Cognitive processes are key for the onset, maintenance and long-term course
of mental illnesses. Tasks have played a central role in providing ever
deeper and more detailed insights into these cognitive processes. However,
recently it has become clear that tasks often have relatively poor
reliability. This is a major challenge that urgently needs to be addressed.
In this project, the student will use computational modelling, item-response
and Bayesian optimal design principles in combination with large-scale online
testing to develop a general procedure to maximise task reliability.
– 1) Huys et al. (2016): Computational psychiatry as a bridge from neuroscience
to clinical applications. Nat Neurosci, 19:404-413
– 2) Huys (2018): Bayesian approaches to Learning and Decision Making. in:
Anticevic and Murray. Computational Psychiatry: Mathematical Modelling of
– 3) Enkavi et al, (2019): Large-scale analysis of test-retest reliabilities of
self-regulation measures. PNAS 116:5472-5477
– 4) Ijaz et al. (2018): Psychological therapies for treatment-resistant
depression in adults. The Cochrane database of systematic reviews, 5:CD010558