A neurocomputational model of altruistic choice and its
implications
Cendri A. Hutcherson
1,4
,
Benjamin Bushong
1,3
, and
Antonio Rangel
1,2
1
Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA
91125, USA
2
Computational and Neural Systems, California Institute of Technology, Pasadena, CA 91125,
USA
3
Department of Economics, Harvard University, Cambridge, MA 02138, USA
4
Department of Psychology, University of Toronto, Toronto, ON M1C 1A4, Canada
Summary
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.
Altruism involves helping others at a cost to the self, not only when such behavior is
supported by strategic considerations like reciprocity or cooperation (
Dufwenberg and
Kirchsteiger, 2004
;
Falk and Fischbacher, 2006
;
Nowak and Sigmund, 1998
), but even in the
absence of expectation for future benefit (e.g. fully anonymous, one-time generosity:
Batson,
2011
;
Fehr and Fischbacher, 2003
). A major goal of neuroeconomics is to develop
neurocomputational models of altruistic choice, specifying which variables are computed,
how they interact to make a decision, and how are they implemented by different brain
circuits. Such models have proven useful in domains such as perceptual decision-making
(
Gold and Shadlen, 2007
;
Heekeren et al., 2008
), simple economic choice (
Basten et al.,
2010
;
Hunt et al., 2012
;
Rangel and Clithero, 2013
), self-control (
Hare et al., 2009
;
Kable
and Glimcher, 2007
;
Peters and Büchel, 2011
;
van den Bos and McClure, 2013
), and social
learning (
Behrens et al., 2008
;
Boorman et al., 2013
). We propose a neurocomputational
Correspondence: chutcher@hss.caltech.edu.
Author Contributions:
C.A.H., B.B., and A.R. designed the experiment. C.A.H. and B.B. collected the data. C.A.H. developed the
model and its predictions. C.A.H. analyzed the data. C.A.H., B.B., and A.R. wrote the paper.
HHS Public Access
Author manuscript
Neuron
. Author manuscript; available in PMC 2016 July 16.
Published in final edited form as:
Neuron
. 2015 July 15; 87(2): 451–462. doi:10.1016/j.neuron.2015.06.031.
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model of simple altruistic choice and test it using behavioral and fMRI data from a modified
Dictator Game in which subjects make choices between pairs of real monetary prizes for
themselves ($
Self
) and another ($
Other
). These choices involve a trade-off between what is
best for the self and what is best for the other, and thus require people to choose to act
selfishly or generously.
Our model assumes that choices are made by assigning an overall value to each option,
computed as the weighted linear sum of two specific attributes: monetary prizes for self and
other. This type of simple value calculation captures a wide range of behavioral patterns in
altruistic choice (
Charness and Rabin, 2002
;
Eckel and Grossman, 1996
;
Engel, 2011
;
Fehr
and Fischbacher, 2002
;
Fehr and Fischbacher, 2003
). Our model also assumes that the
overall value signal is computed with noise and that choices are made using a multi-attribute
version of the Drift-Diffusion Model (DDM:
Ratcliff and McKoon, 2008
;
Smith and
Ratcliff, 2004
). In this algorithm, a noisy relative value signal is integrated at each moment
in time and a choice is made when sufficient evidence has accumulated in favor of one of the
options. This type of algorithm has been shown to provide accurate descriptions of both
choice and reaction time (RT) data (
Busemeyer and Townsend, 1993
;
Hunt et al., 2012
;
Krajbich et al., 2010
;
Milosavljevic et al., 2010
;
Rodriguez et al., 2014
;
Smith and Ratcliff,
2004
), as well as neural response patterns associated with computing and comparing values
(
Basten et al., 2010
;
Hare et al., 2011
;
Hunt et al., 2012
) in many non-social domains.
The model suggests neural implementation of two specific quantities. First, values for the
attributes $
Self
and $
Other
must be computed independently. Second, an overall value signal
must be constructed from the independent attributes. We hypothesized that areas like the
temporoparietal junction, precuneus, or medial prefrontal cortex may compute quantities
related to the value of these attributes. Prior research strongly implicates these regions in
social behavior (
Bruneau et al., 2012
;
Carter and Huettel, 2013
;
De Vignemont and Singer,
2006
;
Decety and Jackson, 2006
;
Hare et al., 2010
;
Jackson et al., 2005
;
Moll et al., 2006
;
Saxe and Powell, 2006
;
Singer, 2006
;
Waytz et al., 2012
;
Zaki and Mitchell, 2011
), although
their precise computational roles remain poorly understood. Inspired by a large body of
work on the neuroeconomics of non-social choice (
Basten et al., 2010
;
Hare et al., 2009
;
Kable and Glimcher, 2007
;
Lim et al., 2013
;
McClure et al., 2004
;
Tom et al., 2007
), we
additionally hypothesized that the integration of specific attribute signals would occur in
ventromedial prefrontal cortex (vmPFC). We explore these hypotheses with our fMRI
dataset.
We also highlight three ways in which the development of a computational model of
altruistic choice can be used to generate novel insights into the nature of altruistic choice.
First, we compare the model’s predictions about RT and neural response for generous versus
selfish choices. We find that, for the best-fitting parameters, the model predicts longer RT
and higher BOLD response in decision-related regions for generous choices, and that the
predicted effect sizes match the observed data. Second, we use simulations to identify how
model parameters influence altruistic behavior, and find that several of these variables
(including the relative importance of benefits to self and other and the decision boundaries
of the DDM) predict observed individual differences in generosity. Third, we show that the
model predicts that generous decisions are sometimes unintended mistakes resulting from
Hutcherson et al.
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