Hebbian Constraint on the Resolution of the Homunculus Fallacy Leads to a Network that Searches for Hidden Cause-Effect Relationships

Authors
András Lorincz
Corresponding Author
András Lorincz
Available Online June 2009.
DOI
https://doi.org/10.2991/agi.2009.36How to use a DOI?
Abstract
We elaborate on a potential resolution of the homunculus fallacy that leads to a minimal and simple auto-associative recurrent `reconstruction network' architecture. We insist on Hebbian constraint at each learning step executed in this network. We find that the hidden internal model enables searches for cause-effect relationships in the form of autoregressive models under certain conditions. We discuss the connection between hidden causes and Independent Subspace Analysis. We speculate that conscious experience is the result of competition between various learned hidden models for spatio-temporal reconstruction of ongoing effects of the detected hidden causes.
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Proceedings
Proceedings of the 2nd Conference on Artificiel General Intelligence (2009)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2009
ISBN
978-90-78677-24-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/agi.2009.36How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - András Lorincz
PY  - 2009/06
DA  - 2009/06
TI  - Hebbian Constraint on the Resolution of the Homunculus Fallacy Leads to a Network that Searches for Hidden Cause-Effect Relationships
BT  - Proceedings of the 2nd Conference on Artificiel General Intelligence (2009)
PB  - Atlantis Press
SP  - 170
EP  - 175
SN  - 1951-6851
UR  - https://doi.org/10.2991/agi.2009.36
DO  - https://doi.org/10.2991/agi.2009.36
ID  - Lorincz2009/06
ER  -