A Neurocomputational Model of Altruistic Choice and Its Implications

Abstract

We propose a neurocomputational model of altruistic choice and test it using behavioral and fMRI data from a task in which subjects make choices between real monetary prizes for themselves and another. We show that a multi-attribute drift-diffusion model, in which choice results from accumulation of a relative value signal that linearly weights payoffs for self and other, captures key patterns of choice, reaction time, and neural response in ventral striatum, temporoparietal junction, and ventromedial prefrontal cortex. The model generates several novel insights into the nature of altruism. It explains when and why generous choices are slower or faster than selfish choices, and why they produce greater response in TPJ and vmPFC, without invoking competition between automatic and deliberative processes or reward value for generosity. It also predicts that when one's own payoffs are valued more than others', some generous acts may reflect mistakes rather than genuinely pro-social preferences.

Figure 1

Task design and model. A) The task consisted of a decision phase, in which subjects chose whether to accept the proposed payment-pair or a default of $50 to both individuals, and a subsequent outcome phase, in which subjects discovered if their choices had been implemented (60% of trials), or reversed, resulting in the non-chosen option (40% of trials). B) Proposed transfers used in the experiment describing $Self and $Other. The alternative was always a transfer of $50 to both subjects. X- and Y-axes represent distance from default offer. The filled area in each transfer is proportional to the percentage of pro-social choices across all subjects. C) In the DDM model, choices are made through the noisy accumulation of a relative value signal (RDV), based on a weighted sum of the amounts $Self and $Other available on each trial. A response occurs when this accumulated value signal crosses a threshold, with an RT equal to the total accumulated time + a non-decision time (NDT) to account for sensory and motor-related processes unrelated to the comparison process itself.

Figure 2

Model fits to behavior. A) Model-predicted vs. observed average generosity across subjects. Dashed 45 degree line represents a perfect match. B) Model-predicted vs. observed overall response time (RT). C) Within-subject acceptance likelihood (mean ± SEM) and D) RT (mean ± SEM) for each of the 9 proposal-types. Observed behavior: grey bars. Predict behavior: blue circles.

Figure 3

Neural responses vary parametrically with A) behavioral preference at the time of choice; B) $Self on each trial; and C) $Other on each trial. D) Conjunction of $Self and $Other in vmPFC. Bar plot (mean ± SEM) shown for illustrative purposes only. Activations displayed at P < .001, uncorrected.

Figure 4

Model implications for RT differences. A) At the fitted parameters, the model predicts generous (G) choices should take longer than selfish (S) choices. B) The model predicts that overall generosity correlates negatively with RT differences on G vs. S choices. C) Observed RTs for G and S choices. D) Observed relationship between average generosity and G vs. S choice RT differences. Bars show mean RT ± SEM. ** P < .001

Figure 5

Model implications for BOLD response (mean ± SEM) during generous (G) vs. selfish (S) choices. Independently defined regions in value-related vmPFC (A) and generosity-related TPJ (B) both show higher response on G choice trials. B) Individual variation in G vs. S choice BOLD response is accounted for by model-predicted comparator differences in both regions. *P=.02; **P=.008.

Figure 6

Model implications for relation between different parameters of the model and behavior. A) Variation in generosity as weights for self and other vary, and B) as threshold starting height and collapse rate vary. C) Variation in likelihood that a generous choice is a mistake as a function of weights for self and other and D) threshold parameters. Dots represent the estimated parameter values for the 51 subjects who completed the fMRI study, jittered randomly by a small amount to allow visualization of subjects with overlapping values.

Figure 7

Model implications for the likelihood that selfish (S) or generous (G) choices are errors. A) A vmPFC region implicated in coding outcome value responded more positively to reversal vs. receipt of G choices compared to S choices (P <. 05, SVC). Differential BOLD response in this region (mean ± SEM) is shown for illustrative purposes only. B) vmPFC response to reversal vs. receipt of G vs. S choices correlated with the DDM-predicted likelihood that a subject’s G choices were more likely to be errors than S choices (i.e. indexing the relief they should feel if those choices are overturned).