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When and How-Long: A Unified Approach for Time Perception.
Michail Maniadakis1, Panos Trahanias1
1Computational Vision and Robotics Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas Heraklion, Greece.
Frontiers in Psychology
|April 12, 2016
Summary
This study enhances artificial cognitive systems by enabling them to encode event timing and duration, crucial for developing temporal cognition and understanding past events.
Area of Science:
- Cognitive Science
- Artificial Intelligence
- Neuroscience
Background:
- The brain encodes environmental dimensions, including 3D space and time.
- Temporal cognition, particularly interval timing, is a growing research area.
- Encoding distant moments and event timing is underdeveloped in artificial systems.
Purpose of the Study:
- To extend a time perception model for encoding event occurrence time and duration.
- To develop artificial systems capable of temporal cognition.
- To enable AI agents to process and recall temporal information about events.
Main Methods:
- Extension of a neural network-based time perception model.
- Integration of the Striatal Beat Frequency (SBF) theory.
- Focus on encoding both event duration and time of occurrence.
Main Results:
- The extended model facilitates the encoding of event occurrence time in memory.
- The model supports skills essential for temporal cognition.
- Artificial systems can now answer time-related questions about past events.
Conclusions:
- Accurate temporal cognition in AI requires encoding both event duration and occurrence time.
- The enhanced model addresses a significant gap in artificial cognitive systems.
- This work paves the way for more sophisticated AI with temporal reasoning abilities.
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