Making decisions under conflict with a continuous mind: from micro to macro time scales

Making decisions is a dynamic process. Especially when we face a decision between conflicting options, different forces seem to drag our mind from one option to the other one (James, 1890), again and again. This process may last for a long time, sometimes only coming to a decision when we are finally forced to choose, e.g. by an important deadline. Psychology and many other disciplines were interested in how humans make decisions from their beginnings on. Many different influences on decisions were discovered (e.g. Kahneman & Tversky, 1979; Todd & Gigerenzer, 2000). In the face of these advances, it seems odd, that knowledge about the ongoing process of reaching a decision is rare and much of the investigation has focused on the final outcome of choice situations (Townsend & Busemeyer, 1995). A very recent approach, called neuroeconomics, started out to investigate what happens behind the scenes of a final decision. Using modern neuroimaging methods, many neuroeconomists explain decision making in the brain in terms of a hierarchy of different neural modules that work together like a big corporation to finally make the best possible decision (Sanfey, Loewenstein, McClure, & Cohen, 2006). However, the focus on neural modules also limits this approach to a quite static view of decision making and many questions, related to the dynamic aspects of decision making, still remain open: How do we continuously control impulsive or habitual tendencies in our decisions when we pursue long-term goals? How do we shift attention back and forth between (goal) relevant properties of choice options? How do we adjust and readjust our focus of attention to relevant information in order to avoid distraction by irrelevant or misleading information? And how are we influenced by the environmental context when we make decisions? The present work aims to show how an approach based on the concepts of dynamic systems theory could complement the module oriented approach and enhance our knowledge of the processes of decision making. Chapter 2 elaborates the limits of the module oriented approach, with a special focus on decisions under conflict, when we are faced with conflicting information, and introduces the principles of a complementary dynamic approach. Chapter 3 deduces the dynamic hypothesis of this work: ongoing processes interactions at different time scales can explain specific cognitive functions without postulating specialized modules for this function. To approach this hypothesis, chapter 4 will develop a theoretical and empirical framework to study decision making dynamically. The empirical part, building on the empirical framework, starts with chapter 5 presenting an EEG experiment. Chapter 6 presents two mouse tracking experiments, and chapter 7 presents a modelling study, reproducing the empirical data of chapters 5 and 6. The general discussion in chapter 8 summarizes the theoretical and empirical results and discusses possible limitations. Finally, chapter 9 discusses the implications of the dynamic approach to decision making, presents an outlook on future research projects, and closes the work by offering a dynamic picture of the processes behind the stage of a final decision.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Technische Universität Dresden

Year

2010

Author dc:creator

• Scherbaum, Stefan

Contributors dc:contributor

• Goschke, Thomas
• Strobel, Alexander

• Entscheidung
• Konflikt
• Kognitive Kontrolle
• Dynamik
• Dynamische System Theorie
• Frequenzanalyse
• EEG
• Mausbewegung
• kognitive Modellierung
• Simon Aufgabe
• Flanker Aufgabe
• Neuroökonomie
• Decision making
• conflict
• cognitive control
• dynamics
• dynamic systems theory
• frequency tagging
• mouse tracking
• cognitive modelling
• Simon Task
• Flanker Task
• Neuroeconomics