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Computational Grounded Cognition: a new alliance between grounded cognition and computational modeling
Giovanni Pezzulo1, Lawrence W Barsalou, Angelo Cangelosi
1Institute of Computational Linguistic "A. Zampolli," National Research Council Pisa, Italy ; Institute of Cognitive Sciences and Technologies, National Research Council Rome, Italy.
Frontiers in Psychology
|January 25, 2013
Summary
Grounded cognition theories propose that all cognitive functions emerge from bodily processes. This research introduces Computational Grounded Cognition, using cognitive robotics to model these embodied and situated cognitive mechanisms.
Area of Science:
- Cognitive Science
- Artificial Intelligence
- Robotics
Background:
- Grounded cognition theories posit that cognition arises from sensory, motor, and affective bodily processes, challenging traditional modular views.
- Existing empirical evidence supports the grounded framework, yet explicit computational models demonstrating mechanistic implementation of higher cognition are scarce.
Purpose of the Study:
- To propose a novel multidisciplinary approach: Computational Grounded Cognition, integrating grounded theories with computational modeling.
- To demonstrate how embodied and situated cognitive agents can mechanistically implement higher cognitive abilities.
Main Methods:
- Advocating for the use of Cognitive Robotics methodology to simultaneously address grounding, embodiment, and situatedness.
- Developing explicit computational models that integrate sensory, motor, and affective processes as intrinsic to cognition.
Main Results:
- The proposed framework, Computational Grounded Cognition, offers a pathway to mechanistically implement higher cognitive functions.
- Cognitive Robotics provides a suitable methodology for studying how embodiment and context shape cognitive development and expression.
Conclusions:
- Computational Grounded Cognition represents a significant advancement in understanding cognition as an embodied and situated phenomenon.
- This approach facilitates the creation of computational models that can bridge the gap between theoretical grounded cognition and empirical implementation.
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