Neural reward representations enable utilitarian welfare maximization

Description: From deciding which meal to prepare for our guests to trading-off the pro-environmental effects of climate protection measures against their economic costs, we often must consider the consequences of our actions for the well-being of others (welfare). Vexingly, the tastes and views of others can vary widely. To maximize welfare according to the utilitarian philosophical tradition, decision makers facing conflicting preferences of others should choose the option that maximizes the sum of subjective value (utility) of the entire group. This notion requires comparing intensities of preferences across individuals. However, it remains unclear whether such comparisons are possible at all, and (if they are possible) how they might be implemented in the brain. Here, we show that humans can both learn the preferences of others by observing their choices, and represent these preferences on a common scale to make utilitarian welfare decisions. On the neural level, multivariate support vector regressions revealed that a distributed activity pattern in the ventromedial prefrontal cortex (VMPFC), a brain region previously associated with reward processing, represented preference strength of others. Strikingly, also the utilitarian welfare of others was represented in the VMPFC and relied on the same neural code as the estimated preferences of others. Together, our findings reveal that humans can behave as if they maximized utilitarian welfare using a specific utility representation and that the brain enables such choices by repurposing neural machinery processing the reward others receive.

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