Skip to main content

Phenomenology, dynamical neural networks and brain function

Buy Article:

$53.17 plus tax (Refund Policy)


Current cognitive science models of perception and action assume that the objects that we move toward and perceive are represented as determinate in our experience of them. A proper phenomenology of perception and action, however, shows that we experience objects indeterminately when we are perceiving them or moving toward them. This indeterminacy, as it relates to simple movement and perception, is captured in the proposed phenomenologically based recurrent network models of brain function. These models provide a possible foundation from which predicative structures may arise as an emergent phenomenon without the positing of a representing subject. These models go some way in addressing the dual constraints of phenomenological accuracy and neurophysiological plausibility that ought to guide all projects devoted to discovering the physical basis of human experience.

Document Type: Research Article


Affiliations: 1: Division of Neurology, Toronto East General Hospital, 825 Coxwell Avenue #4, Toronto ON M4C3E7, Canada 2: Department of Philosophy, Princeton University, 1879 Hall, Princeton, NJ 08544–1006, USA 3: Department of Physiology, University of Toronto, Toronto ON M5S 1A8, Canada

Publication date: June 1, 2000

More about this publication?

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more