Uncovering mental structure through data-driven ontology discovery
Abstract/Contents
- Abstract
- Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggles to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction. These issues are particularly damaging for the study of multifaceted psychological construct and our general understanding of psychology's practical utility. In this dissertation we use the construct of self-regulation as a case-study to develop a data-driven framework for psychological science. Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors and has a rich and varied measurement tradition. Despite the intensive focus of many research programs and clear societal benefits, the psychological components of self-regulation remain poorly understood, and its practical utility as a psychological construct has not been systematically evaluated. In Chapter 3, we derive a psychological ontology from a large dataset of individual differences across a broad range of behavioral tasks, self-report surveys, and self-reported real-world outcomes. Though both tasks and surveys putatively measure self-regulation, they show little empirical relationship. Within tasks and surveys, however, the ontology identifies reliable individual traits and reveals opportunities for theoretic synthesis. In Chapter 4 we evaluate predictive power of the psychological measurements both for health behaviors normally associated with self-regulation, and individual ideological characteristics. We find that while surveys modestly and heterogeneously predict both health and ideological outcomes, tasks largely fail to predict health behaviors. We conclude that self-regulation lacks coherence as a construct, and should be abandoned in favor of a multiply-determined view of so-called self-regulatory outcomes. Finally, in Chapter 5 we introduce a method to extend the psychological ontology, opening the door for a truly cumulative quantitative framework for psychology.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Eisenberg, Ian Weiss |
|---|---|
| Degree supervisor | Poldrack, Russell A |
| Thesis advisor | Poldrack, Russell A |
| Thesis advisor | Cao, Rosa |
| Thesis advisor | McClelland, James L |
| Degree committee member | Cao, Rosa |
| Degree committee member | McClelland, James L |
| Associated with | Stanford University, Department of Psychology. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ian Weiss Eisenberg. |
|---|---|
| Note | Submitted to the Department of Psychology. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Ian Weiss Eisenberg
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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