Founded in the spirit of facilitating the transition from closed scientific enquiry to a more open model we aim to transcending barriers between disciplines, science and the society.
We foster research at crossroads between interdisciplinary life and health sciences, basic understanding of learning processes and novel education technology/methodology testing and implementation, and digital sciences.
This two-day international conference will bring together researchers from various fields - humanities and social sciences, neurosciences and psychiatry, as well as doctoral students in philosophy of psychiatry, biology and medicine - to explore the ways in which philosophy and psychiatry can contribute to each other today.
The aim of this conference is first to present the different ways of practicing and studying psychiatry from a philosophical perspective (from clinical phenomenology to philosophical anthropology, from psychiatry to analytical philosophy applied to psychopathology), and to explore the issues, biases and presuppositions of each ones. But the aim is also to formulate the theoretical or practical objectives of these different approaches, their preferred objects or aspects in the study of psychiatry, and their expectations for the future development of their respective research programmes.Hybrid format available!
Join us in Sorbonne (registrations are still OPEN !!!), or by Zoom.
Meeting ID: 970 1709 4228 Passcode: 293578
Event mostly in French.
How does my own brain influence my research?
The process of scientific research involves our brain in many possible ways, by sensing, by abstracting, by communicating, by drawing, by imagining, by wandering in blurry thoughts, by making decisions. In all these activities, cognitive biases well described in human psychology are at play, which can lead us to wrong paths, misformulated messages, misperception, or simply to not seeing what there is to see. One of the key notions in this talk is expectations, which can operate at abstract levels such as during hypothesis generation, or at more perceptive levels when looking at data (predictive coding). Whether you’re parsing genomes, designing a figure, or simply thinking about phenomena with words in mind, some bias may be affecting you without you being aware of it. Getting to know them will help you avoid pitfalls. I will illustrate several cases of bias by sharing some of my research experience, but during the talk your own examples will probably pop up!