Author response: A unified neural account of contextual and individual differences in altruism

Abstract

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics 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. Editor's evaluation In this important paper, 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. This paper will be of considerable interest to researchers studying the psychological and neural basis of variation in prosocial behavior. https://doi.org/10.7554/eLife.80667.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Altruism – incurring own costs to benefit others – is fundamental for cooperation and productivity in human societies (de Waal, 2008; Piliavin and Charng, 1990). It not only plays crucial roles in shaping socio-political ideology and welfare (e.g. via tax policies and charity; Bechtel et al., 2018; Offer and Pinker, 2017) but is also essential for collective management of challenging situations, such as political, financial, and public health crises. While altruism is thought to be a stable behavioral tendency shaped by the evolutionary advantages of the ability to cooperate, it is unclear why this tendency varies so strongly across individuals, contexts, and cultures (Bester and Güth, 1998; Hamilton, 1964a; Hamilton, 1964b; Lebow, 2018; Piliavin and Charng, 1990). Is altruism governed by a set of unitary neuro-cognitive mechanisms that are engaged to varying degrees in different situations or different people (Tricomi et al., 2010)? Or are there fundamentally different types of altruistic actions that are guided by different neuro-cognitive processes triggered by different contexts (Hein et al., 2016)? From a neurobiological perspective, both these possibilities appear plausible. On the one hand, all altruistic actions necessitate the ability to override self-interest, a parsimonious brain mechanism (Bester and Güth, 1998) that is thought to be facilitated more or less by different contexts and that could be expressed to different degrees in different people (Morishima et al., 2012; Trivers, 1971). On the other hand, empirical observations suggest that altruism varies with a range of factors such as others’ previous actions (e.g. empathy-based vs. reciprocity-based altruism) or their perceived similarity (e.g. social distance; Hein et al., 2016; Vekaria et al., 2017). It is thus often argued that in different contexts or different individuals, superficially similar altruistic actions can be guided by distinct motives (such as personal moral norms, responsibility, or empathy), which may be controlled by fundamentally different types of neurocognitive mechanisms (Hein et al., 2016; Piliavin and Charng, 1990; Zaki and Mitchell, 2011). One specific context factor that is often discussed in this context is the inequality in resources held by the actor and the recipient of a possible distribution: People are more willing to share if they possess more than the recipient (advantageous inequality, ADV) than if they possess less (disadvantageous inequality; DIS) (Charness and Rabin, 2002; Fehr and Schmidt, 1999; Gao et al., 2018; Güroğlu et al., 2014; Morishima et al., 2012; Tricomi et al., 2010). Although this consistent effect has been formalized with the same utility model across contexts, this model needs to comprise two distinct latent parameters quantifying altruism in the two contexts (i.e. decision weights on others’ payoffs that are specific for ADV and DIS), and these are often uncorrelated and differ strongly from each other (Gao et al., 2018; Morishima et al., 2012). These observations, together with distinct psychological accounts for the distribution behaviors in different contexts (i.e. ‘guilt’ in the advantageous and ‘envy’ in the disadvantageous inequality context), imply that altruistic choices in the two contexts may be driven by fundamentally different psychological processes (Fehr and Schmidt, 1999; Gao et al., 2018). Moreover, modeling studies often reveal that these altruism parameters vary strongly between different people for the same choice set (Fehr and Schmidt, 1999), and neuroimaging studies have shown that while distributional behavior in both contexts correlates with activity in brain regions commonly associated with motivation (e.g. the putamen and orbitofrontal cortex), either context also leads to activity in a set of distinct areas (the dorsolateral and dorsomedial prefrontal cortex in advantageous and the amygdala and anterior cingulate cortex in disadvantageous inequality; Gao et al., 2018; Yu et al., 2014). Finally, neuroanatomical research shows that only for advantageous inequality, individual variations in altruistic preferences relate to gray matter volume in the temporoparietal junction (TPJ; Morishima et al., 2012). While these behavioral modeling and neural findings suggest clear contextual and individual differences in altruism, it is still unclear what specific neurocognitive mechanisms these differences could arise from. Previous research on individual and contextual differences in altruism has largely used unitary computational models focusing exclusively on valuation (rather than attempting to separate distinct aspects of the choice process), and has used functional magnetic resonance imaging (fMRI) to identify spatial patterns of neural activity that correlate with valuation processes during wealth distribution behaviors in different contexts (Charness and Rabin, 2002; Fehr and Schmidt, 1999; Gao et al., 2018; Güroğlu et al., 2014; Morishima et al., 2012; Tricomi et al., 2010). For example, recent studies combined computational modeling with fMRI techniques to show that the value of altruistic choice can be modeled as the weighted sum of self- and other-interest, and that different attributes are integrated into an overall value signal correlating with BOLD activity in the ventromedial prefrontal cortex (vmPFC) (Crockett et al., 2017; Crockett et al., 2013; Hutcherson et al., 2015; Hare et al., 2010). However, since these studies neither formally examined the difference in altruistic choices between advantageous and disadvantageous inequality contexts, nor focused on separating different aspects of the decision mechanisms of altruistic choice, they can hardly address the question of whether and how different mechanisms are involved in different types of altruistic actions in different contexts (Crockett et al., 2013; Crockett et al., 2008; Gao et al., 2018). To systematically investigate this issue, it would be beneficial to harness the fact that altruistic decisions – like all choices – are guided by processes unfolding at different temporal stages (Seo and Lee, 2012; Shin et al., 2021; Tump et al., 2020). These processes include (1) initial perception of the objective information related to wealth distribution (e.g. payoff numbers) (Nieder, 2016; Pinel et al., 2004), (2) biased representations of the subjectively decision-relevant information attributes, such as attention-guided weighing of self- vs other-payoffs (Chen and Krajbich, 2018; Teoh et al., 2020), (3) integration of all these attributes and subjective preferences into decision values (Collins and Frank, 2018; Harris et al., 2018; Hutcherson et al., 2015), and (4) final decision processes that transform the decision values into motor responses (O’Connell et al., 2012; Polanía et al., 2014). Taking into account this temporal unfolding of the neurocognitive processes further refines the questions about the origins of differences in altruistic behavior: Do altruistic choices involve different sets of computations throughout all the temporally different processing stages (i.e., initial perceptual processing, valuation, final integrative choice mechanisms) in these different contexts and by different individuals (as suggested by Gao et al., 2018; Tricomi et al., 2010)? Or do individuals mainly perceive and attend to the choice-relevant information differently, before passing on this information to valuation and integrative decision mechanisms devoted to all types of altruistic choices (as suggested by Yu et al., 2014)? Answering these questions by means of modelling and neural recording techniques that allow a detailed focus on different temporal stages of altruistic choice processes could help us understand the biological origins of altruism, reveal why people differ strongly in altruistic behavior, and develop more efficient strategies to facilitate altruism. In the current study, we take such an approach. We combined a modified dictator game that independently varies payoffs to a player versus another person, and thereby also the inequality between both players, with electroencephalography (EEG) and sequential sampling modeling (SSM). This allowed us to identify electrophysiological markers of the initial perceptual processing and biased representation of the decision-relevant information (i.e. stimulus-locked event-related potentials [ERPs] related to the payoffs and the inequality context) as well as of the processes integrating this information into a decision variable used to guide choice (i.e. response-locked evidence accumulation [EA] signals; Balsdon et al., 2021; Hutcherson et al., 2015; Krajbich et al., 2015; Nassar et al., 2019). Thus, our approach differs from that of fMRI studies identifying brain areas involved in the valuation of own and others’ payoffs (Fehr and Schmidt, 1999; Morishima et al., 2012; Sáez et al., 2015), since the temporal resolution of fMRI measures makes it difficult to separate response-locked decision-making processes from stimulus-locked perceptual processes and to examine the independent dynamics of these processes during distribution decisions. Our approach is also motivated by studies of nonsocial decisions showing that SSMs may provide a useful framework for investigating the temporal dynamics of the processes that integrate different choice attributes into the decision outcome (Harris et al., 2018; Maier et al., 2020). Many studies have shown that SSMs can identify these processes not just computationally, but also at the neural level, for both the perceptual (Brunton et al., 2013; Kelly and O’Connell, 2013; Ossmy et al., 2013) and value-based decision making (Glaze et al., 2015; Hutcherson et al., 2015; Pisauro et al., 2017; Polanía et al., 2014). The SSM framework provides a formal way to predict the temporal dynamics of processes that integrate evidence for one choice option over another for the temporal period leading up until choice, and to separate these from initial perceptual processes time-locked to stimulus presentation. Neural signals corresponding to these predicted evidence-accumulation signals have been identified with EEG for perceptual decision making across different sensory modalities or stimulus features (Kelly and O’Connell, 2013; O’Connell et al., 2012; Wyart et al., 2012) as well as for value-based decision making (Pisauro et al., 2017; Polanía et al., 2014). These studies have identified evidence accumulation processes either as the model-free build-up rate of the centroparietal positivity (CPP) (Kelly and O’Connell, 2013; Loughnane et al., 2018; Loughnane et al., 2016; O’Connell et al., 2012) or in SSM-prediction-based neural signals measured over parietal and/or frontal regions (Pisauro et al., 2017; Polanía et al., 2014). Both types of neural signals are commonly interpreted as reflecting integration of the choice-relevant evidence to reach a decision, rather than basic motor planning which is usually identified by a fundamentally different neural signal, the contralateral action readiness potential (Kornhuber and Deecke, 2016; Schurger et al., 2021). The cortical origins of these signals may in principle correspond to locations identified by fMRI studies of corresponding SSM-predicted evidence accumulation traces, but note that these studies were not able to study the temporal dynamics of such signals and to unambiguously separate them into stimulus-locked perceptual versus response-locked decision processes (Gluth et al., 2012; Hare et al., 2011; Hutcherson et al., 2015; Rodriguez et al., 2015). Studies using this approach to investigate different types of decisions have identified different cortical areas that implement evidence-accumulation signals in different choice contexts (e.g. parietal regions specifically for perceptual decision making vs. both frontal and parietal regions for value-based decision making Polanía et al., 2014). This shows that different types of decisions may, even if they are reported via the same manual actions, draw on evidence accumulation computations that are instatiated in distinct brain regions. Moreover, altruistic decisions driven by different motives, or made by individuals with different social preferences, have also been found to involve activity in different neural networks (Hein et al., 2016). Therefore, it is necessary to differentiate whether the contextual and individual differences in altruistic decisions reflect recruitment of different brain areas/signals and/or of different computations that are performed within these brain areas. If different final decision mechanisms (i.e. computational and/or neural mechanisms) were to be involved in the two types of altruistic choices, or in different individuals, we should observe response-locked evidence-accumulation signals in different brain areas (e.g. frontal vs. parietal regions), or even different types of computations, in the two types of inequality contexts and/or different individuals. Conversely, if the same final decision mechanism is employed for both types of choice contexts, we should observe similar evidence-accumulation neural signals in similar brain areas, but systematic variations across contexts and/or individuals in those signals (e.g. responses in different brain areas and/or with different temporal characteristics) related to early perceptual/attentional processing of choice-relevant information, such as the available payoff magnitudes (Harris et al., 2018). Here, we apply this approach and use SSMs fitted to individuals’ wealth distribution behaviors to predict the underlying neural evidence accumulation dynamics. We then employ these predicted EA signals in our EEG analyses to examine whether a similar neural choice system accumulates the choice-relevant evidence in both inequality contexts, or whether distinct neural systems implement this decision process for the different contexts. Then, we examine whether the different features of each choice problem that ultimately need to be integrated into the choice-relevant evidence – that is, the specific payoffs available to oneself and the other person – are initially processed in a different manner for different contexts and in different individuals. This allows us to directly approach the question of whether contextual and individual differences in altruism arise from differences in the decision mechanisms that integrate and compare choice-relevant information at the final stage of the choice process, or rather from differences in the initial processing and biased representation of the choice-relevant information that is ultimately integrated into the final decision mechanism. Results We recorded 128-channel EEG data from healthy participants playing a modified Dictator Game (DG). On each trial of this task, participants played as proposers and chose between two possible allocations of monetary tokens between themselves and an unknown partner. We systematically varied the allocation options from trial to trial so that in half of the trials, participants received less than their partners for both choice options (disadvantageous context [DIS]) and in the other half they got more than their partners for both options (advantageous context [ADV]). These two types of trials were randomly intermixed and were only defined by the size of the payoffs presented on the screen. On each trial, we presented the two options sequentially, to allow clear identification of time points at which the information associated with each option was processed (Figure 1A, see Materials and methods for details). This sequential presentation allowed us to establish the inequality context with the presentation of the first option, without having to explicitly instruct participants about thetwo contexts. We then studied individuals’ sensitivity to self-payoff and other-payoff by focusing on how the choice of the second option depended on the change in these variables from the first to the second option. Importantly, as shown in the payoff schedule of all trials (Figure 1—figure supplement 1), we matched self-/other-payoff differences and the resulting absolute levels of inequality across both contexts and also across the second and the first options (Figure 1—figure supplement 1 middle and right panels). This allowed us to compare choices and response times, model-defined neural choice processes time-locked to the response, and neural processing of different stimulus information (self- and other-payoff) between the two contexts. Figure 1 with 2 supplements see all Download asset Open asset Experimental design and behavioral results. We employed a modified dictator game to measure individuals’ wealth distribution behaviors. (A) Example of display in a single trial. In the task, participants played as proposers to allocate a certain amount of monetary tokens between themselves and anonymous partners. At the beginning of each trial, participants were presented with one reference option in blue and were asked to keep their eyes on the central cross for at least 1 s to start the trial, as indicated by the change in font color from blue to green. When the second option was presented, participants had to choose between the two options within 3 s. The selected option was highlighted in blue before the inter-trial interval. Font color assignment to phases (i.e. blue and green to response) was counterbalanced across participants. (B) Payoff information and context affect choice systematically. The generalized linear mixed-effects model shows the effects of multiple predictors on the probability to choose the second option; (C) Payoff information and context affect response times systematically. The linear mixed-effects model shows the effects of multiple predictors on response times (RTs). ΔS, Self-payoff Change; ΔO, Other-payoff Change; CON, Context; C, Constant; •••, p < 0.001; ••, p < 0.01; •, p < 0.05. Error bars indicate 95% confidence interval (CI) of the estimates, N=38. Based on the model fits and their predicted response-locked evidence accumulation EEG traces, we first tested whether similar or different neural processes (i.e. brain regions or physiological markers) underlie the ultimate choice process in the two inequality contexts, in similarity to how this has been studied for other types of decisions (Polanía et al., 2014). Then, we clarified whether neural processing of the stimulus information – which subsequently feeds into the decision processes – differs across contexts and individuals. For this analysis, we examined stimulus-locked event-related potentials (ERPs), in a way that has also been used to differentiate neural processing of decision-relevant features in non-social value-based decision making (e.g. perceptions of health and taste of food items) (Harris et al., 2018). Finally, we explored how individual differences in altruism are related to large-scale information communications between regions associated with these two sets of processes (i.e. response-locked decision processes and stimulus-locked perceptual processes), by examining inter-regional synchronization in the gamma-band frequency (30–90 Hz). This last analysis was motivated by the consideration that evidence accumulation processes need to integrate evidence input from different neural sources (e.g. perceptual processes) (Polanía et al., 2014), and by the proposal that coherent phase-coupling in the gamma band between different groups of neurons may serve as a fundamental process of neural communication for information transmission (Bosman et al., 2014; Fries, 2009; Fries, 2005; Vinck et al., 2013), as already shown for non-social value-based decisions (Polanía et al., 2014; Siegel et al., 2008). Behavior: Altruism depends differentially on self- and other-payoffs across contexts Before performing model-based analyses, we ran model-free linear mixed-effects regressions to establish that the choice-relevant information (i.e. self-payoff, other-payoff, and inequality context [ADV and DIS]) indeed systematically affects individual wealth distribution choices. These analyses confirmed that both self-payoff and other-payoff were important factors underlying individuals’ choices. Specifically, participants chose the second option more often when either they or the receiver profited more from this choice (main effect Self-payoff Change (ΔS): beta = 3.77, 95% CI [3.65–3.89], p < 0.001; main effect Other-payoff Change (ΔO): beta = 0.56, 95% CI [0.51–0.61], p < 0.001, ΔS(ΔO): participants’ own (partners’) payoff change between the second and the first option) (Supplementary file 1, Figure 1B). However, participants were less influenced by changes in their own payoff when they had more money than the other (ADV, interaction Self-payoff Change (ΔS) and Context (CON): beta = –0.35, 95% CI [-0.47 to –0.23], p < 0.001) or when the receiver got lower payoffs from this choice (decreasing other-payoff, interaction Self-payoff Change (ΔS) and Other-payoff Change (ΔO): beta = 0.13, 95% CI [0.01–0.24], p = 0.03). This latter effect was particularly marked when the participants had more money than the receiver (ADV; three-way interaction Self-payoff Change (ΔS), Other-payoff Change (ΔO), and Context (CON): beta = 0.23, 95% CI [0.11–0.35], p < 0.001; Supplementary file 1, Figure 1B). For visualizations of these effects, see Appendix 1 and Figure 1—figure supplement 2A, for confirmation by model-based analyses reported, see Appendix 1. Note that we also constructed simpler models without interaction effects and/or main effects, but model comparison analyses favored the full model (Supplementary file 1). An additional linear mixed-effects regression model suggested that the presentation order (i.e. first or second) of options would not affect individuals’ equal/unequal choices (see Appendix 1 and Supplementary file 2). Self-payoff, other-payoff, and context also jointly affected how quickly participants took their decisions. Choices were faster for larger absolute values of self-payoff change (main effect Self-payoff Change (|ΔS|): beta = –0.09, 95% CI [-0.11 to –0.07], p < 0.001) and other-payoff change (main effect of Other-payoff Change (|ΔO|): beta = –0.03, 95% CI [-0.05 to –0.003], p = 0.03) (Figure 1C). Again, both these effects were different for the two inequality contexts, with response times more strongly affected in the disadvantageous inequality context (interaction between Self-payoff Change (|ΔS|) and Context (CON): beta = 0.04, 95% CI [0.02–0.06], p < 0.001; interaction between Other-payoff Change (|ΔO|) and Context (CON): beta = 0.03, 95% CI [0.01–0.06], p = 0.005; Supplementary file 3, Figure 1C). These effects are consistent with the central assumption of the SSM framework that stronger (weaker) evidence will speed up (slow down) evidence accumulation and resulting choice, thereby already suggesting that an SSM-based decision process may integrate self- and other-payoff to guide individual decisions (For visualizations of these effects, see Figure 1—figure supplement 2B). Model-based EEG reveals similar parietal evidence accumulation across contexts To address the question of whether distribution choices are supported by similar or different neural decision processes across both inequality contexts, we fitted a dynamical sequential sampling model (SSM) to participants’ behavioral data and used it to predict neural evidence accumulation (EA) signals for the two contexts. Our analyses revealed comparable SSM-based EA signals over similar parietal regions for both contexts and no context-specific EA signals that would indicate the use of fundamentally different final choice mechanisms in the different contexts. Specifically, we first fitted the SSM by categorizing trials as ‘equal’ or ‘unequal’ choices, based on whether the participant selected the option with more equal or less equal distribution of monetary tokens between both players. For each trial, the model used the subjective value difference (VD) between the more equal option and the more unequal option (computed using the Charness-Rabin utility model, see Materials and methods) as its input to predict moment-by-moment evidence accumulation signals until the timepoint when the decision was made. For this, we used the Ornstein-Uhlenbeck choice model (OU), which assumes a leaky accumulation-to-bound process (Bogacz et al., 2006): (1) EA(t+1)=EA(t)+λc, s×EA(t)+ κc, s×VDc, s,idt+N(0,σ) (2) VDc, s,i=1-ωc, s×Ec, s,iS+ωc, s×Ec, s,iO- 1-ωc, s×Ic, s,iS+ωc, s×Ic, s,iO with indices c for conditions (c = DIS for disadvantageous inequality context, c = ADV for advantageous inequality context), s for participants (s = 1,..., Nparticipants), and i for trials (i = 1,..., Ntrials). Ec, s,iS (Ic, s,iS) indicates participants’ payoff of the equal (unequal) option in condition c, for participant s and trial i; Ec, s,iO (Ic, s,iO) indicates the partners’ payoff in the equal (unequal) option in condition c, for participant s and trial i. This model allowed us to fit several free parameters that correspond to different aspects of preference and the choice process: the relative decision weight on others ω (altruistic preference), decision threshold α (response caution), starting point β (response bias), and drift rate modulator κ (sensitivity to equality-related information), as well as parameters which are less plausible to be linked to the cognitive or neural mechanisms underlying valuation or decision processes, including leak strength λ and non-decision time τ (see Materials and methods for a detailed model description). By including these parameters, we could examine the effects of context on both basic altruistic preference (i.e. ω) and the final decision process that integrates the subjective values passed on from perception and valuation processes (i.e. α, β, and κ). Although the payoff of each option was fixed for each trial, participants still had to accumulate evidence by calculating and comparing the difference in payoffs between options, so the decision time limit (3 s) may have accelerated the evidence accumulation speed when the decision process approached the limit. The leak strength parameter λ thus captured how the decision process adaptively controls the acceleration or deceleration of evidence accumulation. Please see Materials and methods for the comparison of alternative models and the details of the best-fitting model we used for our analysis. To simulate the evidence accumulation process, we averaged 500 EA traces generated by the participant-specific fitted model for the given context and the payoffs on each trial. Model simulations showed that these traces were good approximations of the EA processes underlying choice, since the fitted model could both capture choices and RTs across the two contexts. For both types of choices (equal/unequal) and contexts (ADV and DIS), the sensitivity/specificity of the data simulated by the model was higher than 83% (Figure 2A left panel) and the balanced accuracy was higher than 89% (Figure 2A right panel, Materials and methods). The model also correctly captured response speed effects, predicting that choices are faster during advantageous inequality overall (RT in ADV: 0.80±0.04 s, DIS: 0.84±0.04 s, ADV vs. DIS: 95% CI [-0.06,–0.02], Cohen’s d = –0.83, t(37) = –5.12, p < 0.001), and faster for equal (unequal) c