Departmental Colloquia Series: Professor Micah Galen Allen

The Department of Psychology invites to a lecture by:

Professor Micah Galen Allen, University of Aarhus, Denmark, and University of Cambridge, United Kingdom.

on Thursday 23rd January 2020, 15:15 – 16:45.

The title of the lecture is "Interoceptive Self-Inference: Building a Psychophysical and Computational Approach".

The lecture takes place at the Library, Faculty of Social Sciences, Audit 1, Gothersgade 140, 1353 Copenhagen K.

After the lecture the Department invites to a reception at Øster Farimagsgade 2A, 2nd floor, room 03-2-M202, 1353 Copenhagen K.

Faculty, students and others with interest are welcome. 


Beginning with William James, psychological theory has long placed interoception – the processing, perception, and awareness of signals arising from within the visceral body – at the core of emotional self-awareness. Today, interest in understanding the neurobiology and psychology of interoception is greater than ever, driven in part by the possibility that disordered interoception is a trans-diagnostic risk factor for mental illness. This has led to the rise of pseudo-computational theories of interoceptive inference, based in the notion of predictive processing, in which interoceptive awareness is argued to arise from the precision-weighted integration of interoceptive predictions and prediction errors in the central nervous system. While initially promising, the excitement surrounding this research paradigm is currently limited by the generally poor quality of existing means to measure, manipulate, and quantitatively model interoceptive inference. In this talk, I will first review some of the existing limitations of measuring interoception and present a new psychophysical approach that overcomes many of these issues. I will further present a new computational model characterizing the interplay of interoceptive and exteroceptive predictions in emotional awareness and give a sketch of our upcoming large-scale neuroimaging study of interoceptive inference.