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Distinct and Overlapping Brain Areas Engaged during Value-Based, Mathematical, and Emotional Decision Processing.
Chun-Wei Hsu1, Joshua O S Goh2
1Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan UniversityTaipei, Taiwan; Department of Psychology, University of PlymouthPlymouth, UK.
Frontiers in Human Neuroscience
|July 5, 2016
Summary
Value-based decision-making involves both mathematical and emotional processing. This study found that neural activity during value decisions more closely resembles mathematical processing, especially in subcortical regions.
Area of Science:
- Neuroscience
- Cognitive Science
- Decision-Making Research
Background:
- Human decision-making relies on cognitive (mathematical) and affective (emotional) processes.
- The neural underpinnings of how these processes interact during value-based choices are not fully understood.
Purpose of the Study:
- To investigate the neural overlap between value-based decision-making, mathematical processing, and emotional processing.
- To determine the relative contribution of cognitive versus affective systems in value-based choices.
Main Methods:
- Functional magnetic resonance imaging (fMRI) experiment with within-subject design.
- Participants performed three tasks: value-based decision-making, mathematical calculation, and emotional face evaluation.
- Analysis of overlapping and unique neural regions engaged across tasks.
Main Results:
- All tasks engaged widespread cortical areas (frontal, parietal, motor, somatosensory, visual).
- Mathematical and value tasks shared overlapping regions in the bilateral striatum.
- Value-based decisions showed greater neural similarity to mathematical processing than emotional processing, particularly in subcortical areas.
Conclusions:
- Value-based decision-making recruits neural regions involved in both mathematical and emotional processing.
- Mathematical processing, especially within subcortical structures, plays a more dominant role in value-based decisions.
- Emotional processing engages broader neural networks compared to the more focal activations seen in value and mathematical tasks.
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