The dark room problem in predictive processing and active inference, a legacy of cognitivism?

Baltieri, Manuel and Buckley, Christopher (2019) The dark room problem in predictive processing and active inference, a legacy of cognitivism? ALIFE 2019, Newcastle upon Tyne, UK, 29 July - 2nd August, 2019. Published in: Proceedings of the Artificial Life Conference 2019 (ALIFE 2019). (31) 40-47. MIT Press, Cambridge, MA.

[img] PDF - Accepted Version
Download (281kB)
[img] PDF - Published Version
Available under License Creative Commons Attribution.

Download (287kB)

Abstract

The free energy principle describes cognitive functions such as perception, action, learning and attention in terms of surprisal minimisation. Under simplifying assumptions, agents are depicted as systems minimising a weighted sum of prediction errors encoding the mismatch between incoming sensations and an agent’s predictions about such sensations. The “dark room” is defined as a state that an agent would occupy should it only look to minimise this sum of prediction errors.
This (paradoxical) state emerges as the contrast between the attempts to describe the richness of human and animal behaviour in terms of surprisal minimisation and the trivial solution of a dark room, where the complete lack of sensory stimuli would provide the easiest way to minimise prediction errors, i.e., to be in a perfectly predictable state of darkness with no incoming stimuli. Using a process theory derived from the free energy principle, active inference, we investigate with an agent-based model the meaning of the dark room problem and discuss some of its implications for natural and artificial systems. In this set up, we propose that the presence of this paradox is primarily due to the long-standing belief that agents should encode accurate world models, typical of traditional (computational) theories of cognition.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Evolutionary and Adaptive Systems Research Group
Depositing User: Lucy Arnold
Date Deposited: 11 Sep 2019 14:18
Last Modified: 11 Sep 2019 14:18
URI: http://sro.sussex.ac.uk/id/eprint/86095

View download statistics for this item

📧 Request an update
Project NameSussex Project NumberFunderFunder Ref
Distributed neural processing of self-generated visual input in a vertebrate brainG2144BBSRC-BIOTECHNOLOGY & BIOLOGICAL SCIENCES RESEARCH COUNCILBB/P022197/1