Javascript must be enabled for the correct page display

Working memory control: Modeling task-general knowledge

Hoef, M.J.L. van de (2017) Working memory control: Modeling task-general knowledge. Master's Thesis / Essay, Human-Machine Communication.

[img]
Preview
Text
Thesis_MvH_Final.pdf - Published Version

Download (2MB) | Preview
[img] Text
toestemming.pdf - Other
Restricted to Backend only

Download (75kB)

Abstract

In today’s scientific research, computational models are used to test and expand our theories on human cognition. The models, however, are mostly used to study tasks individually and thus, generalization between computational models is limited. In the present body of work, we examined working memory control, which we theorized to be task-general. Individuals typically rely on one of two control strategies when exercising cognitive control, namely on a proactive or a reactive control strategy, according to the dual-mechanisms of control (DMC) framework. In the current study, three tasks were performed on which participants were expected to benefit from a proactive strategy. In addition, computational models were developed in the PRIMs cognitive architecture, to assess whether variations in performance were the result of differences in the participants’ cognitive control strategies. We found that participants who are proactive perform more accurately than those who are reactive on two out of three tasks. On the last task, results were marginally significant. In addition, we demonstrated the proactiveness index to measure more than the mere difference between fast and slow performing participants. The model results, however, did not confirm the variations in performance to be caused by the participants’ control strategies. Two explanations were proposed, which are not mutually exclusive. Either individuals mainly adopt proactive control with the strategy to prepare rather than the strategy to rehearse, or the two control modes of the DMC framework did explain only parts of the variations in performance that we found in the behavioral data.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Human-Machine Communication
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:33
Last Modified: 15 Feb 2018 08:33
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/16214

Actions (login required)

View Item View Item