Topic: phenomenology

Over the past thirty years or so, cognitive neuroscience has made spectacular progress understanding the biological mechanisms of consciousness. Consciousness science, as this field is now sometimes called, was not only inexistent thirty years ago, but its very name seemed like an oxymoron: how can there be a science of consciousness? And yet, despite this scepticism, we are now equipped with a rich set of sophisticated behavioural paradigms, with an impressive array of techniques making it possible to see the brain in action, and with an ever-growing collection of theories and speculations ab

The role of the interface between internal and external factors which shape an agent's experience have been considered for millennia. In the 21st century, we have access to new technologies and ideas, but what are the breakthroughs we need that will afford new discoveries? Similarly, what disciplinary boundaries are appropriate as opposed to inhibitory; how much does neuroscience overlap with or have functional or explanatory distinctness from psychology, or other disciplines within the original umbrella of the Cognitive Science project? These questions drive our work in methods for cognition

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.

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.