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Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
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A Model of Unified Perception and Cognition.
Pei Wang1, Christian Hahm1, Patrick Hammer2
1Department of Computer and Information Sciences, Temple University, Philadelphia, PA, United States.
Frontiers in Artificial Intelligence
|April 28, 2022
Summary
This study introduces a novel perception approach for the intelligent system NARS, emphasizing the perceiver
Area of Science:
- Artificial Intelligence
- Cognitive Science
- Perception Systems
Background:
- Current AI perception models often lack subjective and goal-driven elements.
- The distinction between perception and cognition in AI is often treated as qualitative rather than quantitative.
Purpose of the Study:
- To present a new perception framework for the general-purpose intelligent system NARS.
- To integrate subjective and goal-oriented aspects into AI perception.
- To align AI perception with cognitive science findings for Artificial General Intelligence (AGI).
Main Methods:
- Designing NARS's perception functionality based on three core principles: perceiver-dependency, goal-initiation, and unified perception-cognition.
- Describing NARS system aspects relevant to the proposed perception approach.
- Comparing the NARS approach with existing AI perception methodologies.
Main Results:
- The NARS approach integrates subjective experience and goal-driven processes into perception.
- Perception in NARS is fundamentally unified with cognition.
- The NARS design offers a more suitable model for perception in Artificial General Intelligence (AGI).
Conclusions:
- The proposed NARS perception model, grounded in cognitive science, offers a more effective approach for AGI.
- Perception is best understood as an active, perceiver-centric process.
- Unifying perception and cognition is crucial for advanced intelligent systems.
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