Using a symbolic process model as input for model-based fMRI analysis: locating the neural correlates of problem...

Area of Science:

• Cognitive Neuroscience
• Neuroimaging
• Computational Psychiatry

Background:

• Multitasking requires frequent updating of task information, with the 'problem state resource' acting as a known bottleneck.
• Functional magnetic resonance imaging (fMRI) is a key tool for understanding brain activity during cognitive tasks.

Purpose of the Study:

• To employ a model-based fMRI analysis using a high-level symbolic process model to investigate the neural correlates of 'problem state replacements' during a triple-task.
• To identify specific brain regions responsible for temporary and long-term storage of problem states during complex cognitive tasks.

Main Methods:

• A computational process model was developed and fitted to behavioral data from participants performing a demanding triple-task.
• fMRI data were analyzed by regressing the computational model's activity against the BOLD signal to pinpoint neural correlates.
• The study utilized a model-based fMRI approach with a high-level symbolic cognitive model.

Main Results:

• The inferior parietal lobule was identified as the brain region responsible for the temporary representation of problem states.
• The inferior frontal gyrus was identified as the brain region involved in the long-term storage of problem states.
• The model-based fMRI analysis successfully located neural correlates associated with managing task information.

Conclusions:

• Model-based fMRI analyses are feasible and effective when using high-level symbolic cognitive models.
• This approach enables fine-grained exploratory research into the neural underpinnings of complex cognitive functions like multitasking.
• The findings highlight distinct neural substrates for temporary and long-term management of task-related information.