Venue: Mi3/LMU, Gabelsbergerstrasse 62, 80339 München
Our winter semester sees the return of our series of interdisciplinary lab meetings, featuring speakers bringing together perspectives from philosophy, cognitive neuroscience and more.
Below are a couple of our featured talks:
Wednesday, January 28, at 10:15 (Munich time):
Judith Burkart (Universität Zürich, Director of the Institute for Evolutionary Anthropology)
On the origins of the (human) mind: Cooperative breeding and altercentric cognition
Abstract: Humans have evolved as cooperatively breeding great apes, which was essential for the emergence of many of our species-specific traits. Based on our comparative work with marmoset monkeys, the only cooperatively breeding primates in addition to humans, we explore how these breeding system facilitated our psychological predispositions towards prosociality, joint action, and information donation. More broadly, this work also challenges traditional views of how social minds emerge. Rather than moving from an initial egocentric stage to eventually understanding other minds, empirical results from both developmental and comparative psychology suggest this view may be turned upside down: minds may start out altercentric, rather than egocentric, both in development and evolution.
Wednesday, February 4 at 10:15 (Munich time):
Heike Felzmann (University of Galway, School of History and Philosophy & Data Science Institute; Research Ireland Insight Centre for Data Analytics, Galway)
Mental health diagnosis by AI: the promise of precision psychiatry?
Abstract: Psychiatric diagnosis has long been considered imprecise and substantially shaped by social factors, including the social processes underlying the consensus-based development of diagnostic manuals. While a reasonable level of reliability has been achieved at least in some areas of psychiatric diagnosis, the variety of presentations and limited availability of precise objective diagnostic markers remains a concern for many conditions. The increased availability of large scale, multi-modal health data and new methods of analysing large scale data open up a new avenue for potential progress in the scientific basis of psychiatric practice. AI detection of distinctive diagnostic features, identification of subtypes and AI assisted diagnosis appear to be within reach, with potentially impact on the future practice of psychiatry. In my paper I will discuss some current developments with regard to AI use for the identification and understanding of common psychiatric conditions and consider their conceptualisation as contribution to “precision psychiatry”. I will explore the envisaged benefits and potential ethical challenges related to pursuing AI detection of psychiatric conditions and the potential development of AI assisted approaches to diagnosis and will argue that the promise of transformation of psychiatric practice into “precision psychiatry” is not only unlikely to emerge from such findings but is also associated with considerable ethical challenges.