Distinct Patterns of Activity Underlie the Motivation to Be Fair

Post by Shireen Parimoo

What's the science?

Why are people motivated to be fair? People can be fair for prosocial reasons when they value the well-being of others, or for strategic reasons when being unfair might cost them something. In the ultimatum game, which is often used to evaluate fairness, people offer to split a sum of money with a recipient who accepts or rejects the offer. Participants typically offer 40% of the sum, which suggests that they could be acting prosocially by providing a nearly equal split. Conversely, they could be acting strategically to ensure that the recipient does not reject the offer. The ultimatum game activates regions of the brain like the dorsolateral prefrontal cortex (dlPFC) that are involved in strategic processing. Prosocial behavior is thought to be supported by Theory of Mind (ToM), which is the ability to empathize with and understand other people’s mental states. No study has yet to examine the pattern of activity in brain regions belonging to the ToM network while people make fair or unfair decisions. This week in Social Cognitive and Affective Neuroscience, Speer and Boksem used functional magnetic resonance imaging to distinguish between patterns of activity associated with prosocial and strategic motivations in the cognitive control and ToM networks.

How did they do it?

Thirty-one young adults played the ultimatum game (UG) and the dictator game (DG) while undergoing functional magnetic resonance imaging scanning. They had to split €20 and could offer between €0-14 to their opponent. Half of the trials consisted of the UG and the other half of the trials consisted of the DG. Unlike the UG, there is no strategic advantage to offering a fair split in the DG, as opponents cannot reject offers made by participants. To evaluate behavior, the authors calculated the difference between the amount of money that participants offered in the two games. Participants were categorized as selfish players if there was a large difference in their offers between the two games, which suggests that they were acting strategically during the UG by offering more money to their opponent.

The authors examined patterns of activity in the ToM and cognitive control networks during the two games. First, they used Neurosynth (an online database of fMRI studies) to identify brain regions that are often active during ToM and cognitive control tasks, which included the temporoparietal junction (TPJ) and the medial prefrontal cortex (mPFC) in the ToM network and the dlPFC and posterior cingulate cortex (PCC) in the cognitive control network. For each participant, they created a model (a support vector machine classifier) to distinguish between the two games based on the pattern of activity in these networks and in individual brain regions. The classifier was trained on brain activity on a subset of UG and DG trials and then tested with a different set of trials to predict whether the pattern of activity corresponds to the UG or the DG. They correlated classifier performance with behavior to determine how patterns of activity related to participants’ motivations in the two games. Finally, to identify other brain regions that might be differentially activated by the two games, the authors applied the classifier to the whole brain by targeting a small area at a time and then correlated classifier performance with behavior.

What did they find?

In general, people made higher offers to their opponents in the UG than in the DG. There were large individual differences in motivation, as prosocial participants made similar offers between the two games whereas selfish players offered comparatively less money to their opponent in the DG. Classification accuracy in the ToM and cognitive control networks was related to behavior. Distinct patterns of activity in these networks were found to underlie prosocial and strategic motivations, as the classifier was more accurate at distinguishing between the two games when participants were behaving strategically than when they were driven by prosocial motivations.

Patterns of activity in individual regions of the ToM and cognitive control networks also differed between prosocial and selfish players. For example, activity in the left TPJ was more different across the two games in selfish players than in prosocial players. Similarly, classification accuracy in the bilateral dlPFC and PCC was higher when the difference in offers was larger, suggesting that the pattern of activity was more distinct between the two games in selfish than in prosocial players. Finally, classifier performance in other regions like the bilateral TPJ, mPFC, and the left IFG was also related to behavior. These results indicate that prosocial players exhibited similar patterns of activity in the two games because they did not differentially engage in strategic and prosocial reasoning. On the other hand, selfish players engaged regions in the ToM and cognitive control network differently when they were motivated to behave strategically in the UG game, even when their offers do not differ from prosocial individuals.

What's the impact?

This study is the first to demonstrate that distinct patterns of activity in the ToM and cognitive control networks underlie prosocial and strategic motivations. Importantly, these results provide a deeper insight into how people rely on both cognitive control processes and ToM processes like empathy to make fairness decisions. 

Speer and Boksem. Decoding fairness motivations from multivariate brain activity patterns. Social Cognitive and Affective Neuroscience (2020). Access the original scientific publication here.