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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Published on: November 2, 2012
A computational model of event segmentation from perceptual prediction
Jeremy R Reynolds1, Jeffrey M Zacks, Todd S Braver
1Department of Psychology, Washington UniversityDepartments of Psychology and Radiology, Washington University.
Cognitive Science
|June 4, 2011
Summary
Our simulations show that recognizing recurring patterns helps systems predict stimuli, identify event boundaries via prediction errors, and improve future predictions. This explains how we perceive continuous activity as distinct events.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Machine Learning
Background:
- Continuous activity is often perceived as discrete events.
- This partitioning may stem from recurring dynamic patterns in experience.
- Such patterns create reliable sequential dependencies guiding perception.
Purpose of the Study:
- Investigate if statistical structure within events aids perception.
- Determine if systems can self-organize internal representations.
- Explore how systems learn to update these representations.
Main Methods:
- Computer simulations exploring pattern recognition.
- Modeling of internal representations and prediction error.
- Analysis of self-organizing learning mechanisms.
Main Results:
- Experience with recurring patterns improves stimulus prediction.
- Event boundaries are identified by prediction error increases.
- Learned event boundaries enhance subsequent activity prediction.
Conclusions:
- Statistical learning of recurring patterns is key to event perception.
- Prediction error signals are crucial for identifying event transitions.
- Self-organizing systems can learn to segment continuous activity effectively.
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