A unified neural account of contextual and individual differences in altruism
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This paper will be of considerable interest to researchers studying the psychological and neural basis of variation in prosocial behavior. The authors use a sophisticated combination of computational modeling and EEG to show that variation in generosity produced by changes in context (i.e., disadvantageous vs. advantageous inequality) and variation due to individual differences in concern for others both seem to occur early, during the perceptual or valuation stage of a choice, rather than later on during choice comparison. However, these two sources of variation also appear to operate through distinct mechanisms during this stage of processing, which spurs further questions about the drivers of human prosocial behavior.
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Abstract
Altruism is critical for cooperation and productivity in human societies but is known to vary strongly across contexts and individuals. The origin of these differences is largely unknown, but may in principle reflect variations in different neurocognitive processes that temporally unfold during altruistic decision making (ranging from initial perceptual processing via value computations to final integrative choice mechanisms). Here, we elucidate the neural origins of individual and contextual differences in altruism by examining altruistic choices in different inequality contexts with computational modeling and electroencephalography (EEG). Our results show that across all contexts and individuals, wealth distribution choices recruit a similar late decision process evident in model-predicted evidence accumulation signals over parietal regions. Contextual and individual differences in behavior related instead to initial processing of stimulus-locked inequality-related value information in centroparietal and centrofrontal sensors, as well as to gamma-band synchronization of these value-related signals with parietal response-locked evidence-accumulation signals. Our findings suggest separable biological bases for individual and contextual differences in altruism that relate to differences in the initial processing of choice-relevant information.
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eLife assessment
This paper will be of considerable interest to researchers studying the psychological and neural basis of variation in prosocial behavior. The authors use a sophisticated combination of computational modeling and EEG to show that variation in generosity produced by changes in context (i.e., disadvantageous vs. advantageous inequality) and variation due to individual differences in concern for others both seem to occur early, during the perceptual or valuation stage of a choice, rather than later on during choice comparison. However, these two sources of variation also appear to operate through distinct mechanisms during this stage of processing, which spurs further questions about the drivers of human prosocial behavior.
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Reviewer #1 (Public Review):
This study reports the results of a computational and EEG analysis of altruistic decision making. The authors intend to examine whether fundamentally different mechanisms operate to drive altruistic decision making in different contexts, which they here manipulate by examining choices in the realm of advantageous and disadvantageous inequality. The authors find that changes in self payoff are encoded in opposite manners in the two contexts, but that a similar evidence accumulation mechanism leading up to the time of response seems to operate equally in both. In addition, they find that individual differences in generosity are predicted more by differences in sensitivity to change in the other's payoff in the disadvantageous inequality condition, and by stronger phase coupling between sensors related to this …
Reviewer #1 (Public Review):
This study reports the results of a computational and EEG analysis of altruistic decision making. The authors intend to examine whether fundamentally different mechanisms operate to drive altruistic decision making in different contexts, which they here manipulate by examining choices in the realm of advantageous and disadvantageous inequality. The authors find that changes in self payoff are encoded in opposite manners in the two contexts, but that a similar evidence accumulation mechanism leading up to the time of response seems to operate equally in both. In addition, they find that individual differences in generosity are predicted more by differences in sensitivity to change in the other's payoff in the disadvantageous inequality condition, and by stronger phase coupling between sensors related to this delta-other signal and sensors related to the evidence accumulation signal.
This study makes a valuable contribution by combining a sophisticated suite of modelling and neurophysiological analyses to shed light on the decision parameter adjustments that inform altruistic decisions in different contexts. The conclusions regarding those adjustments appear well supported by the data. One aspect that could be clarified is that there is an apparent discrepancy between the cross-condition bound adjustments identified by the modelling and the absence of any corresponding neural evidence accumulation signal amplitude difference.
One of the stated overarching goals of this study is to determine whether the neural mechanisms and circuits for altruistic decisions are context-specific or general. The manuscript would benefit from greater clarity on this point, in particular defining what is meant by 'mechanisms' and what qualitative and quantitative criteria should be applied when identifying them as distinct versus common. As all decisions in this study are reported via the same manual actions it seems implausible that there would be no overlap at all in the circuits and mechanisms involved. In addition, the prior literature has demonstrated that even individual neurons can trace different computations depending on the circumstances. Therefore, it is necessary to clarify whether the authors are searching for context-dependence in the brain areas/signals that are recruited and/or in the computations that are performed within a brain area.
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Reviewer #2 (Public Review):
Recent work in the neurosciences has suggested that decision making in most domains consists of computations at multiple stages. In value-based choices, initial evidence is perceived, categorized, and evaluated, then accumulated over time in a process that essentially compares the relative value of two options, until the accumulated evidence passes a threshold for choice. Although previous work has shown that this basic structure also applies to decisions in the domain of prosocial choice, it has remained unclear at what stage of this decision process variation in prosocial choices arises. The authors aimed to resolve this issue by using a combination of computational modeling and EEG, applied to a choice paradigm that evokes variation in altruistic behavior through two distinct routes: exogenous variation …
Reviewer #2 (Public Review):
Recent work in the neurosciences has suggested that decision making in most domains consists of computations at multiple stages. In value-based choices, initial evidence is perceived, categorized, and evaluated, then accumulated over time in a process that essentially compares the relative value of two options, until the accumulated evidence passes a threshold for choice. Although previous work has shown that this basic structure also applies to decisions in the domain of prosocial choice, it has remained unclear at what stage of this decision process variation in prosocial choices arises. The authors aimed to resolve this issue by using a combination of computational modeling and EEG, applied to a choice paradigm that evokes variation in altruistic behavior through two distinct routes: exogenous variation in the inequality context of a choice (i.e., advantageous vs. disadvantageous inequality), and endogenous variation as a function of individual differences in prosocial preferences.
One of the strengths of this approach (particularly the use of EEG) over previous studies is that the authors can use the timing and nature of the EEG signals to disentangle both HOW preferences evolve, and WHEN differences evoked by context or individual preference emerge. This work very clearly shows that late-stage choice comparison processes, locked to the time of response (i.e., the evidence accumulation phase of a choice) are likely NOT where variations in altruistic choice arise. Instead, the evidence points to a set of distinct signals that occur time-locked to the onset of an option that enables participants to make a choice, which implies that the computations driving choice behavior likely occur at the perceptual and/or valuation stage. This is not wholly surprising, but is interesting and important to verify.
A second potential strength of this approach is that the methodology allows the authors to determine whether the observed signals more strongly resemble encoding of the overall magnitude of outcomes to self and others, or instead are more related to signals sensitive to distributional values (i.e., inequality/fairness). The evidence here paints a quite intriguing, but somewhat mixed picture, in my view, and I think needs to come with more caveats than the authors currently acknowledge. The authors claim that their evidence supports the idea that people are making choices by considering inequality, rather than by computing outcomes for self or other directly. The lack of a consistently-signed association between EEG signals and either self or other outcome magnitude across contexts is not consistent with the idea that values are encoded in terms of self and other, which has sometimes been argued from fMRI data. However, I also do not think they are fully consistent with the authors' claims that they are observing signals related directly to fairness considerations either. Fairness/inequality, as typically defined by economic models of social preferences, involves computing the differences between self and other payoffs. The authors find ERP signals scaling with payoff changes for self but not other. Those signals do move in opposite directions in the two inequality contexts, which is why the authors interpret this as meaning that these ERP signals represent some calculation related to fairness. But there is no sensitivity of these signals to payoff change for the other, suggesting that these signals are not precisely driven by fairness as it is canonically conceived. Instead, it seems that these signals might reflect something about how people orient to self outcomes differently in the two contexts. This actually is an intriguing finding, but is somewhat difficult to interpret, since it is not wholly clear what these ERP signals represent (i.e., are they related to perception, valuation, attention, etc.?). Moreover, as the authors acknowledge in their discussion, the design of the study, with its presentation of a first option that determines the inequality context and a second option that determines the relative values of the options, means that it is difficult to know when and how one would expect to see raw self and other values as opposed to comparative value signals related to differences in self and other. Finally, the sensitivity (or lack thereof) of EEG to more subcortical signals means that it is not clear one is getting a whole picture of the computations driving choice. Thus, I think the conclusion that behavior is related to inequality processing rather than to a focus on self- and other-payoffs directly, while intriguing, needs to be tempered a bit.
What also seems somewhat puzzling is that the behavioral and neural signals do not always seem fully consistent with one another, or with prior research. For example, behaviorally people seem to put more weight on others' payoff changes in the advantageous inequality context. And in other work (Morishima et al., 2012), it is behavioral variation in the advantageous context that correlates with neural (anatomical) variation. Yet here, there are no EEG signals that encode changes in other outcomes as a main effect in the advantageous context, and it is individual variation in encoding of others' payoffs in the DISADVANTAGEOUS context that relate to individual differences in equality-seeking in that context. Thus, it is actually in the context where one would expect people to be paying *less* attention to other outcomes (based on the modeling parameters) that neural signals seem to be *more* sensitive to those outcomes. This doesn't mean that these signals aren't interesting, but it does point to a need to more fully understand what they represent before coming to firm conclusions about what they actually mean, computationally and psychologically.
Thus, I think this paper will likely have an impact on the field largely for the intriguing questions it raises about how people make altruistic choices rather than for providing definitive answers. This is an important contribution and researchers will, I expect, find this paper thought-provoking.
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