Are individual differences quantitative or qualitative? An integrated behavioral and fMRI MIMIC approach

Area of Science:

• Cognitive Neuroscience
• Neuroimaging
• Decision Science

Background:

• Growing interest in understanding individual differences in cognitive processes.
• Need for methods to distinguish quantitative vs. qualitative variations in these differences.
• Limitations of current approaches in characterizing the nature of individual variability.

Purpose of the Study:

• To propose and validate the Multiple Indicators Multiple Causes (MIMIC) model for combined behavioral and fMRI data.
• To empirically determine whether individual differences in decision-making are quantitative or qualitative.
• To apply the MIMIC model to existing datasets to illustrate its utility.

Main Methods:

• Development and simulation of the Multiple Indicators Multiple Causes (MIMIC) model.
• Integration of behavioral data and functional magnetic resonance imaging (fMRI) data.
• Re-analysis of decision-making datasets from Van Duijvenvoorde et al. (2016) and Blankenstein et al. (2018).

Main Results:

• Simulation confirmed adequate power and satisfactory parameter recovery for the MIMIC model.
• Individual differences in Van Duijvenvoorde et al. (2016) were qualitative, reflecting distinct decision strategies (expected value vs. loss minimization) linked to vmPFC activity.
• Individual differences in Blankenstein et al. (2018) were quantitative, related to risk aversion levels and vmPFC activity.

Conclusions:

• The MIMIC model effectively distinguishes between quantitative and qualitative individual differences in cognitive neuroscience.
• Empirical determination of the nature of individual differences is crucial, moving beyond assumptions.
• The findings highlight the role of vmPFC activity in modulating decision strategies and risk aversion.