Dr. Franziska Knolle

Keywords:
Computational modelling, cognition, decision making, language, psychosis, fMRI, EEG

Research methods:
In my research, I am using computational and machine learning approaches to explore novel behavioural paradigms as well as f/MRI and EEG data.

Brief research description:
I am using the framework of predictive processing to understand cognition in healthy states and mental disorders, especially psychosis. My research has the following two overarching questions: Can potentially dysfunctional predictive processing used to explain symptoms in psychiatric disorders, especially those of psychosis? Is it possible to use computational parameters, extracted from behavioural paradigms and imaging tasks, to predict risk for or functional outcome of psychosis or other psychiatric disorders?

GSN students: 

Selected publications:

Kesby, J., Murray, G.K., Knolle, F. (2021). Neural circuitry of salience and reward processing in psychosis. Biological Psychiatry GOS.

Haarsma, J., Knolle, F., Griffin, J.D., Taverne, H. Mada, M., Goodyer, I.M., the NSPN Consortium, Fletcher, P.C., Murray, G.K. (2020). Influence of prior beliefs on perception in early psychosis: effects of illness stage and hierarchical level of belief. Journal Abnormal Psychology ,129(6), 581-598.

Ermakova*, A.O., Knolle*, F., Justicia, A., Bullmore, E.T., Jones, P.J., Robbins, T.W., Fletcher, P.C., & Murray, G.K. (2018). Abnormal reward prediction error signalling in antipsychotic naïve individuals with first episode psychosis or clinical risk for psychosis. Neuropsychopharmacology, 43, 1691–1699. * Joint first author.

Knolle, F., Goncalves, R. & Morton, J. (2017). Sheep recognise familiar and unfamiliar human faces from 2D images. Royal Society Open Science, 4: 171228.

Knolle, F., Schröger, E., & Kotz, S. A. (2013). Cerebellar contribution to the prediction of self-initiated sounds. Cortex, 49(9), 2449-2461.