filmov
tv
Consciousness Talks 10: Mathematical phenomenology & category theory

Показать описание
Matteo Grasso, University of Wisconsin-Madison, USA
Title: “Of Grids and Maps”
Bio: I am a postdoctoral researcher at the University of Wisconsin–Madison since 2018. I do research in philosophy and computational neuroscience. In my research I contribute to the development of Integrated Information Theory (IIT), its metaphysical underpinnings, and its application to the study of the content of experience (with particular focus on phenomenal concepts).
Abstract: Neuroscientific methods successfully account for a system’s functional properties, but leave out the subjective properties of the accompanying experience. According to IIT, phenomenology can be studied scientifically by unfolding the cause-effect structure specified by a system. To illustrate how, in this talk I compare two systems (a grid and a map) to show that they can be functionally equivalent in performing fixation, but only one can specify a cause-effect structure that accounts for the extendedness of phenomenal space.
Steven Phillips, AIST, Japan
Title: “Data spaces: category (sheaf) theory and phenomenology”
Bio: Steven Phillips, PhD
Chief Senior Researcher (上級主任研究員)
Mathematical Neuroscience Group
Human Informatics and Interaction Research Institute
National Institute of Advanced Industrial Science and Technology (AIST)
Research: Category theory approaches to cognition.
Abstract: In this talk, I’ll introduce the formal concept of a (pre)sheaf as data attached to a topological space. Sheaves capture the notion of patching local sources of information to form a global whole, e.g., the binding of visual features such as colour and shape. The formal theory appears to be closely related to the foundational properties asserted by the Information Integration Theory (IIT) for phenomenology. A comparison is intended to engender discussion on ways that phenomenology may benefit from a sheaf theory, or (more generally) a category theory approach.
Title: “Of Grids and Maps”
Bio: I am a postdoctoral researcher at the University of Wisconsin–Madison since 2018. I do research in philosophy and computational neuroscience. In my research I contribute to the development of Integrated Information Theory (IIT), its metaphysical underpinnings, and its application to the study of the content of experience (with particular focus on phenomenal concepts).
Abstract: Neuroscientific methods successfully account for a system’s functional properties, but leave out the subjective properties of the accompanying experience. According to IIT, phenomenology can be studied scientifically by unfolding the cause-effect structure specified by a system. To illustrate how, in this talk I compare two systems (a grid and a map) to show that they can be functionally equivalent in performing fixation, but only one can specify a cause-effect structure that accounts for the extendedness of phenomenal space.
Steven Phillips, AIST, Japan
Title: “Data spaces: category (sheaf) theory and phenomenology”
Bio: Steven Phillips, PhD
Chief Senior Researcher (上級主任研究員)
Mathematical Neuroscience Group
Human Informatics and Interaction Research Institute
National Institute of Advanced Industrial Science and Technology (AIST)
Research: Category theory approaches to cognition.
Abstract: In this talk, I’ll introduce the formal concept of a (pre)sheaf as data attached to a topological space. Sheaves capture the notion of patching local sources of information to form a global whole, e.g., the binding of visual features such as colour and shape. The formal theory appears to be closely related to the foundational properties asserted by the Information Integration Theory (IIT) for phenomenology. A comparison is intended to engender discussion on ways that phenomenology may benefit from a sheaf theory, or (more generally) a category theory approach.
Комментарии