Integrating Theoretical Models with Functional Neuroimaging.

Area of Science:

• Cognitive Neuroscience
• Computational Psychology

Background:

• Mathematical modeling of cognition has a long history in psychology.
• Human functional neuroimaging has significantly advanced cognitive neuroscience since the 1990s.

Purpose of the Study:

• To review recent studies combining formal modeling and neuroimaging.
• To highlight novel insights into human cognitive mechanisms.

Main Methods:

• Integration of computational models with human functional neuroimaging data.
• Application of diverse analytic and model-inspired approaches.
• Development of tailored solutions for linking multi-parameter models with neural data.

Main Results:

• Combined approaches yield models with greater specificity and rigor than behavioral studies alone.
• Novel insights were gained across perception, attention, memory, categorization, and cognitive control.
• Creative, individualized solutions are often necessary for robust model-neural data integration.

Conclusions:

• Model-based cognitive neuroscience is a powerful approach for understanding cognition.
• Future developments hold great potential for advancing theoretical understanding of cognitive processes.
• This integrated methodology can model both low-level and high-level cognitive functions.