A signal-detection approach to modeling forgiveness decisions

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2017
Autor:innen
Luan, Shenghua
Katsikopoulos, Konstantinos
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Zusammenfassung

Whether to forgive is a key decision supporting cooperation. Like many other evolutionarily recurrent decisions, it is made under uncertainty and requires the trade-off of costs and benefits. This decision can be conceptualized as a signal detection or error management task: Forgiving is adaptive if a relationship with the “harmdoer” will be fitness enhancing and not adaptive if the relationship will be fitness reducing, and the decision should be biased toward lowering the likelihood of the more costly error, which depending on the context may be either erroneously not forgiving or forgiving. Building on such conceptualization, we developed two cognitive models and examined how well they described participants' forgiveness decisions in hypothetical scenarios and predicted their decisions in recalled real-life incidents. We found that the models performed similarly and generally well—around 80% in describing and 70% in prediction. Moreover, this modeling approach allowed us to estimate the decision bias of each participant; we found that the biases were generally consistent with the prescriptions of signal detection theory and were directed at reducing the more costly error. In addition to testing mechanistic models of the forgiveness decision, our study also contributes to forgiveness research by applying a novel experimental method that studied both hypothetical and real-life decisions in tandem. These models and experimental methods could be used to study other evolutionarily recurrent problems, advancing understanding of how they are solved in the mind.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
150 Psychologie
Schlagwörter
Cooperation, Forgiveness, Fast-and-frugal trees, Franklin's rule, Error management theory, Signal detection theory
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690TAN, Jolene H., Shenghua LUAN, Konstantinos KATSIKOPOULOS, 2017. A signal-detection approach to modeling forgiveness decisions. In: Evolution and Human Behavior. 2017, 38(1), pp. 27-38. ISSN 1090-5138. eISSN 1879-0607. Available under: doi: 10.1016/j.evolhumbehav.2016.06.004
BibTex
@article{Tan2017-01signa-46068,
  year={2017},
  doi={10.1016/j.evolhumbehav.2016.06.004},
  title={A signal-detection approach to modeling forgiveness decisions},
  number={1},
  volume={38},
  issn={1090-5138},
  journal={Evolution and Human Behavior},
  pages={27--38},
  author={Tan, Jolene H. and Luan, Shenghua and Katsikopoulos, Konstantinos}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46068">
    <dcterms:title>A signal-detection approach to modeling forgiveness decisions</dcterms:title>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/52"/>
    <dcterms:abstract xml:lang="eng">Whether to forgive is a key decision supporting cooperation. Like many other evolutionarily recurrent decisions, it is made under uncertainty and requires the trade-off of costs and benefits. This decision can be conceptualized as a signal detection or error management task: Forgiving is adaptive if a relationship with the “harmdoer” will be fitness enhancing and not adaptive if the relationship will be fitness reducing, and the decision should be biased toward lowering the likelihood of the more costly error, which depending on the context may be either erroneously not forgiving or forgiving. Building on such conceptualization, we developed two cognitive models and examined how well they described participants' forgiveness decisions in hypothetical scenarios and predicted their decisions in recalled real-life incidents. We found that the models performed similarly and generally well—around 80% in describing and 70% in prediction. Moreover, this modeling approach allowed us to estimate the decision bias of each participant; we found that the biases were generally consistent with the prescriptions of signal detection theory and were directed at reducing the more costly error. In addition to testing mechanistic models of the forgiveness decision, our study also contributes to forgiveness research by applying a novel experimental method that studied both hypothetical and real-life decisions in tandem. These models and experimental methods could be used to study other evolutionarily recurrent problems, advancing understanding of how they are solved in the mind.</dcterms:abstract>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/46068"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dcterms:issued>2017-01</dcterms:issued>
    <dc:contributor>Luan, Shenghua</dc:contributor>
    <dc:contributor>Tan, Jolene H.</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dc:creator>Tan, Jolene H.</dc:creator>
    <dc:language>eng</dc:language>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-06-21T13:11:24Z</dcterms:available>
    <dc:creator>Luan, Shenghua</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/52"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-06-21T13:11:24Z</dc:date>
    <dc:creator>Katsikopoulos, Konstantinos</dc:creator>
    <dc:contributor>Katsikopoulos, Konstantinos</dc:contributor>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Ja
Diese Publikation teilen