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Title RELPH: A Computational Model for Human Decision Making
URL
Publication Date
University/Publisher University of Waterloo
Abstract The updating process, which consists of building mental models and adapting them to the changes occurring in the environment, is impaired in neglect patients. A simple rock-paper-scissors experiment was conducted in our lab to examine updating impairments in neglect patients. The results of this experiment demonstrate a significant difference between the performance of healthy and brain damaged participants. While healthy controls did not show any difficulty learning the computer’s strategy, right brain damaged patients failed to learn the computer’s strategy. A computational modeling approach is employed to help us better understand the reason behind this difference and thus learn more about the updating process in healthy people and its impairment in right brain damaged patients. Broadly, we hope to learn more about the nature of the updating process, in general. Also the hope is that knowing what must be changed in the model to “brain-damage” it can shed light on the updating deficit in right brain damaged patients. To do so I adapted a pattern detection method named “ELPH” to a reinforcement-learning human decision making model called “RELPH”. This model is capable of capturing the behavior of both healthy and right brain damaged participants in our task according to our defined measures. Indeed, this thesis is an effort to discuss the possible differences among these groups employing this computational model.
Subjects/Keywords computational Modeling; Updating; neglect; reinforcement learning
Language en
Country of Publication ca
Record ID handle:10012/7883
Repository waterloo
Date Retrieved
Date Indexed 2019-06-26

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…43 Figure 16. Reinforcement learning with only a reward signal as an external feedback ........................... Figure 17. Unsupervised learning with absolutely no external feedback ................................................. Figure 18…

…one of all the participants in HC group. .............................. 37 viii Figure 13. The schematic representation of the different learning mechanisms in the brain ............... 40 Figure 14. Supervised learning with error signal back…

…propagating to the system as a training signal.... Figure 15. The cortico-cerebellar connection in the brain suggested to be involved in supervised learning in brain…

…known related facts about the human neural systems involved in learning and decision making. 1 Motivation and Summary of Neglect Hemineglect, also known as Hemispatial neglect or simply neglect, is a common consequence of injury to the right side of…

…spatial tasks; they also involve nonspatial deficits as well. Position priming and statistical learning are two frameworks that have been employed in our lab to investigate the non-spatial impairment in neglect patients. The priming effect refers to how…

…neglect participants were a bit faster to respond when the target repeated in the same position; this priming benefit was not significant compared to non-primed trials. Statistical learning is another suitable framework to study non-spatial updating (…

…Turk-Browne, Isola, Scholl, & Treat, 2008; Aslin & Newport, 2012). Statistical learning helps humans to learn the regularities distributed in space and time. Thus both statistical learning and the priming effect are quite similar. In fact, some…

…researchers believe the concepts are so closely related that priming might be a form of statistical learning (Walthew & Gilchrist, 2006). To investigate statistical learning in neglect patients, Shaqiri and Anderson conducted the “hot spot…

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