IMPLICIT SEQUENCE LEARNING - A Case Study with a 4–2–4 Encoder Simple Recurrent Network

Stefan Glüge, Ronald Böck, Andreas Wendemuth

Abstract

Without any doubt the temporal order inherent in a task is an important issue during human learning. Recurrent neural networks are known to be a useful tool to model implicit sequence learning. In terms of the psychology of learning, recurrent networks might be suitable to build a model to reproduce the data obtained from experiments with human subjects. Such model should not just reproduce the data but also explain it and further make verifiable predictions. Therefore, one basic requirement is an understanding of the processes in the network during learning. In this paper, we investigate how (implicitly learned) temporal information is stored/represented in a simple recurrent network. To be able to study detailed effects we use a small network and a standard encoding task for this study.

References

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Paper Citation


in Harvard Style

Glüge S., Böck R. and Wendemuth A. (2010). IMPLICIT SEQUENCE LEARNING - A Case Study with a 4–2–4 Encoder Simple Recurrent Network . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 279-288. DOI: 10.5220/0003061402790288


in Bibtex Style

@conference{icnc10,
author={Stefan Glüge and Ronald Böck and Andreas Wendemuth},
title={IMPLICIT SEQUENCE LEARNING - A Case Study with a 4–2–4 Encoder Simple Recurrent Network},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={279-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003061402790288},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - IMPLICIT SEQUENCE LEARNING - A Case Study with a 4–2–4 Encoder Simple Recurrent Network
SN - 978-989-8425-32-4
AU - Glüge S.
AU - Böck R.
AU - Wendemuth A.
PY - 2010
SP - 279
EP - 288
DO - 10.5220/0003061402790288