A Neurocomputational Model of Altruistic Choice and Its Implications.
Level 4 - case-series / case-control
Cross-sectional laboratory behavioral and fMRI study with computational modeling (graded by design analogy).
PubMed 26182424 · doi:10.1016/j.neuron.2015.06.031
What was done
Participants performed a task making choices between real monetary prizes for themselves versus another person while behavioral data and functional magnetic resonance imaging (fMRI) were collected. The authors tested a multi-attribute drift-diffusion model in which choices emerge from accumulating a relative value signal that linearly weights payoffs for self and other, evaluating neural signals in the ventral striatum, temporoparietal junction (TPJ), and ventromedial prefrontal cortex (vmPFC).
What was found
The model accounted for choice distributions, reaction times, and neural responses in the ventral striatum, TPJ, and vmPFC. It explained differences in response speed between generous and selfish choices and the increased recruitment of TPJ and vmPFC during generous choices without assuming dual-process competition or intrinsic reward value for generosity. It also predicted that some generous decisions reflect decision mistakes when self-payoff is weighted higher than others'. The abstract reports no numerical statistics, sample sizes, or effect sizes.
Why it matters
The study shows that altruistic decisions and apparent generosity errors can be explained by a unified value-accumulation model rather than competing automatic and deliberative neural processes.
Limits
The abstract does not disclose the sample size, participant characteristics, or quantitative goodness-of-fit metrics. The results derive from an artificial monetary decision task, which may not capture real-world social altruism.
Cited by
- context Generosity toward others activates brain regions associated with positive emotion more strongly than performing self-interested actions.