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Conference Paper/Proceeding/Abstract 757 views 35 downloads

Regret from Cognition to Code

Alan Dix, Genovefa Kefalidou Orcid Logo

Lecture Notes in Computer Science, Volume: 13230, Pages: 15 - 36

Swansea University Author: Alan Dix

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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...

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Published in: Lecture Notes in Computer Science
ISBN: 9783031124280 9783031124297
ISSN: 0302-9743 1611-3349
Published: Cham Springer International Publishing 2022
Online Access: Check full text

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.
Keywords: Regret; Cognitive model; Emotion; Machine learning; Human-Computer Interaction
College: Faculty of Science and Engineering
Start Page: 15
End Page: 36

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