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Developing and Validating a Model-Based Method for Measuring People’s Subjective Planning Costs
Get PDFBei der Planung müssen Menschen den Nutzen zusätzlicher Erwägungen gegen
ihre Kosten abwägen. Menschen, die dazu neigen, nicht zu planen,
erzielen in der Regel schlechtere Ergebnisse in verschiedenen Bereichen
des Lebens, die sich auf ihr Wohlbefinden auswirken, einschließlich
Gesundheit, Finanzen und Bildung. Es gibt mehrere Fragebögen, um zu
messen wie sehr Menschen dazu neigen die Zukunft zu planen (z. B., die
Fragebögen zur Erfassung der Planungsneigung: Lynch et al., 2010,
der Berücksichtigung zukünftiger
Konsequenzen: Strathman et al., 1994, und der
Zukunftsorientierung: Steinberg et al., 2009). Diese Maße können jedoch als
subjektiv angesehen werden und könnten durch soziale Erwünschtheit oder
Reaktivitätseffekten verfälscht werden, wie z. B. dem Wunsch der
Menschen, im Einklang mit gesellschaftlichen Werten zu antworten, um
"gut" zu erscheinen.
In dieser Dissertation entwickelte ich ein objektives,
verhaltensbasiertes, mathematisches Verfahren zur Messung individueller
Unterschiede darin, wie Menschen planen. Anstatt eine Person zu bitten,
selbst zu berichten, wie sehr sie dazu neigt, zukünftige Konsequenzen in
Betracht zu ziehen, schätzt meine Methode die subjektiven Planungskosten
einer Person mithilfe einer Kombination aus einer Planungsaufgabe und
einem rationalen kognitiven Prozessmodelll (Lieder and Griffiths, 2020).
Außerdem stelle ich eine Studie vor, die Fragebogenmessungen mit dieser
Methode kombiniert. In dieser Studie untersuche ich, ob und wie
individuelle Unterschiede in den subjektiven Planungskosten die Symptome
psychiatrischer Störungen vorhersagen können, und wie sehr mein
verhaltensbasiertes Maß der subjektive Planungskosten mit dem Erleben
der Person korreliert ist.
Die Grundlage meiner Methode zur Messung individueller Unterschiede in
der Planung ist ein kognitives Prozessmodell das die Planungsstrategien
von Personen mit unterschiedlichen Planungskosten vorhersagt . In einer
Situation, in der es unwahrscheinlich ist, dass sich lohnen würde zu
planen, oder in der planen besonders schwierig ist, könnte es sinnvoll
sein, überhaupt nicht zu planen. Hier untersuche ich, wie
Planungsstrategien, die suboptimal wirken, in Wirklichkeit optimal sein
können, wenn man die subjektiven Planungskosten berücksichtigt, die ein
Individuum empfindet. Insbesondere untersuche ich, wie erhöhte Kosten
för vorausschauendes Planen zu suboptimaler Entscheidungen führen
können, indem ich ein Prozessverfolgungsparadigma verwende (das
Mouselab-MDP-Paradigma Callaway et al., 2017). Zu diesem Zweck
erweitere ich ein bestehendes ressourcenrationales Planungsmodell um
subjektive Planungskosten, die durch eine Kostenfunktion mit mehreren
Parametern erfasst werden. Ich zeige, dass dieses Modell menschliches
Planen besser erklärt als einfachere Modelle und alternative
Erklärungen. Dieses Modell liefert eine mechanistische Erklärung dafür,
warum manche Menschen sich auf bestimmte Formen scheinbar suboptimaler
Planung verlassen.
Darüber hinaus wende ich Bayesian Inverse Reinforcement Learning an, um
die Parameter dieser Planungskostenfunktion zu schätzen. In einem
Experiment, in dem die Planungskosten von Personen manipuliert werden,
zeige ich, dass individuelle Unterschiede in den Kosten des
vorausschauenden Planens für etwa 70% der Versuchspersonen
zuverlässig erfasst werden können. Dieser Parameter könnte ein
nützliches Maß dafür sein, wie sehr eine Person dazu neigt in die
Zukunft zu planen (z. B. langfristige versus kurzfristige Planung).
Ich präsentiere die Ergebnisse einer Studie, die untersucht, ob
individuelle Unterschiede in den subjektiven Planungskosten mit den
Symptomen psychiatrischer Störungen sowie mit anderen Fragebögen wie
Lebenszufriedenheit, Bedauern und Planungsverhalten zusammenhängen
könnten. Ich finde keine prädiktive Beziehung zwischen den geschätzten
kognitiven Kosten und den Selbstauskünften der Versuchspersonen. In
explorativen Analysen finde ich jedoch mehrere kleinere Korrelationen
zwischen den geschätzten Planungskosten und einzelnen Fragebögen. Diese
explorative Arbeit erweitert die bestehende Literatur über die Struktur
individueller Unterschiede im Planen zwischen verschiedenen psychischen
Störungen.
Abschließend skizziere und diskutiere ich die möglichen Grenzen der
Methode und künftige Studien, die erforderlich sind, bevor diese Methode
in einem größeren, realistischeren Rahmen angewendet werden kann.When planning, people have to trade off between the costs and benefits
of additional deliberation. People who tend not to plan may have worse
outcomes in various domains which impact well-being such as health,
finances, and academics. Several self-report measures exist to quantify
people's tendency to plan into the future (e.g., the Propensity to Plan
Scale: Lynch et al., 2010, the Consideration of Future
Consequences Scale: Strathman et al., 1994, and the Future
Orientation Scale: Steinberg et al., 2009). However, these measures can be
seen as subjective and might be influenced by social-desirability bias
or demand characteristics such as people wanting to answer in alignment
with societal values to appear "good".
In this dissertation, I present a computational method for objectively
quantifying individual differences in planning via subjective planning
costs. Instead of asking a person to self report how much they tend to
consider future outcomes, I quantify people's subjective planning costs
using a combination of a planning task and a resource-rational
computational model (Lieder and Griffiths, 2020). Furthermore, I present a
study pairing questionnaire measures with this method. In this study, I
investigate whether and how individual differences in subjective
planning costs can predict symptoms of psychiatric disorders, as well as
if subjective planning costs can predict people's scores on self-report
measures for similar constructs.
The foundation of my method for quantifying individual differences in
planning is a computational model for measuring subjective planning
costs. In a situation where planning is unlikely to pay off, or where
planning is particularly hard, it might make sense not to plan at all.
Here I investigate how planning which looks suboptimal, may, in fact, be
optimal with respect to the subjective planning costs experienced by an
individual. In particular, I investigate how costs such as a planning
depth cost (i.e., cost for looking into the future) might lead to
suboptimal planning using a process-tracing paradigm (the Mouselab-MDP
paradigm Callaway et al., 2017). To do so, I extend an existing
resource-rational model of planning to include subjective planning costs
captured by a cost function with multiple parameters. I show that this
model explains human planning better than simpler candidate models and
other alternative models. This model provides a mechanistic account for
why some people might engage in particular forms of seemingly suboptimal
planning.
Furthermore, I introduce the application of Bayesian Inverse
Reinforcement Learning to infer these cost weights for individuals. I
show, in an experiment where people's planning costs are manipulated,
that individual differences in a planning depth cost weight can be
reliably recovered for around 70% of people. The planning depth cost
weight could be useful as a measure of a person's propensity to plan
into the future (e.g., far-sighted versus short-sighted planning).
I present the results from a study investigating whether individual
differences in subjective planning costs might be related to symptoms of
psychiatric disorders as well as other self-report measures, such as
life satisfaction, regrets, and planning behaviors. I find no predictive
relationship between inferred cognitive cost weights and self-report
measure scores. However, I do find, in exploratory analyses, several
smaller correlations between cognitive cost weights and self-report
measure scores. This exploratory work expands on the existing literature
on the structure of planning differences across different mental
disorders.
Finally, I outline and discuss the possible limitations of the method,
and future studies needed before these methods could be applied to a
larger-scale, more real-world setting
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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Measuring the costs of planning
Get PDFWhich information is worth considering depends on how much effort it would take to acquire and process it. Fromthis perspective peoples tendency to neglect considering the long-term consequences of their actions (present bias) mightreflect that looking further into the future becomes increasingly more effortful. In this work, we introduce and validatethe use of Bayesian Inverse Reinforcement Learning (BIRL) for measuring individual differences in the subjective costsof planning. We extend the resource-rational model of human planning introduced by Callaway, Lieder, et al. (2018) byparameterizing the cost of planning. Using BIRL, we show that increased subjective cost for considering future outcomesmay be associated with both the present bias and acting without planning. Our results highlight testing the causal effectsof the cost of planning on both present bias and mental effort avoidance as a promising direction for future work
Dispelling the Myths Behind First-author Citation Counts
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