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Updated: Jul 6, 2026
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Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity
Published on: March 18, 2019
Prediction of aperiodic target sequences by saccades
Manabu Shikauchi1, Shin Ishii, Tomohiro Shibata
1Graduate School of Information Science, Nara, Institute of Science and Technology (NAIST), Ikoma, Nara, Japan. manabu-s@is.naist.jp
Behavioural Brain Research
|March 25, 2008
Summary
Humans can predict complex, aperiodic target sequences by learning the underlying Auto-Regressive (AR) dynamics, outperforming random guessing. This demonstrates predictive learning beyond simple pattern memorization.
Area of Science:
- Cognitive Science
- Neuroscience
- Human Prediction
Background:
- Investigating human predictive capabilities for complex, aperiodic sequences.
- Exploring prediction beyond short-term pattern memorization.
Purpose of the Study:
- To determine if humans can predict aperiodic target sequences generated by Auto-Regressive (AR) processes.
- To assess the extent to which participants utilize knowledge of AR dynamics for prediction.
Main Methods:
- Recording saccadic eye movements during a prediction task.
- Generating aperiodic target sequences using Auto-Regressive (AR) processes.
- Comparing prediction accuracy against random and optimal guesses for shuffled sequences.
Main Results:
- Human prediction of AR sequences was significantly better than random guessing.
- Performance on AR sequences surpassed optimal prediction for random sequences.
- Evidence of learning AR dynamics was observed, though not fully optimal.
Conclusions:
- Humans can learn and utilize underlying dynamics of aperiodic sequences for prediction.
- Predictive learning extends beyond memorizing short patterns.
- Partial identification of AR dynamics influences prediction attempts.
