Differences in Discounting Behavior and Brain Responses for Food and Money Reward

M. Markman, E. Saruco, S. Al-Bas, B. A. Wang, J. Rose, K. Ohla, S. Xue Li Lim, D. Schicker, J. Freiherr, M. Weygandt, Q. Rramani, B. Weber, J. Schultz,B. Pleger

ENEURO(2024)

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摘要
Most neuroeconomic research seeks to understand how value influences decision -making. The influence of reward type is less well understood. We used functional magnetic resonance imaging (fMRI) to investigate delay discounting of primary (i.e., food) and secondary rewards (i.e., money) in 28 healthy, normal -weighted participants (mean age = 26.77; 18 females). To decipher differences in discounting behavior between reward types, we compared how well -different option -based statistical models (exponential, hyperbolic discounting) and attribute -wise heuristic choice models (intertemporal choice heuristic, dual reasoning and implicit framework theory, trade-off model) captured the reward -specific discounting behavior. Contrary to our hypothesis of different strategies for different rewards, we observed comparable discounting behavior for money and food (i.e., exponential discounting). Higher k values for food discounting suggest that individuals decide more impulsive if confronted with food. The fMRI revealed that money discounting was associated with enhanced activity in the right dorsolateral prefrontal cortex, involved in executive control; the right dorsal striatum, associated with reward processing; and the left hippocampus, involved in memory encoding/retrieval. Food discounting, instead, was associated with higher activity in the left temporoparietal junction suggesting social reinforcement of food decisions. Although our findings do not confirm our hypothesis of different discounting strategies for different reward types, they are in line with the notion that reward types have a significant influence on impulsivity with primary rewards leading to more impulsive choices.
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关键词
attribute-wise models,computational modeling,delay discounting,neuroimaging,option-based models,primary and secondary reward
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