Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions

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. This article is categorized under: Economics > Interactive Decision-Making Psychology > Emotion and Motivation Neuroscience > Cognition Economics > Individual Decision-Making.

Keywords: decision neuroscience; drift diffusion models; prosociality; social affect and cognition (theory of mind); social choice tasks.

FIGURE 1

Game‐theoretical paradigm and computational model of altruistic choice. (a) Altruistic choice in a modified dictator game (“choose the option that you prefer”). (b) Computational model of altruism (multi‐attribute extension of a drift diffusion model). The model characterizes the decision process as the accumulation of a noisy value signal that evolves dynamically over time (t). The value signal represents the relative desirability of available choice options (relative decision value, RDV). Blue and red lines represent trajectories of the value signal in favor of Option A or B, respectively. The value signal results from a linear combination of weighted features of the decision problem (e.g., gains for self, others, and fairness; represented as numerical values in each trial: $Self, $Other, |$Self‐$Other|). A decision is reached when the value signal crosses a critical threshold (upper or lower barrier, b or b′). Fitting the model to the observed behavior (choices and reaction times) yields parameters that characterize the decision process. Potential parameters include the weights that individuals place on each choice‐relevant feature (w_Self, w_Other, W_Fair); non‐decision time (NDT; accounts for sensory and motor‐related processes unrelated to the value comparison process itself); the decision threshold; or the start point of the value signal (accounts for initial biases in favor of one available alternative). Drift diffusion models of altruism have been shown to capture generous choices, reaction times (Krajbich et al., 2015), and even neural responses in altruism tasks (Hutcherson et al., ; Tusche & Hutcherson, 2018)

FIGURE 2

Brain regions involved in (pro)social decision‐making. Process‐specific networks are color‐coded for illustrative purposes. The schematic assignment of brain areas to mental functions represents a simplified account of popular assumptions about their functional role in (prosocial) decision‐making. We acknowledge that these brain areas have also been linked to other mental processes. AI, anterior insula; DLPFC, dorsolateral prefrontal cortex; MCC, mid cingulate cortex; MPFC, medial prefrontal cortex; STS, superior temporal sulcus; TP, temporal pole; TPJ, temporoparietal junction; VS, ventral striatum; VMPFC, ventromedial prefrontal cortex

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