- Title
- Extending and testing the components of evidence accumulation models of decision-making
- Creator
- Evans, Nathan
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2017
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- Past decades of research within the area of decision-making has had a large focus on advancing process models, which are models that contain theoretically meaningful parameters, in order to better understand the processes that underlie decision-making. One of the most popular types of process models within decision-making research has been evidence accumulation models (EAMs), which propose that decision-making is made up of some process where evidence for the various alternates accumulate over the course of a decision, until the evidence reaches some threshold value, where the decision is triggered. Importantly, EAMs have enjoyed a great deal of success in being fit to empirical data, being able to successfully account for a wide range of phenomena and helping to answer theoretical questions that would have been near impossible without the use of a process model. This thesis aims to accomplish 3 main goals with EAMs: to extend EAMs to new research areas to help solve novel empirical questions, to test newly proposed components that could potentially be added to existing EAMs, and to propose a new method of how to test between models in the EAM framework to answer empirical research questions. The first goal is addressed in Chapters 2 and 3, which applies the linear ballistic accumulator (LBA) to personality and genetics research, respectively, which are two areas previously unexplored with EAMs. The second goal is addressed in Chapters 4 and 5, which assesses whether a newly proposed component of EAMs, a threshold that decreases over the course of a trial (collapsing threshold; or a mathematically similar urgency signal), can be justified in empirical data. The final goal is addressed in Chapter 6, which presents a new method of calculating Bayes factors - a method of selecting between competing models - for the LBA using Monte-Carlo integration and general-purpose graphics processing unit computing. Generally speaking, the findings indicate that EAMs are capable of extending to the fields of personality and genetics, that the proposed component of a collapsing threshold is not necessarily justified within the EAM framework, and that the use of Bayes factors through Monte-Carlo integration improves upon previous methods of model selection.
- Subject
- decision-making; evidence accumulation models; mathematical models of cognition
- Identifier
- http://hdl.handle.net/1959.13/1333595
- Identifier
- uon:27106
- Rights
- Copyright 2017 Nathan Evans
- Language
- eng
- Full Text
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT01 | Thesis | 1 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 157 KB | Adobe Acrobat PDF | View Details Download |