Guess first or second-guess: the neural signature of prediction-based learning in adolescents and young adults

An alarming number of young adolescents fail in school for reasons not related
to their intellectual capacities or their eagerness to learn. This discrepancy
between students* school achievement and their learning potential suggests that
the educational approach in early adolescence does not provide a good enough
fit with students* learning skills. The goal of this project is to get a better
understanding of the neurocognitive mechanisms underlying different
instructional approaches and to investigate potential differences in learning
mechanisms between young adolescents and young adults.

For this purpose, we will measure brain activation during a learning task with
two conditions: direct instruction and prediction-based learning. We
hypothesize that instruction-based learning draws more upon brain circuits
classically associated with declarative memory (medial temporal lobe system),
whereas learning from predictions will rely more on feedback-based learning
circuits (striatal system). The prefrontal cortex will likely play a role in
modulating the interaction between medial temporal lobe and striatal systems.
Critically, we expect to find differences the recruitment of these learning
systems between adolescents and adults, which may provide important leads for
the design of educational material that takes into account the relative
strengths and weaknesses of adolescents* learning systems.

The study will address two key questions:
1. Which brain systems are involved during learning by making predictions
versus receiving direct instruction on a numerical fact learning task?
2. Do adolescents and young adults differ in the recruitment of these brain
systems during numerical fact learning?

Besides the main research questions, we will also address two exploratory
questions:
1. Can differences in learning be explained by differences in cognition and
self-regulation?
2. Can differences in learning be explained by differences in the intrinsic
network structure of the brain?

The current proposal involves experimental research, combining measures of
neural activity with behavioural assessments. We will measure brain activation
using functional Magnetic Resonance Imaging (fMRI) while participants are
performing a computerized numerical fact learning task. The learning task
involves two conditions: direct instruction and prediction-based learning. In
addition, we will collect resting-state MRI to measure functional connectivity
in the underlying brain networks that are relevant to the learning task.
Outside the scanner, memory for the numerical facts will be assessed.
Furthermore, we will measure cognitive functioning on a battery of tasks and
participants will fill out a couple of questionnaires about executive functions
and learning strategies. All measurements are non-invasive.

There are no known risks associated with participating in the proposed
measurements. MRI is a noninvasive technique involving no catheterizations or
introduction of exogenous tracers. Numerous children and adults have undergone
MRI studies without apparent harmful consequences. Some people become
claustrophobic while inside the magnet and in these cases the study will be
terminated immediately at the subject's request. The only absolute
contraindications to MRI studies are the presence of intracranial or
intraocular metal, or a pacemaker. Relative contraindications include pregnancy
and claustrophobia. Subjects who may be pregnant, who may have metallic foreign
bodies in the eyes or head, or who have cardiac pacemakers will be excluded
because of potential contraindications of MRI in such subjects. Although there
is no direct benefit to the participants from this proposed research, there are
greater benefits to society. Not only are the insights gained by this study
important for theory development, they can also be used to inform educational
programs tailored to the abilities and needs of young adolescents. More
generally, knowledge about normal development is critical to aid in the
understanding of cases of abnormal development, as seen in children with
Attention Deficit Hyperactivity Disorder, autism spectrum disorder, or
traumatic brain injury.