Alchihabi, Abdullah
Kivilicim, Baran B.
Newman, Sharlene D.
Yarman Vural, Fatoş Tunay
Tower of London (TOL) is a classic problem solving task to study high-level cognitive processes. In this paper, using the TOL experiment, we aim to investigate the activation and relations of anatomic regions during the planning and execution phases of the problem solving task. We propose a dynamic sparse network representation estimated from the fMRI brain volumes at all time instances. This representation, called Dynamic Mesh Network, enables us to analyze the network properties of the brain under planning and execution stages of a TOL problem. Results indicate that activation during the planing phase is relatively higher than during the execution phase in most of the anatomic regions. Also, the connectivity between the anatomic regions is denser and stronger during the planing phase, compared to the execution phase.


Improvement of temporal resolution of fMRI data for brain decoding
Varol, Emel; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2022-2-10)
In this study, we aim to increase the accuracy of the mapping between the states of the brain and problem-solving phases namely planning and execution. To create a computational model to generate the mapping, an fMRI dataset obtained from subjects solving the Tower of London problem has been used. fMRI data is suitable for this problem as it provides regional and time-varying changes in brain metabolism. However, developing the model using fMRI data is not trivial. Generally, fMRI data has a very large feat...
Analyzing Complex Problem Solving by Dynamic Brain Networks
Alchihabi, Abdullah; Ekmekci, Ömer; Kivilcim, Baran B.; Newman, Sharlene D.; Yarman Vural, Fatos T. (2021-12-01)
Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested...
On the Entropy of Brain Anatomic Regions for Complex Problem Solving
Degirmendereli, Gonul Gunal; Newman, Sharlene D.; Yarman Vural, Fatoş Tunay (2019-01-01)
In this paper, we aim to measure the information content of brain anatomic regions using the functional magnetic resonance images (fMRI) recorded during a complex problem solving (CPS) task. We, also, analyze the brain regions, activated in different phases of the problem solving process. Previous studies have widely used machine learning approaches to examine the active anatomic regions for cognitive states of human subjects based on their fMRI data. This study proposes an information theoretic method for ...
Effects of cooperatlye and indiyidualistic problem solying methods on mathematical problem solving performance
Koç, Yusuf; Bulut, Safure (2002-01-01)
The purpose of the present study was to investigate the effects of the cooperatiye problem solving method (CPSM) and the individualistic problem solving method (IPSM) on seventh grade students' mathematical problem solving performance (MPSP). in this quasiexperimentalresearch study, seventh grade "percents unit" was covered. After analyzing the data by using the multivariate analysis of covariance it was found thatCPSM and IPSM groups had statistically significantly greater mean scores than the traditional ...
Gunal Degirmendereli, Gonul; Yarman Vural, Fatoş Tunay; Department of Cognitive Sciences (2022-2)
In this thesis, we propose an information theoretic method for the representation of human brain activity to decode mental states of a high-order cognitive process, complex problem solving (CPS) using functional magnetic resonance images. First, we aim to identify the active regions and represent underlying cognitive states by measuring the information content of anatomical regions for expert and novice problem solvers during the main phases of problem solving, namely planning and execution. Based on Shann...
Citation Formats
A. Alchihabi, B. B. Kivilicim, S. D. Newman, and F. T. Yarman Vural, “A DYNAMIC NETWORK REPRESENTATION OF FMRI FOR MODELING AND ANALYZING THE PROBLEM SOLVING TASK,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53639.