiplications), and the role of cognitive abilities, such as phonological skill, and affective factors, such as attitudes towards math, in explaining these differences in brain activation.
Particularly relevant regarding this second research line is the fact that I’ve studied longitudinal data, which means that we had children come to the lab to do math (let’s call it “time 1”) and then we invited them back to the lab two years later to do math in the lab again (let’s call it “time 2”). This design allows us to answer very interesting questions regarding the development of math skill. For example: Can we identify the brain regions that are activated at time 1 and that predict that children will become better at math over time (comparing time 2 with time 1)? Can we identify the brain regions activated at time 1 that predict that children would struggle with math later on?
Longitudinal designs also offer insights into the temporal precedence of specific math abilities. For example, when you go to the supermarket and you pick the shortest checkout line you are most likely not counting how many persons are in each line, but just simply estimating quantity. Some researchers believe that this “estimation” skill is what humans use to build their ability to do math with symbols (i.e. Arabic digits), such as arithmetic. Well, while these two skills are associated with one another, a longitudinal design allows us to study whether estimation skill is that crucial to explain symbolic math (estimation at time 1 explains changes in symbolic math from time 1 to time 2) or whether it is the other way around and it is actually our ability to do math with symbols what “refines” this estimation skill (symbolic math at time1 explaining changes in estimation skill from time 1 to time 2). These are fascinating questions!
