Conference Paper/Proceeding/Abstract 553 views
Regret from Cognition to Code
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...
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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URI: | https://cronfa.swan.ac.uk/Record/cronfa59810 |
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2022-09-28T14:42:29.2814409 v2 59810 2022-04-12 Regret from Cognition to Code e31e47c578b2a6a39949aa7f149f4cf9 0000-0002-5242-7693 Alan Dix Alan Dix true false 2022-04-12 SCS 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. Conference Paper/Proceeding/Abstract Lecture Notes in Computer Science 13230 15 36 Springer International Publishing Cham 9783031124280 9783031124297 0302-9743 1611-3349 Regret; Cognitive model; Emotion; Machine learning; Human-Computer Interaction 25 9 2022 2022-09-25 10.1007/978-3-031-12429-7_2 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Not Required 2022-09-28T14:42:29.2814409 2022-04-12T19:36:10.5027749 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Alan Dix 0000-0002-5242-7693 1 Genovefa Kefalidou 0000-0002-2889-7564 2 Under embargo Under embargo 2022-04-12T19:41:05.5421602 Output 2851642 application/pdf Accepted Manuscript true 2023-09-25T00:00:00.0000000 true eng |
title |
Regret from Cognition to Code |
spellingShingle |
Regret from Cognition to Code Alan Dix |
title_short |
Regret from Cognition to Code |
title_full |
Regret from Cognition to Code |
title_fullStr |
Regret from Cognition to Code |
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Regret from Cognition to Code |
title_sort |
Regret from Cognition to Code |
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e31e47c578b2a6a39949aa7f149f4cf9 |
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e31e47c578b2a6a39949aa7f149f4cf9_***_Alan Dix |
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Alan Dix |
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Alan Dix Genovefa Kefalidou |
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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. |
published_date |
2022-09-25T04:17:23Z |
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11.013731 |