Quantitative estimation from multiple cues [Elektronische Ressource] : test and application of a new cognitive model / von Bettina von Helversen

Bettina Von Helversen

Sujets

• Psychologie

Informations

• Publié par: humboldt-universitat_zu_berlin
• Publié le: 01 janvier 2008
• Nombre de lectures: 16
• Langue: English
• Poids de l'ouvrage: 1 Mo

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Quantitative Estimation from Multiple Cues:
Test and Application of a New Cognitive Model



D i s s e r t a t i o n

zur Erlangung des akademischen Grades
Dr. rer. nat. im Fach Psychologie



eingereicht an der
Mathematisch-Naturwissenschaftlichen Fakultät II
der Humboldt-Universität zu Berlin


von
Dipl. Psych. Bettina von Helversen Quantitative Estimation from Multiple Cues:
Test and Application of a New Cognitive Model

D i s s e r t a t i o n
zur Erlangung des akademischen Grades
Dr. rer. nat. im Fach Psychologie

eingereicht an der
Mathematisch-Naturwissenschaftlichen Fakultät II
der Humboldt-Universität zu Berlin

von
Dipl. Psych. Bettina von Helversen,
geboren am 27.12.1977 in Freiburg im Breisgau

Präsident der Humboldt-Universität zu Berlin
Prof. Dr. Christoph Markschies

Dekan der Mathematisch-Naturwissenschaftlichen Fakultät II
Prof. Dr. Wolfgang Coy


Gutachter/Gutachterin
1. Prof. Gerd Gigerenzer
2. Prof. Peter Frensch
3. Prof. Peter Juslin

Tag der Verteidigung: 18.01.2008
Acknowledgements
This dissertation is the result of research I have carried out at the Center for Adaptive
Behavior and Cognition (ABC) of the Max Planck Institute for Human Development (MPI)
as a fellow of the LIFE Research school.
First and foremost I would like to dearly thank my advisor Jörg Rieskamp for his time,
unfailing support and outstanding mentoring throughout the last three years. I deeply
appreciate that he was always there when I needed advice. Jörg’s unique ability to reduce a
complex problem to a simple question and to combine scientific and methodological rigor
with a pragmatic approach, encouraged me to search for simple solutions. Further, I would
like to thank my colleagues and friends at the ABC Research group and in the LIFE
Research School who with their warm atmosphere, critical minds, and helpful advice made
my dissertation possible, and the last three years an exciting time. I’m deeply thankful to
Gerd Gigerenzer for providing me the opportunity to work in this wonderful environment
and Paul Baltes, and the LIFE directors for giving me the chance to enjoy the truly
stimulating experience of being part of the LIFE program.
I’m very grateful to Peter Juslin, Linnea Karlsson, and Henrik Olsson for generously
sharing their data with me, enabling me to include a reanalysis of their study in my
dissertation. Further I’d like to thank Lael Schooler, Henrik Olsson, Konstantinos
Katsikopoulos, Stefan Krauss, Peter Frensch, and Richard Gonzalez for their advice and
comments on prior versions of the manuscripts. I also thank Jing Qian, Benjamin
Scheibehenne, Rui Mata, Thorsten Pachur, Andreas Wilke, Wolfgang Gaißmeier, and Tim
Johnson for their thought-provoking comments, interesting discussions, and most of all their
friendship. Above all I want to thank Jutta Mata, for her friendship, patience,
encouragement, invaluable help and advice on countless occasions throughout the last three
years.
Further I would like to thank Gregor Caregnato and all the student assistants for their
help and patience in collecting my data and developing the experimental programs; Christian
Elsner for his technical as well as emotional support; Uwe Czienkowski for encouraging me
to program the experiments myself and his help with any problems I encountered on the way
and Anita Todd for her thorough editing of my manuscripts.
I thank my family and friends who gave me the strength to go forward and helped me
find time to relax and enjoy life while working on my dissertation; my Nonna Ruth for her
unfaltering belief in me; my brothers Thomas and Martin for being there, whenever I need
them and my parents for their unconditional support. Their dedication to science, insatiable
curiosity and questioning minds raised my interest in science and encouraged me to pursue
an academic career.
English Summary
How do people make quantitative estimations, such as estimating a car’s selling price?
Often people rely on cues, information that is probabilistically related to the quantity they
are estimating. For instance, to estimate the selling price of a car they could use information,
such as the car’s manufacturer, age, mileage, or general condition. Traditionally, linear
regression type models have been employed to capture the estimation process. These models
assume that people weight and integrate all information available to estimate a criterion. In
my dissertation, I propose an alternative cognitive theory for quantitative estimation: The
mapping model, inspired by the work of Brown and Siegler (1993) on metrics and mappings,
offers a heuristic approach to decision making. In the first part of my dissertation, I laid the
theoretical foundation for the mapping model, and tested this against established alternative
models of estimation, namely, linear regression, an exemplar model, and a simple estimation
heuristic. The mapping model provided a valid account of people’s estimates outperforming
the other models in a variety of conditions. Consistent with the “adaptive toolbox” approach
on decision making (Gigerenzer & Todd, 1999), which model was best in predicting
participants’ estimations was a function of the task environment. In the second part of my
dissertation, I further investigated how task characteristics influence the models’ ability to
predict participants’ estimations by focusing on the assumptions the models make about the
estimation process: While the exemplar model relies on the establishment of an exemplar
memory base, the mapping model requires the abstraction of knowledge. I examined how
different task features affect these assumptions and thus explain shifts in processing
contingent on the task structure. My results indicate that explicit knowledge about the cues is
decisive. When knowledge about the cues was available, the mapping model was the best
model; however, if knowledge about the task was difficult to abstract, participants’
estimations were best described by the exemplar model. In the third part of my dissertation, I
applied the mapping model in the field of legal decision making. In an analysis of fining and
incarceration decisions, I showed that the prosecutions’ sentence recommendations were
better captured by the mapping model than by legal policy modeled with a linear regression.
These results indicated that the mapping model is a valid model which can be applied to
model actual estimation processes outside of the laboratory. Furthermore, they suggest that
deviations from legal policy can be explained by considering the cognitive processes of the
decision maker.
Deutsche Zusammenfassung
Wie schätzen Menschen quantitative Größen wie zum Beispiel den Verkaufspreis
eines Autos? Oft benutzen Menschen zur Lösung von Schätzproblemen sogenannte Cues,
Informationen, die probabilistisch mit dem zu schätzenden Kriterium verknüpft sind. Um
den Verkaufspreis eines Autos zu schätzen, könnte man zum Beispiel Informationen über
das Baujahr, die Automarke, oder den Kilometerstand des Autos verwenden. Um
menschliche Schätzprozesse zu beschreiben, werden häufig linear additive Modelle
herangezogen. Diese Modelle nehmen an, dass Menschen alle Informationen, die sie zur
Verfügung haben, gewichten und dann zu einer Schätzung integrieren, indem sie die
gewichteten Informationen addieren. In meiner Dissertation schlage ich ein alternatives
Modell zur Schätzung quantitativer Größen vor. Das Mapping-Modell präsentiert einen
heuristischen Ansatz auf der theoretischen Grundlage von Brown und Sieglers (1993) Arbeit
zu metrics und mappings. Im ersten Kapitel meiner Dissertation lege ich die theoretische
Basis des Mapping-Modells dar und teste es gegen weitere, in der Literatur etablierte,
Schätzmodelle wie zum Beispiel eine lineare Regression, ein Exemplar-Modell und eine
Schätzheuristik. Es zeigte sich, dass das Mapping-Modell unter unterschiedlichen
Bedingungen in der Lage war, die Schätzungen der Untersuchungsteilnehmer akkurat
vorherzusagen. Allerdings bestimmte die Struktur der Aufgabe ― im Einklang mit dem
Ansatz der „adaptiven Werkzeugkiste“(Gigerenzer & Todd, 1999) ― im großen Maße,
welches Modell am besten geeignet war, die Schätzungen zu erfassen. Im zweiten Kapitel
meiner Dissertation greife ich diesen Ansatz auf und untersuche, in wie weit das
Zusammenspiel von Aufgabenstruktur und den Annahmen, die die Modelle zum
Schätzprozess machen, bestimmt, welches Modell die Schätzprozesse am Besten beschreibt.
Das Exemplar-Modell setzt die Speicherung von Exemplaren im Gedächtnis voraus,
während das Mapping-Modell die Abstraktion von explizitem Wissen über die Aufgabe
postuliert. Meine Ergebnisse zeigten, dass die Struktur der Aufgabe beeinflusste, welches
Modell die kognitiven Prozesse am Besten beschrieb. Das Mapping-Modell war am Besten
dazu geeignet die Schätzungen der Versuchsteilnehmer zu beschreiben, wenn explizites
Wissen ü