Conference Paper/Proceeding/Abstract 542 views
Regret from Cognition to Code
,
Genovefa Kefalidou
Lecture Notes in Computer Science, Volume: 13230, Pages: 15 - 36
Swansea University Author:
Alan Dix
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1007/978-3-031-12429-7_2
Abstract
Regret seems like a very negative emotion, sometimes even debilitating. However, emotions usually have a purpose -- in the case of regret to help us learn from past mistakes. In this paper we first present an informal cognitive account of the way regret is built from a wide range of both primitive a...
| Published in: | Lecture Notes in Computer Science |
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| ISBN: | 9783031124280 9783031124297 |
| ISSN: | 0302-9743 1611-3349 |
| Published: |
Cham
Springer International Publishing
2022
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa59810 |
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| Abstract: |
Regret seems like a very negative emotion, sometimes even debilitating. However, emotions usually have a purpose -- in the case of regret to help us learn from past mistakes. In this paper we first present an informal cognitive account of the way regret is built from a wide range of both primitive and more sophisticated mental abilities. The story includes Skinner-level learning, imagination, emotion, and counter-factual reasoning. When it works well this system focuses attention on aspects of past events where a small difference in behaviour would have made a big difference in outcome -- precisely the most important lessons to learn. The paper then takes elements of this cognitive account and creates a computational model, which can be applied in simple learning situations. We find that even this simplified model boosts machine learning reducing the number of required training samples by a factor of 3--10. This has theoretical implications in terms of understanding emotion and mechanisms that may cast light on related phenomena such as creativity and serendipity. It also has potential practical applications in improving machine leaning and maybe even alleviating dysfunctional regret. |
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| Keywords: |
Regret; Cognitive model; Emotion; Machine learning; Human-Computer Interaction |
| College: |
Faculty of Science and Engineering |
| Start Page: |
15 |
| End Page: |
36 |