Related Experiment Video
Updated: Nov 1, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
Published on: June 21, 2022
Apophatic science: how computational modeling can explain consciousness
Will Bridewell1, Alistair M C Isaac2
1Navy Center for Applied Research in Artificial Intelligence, U.S. Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC 20375, USA.
Abstract:
This study introduces a novel methodology for consciousness science. Consciousness as we understand it pretheoretically is inherently subjective, yet the data available to science are irreducibly intersubjective. This poses a unique challenge for attempts to investigate consciousness empirically. We meet this challenge by combining two insights. First, we emphasize the role that computational models play in integrating results relevant to consciousness from across the cognitive sciences. This move echoes Alan Newell's call that the language and concepts of computer science serve as a lingua franca for integrative cognitive science. Second, our central contribution is a new method for validating computational models that treats them as providing negative data on consciousness: data about what consciousness is not. This method is designed to support a quantitative science of consciousness while avoiding metaphysical commitments. We discuss how this methodology applies to current and future research and address questions that others have raised.
Related Concept Videos
Understanding Consciousness
Sleep, a crucial state, is characterized by reduced...
Introduction to Cognitive Psychology
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
Subconsciousness and No Awareness
An illustrative example of subconscious processing is its role in problem-solving. Often, individuals...
Concepts and Prototypes
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
High-Level and Low-Level Awareness
Mechanistic Models: Overview of Compartment Models

