From faces to prosocial behavior: cues, tools, and mechanisms

Abstract

In this review we ask how looking at people's faces can influence prosocial behaviors towards them. Components of this process have often been studied by disparate literatures: one focused on perception and judgment of faces, using both psychological and neuroscience approaches; and a second focused on actual social behaviors, as studied in behavioral economics and decision science. Bridging these disciplines requires a more mechanistic account of how processing of particular face attributes or features influences social judgments and behaviors. Here we review these two lines of research, and suggest that combining some of their methodological tools can provide the bridging mechanistic explanations.

Keywords: altruism; drift-diffusion model; eyetracking; face perception; social judgments.

Figure 1

Brain regions. The left panel of the figure shows schematically some of the brain regions mentioned in the text. Note that several of these (e.g. amygdala, insula) are in fact interior to the brain so that their location here represents where they are if projected onto the lateral surface. The right panel illustrates the technical equipment in functional magnetic resonance imaging (fMRI) studies that measure and map brain activity. This technique is noninvasive and safe.

Figure 2

Tools. The upper panel illustrates model-based analysis of eyetracking data that yield saliency weights onto specific features of visual stimuli. In the current example, these features were defined for complex, real-world visual scenes and their weights were computed using advanced machine learning algorithms. The high weight for faces reflects the fact that, when looking at a scene, viewers tend to fixate faces most frequently (modified from Wang et al., 2015). The bottom panel illustrates a driftdiffusion model as applied to altruistic choices. The curves (blue) plot the relative decision value in favor of one or the other behavioral option (e.g. act prosocially or selfishly), as a function of time during the decision process. This accumulation of evidence over time is stochastic and noisy, as reflected in the moment-by-moment fluctuations of the plots. A decision is made once we have enough evidence accumulated and one of the thresholds is reached (upper and lower black lines). Critically, how much attention is paid to choice-options or relevant features can bias the evolution of the curve. In this framework, attention (e.g. as measured in gaze behavior) can bias the decision in favor of a generous decision, as illustrated by the light blue line that reaches the upper decision barrier earlier than in case of the dark blue line. Modified from (Hutcherson, Bushong, & Rangel, 2015).