Computing Social Value Conversion in the Human Brain

Abstract

Social signals play powerful roles in shaping self-oriented reward valuation and decision making. These signals activate social and valuation/decision areas, but the core computation for their integration into the self-oriented decision machinery remains unclear. Here, we study how a fundamental social signal, social value (others' reward value), is converted into self-oriented decision making in the human brain. Using behavioral analysis, modeling, and neuroimaging, we show three-stage processing of social value conversion from the offer to the effective value and then to the final decision value. First, a value of others' bonus on offer, called offered value, was encoded uniquely in the right temporoparietal junction (rTPJ) and also in the left dorsolateral prefrontal cortex (ldlPFC), which is commonly activated by offered self-bonus value. The effective value, an intermediate value representing the effective influence of the offer on the decision, was represented in the right anterior insula (rAI), and the final decision value was encoded in the medial prefrontal cortex (mPFC). Second, using psychophysiological interaction and dynamic causal modeling analyses, we demonstrated three-stage feedforward processing from the rTPJ and ldPFC to the rAI and then from rAI to the mPFC. Further, we showed that these characteristics of social conversion underlie distinct sociobehavioral phenotypes. We demonstrate that the variability in the conversion underlies the difference between prosocial and selfish subjects, as seen from the differential strength of the rAI and ldlPFC coupling to the mPFC responses, respectively. Together, these findings identified fundamental neural computation processes for social value conversion underlying complex social decision making behaviors.

Breaking human social decision making into multiple components and then putting them together again

“…In our daily life, we make value-based decisions not only for our own interest but also for the benefit of other individuals (e.g., charitable donation involving decisionmaking about how resources are allocated between oneself and others). In such decision-making, an individual computes the value of each option by considering both self and others' reward outcomes in line with her social preference such as warm-glow, inequity-aversion and envy-aversion (Crockett et al, 2017;Fehr & Shmidt, 1999;Fehr & Camerer, 2007;Fukuda et al, 2019;Harbaugh et al, 2007;Hula et al, 2018;Sanfey et al, 2003;Takahashi et al, 2009), suggesting that multiple types of information are represented in the brain to guide choice. One study (Hutcherson et al, 2015) examined simple decisions about different allocations of monetary reward between oneself and an anonymous partner.…”

“…In most of the computational models mentioned above, the form of the integration of the variables is to compute a subjective value or choice probability for a given option. For example, in models of decision-making for others (e.g., Fukuda et al, 2019;Hutcherson et al, 2015), a subjective value for each option is computed by integrating information about the amount of reward delivered to oneself and others (Fig 2A). In the contexts of observational learning (e.g., Burke et al, 2010;Suzuki et al, 2012), leaning signals from others' choices and reward outcomes are integrated to compute the value of each option (Fig 2B).…”

“…On the other hand, to our knowledge, only a few studies (Fukuda et al, 2019;Hampton et al, 2008;Hill et al, 2017;Suzuki et al, 2015) have examined both of the two criteria. For example, as mentioned in the previous section, Suzuki et al (Suzuki et al, 2015) first identified three key computational variables and their neural correlates for consensus formation in a group: vmPFC encoding one's own preference for each of the available options, TPJ encoding group-members' prior choices and IPL encoding one's inference about how much each option was stuck to by the other group-members ( Fig.…”

“…In the context of decision-making for others, one study (Fukuda et al, 2019) tested for areas meeting the two criteria, by combining two types of connectivity analysis:…”

Functional specialization within the inferior parietal lobes across cognitive domains

“…Notably, there is a scarcity of existing studies directly devoted to investigating the left versus right IPL in several different psychological tasks (see 42,43 for exceptions). The few existing studies committed to the effective connectivity of the IPL mainly focused on task-related connectivity changes between an IPL subregion and other areas of a specialized network for a single cognitive domain, typically in one hemisphere only 34,[44][45][46] . As such, our results usher towards a broader perspective.…”