Apophatic science: how computational modeling can explain consciousness.

A new methodology for consciousness science treats computational models as providing negative data—information about what consciousness is not—rather than positive evidence. This approach avoids metaphysical commitments while supporting quantitative research. It combines computational modeling as an integrative framework across cognitive sciences, echoing Alan Newell's call for computer science concepts as a common language, with a validation method that uses models to constrain theories by ruling out alternatives. The methodology addresses the challenge that consciousness is inherently subjective while scientific data are intersubjective, enabling empirical investigation without resolving philosophical debates.

Study at a glance

Abstract

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.