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Updated: Jun 21, 2025
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Published on: December 15, 2023
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Probabilistic prediction and context tree identification in the Goalkeeper game.
Noslen Hernández1, Antonio Galves2, Jesús E García3
1INTHERES, Université de Toulouse, INRAe, ENVT, Toulouse, France.
Scientific Reports
|July 5, 2024
Summary
Predicting probabilistic event sequences is complex. Key factors include context tree shape, sequence entropy, and underlying periodicity, influencing how learners identify event structures.
Area of Science:
- Cognitive Science
- Machine Learning
- Probability Theory
Background:
- Understanding how humans learn and predict complex event sequences is crucial.
- Probabilistic sequences are ubiquitous in natural and artificial systems.
Purpose of the Study:
- To identify features that increase the difficulty of predicting event sequences generated by stochastic chains.
- To model the cognitive procedures learners use to discern sequence structures.
Main Methods:
- Participants acted as goalkeepers predicting penalty kick directions (left, center, right).
- Sequences were generated by a variable-length memory stochastic chain.
- Analysis focused on context tree shape, sequence entropy, and periodicity.
Main Results:
- Sequence predictability is influenced by context tree shape, entropy, and underlying periodicity.
- Learners' ability to predict sequences is linked to these identified features.
- More effective learners showed reduced reliance on their own past predictions.
Conclusions:
- The study elucidates key factors governing the learnability of stochastic event sequences.
- Findings offer insights into human sequence learning mechanisms and predictive strategies.
- This research contributes to models of cognitive sequence identification and structure learning.
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