Dynamics of individual learning
Author
Rahman, RousselOther Contributors
Gray, Wayne D., 1950-; Sims, Christopher Robert; Fajen, Brett R.; Gigerenzer, Gerd;Date Issued
2022-08Subject
Cognitive scienceDegree
PhD;Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute (RPI), Troy, NY. Copyright of original work retained by author.; Attribution-NonCommercial-NoDerivs 3.0 United StatesMetadata
Show full item recordAbstract
This work focuses on understanding how individuals learn complex tasks. Most real-world tasks that we learn and need help with, are complex. Yet most of our knowledge about human learning is based on simple tasks. We think the main reason is the lack of appropriate tools of analysis, as learning curves in complex tasks must be studied from an individual-specific perspective due to large individual differences of methods. Therefore, as our first step, we developed a novel, uncertainty-based tool -- the SpotLight -- to model changes in uncertainty of individual performance and highlight the plateaus, dips, and leaps at different levels of complex task performance. We applied the SpotLight to investigate the changes of individuals' methods while learning the complex game of Space Fortress (SF). Our results indicate that, underneath a sea of individual differences of methods, individuals applied a common, simple heuristic to recurrently update methods. While our SpotLight analysis helped us identify similarities across individuals, it also revealed scopes of improving measures of complex task performance to prevent false negatives of training benefits. Application of heuristics to update methods, as observed for our SF players, indicates at boundedly rational behavior to deal with the computational complexity of finding appropriate methods for complex tasks. Therefore, in our final study, we investigated how an individual searches for better methods while learning the complex game of Ms. Pacman. Our results indicate an efficient approach by the individual to search for better methods, by developing and refining a set of elementary, heuristic-based methods. Finally, we tie together the four studies and discuss how we may progress towards a boundedly rational theory of learning complex tasks.;Description
August 2022; School of Humanities, Arts, and Social SciencesDepartment
Dept. of Cognitive Science;Publisher
Rensselaer Polytechnic Institute, Troy, NYRelationships
Rensselaer Theses and Dissertations Online Collection;Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 license. No commercial use or derivatives are permitted without the explicit approval of the author.;Collections
Except where otherwise noted, this item's license is described as CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons
Attribution-Noncommercial-No Derivative Works 3.0 license. No commercial use or derivatives
are permitted without the explicit approval of the author.