Quotation Human, Soheil, Bidabadi, Golnaz, Peschl, Markus. 2017. Learning to Satisfy Needs: Predictive Processing vs. Deep Learning. EUCognition, Zurich, Switzerland, 23.11.-24.11.




Need satisfaction has a key role in survival and wellbeing of biological cognitive agents. As a basic cognitive capability which presents early in development and throughout the lifespan, need satisfaction can be seen as an inspiring case for development of more human-like learning and thinking machines. In this paper, we first define the problem of need satisfaction and reformulate it as a learning problem. We then argue that deep learning is not an appropriate learning approach for development of computational cognitive models of need satisfaction. Finally, we introduce an alternative conceptual learning model for need satisfaction based on predictive processing (PP).


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Poster presented at an academic conference or symposium
Language English
Title Learning to Satisfy Needs: Predictive Processing vs. Deep Learning
Event EUCognition
Date 23.11.-24.11.
Location Zurich
Country Switzerland
Year 2017
URL https://www.eucognition.org/index.php?page=2017-zurich-programme


Open Data for Local Communities
Incentivising Open Data Exploration through Needs Management
Human, Soheil (Details)
Bidabadi, Golnaz (Department of Computer Engineering, San Jose ́ State University, San Jose ́, United States/USA)
Peschl, Markus (Cognitive Science Research Platform & Department of Philosophy, University of Vienna, Vienna, Austria)
Institute for Data, Process and Knowledge Management (AE Polleres) (Details)
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