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  <doc>
    <id>952</id>
    <completedYear>2024</completedYear>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted>2024</thesisYearAccepted>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>VII, 247 Seiten : Illustrationen, Diagramme</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>doctoralthesis</type>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2024-11-13</publishedDate>
    <thesisDateAccepted>2024-10-18</thesisDateAccepted>
    <title language="eng">Modeling the complex relationship between task difficulty, accuracy, response time, and confidence</title>
    <abstract language="eng">Confidence, a subjective evaluation about the correctness of one's own decision, is influenced by many aspects of the decision itself, for example the level of difficulty. Empirically, the time it takes to make a decision is also related to confidence, such that faster decisions are usually accompanied with higher confidence compared to slower decisions. The mathematical framework of sequential sampling models offers a way to formalize a unified theory of the complex relationship between task difficulty, response times, and confidence judgments. The three studies in this thesis utilized the sequential sampling framework to yield insight into the decision dynamics and computations that give rise to confidence judgments in perceptual decisions.&#13;
In Study 1 and 2, a variety of dynamical confidence models are compared with respect to their fit to empirical data from experiments comprising different visual discrimination tasks with a difficulty manipulation. In Study 1, I formulated the dynamical weighted evidence and visibility (dynWEV) model and in Study 2, I refined the model to the dynamical visibility, time, and evidence model (dynaViTE) model. The dynaViTE model describes the decision process as a drift diffusion model. In addition, dynaViTE assumes a period of post-decisional accumulation of evidence and a parallel accumulation of visibility evidence, which accrues evidence about task difficulty. In dynaViTE, confidence is determined by a combination of decision evidence and visibility evidence in the form of a weighted sum, penalized by the duration of evidence accumulation. &#13;
In Study 1, I compared different dynamical confidence models, which are based on either a race of accumulators or the drift diffusion model, on their fit to data from two experiments comprising different visual discrimination tasks with a difficulty manipulation. The newly proposed dynWEV model outperformed all competitors suggesting that confidence is not only determined by evidence about stimulus identity but is influenced by independent evidence about task difficulty, which is conveyed by choice-irrelevant stimulus features. &#13;
Study 2 examined the role of the duration of evidence accumulation in the computation of confidence. Theoretically supported by a formal analysis of confidence in a Bayesian optimal observer model, I generalized the dynWEV model to include a penalization of the time it takes to accumulate evidence and form a decision in the computation of confidence, resulting in the dynaViTE model. The dynaViTE model is compared against three of its special cases that either ignore accumulation time, visibility evidence, or both accumulation time and visibility evidence in the computation of confidence. We used previously published empirical data from four experiments, three visual discrimination tasks with a difficulty manipulation, and one visual two-alternative forced choice task with a difficulty and a speed-accuracy manipulation. The results suggest that human observers indeed explicitly consider accumulation time when making confidence judgments. However, depending on the task at hand, either visibility accumulation or accumulation time may be less relevant in the formation of confidence, which suggests that some stimuli or experimental tasks leads to different strategies in confidence computations. The third study showcases the R package dynConfiR, which includes an implementation of several dynamical confidence models together with high-level functions for parameter estimation and prediction of response time distributions. The package offers an efficient and intuitive model fitting function, which simplifies the workflow for modeling studies with confidence and response time data to calling a few lines of code. Thus, the dynConfiR package provides useful tools for analyzing response time and confidence data across a variety of experiments.&#13;
In sum, the dynaViTE model forms a computational model, which formalizes the relationship between task difficulty, choice, response time, and confidence judgment. The dynaViTE model extends previous theories of confidence by including the parallel assessment of independent evidence about task difficulty and the incorporation of accumulation time in the computation of confidence. The studies presented in this thesis deliver substantial evidence in supporting both features, the parallel accumulation of visibility and the incorporation of accumulation time in the computation of confidence. This thesis thus forms an important contribution to the growing research on decision mechanics and confidence and thereby improve our understanding how confidence arises in perceptual decisions.</abstract>
    <subTitle language="eng">[cumulative dissertation]</subTitle>
    <identifier type="doi">10.17904/ku.opus-952</identifier>
    <identifier type="urn">urn:nbn:de:bvb:824-opus4-9527</identifier>
    <enrichment key="DOI_Checkbox">1</enrichment>
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    <licence>Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Sebastian Hellmann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kognitive Psychologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mathematische Psychologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Wahrnehmung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Entscheidungsfindung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Konfidenzintervall</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Aufmerksamkeitsumfang</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Statistik</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mathematische Methode</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bayes-Entscheidungstheorie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kognitiver Prozess</value>
    </subject>
    <collection role="ddc" number="150">Psychologie</collection>
    <collection role="ccs" number="">Psychology</collection>
    <collection role="institutes" number="">Philosophisch-Pädagogische Fakultät</collection>
    <thesisPublisher>Katholische Universität Eichstätt-Ingolstadt</thesisPublisher>
    <thesisGrantor>Katholische Universität Eichstätt-Ingolstadt</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-ku-eichstaett/files/952/DissertationSebastianHellmann_2024.pdf</file>
  </doc>
  <doc>
    <id>834</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted>2023</thesisYearAccepted>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>137 Seiten : Illustrationen, Diagramme</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>doctoralthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-06-27</publishedDate>
    <thesisDateAccepted>2023-05-05</thesisDateAccepted>
    <title language="deu">Anwendbarkeit der Optimal Foraging Theory in der kognitiven Psychologie</title>
    <abstract language="deu">Die Optimal Foraging Theory (OFT) bezeichnet eine Reihe mathematischer Entscheidungsmodelle, die Entscheidungen als mathematisches Optimierungsproblem mit dem Ziel der Maximierung des Nutzens bei Minimierung der Kosten verstehen. Die OFT ist in der Verhaltensökologie äußerst erfolgreich darin, das Verhalten einer Vielzahl von Spezies zu beschreiben und vorherzusagen. Ziel der Dissertation ist es darum zu untersuchen, inwieweit die beiden zentralen Modelle der OFT - das klassische Patch Model und Prey Model - auf menschliches, abstraktes Entscheidungsverhalten anwendbar sind. Dazu werden experimentelle Paradigmen entwickelt, die als virtuelle Spiele am Computer dargeboten werden können und es erlauben sowohl die qualitativen als auch die quantitativen Vorhersagen der Modelle empirisch zu prüfen. &#13;
Die Auswertung der verschiedenen Experimentalserien erfolgt mittels Sequential Bayes Factor-Designs. Die Befunde stützen die zentralen qualitativen Annahmen der OFT: Wie vom Patch Model vorhergesagt, ist die Entscheidungsstrategie zum einen abhängig von der zeitlichen Verfügbarkeit potenzieller Alternativen (Travel Time), zum anderen von der Güte der Patches (Patch Quality). Jedoch zeigt sich ein Einfluss der Reisezeithistorie, der umso ausgeprägter ist, je stärker die Reisezeiten variieren und je aktueller eine erlebte Reisezeit ist. Dies deutet darauf hin, dass anders als vom Patch Model vorhergesagt ein Tracking der Historie erfolgt. Quantitativ werden nahezu durchgängig längere Verweildauern beobachtet als gemäß dem Patch Model optimal. In Übereinstimmung mit den qualitativen Vorhersagen des Prey Models ist die Entscheidungsstrategie abhängig von der Encounter Rate der reichhaltigen Ressourcen, nicht aber von der Encounter Rate der wenig profitablen Ressourcen. Dies ist unabhängig davon zu beobachten, ob die Encounter Rate über die Suchkosten, die relative Häufigkeit oder eine Kombination beider Faktoren manipuliert wird. Entgegen der quantitativen Alles-oder-nichts-Regel des Prey Models werden jedoch partielle Präferenzen beobachtet. Insgesamt scheinen Menschen ihre Entscheidungsstrategie in Einklang mit dem Patch und Prey Model adaptiv an die Merkmale der Entscheidungssituation anzupassen, jedoch nicht in einer vollumfänglich optimalen Weise. Dies deckt sich mit einem Großteil der verhaltensökologischen Befunde. Offenbar basiert das Verhalten in abstrakten Entscheidungssituationen auch heute noch auf denselben Mechanismen, die sich ursprünglich im Kontext der Nahrungssuche durch natürliche Selektion entwickelt haben.</abstract>
    <abstract language="eng">Optimal Foraging Theory (OFT) refers to a set of mathematical decision models that understand decisions as a mathematical optimization problem, aiming to maximize benefits while minimizing costs. In behavioral ecology, OFT has been highly successful in describing and predicting the behavior of a wide range of species. The goal of this thesis is to investigate the extent to which the two central models of OFT, the classical Patch Model and Prey Model, are applicable to human abstract decision-making. To achieve this, experimental paradigms are developed that can be presented as virtual games on a computer, allowing for empirical testing of both the qualitative and quantitative predictions of the models. &#13;
Several  experimental series were analyzed using Sequential Bayes Factor designs. The findings support the central qualitative assumptions of OFT. As predicted by the Patch Model, the decision strategy depends on the temporal availability of potential alternatives (travel time) and the quality of patches. However, an influence of travel time history is observed, which is more pronounced the more travel times vary and the more recent an experienced travel time is. This suggests that, contrary to the predictions of the Patch Model, a tracking of travel time history occurs. Quantitatively, observed residence times are longer than what is optimal according to the Patch Model. In line with the qualitative predictions of the Prey Model, the decision strategy depends on the encounter rate of rich resources but not on the encounter rate of less profitable resources. This holds true regardless of whether the encounter rate is manipulated through search costs, relative frequency, or a combination of both factors. However, contrary to the quantitative all-or-nothing rule of the Prey Model, partial preferences are observed. Overall, humans seem to adapt their decision strategy in accordance with the characteristics of the decision situation, but not in a fully optimal manner, aligning with a substantial body of behavioral ecological findings. Apparently, behavior in abstract decision situations still relies on the same mechanisms that originally evolved in the context of foraging through natural selection.</abstract>
    <identifier type="doi">10.17904/ku.opus-834</identifier>
    <identifier type="urn">urn:nbn:de:bvb:824-opus4-8340</identifier>
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    <licence>Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Christina Lisanne Breitenladner</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kognitive Psychologie</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Optimal Foraging Theory</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Marginal Value Theorem</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Decision-Making</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Verhaltenspsychologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Experimentelle Psychologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mathematische Psychologie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mathematische Methode</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Entscheidungsverhalten</value>
    </subject>
    <collection role="institutes" number="">Philosophisch-Pädagogische Fakultät</collection>
    <thesisPublisher>Katholische Universität Eichstätt-Ingolstadt</thesisPublisher>
    <thesisGrantor>Katholische Universität Eichstätt-Ingolstadt</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-ku-eichstaett/files/834/Diss_FINAL_20230531_PDFA.pdf</file>
  </doc>
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