Measuring, monitoring, and maintaining memories in a partially observable mind The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Suchow, Jordan William. 2014. Measuring, monitoring, and maintaining memories in a partially observable mind. Doctoral dissertation, Harvard University. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:12274120 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Measuring, monitoring, and maintaining memories in a partially observable mind a dissertation presented by Jordan William Suchow to The Department of Psychology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Psychology Harvard University Cambridge, Massachusetts April 2014 ©2014 – Jordan William Suchow all rights reserved. Dissertation advisor: Professor George A. Alvarez Jordan William Suchow Measuring, monitoring, and maintaining memories in a partially observable mind Abstract Visual memory holds in mind details of objects, textures, faces, and scenes. After initial expo- sure to an image, however, visual memories rapidly degrade because they are transferred from iconic memory, a high-capacity sensory buffer, to working memory, a low-capacity maintenance system. How does visual memory maintenance work? This dissertation builds the argument that the main- tenance of short-term visual memories is analogous to the act of breathing: it is a dynamic process with a default behavior that explains much of its usual workings, but which can be observed, overrid- den, and controlled. Chapter 1 shows how the act of trying to remember more information causes people to forget faster and to remember less (“load-dependent forgetting” and “overreaching”). It then shows how the paradigm of evolution can be applied to the problem of maintenance, with memories competing over a limited memory-supporting commodity, explaining these effects. Chap- ter 2 presents experiments on metamemory, the ability of people to observe and make decisions about their own memories. The experiments isolate a component of metamemory that monitors a memory’s quality as it degrades over time. Chapter 3 connects memory to metamemory, drawing on work from reinforcement learning and decision theory to liken the problem of memory maintenance to that of an agent who sequentially decides what to prioritize in a partially observable mind. iii Contents 0 Introduction lj 0.0 An analogy to breathing .................................. 1 0.1 Thesis ........................................... 2 0.2 Plan of the dissertation .................................. 3 0.3 Working memory ..................................... 4 0.4 Metamemory ....................................... 7 0.5 Directed forgetting .................................... 8 0.6 Summary ......................................... 9 1 Evolutionary dynamics of visual memory ljLj 1.0 Abstract .......................................... 10 1.1 Introduction ........................................ 11 1.2 Results ........................................... 14 1.2.0 Memory stability depends on load ....................... 14 1.2.1 Crossovers and overreaching .......................... 14 1.2.2 Evolutionary model ............................... 16 1.3 Discussion ......................................... 22 1.4 Methods .......................................... 25 1.5 Additional results ..................................... 28 1.5.0 Individual differences .............................. 29 1.5.1 Individual differences in the classic model ................... 29 1.5.2 Variability in the pure death model ....................... 29 1.5.3 Variability in the sudden death model ..................... 30 1.5.4 Variability in the evolutionary model ...................... 32 1.5.5 The effects of practice .............................. 32 1.5.6 Laboratory replication .............................. 37 2 Looking inwards and back: realtime monitoring of visual working mem- ories njLj 2.0 Abstract .......................................... 40 2.1 Introduction ........................................ 41 2.2 Methods .......................................... 43 iv 2.2.0 Logic of the task: isolating realtime monitoring . 43 2.2.1 Implementation of the task ........................... 45 2.2.2 Stimuli and presentation ............................ 46 2.2.3 Participants ................................... 47 2.2.4 Data analysis ................................... 47 2.3 Results ........................................... 48 2.3.0 Results: Two-component mixture model .................... 50 2.3.1 Results: Adding a swap component ....................... 50 2.3.2 Results: No guessing .............................. 51 2.4 Discussion ......................................... 53 2.5 Conclusion ........................................ 55 3 Controlling working memory maintenance Ǎǎ 3.0 Introduction ........................................ 56 3.1 Exp. 1: Efficiency of directed remembering ....................... 59 3.1.1 Methods ..................................... 60 3.1.2 Results ...................................... 62 3.1.3 Interim discussion ................................ 65 3.2 Exp. 2: Self-directed remembering ............................ 65 3.2.1 Methods ..................................... 67 3.2.2 Results ...................................... 68 3.2.3 Interim discussion ................................ 68 3.3 Computational framework: Markov decision process . 72 3.3.1 State space .................................... 73 3.3.2 Set of possible actions .............................. 74 3.3.3 Transition model ................................. 74 3.3.4 Reward function ................................. 75 3.3.5 Policies: unconditional ............................. 76 3.3.6 Policies: conditional ............................... 77 3.3.7 Partially observable minds ........................... 81 3.4 Discussion ......................................... 84 4 Conclusion ǐǐ 4.0 Overview ......................................... 88 4.1 Final thoughts ....................................... 90 Appendix A Forgetting functions of visual memory ǑNJ A.0 Model #1: Classic ..................................... 93 A.1 Model #2: Pure death ................................... 93 A.2 Model #3: Sudden death ................................. 94 A.3 Model #4: Evolutionary model .............................. 95 v Appendix B Data analysis with the MemToolbox ljLjNj B.1 Abstract ..........................................103 B.2 Introduction ........................................104 B.3 The MemToolbox .....................................105 B.3.1 The standard workflow .............................106 B.3.2 The Bayesian workflow .............................108 B.3.3 Posterior predictive checks . 111 B.3.4 Hierarchical modeling ..............................113 B.3.5 Model comparison ................................114 B.4 Availability, contents, & help ...............................116 B.5 Conclusion ........................................116 References ljNjlj vi In memory of Sol Graff, my grandfather. vii Acknowledgments Thank you to George Alvarez, my advisor. Thank you to the other members of my dissertation committee: Patrick Cavanagh, Martin Nowak, and Dan Schacter. It’s a pleasure to have learned from you. Thanks also to Ken Nakayama and Yaoda Xu for contributing to the great environment in the vision- lab. Thank you to fellow members of the visionlab and psychology department, past and present: Arash Afraz, Jorge Almeida, Sam Anthony, Eve Ayeroff, Julie Belkova, Katie Bettencourt, Tim Brady, Donal Cahill, Sasen Cain, Jon Cant, Ramakrishna Chakravarthi, Garga Chatterjee, Sarah Cohan, Michael Cohen, Joe DeGutis, Judy Fan, Daryl Fougnie, Lúcia Garrido, Laura Germine, Jon Gill, Jason Haberman, Fred Halper, Morgan Henry, Laura Herman, Su Keun Jeong, Justin Jungé, Talia Konkle, Andy Leber, Bria Long, Camille Morvan, Marnix Naber, Maryam Vaziri Pashkam, Irene Pepperberg, Julie Rhee, Adena Schachner, Anna Shafer-Skelton, Mirta Stantic, Viola Störmer, Charles Stromeyer, Roger Strong, Arin Tuerk, Ruosi Wang, Jeremy Wilmer, Daw-An Wu, Jiedong Zhang, and Xiaoyu Zhang. Thank you to Celia Raia and other members of the psychology department staff for help in navigating the process. Thank you to Trinidad Zuluaga and Sarah Cormiea for their assistance with data collection. Chapter 1 is in collaboration with Ben Allen, Martin Nowak, and George Alvarez. Chapter 2 is in col- laboration with Daryl Fougnie and George Alvarez. Appendix A is in collaboration with Ben Allen. Appendix B is in collaboration with Tim Brady, Daryl Fougnie, and George Alvarez. Thank you to Ben Allen for many helpful conversations. Thank you to Ned Block, Josh Greene, Susan Carey, Jeremy Wolfe, Guven Guzeldere, and Stefano Anzellotti for their questions and thoughts at cbb lunch. Thanks to Justin Halberda, Steve Luck, Wei Ji Ma, and Christopher Tyler for comments at vss. Thanks to David Rand for the introductions and encouraging words. viii Thank you to Douglas Hofstadter for playing along, much to my delight. Some of the computations in this thesis were run on the Odyssey cluster, supported by the fas Science Division Research Computing Group. This work was in part supported by a National Science