Promising behavioral evidence suggests that we may become more prosocial as we age; Reduced reward activity in the brain in response to self-gains & increased reward activity to others' gains may underlie age-related changes in altruism

Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions. Anita Tusche, Lisa M. Bas. WIREs Cognitive Science, August 2 2021. https://doi.org/10.1002/wcs.1571

Abstract: This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decision-making and other-regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism. To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanisms—pertinent to a broad range of value-based decisions—and social emotions and cognitions commonly recruited when our decisions involve other people. Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision-making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research.

6 SYNTHESIS AND FUTURE DIRECTIONS

6.1 Integrating social affect and cognition into neurocomputational models of altruism

Significant strides have been made in research on human altruism. Fast-growing fields like neuroeconomics have pushed applications of a neurocomputational framework to understand social decision-making. Research in social neuroscience has started to unravel the impact of distinct facets of affective and cognitive social processes on prosocial behaviors. Integrating these two research lines provides an exciting path forward. We propose two tangible advancements.

First, a computational framework can help to reduce the ambiguity of concepts studied in social and affective research on altruistic choice. Despite significant progress, conceptual and neural components of social affect and cognition are still underspecified. Social processes relevant to altruism (e.g., empathy) represent complex, multilevel phenomena (e.g., the valence and arousal associated with an affective state). To date, we know little about how these components are encoded in the brain and, more importantly, contribute to decision-making. Mapping parameters of computational models on discrete components of the social process may offer crucial insights (Roberts & Hutcherson, 2019). This mapping can be direct, linking a specific model parameter to a concept, or indirect through a mediating psychological mechanism (Figure 3). This operationalization in a neurocomputational framework enables researchers to test predictions of the models, which in turn can inform theories (for a review on how computational modeling approaches like DDMs enable studies on affect, see Roberts & Hutcherson, 2019). Neurocomputational frameworks of social affect and cognition are still in their infancy. However, recent work in the domain of social learning (Lockwood & Klein-Flügge, 2020; Rosenthal et al., 2019) and strategic decision-making (Hill et al., 2017; Rusch et al., 2020) highlights the potential for neurocomputational approaches to study social processes in altruism.

FIGURE 3

Mapping affective science concepts to estimates of computational models. Reprinted from Roberts and Hutcherson (2019) (Fig. 1), Copyright 2019, with permission from Elsevier

This brings us to our second point. There is a wide agreement that multiple computational processes occur in parallel during altruistic decision-making. Our understanding of where these processes are computed in the brain has advanced significantly over the last decade. For instance, we highlighted several brain regions involved in value computation, cognitive control, and social processes like empathy or mentalizing in altruism (Figure 2). We also reviewed prior evidence on the neural underpinnings of key variables that guide value computations during altruistic choice (e.g., gains for oneself or others). How these components are integrated in the brain to produce coherent behaviors is less established (Suzuki & O'Doherty, 2020). Examining patterns of connectivity between brain areas that encode distinct choice-relevant computations may shed light on this question. Simply put, brain areas involved in altruistic choice do not act in isolation. They are embedded in interconnected networks. There is a trend in neuroimaging research to move away from narrow localization towards analyzing distributed brain networks. Suppose we aim to probe how other-regard is integrated into altruistic decision-making. Researchers can examine connectivity patterns between brain regions that perform other-regarding computations (e.g., TPJ) and those believed to encode the integrated subjective value of available choice options (e.g., VMPFC, Figure 2) (Hare et al., 2010; Park et al., 2017). Several analysis tools exist to examine functional connectivity patterns in the brain (e.g., psycho-physiological interaction analysis [PPI], Friston et al., 1997; dynamical causal modeling [DCM], Friston et al., 2003). Meta-analytic evidence suggests that PPI represents a reliable methodological approach to examine functional integration in the brain (Smith et al., 2016). Likewise, empirical evidence highlights the test–retest reliability of the DCM approach to study connectivity patterns in the brain (Frässle et al., 2015). One significant advantage of DCM is that it allows inferences about the directionality of the connectivity (e.g., from brain area A to area B). Functional and structural properties of neural networks can also be linked to estimates of formal models of social preferences. This approach has been shown to reveal social motives that guide altruistic decisions. For example, in a study that used DCM, functional coupling from the MCC to AI has been linked to empathy-driven altruistic motivations (modified dictator game) (Hein et al., 2016). Positive connectivity from the AI to VS has been linked to prosocial decisions driven by reciprocity motives. Reciprocity in this context refers to the motivation to respond in kind (i.e., the desire or expectation that a generous behavior will be returned). In other words, the results suggest that distinct social motives have different neurophysiological representations in the brain at the level of functional networks (Hein et al., 2016). These results echo our earlier argument: while resulting behaviors (generous choice) look alike, underlying social motives can be revealed through a multi-disciplinary computational framework. More generally, the combination of computational modeling, neuroimaging, and connectivity analysis will likely advance studies on how distinct computations are integrated in the brain to guide behaviors (for a general discussion beyond altruism, see Suzuki & O'Doherty, 2020). This approach may also inform us about how network configurations change due to situational or dispositional differences in empathy and mentalizing in altruism (or other key computational variables).

6.2 Neurocomputational models of altruism across the lifespan

Other-regarding behaviors emerge during infancy (Dunfield et al., 2011), and lifespan changes in childhood and adolescence have inspired a good deal of research (for an overview, see Eisenberg et al., 2007). Only recently, the field has started to examine age-related changes in altruism in late adulthood. Understanding other-regard in the elderly is essential for one apparent reason: global populations continue to grow older. By 2050, one in six people may be aged 65 or older (Kamiya et al., 2020). Consequently, changes in social preferences in late adulthood have significant social and economic consequences. Promising behavioral evidence suggests that we may become more prosocial as we age (for a recent overview, see Mayr & Freund, 2020, but see Bailey et al., 2020; Rieger & Mata, 2015; Wiepking & James, 2013). This effect holds when researchers control for differences in wealth across age groups (Kettner & Waichman, 2016). For example, charitable giving and volunteering increase across adulthood up to 70 years (Freund & Blanchard-Fields, 2014). While intriguing, these findings do not tell us why and how other-regard changes across the adult lifespan. We argue that an interdisciplinary, computational framework is uniquely suited to provide answers to these questions.

Preliminary research on altruism in the elderly draws on various measures like donations (Bekkers & Wiepking, 2011), surveys (Bekkers, 2010), and economic games (e.g., dictator game) (Engel, 2011; Kettner & Waichman, 2016; Matsumoto et al., 2016; Rosi et al., 2019) (for a review of age-related changes in economic games, see Lim & Yu, 2015). However, studies combining the perspectives and analysis tools from neuroscience, psychology, and behavioral economics are still rare. To illustrate the potential of a multi-level approach, we turn to the example of a recent study that bridged this gap. The results suggest that reduced reward activity in the brain in response to self-gains and increased reward activity to others' gains may underlie age-related changes in altruism (Hubbard et al., 2016). In other words, neural evidence suggests that the elderly may genuinely care more about others' well-being. We propose that incorporating formal models can provide even more insight into other-regard. For instance, formal models could quantify age-related changes in contributions of gains for oneself and others and link these estimates of model parameters to the brain's functional and structural properties. Model-based approaches also allow researchers to delineate the role of distinct social motives (e.g., maximizing others' gain vs. fairness). A recent behavioral study combined data from an economic game and computational modeling to examine age-related differences in other-regarding motives (Cho et al., 2020). The study used formal models (Dufwenberg & Kirchsteiger, 2004; Fehr & Schmidt, 1999) to delineate how young and older adults take intention- and outcome-based fairness into consideration during social decision-making. The parameter estimates of formal models suggest that older adults focus more on fair outcomes to guide their decisions and less on other's intentions. These findings explain why observable behaviors change as we grow older. Specifically, the results illuminate age-related changes in the relative importance of choice features and motives. In sum, we propose that an interdisciplinary, neurocomputational framework can advance our understanding of age-related changes in altruism.

Social neuroscience offers another window into lifespan changes of altruism and why the elderly may genuinely care more about others' welfare. Popular accounts suggest that the motivation to make strong emotional connections with others increases in older people (socioemotional selectivity theory; Carstensen et al., 2003). Consequently, researchers have examined emotional processes relevant to altruism throughout adulthood. This includes the emotional consequences of helping others (Bjälkebring et al., 2016) and emotional precursors of social decisions like empathy. Older individuals report greater empathy and empathic concern for others than their middle-aged and young counterparts (Sun et al., 2018; Sze et al., 2012), which partly accounts for age-related increases in prosocial behavior (Sze et al., 2012) (for a nuanced review on age-related changes in facets of empathy and mentalizing, see Beadle & De la Vega, 2019). These findings fit into a growing body of evidence that distinct facets of social cognition age differently. Empathy seems to be intact in old age, and empathic concern for others' well-being is even elevated (Reiter et al., 2017; Wieck & Kunzmann, 2015). Other components such as mentalizing or meta-cognition decline in late adulthood (Reiter et al., 2017; for evidence on age-differences when inferring others' intentions, see Reiter et al., 2021). Neuroimaging evidence on the aging brain provides insights into the neurobiological underpinning of these differential trajectories of social processes in late adulthood and decision-making (for reviews, see Beadle & De la Vega, 2019; Lighthall, 2020). Research on this topic is still in its infancy. Preliminary evidence suggests that core brain areas involved in affective processing seem to maintain their structural integrity during healthy aging (Mather, 2012). In light of this evidence, it would seem plausible that older adults rely more heavily on affective processes to guide altruistic decisions. Consistent with this notion, empathy-inducing messages increased altruism in a dictator game in the elderly more than in younger adults (Beadle et al., 2015). In sum, neuroimaging studies, together with formal models of altruism, are uniquely suited to elucidate the origins of process-specific inputs into social decisions in the elderly.