No Cover Image

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

Check full text

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...

Full description

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
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2022-04-12T18:52:35Z
last_indexed 2023-01-11T14:41:19Z
id cronfa59810
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>59810</id><entry>2022-04-12</entry><title>Regret from Cognition to Code</title><swanseaauthors><author><sid>e31e47c578b2a6a39949aa7f149f4cf9</sid><firstname>Alan</firstname><surname>Dix</surname><name>Alan Dix</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-04-12</date><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.</abstract><type>Conference Paper/Proceeding/Abstract</type><journal>Lecture Notes in Computer Science</journal><volume>13230</volume><journalNumber/><paginationStart>15</paginationStart><paginationEnd>36</paginationEnd><publisher>Springer International Publishing</publisher><placeOfPublication>Cham</placeOfPublication><isbnPrint>9783031124280</isbnPrint><isbnElectronic>9783031124297</isbnElectronic><issnPrint>0302-9743</issnPrint><issnElectronic>1611-3349</issnElectronic><keywords>Regret; Cognitive model; Emotion; Machine learning; Human-Computer Interaction</keywords><publishedDay>25</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-09-25</publishedDate><doi>10.1007/978-3-031-12429-7_2</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm>Not Required</apcterm><funders/><projectreference/><lastEdited>2024-07-29T15:58:21.8521962</lastEdited><Created>2022-04-12T19:36:10.5027749</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Alan</firstname><surname>Dix</surname><order>1</order></author><author><firstname>Genovefa</firstname><surname>Kefalidou</surname><orcid>0000-0002-2889-7564</orcid><order>2</order></author></authors><documents><document><filename>59810__23827__3ded2c847da740c2b2d91f6b2992feb7.pdf</filename><originalFilename>Regret___CIFMA_final-20220412-1925.pdf</originalFilename><uploaded>2022-04-12T19:41:05.5421602</uploaded><type>Output</type><contentLength>2851642</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2023-09-25T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling v2 59810 2022-04-12 Regret from Cognition to Code e31e47c578b2a6a39949aa7f149f4cf9 Alan Dix Alan Dix true false 2022-04-12 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 COLLEGE CODE Swansea University Not Required 2024-07-29T15:58:21.8521962 2022-04-12T19:36:10.5027749 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Alan Dix 1 Genovefa Kefalidou 0000-0002-2889-7564 2 59810__23827__3ded2c847da740c2b2d91f6b2992feb7.pdf Regret___CIFMA_final-20220412-1925.pdf 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
title_full_unstemmed Regret from Cognition to Code
title_sort Regret from Cognition to Code
author_id_str_mv e31e47c578b2a6a39949aa7f149f4cf9
author_id_fullname_str_mv e31e47c578b2a6a39949aa7f149f4cf9_***_Alan Dix
author Alan Dix
author2 Alan Dix
Genovefa Kefalidou
format Conference Paper/Proceeding/Abstract
container_title Lecture Notes in Computer Science
container_volume 13230
container_start_page 15
publishDate 2022
institution Swansea University
isbn 9783031124280
9783031124297
issn 0302-9743
1611-3349
doi_str_mv 10.1007/978-3-031-12429-7_2
publisher Springer International Publishing
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description 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 2022-09-25T15:58:20Z
_version_ 1805925850037616640
score 11.037603

Similar Items