The neural mechanisms of learning and using cognitive models
Animals are strikingly adapted to complex environments. A fundamental question in neuroscience is
how the brain constructs simplified cognitive models that allow an animal to adapt to a complex and
high dimensional environment, an issue currently challenging both theoretical and empirical
research.
The present project aims at shedding light on this problem both at the computational and empirical
(behavioural and neural) levels. Using computer-based tasks, we will manipulate the complexity and
relevance of task features to study how humans build and exploit cognitive models of the task. In
addition, we will manipulate the reward associated with task performance and time, to test whether
such manipulations change the strategies used. We will adopt a computational neuroscience
approach and integrate behavioural and fMRI data to study the neural mechanisms underlying the
formation and use of cognitive models.
Methods and what it will involve for you
Initially you will be involved in developing and piloting a behavioral task adopting, among other
methodologies, eye-tracking. This task will form the basis for an fMRI study in healthy volunteers.
You will have the opportunity to learn task development in Cogent and statistical analysis of
behavioral and neural data. Preferred applicants will have strong conceptual knowledge about
statistical analysis and basic programming/scripting skills (matlab).
Your role will be to:
1. Assist with the development of a computer-based task.
2. Recruit healthy volunteers to participate in a behavioral and fMRI study (using our
department website etc.).
3. Conduct a behavioral and fMRI experiment throughout all the phases. You will initially be
supervised until you can carry on independently.
4. You will be encouraged, but not required, to take an active role in data analysis. If interested,
the researchers will be happy to provide help and guidance in this respect.
If you are interested, please send an email and a copy of your CV to:
Dr Francesco Rigoli, f.rigoli@ucl.ac.uk