Generative processing and the state of knowledge Restricted; Files Only

Abstract

How do we effectively grow and change knowledge? This question has implications for cognitive science research, education, and day-to-day functioning. Research suggests that generative processes, wherein one goes beyond the material that is directly given to create their own information, may be particularly supportive of knowledge building (Chi, 2009; Wittrock, 1974). For example, generating one’s own information leads to better retrieval than reading the same information (e.g., Slamecka & Graf, 1978). Yet little is known about how generative processes compare with other forms of deep engagement with material, such as actively rephrasing information. It is possible that some observed benefits of generation may be attributable to deep, active engagement rather than generation specifically. Further, the generative process self-derivation through memory integration, the integration of multiple facts to derive novel information, has not been investigated in this context. This is a gap in the literature, because self-derivation has links to semantic knowledge accumulation (Bauer et al., 2020) and academic achievement metrics such as GPA (Varga et al., 2019). To bridge these gaps, across two studies, we examined the influence of the generative process of self-derivation compared with an active rephrase control. In Study 1, we investigated how these learning processes influence retrieval, application, and organization of neuroscience information. We found that generating in any form, across conditions, was beneficial for multiple outcomes. Further, self-derivation led to high retrieval when the process was successfully engaged. In Study 2, we investigated the influence of self-derivation on revision of educationally-relevant misconceptions. We found that generating one’s own information to contradict a misconception was related to higher performance within the self-derivation condition, but overall, rephrasing led to higher or equal misconception revision than generative processing. Together, these studies offer several major contributions. First, self-derivation is a mechanism by which knowledge is built, sometimes above and beyond other forms of learning. Second, there is an outcome-based differentiation between active versus generative learning for some, but not all, learning contexts. Finally, individual differences in how learners engage with learning manipulations must be considered in learning and memory research and may have important implications for classrooms. 

Table of Contents

General Introduction ………………………………………………………………….........1

Study 1 …………………………………………………………………………………………..12

    Introduction ……………………………………………………………………………......13

Method ………………………………………………………………………………….......22

Results …………………………………………………………………………................34

    Discussion ……………………………………………………………………………….....48

Acknowledgments………………………………………………………………………....59

References …………………………………………………………………………….....…60

    Supplemental Materials……..…………………………………………………….........68

Study 2 ………………………………………………………………………………..............70

    Introduction …………………………………………………………………..................71

    Method ………………………………………………………………………………….......79

    Results ………………………………………………………………………....................89

    Discussion ………………………………………………………….............................95

    Acknowledgments…………………………………………………………………..……104

References ……………………………………………………………………………......105

Supplemental Materials.………………………………………………………………..112

Supplemental References……………………………………………………………….122

General Discussion ………………………………………………………………………….123

General References ………………………………………………………………………….136

About this Dissertation

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School	Laney Graduate School
Department	Psychology
Subfield / Discipline	Cognition and Development
Degree	Ph.D.
Submission	Dissertation
Language	English
Research Field	Psychology, Cognitive
Keyword	Semantic Memory Generative Learning Classroom Learning
Committee Chair / Thesis Advisor	Patricia Bauer, Emory University
Committee Members	Arber Tasimi, Emory University Phillip Wolff, Emory University Lynne Nygaard, Emory University Joseph Manns, Emory University Andrew Kazama, Emory University

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