ArticlePDF Available
PSYCHOLOGICAL
INQUIRY
An International Journal for the
Advancement of Psychological Theory
Volume 22, Number 3 2011
TARGET ARTICLE
Jamil Zaki
Kevin Ochsner
Reintegrating the Study of Accuracy Into Social Cognition Research 159
COMMENTARIES
Bahador Bahrami
Chris D. Frith
Interacting Minds: A Framework for Combining Process- and
Accuracy-Oriented Social Cognitive Research
183
Nicholas Epley
Tal Ey a l
Integrations Need Both Breadth and Depth: Commentary on Zaki and Ochsner 187
Judith A. Hall
C. Randall Colvin
On Making a Thriving Field Even Better: Acknowledging the Past and
Looking to the Future
193
William Ickes Everyday Mind Reading Is Driven by Motives and Goals 200
David A. Kenny History, Dialectics, and Dynamics 207
Christian Keysers
Lawrie S. McKay
How to Make Social Neuroscience Social 210
Marco Iacoboni Social Cognition, Accuracy, and Physiology 217
Elysia R. Todd
David C. Funder
Organizing Interactions in the Study of Judgmental Accuracy 219
REPLY
Jamil Zaki
Kerin Ochsner
On the Reintegration of Accuracy in Social Cognition Research: Let Us
Count the Ways
221
Psychological Inquiry, 22: 159–182, 2011
Copyright C
Taylor & Francis Group, LLC
ISSN: 1047-840X print / 1532-7965 online
DOI: 10.1080/1047840X.2011.551743
TARGET ARTICLE
Reintegrating the Study of Accuracy Into Social Cognition Research
Jamil Zaki
Department of Psychology, Harvard University, Cambridge, Massachusetts
Kevin Ochsner
Department of Psychology, Columbia University, New York, New York
Understanding the contents of other minds is a vital and ubiquitous task that hu-
mans perform with impressive skill. As such, it is surprising that the majority of
social cognition research—whether behavioral or neuroscientific—focuses on the
processes people use when attempting to understand each other while ignoring how
well those attempts fare. Here we review historical reasons for the contemporary
dominance of process-oriented research as well as the resurgence in the last decades
of new approaches to studying interpersonal accuracy. Although in principle both
the accuracy-oriented and process-oriented approaches study related aspects of the
same phenomena, in practice they have made strikingly little contact with each other.
We argue that integrating these approaches could expand our understanding of social
cognition, both by suggesting new ways to synthesize extant data and generate novel
predictions and lines of research, and by providing a framework for accomplishing
such an integration. This integration can be especially useful in highlighting the
deeply contextualized nature of the relationships between social cognitive processes,
accuracy, and adaptive social behavior.
Introduction
One of human beings’ most impressive accomplish-
ments is our ability to understand what other peo-
ple are intending, thinking, and feeling. This requires
perceivers (individuals focusing on someone else) to
translate the observable behaviors of social targets (in-
dividuals who are the focus of perceivers’ attention)
into inferences about those targets’ physically invisi-
ble but psychologically real, internal states. Whether
we’re sniffing out the intentions of a used car sales-
man or figuring out the right thing to say to an upset
friend, such inferences are critical to acting adaptively
in social situations. Luckily, we are consummate ex-
perts at this task, accurately reading the internal mental
states that guide other’s behavior with an ease and skill
that would be shocking if it wasn’t so universal (Fiske,
1992; Swann, 1984).
For simplicity, we refer to this ability as mind per-
ception, following Epley and Waytz (2009). Unlike
global terms like “social cognition” or “person percep-
tion,” which can refer to the whole host of faculties we
bring to bear in understanding all manner of transient
and enduring characteristics of other people, mind per-
ception zeroes in on the specific task and accomplish-
ment of understanding others’ internal mental states.
As such, for present purposes, it provides a convenient
shorthand for referring specifically to this ability.
So how do perceivers draw inferences about tar-
gets’ minds, and why are we so adept at it? Given the
importance of these two questions, it is unsurprising
that they have been a central focus of psychological
research for the greater part of a century, and more
recently have gained a great deal of attention in neu-
roscience. What is surprising, however, is the lopsided
way this attention has been distributed, focusing almost
entirely on answering the first—but not the second—
of these questions. The lion’s share of contemporary
research focuses on characterizing the cognitive and
neural processes perceivers engage when encounter-
ing other minds, while typically ignoring whether
the engagement of these processes leads to accurate
159
ZAKI AND OCHSNER
inferences about those minds. In other words, the ma-
jority of relevant research has focused on the question
of how perceivers respond to other minds, but not how
well they understand those minds. Although relatively
neglected, this second question is of clear importance,
as the goal of everyday social cognition is not to sim-
ply draw any type of inference about targets but to use
accurate inferences to guide social behavior.
How did this state of affairs come to pass, what
are its implications for the field, and should we do
anything about it? To address these questions, the re-
mainder of this article is divided into four main parts.
In the first, we review the central role accuracy once
played in social psychological research and the his-
torical trends responsible for its abandonment in fa-
vor of a near monopoly of process-oriented research.
Here we also briefly review the current understand-
ing of mind perception processes gleaned from behav-
ioral and neuroscientific research (for more compre-
hensive reviews, see Decety & Jackson, 2004; Fiske
& Taylor, 2007; Gilbert, 1998; Keysers & Gazzola,
2007; Macrae & Bodenhausen, 2000; Mitchell, 2009a;
Saxe, Carey, & Kanwisher, 2004) as well as the com-
paratively smaller number of research programs that
have developed novel approaches to measuring inter-
personal accuracy (again, for more comprehensive re-
views, see Funder, 1995; Ickes, 1997; Jussim, 2005;
Kenny & Albright, 1987; Kruglanski, 1989; Swann,
1984).
The second section builds on this historical foun-
dation by arguing that a critical missing element in
mind perception research is the integration of process-
oriented and accuracy-oriented approaches within
studies and research programs. The essential argument
is that social cognition should reclaim its past tradi-
tion of thinking about accuracy and combine it with
the current focus on processes. Here we motivate and
describe a framework for integrating these usually in-
dependent approaches. This framework draws equally
from neuroscientific and behavioral research to explain
social cognitive phenomena at multiple levels of analy-
sis, including neural systems, psychological processes,
behavioral accuracy, and other outcomes such as so-
cial well-being and the social deficits that characterize
many psychiatric disorders.
The third section highlights five kinds of novel
insights and predictions that can emerge from focusing
on the relationships between mind perception pro-
cesses and accuracy—as opposed to either in isolation.
First, an integrated view can help to dispel some
incorrect, but pervasive, assumptions about mind per-
ceivers’ abilities to be accurate, or the sources thereof.
Second, integrating processes and outcomes allows
for an interactionist approach to understanding when a
given social cognitive process contributes to accuracy
about others. Third, an integrated approach enables
researchers to connect social cognitive processes with
their ostensive goals: Skillfully navigating the social
world and maintaining positive interpersonal rela-
tionships. Fourth, this multilevel approach can offer
new insights about parallels between the mechanisms
underlying mind perception and other, seemingly
disparate cognitive domains. Fifth, this approach
offers novel ways to study the social cognitive deficits
that characterize many psychiatric illnesses.
In the fourth and last section, we conclude by
summarizing the central arguments of the article and
touching on the ways an integrative approach can
move beyond the specific examples of mind perception
considered here and be applied to the study of other
domains of social cognition and person perception,
including prospection and dispositional inference.
Where Are We and How Did We Get Here?
The Rise and Fall of Accuracy Research
In important ways, the dominance of process-
oriented approaches to mind perception research stems
from a revolution older (and slightly less hostile) than
Cuba’s. In the first half of the 20th century, a cen-
tral project of social psychology was determining the
sources of accurate interpersonal inferences. Scores
of studies were run with the goal of characterizing so-
called good judges—that is, individuals naturally adept
at understanding other minds—who could be tapped
for jobs requiring interpersonal understanding, such as
being judges or therapists. This work drew on a large,
sometimes poorly organized, slew of criteria, both for
defining and predicting accuracy. Depending on the
study, accuracy was defined as a perceiver’s ability to
recognize emotional facial expressions in photographs,
provide personality ratings of targets that agreed with
expert opinions, group consensus, or targets’ self rat-
ings, or to predict the behavior of target individuals or
groups. Predictors of accuracy varied just as broadly
and included gender, socioeconomic status, training in
psychology, number of siblings, general intelligence,
and aesthetic sensibility. Perhaps unsurprisingly, this
proliferation of independent and dependent variables
led to an explosion of relationships being tested, and
“good judges” often eluded capture behind a tangle
of correlations and effect sizes. Nevertheless, accu-
racy continued to enjoy a privileged status among
research topics; in summarizing more than 50 stud-
ies of “good judges” conducted by the mid-20th cen-
tury, Taft (1955) remained confident that “the practical
importance of [accuracy research] in psychology is
obvious.
Writing a quarter century later, Schneider, Hastorf,
and Ellsworth (1979) likely would have surprised Taft
with their conclusion that, amidst the zeitgeist of so-
cial cognition research, “the accuracy issue [had] all but
160
THE REINTEGRATION OF ACCURACY
faded from view” (p. 222). What happened in the inter-
vening years? Although there are many explanations,
among the most salient are the arguments presented
by Lee Cronbach and his colleagues (Cronbach, 1955;
Gage & Cronbach, 1955) that were published only a
few months after Taft’s review. Cronbach presented a
simple methodological criticism of the search for good
judges: Quantifying accuracy as a subtraction between
a perceiver’s judgment and those of a target or group
ignored a host of factors that could affect perceivers’
apparent accuracy. For example, a perceiver with an
accurate base rate for extraversion in the population
could blindly apply that base rate to judge individuals
she had never met and still appear to be a relatively
“good judge.” In other words, Cronbach asserted that
accuracy researchers were not measuring what they
thought they were measuring—accuracy—but instead
were measuring the influence of other, potentially less
interesting phenomena.
A close read of the publications by Cronbach,
Nathaniel Gage, and others suggests that they did not
intend for their criticism to upend accuracy research
altogether. Instead, they hoped that more sophisticated
methods could help clarify the sources of accuracy by
decomposing them into a number of constituent parts.
In fact, many of the factors they described as relevant to
accuracy (e.g., the use of stereotypes, assumed similar-
ity between perceivers and targets) are quite similar to
mind perception processes studied now (see the Expe-
rience Sharing and Mental State Attribution sections).
Gage and Cronbach presciently suggested that person
perception research would be served best by model-
ing the relationship between these processes (which
they referred to as “intermediary keys”) and the ac-
curacy or inaccuracy of resulting perceptions (Gage,
Leavitt, & Cronbach, 1956). However, in lieu of tak-
ing up this challenge, accuracy researchers reacted by
“crowding the exits” (Gilbert, 1998, p. 91), abandon-
ing their endeavors almost completely for more than 25
years (Funder, 1987, 1995; Gilbert, 1998; Ickes, 1997;
Ickes et al., 2000; Kenny & Albright, 1987).
Why did accuracy researchers so eagerly jump ship?
Retellings of this historical trend converge on two
points. First, the search for good judges suffered from
a dearth of organizing principles and, as such, pro-
duced a jumble of loosely related findings, in which
some factors predicted some types of accuracy some
of the time (Dymond, 1949; Taft, 1955). Findings of
any one study in this field were often idiosyncratic and
irreproducible, and did not sum into programmatic,
generative theoretical models. Second, no matter how
organized accuracy researchers might have made their
search for good judges, those judges—at least in the
way researchers conceived of them—may have been
more myth than reality. Early accuracy research staked
much of its identity on isolating personality traits that
would predict a perceiver’s accurate inferences about
targets’ personality traits. In both cases, traits were
defined as monolithic, stable qualities that should pre-
dict behavior across contexts. A good judge was con-
sidered to be someone who would accurately assess
all social targets in all situations, and targets’ self-
reported personality traits were considered to be sim-
ilarly constant. In the last 40 years, however, the idea
of traits as invariant predictors of behavior has given
way to an interactionist perspective, which describes
behavior as fundamentally dependent on both individ-
uals and the situations they encounter (Bandura, 1978;
Mischel, 1968, 1973; Mischel & Shoda, 1995). This
reconceptualization—and the fact that the majority of
accuracy research preceded it—makes it unsurprising
that the search for good judges was plagued by the
low correlations and inconsistent relationships that also
characterized many trait-based predictions of behavior.
The general challenge to personality research issued
by Mischel and others (Mischel, 1968, 1973; Mischel
& Shoda, 1995) signaled a growing shift in attention to-
ward the processes (or cognitive and affective “units”)
mediating the relationships between individuals and
situations as input and behavior as output. A similar
shift took place in psychology more broadly (Neisser,
1967) and in due time took over the troubled field of
interpersonal perception research.
The Reign of Process
Focusing on social cognitive processes, as opposed
to accuracy, has proven extremely generative, as it al-
lowed researchers to “zoom in” on more tractable ele-
ments of mind perception, produced replicable findings
and generated relatively simple and falsifiable theoret-
ical claims (for fantastic reviews of the process ap-
proach and its major findings, see Chaiken & Trope,
1999; Gilbert, 1998). At its root, process research was
strongly influenced by Heider (1958), who suggested
parallels between mind perception and object percep-
tion. Gage and Cronbach alluded to these parallels as
well, arguing that mind perception and visual percep-
tion both are “dominated far more by what the Judge
brings to it than what he takes in during it” (Gage &
Cronbach, 1955, p. 420).
Following the leads of Heider, Gage, and Cronbach,
process-oriented researchers set their sights inside per-
ceivers’ heads. Instead of concerning themselves with
the accuracy of perceivers’ inferences about targets’
reported states or observable behaviors—or even with
actual social targets in any way—process models fo-
cused on a set of cognitive “tools” perceivers bring
to bear when drawing inferences about targets in gen-
eral. Although many such tools have been described
(Ames, 2004; Chaiken & Trope, 1999; E. Smith & De-
Coster, 2000), here we focus on two that have gained
a great deal of attention in both psychological and
161
ZAKI AND OCHSNER
neuroscience research: experience sharing and mental
state attribution.
Experience Sharing
President Bill Clinton famously claimed to “feel the
pain” of Americans suffering during tough economic
times. Perceivers of all stripes share the intuition that
they vicariously experience the internal states of oth-
ers. This idea also is found in 18th-century moral phi-
losophy (Smith, 1790/2002), aesthetic theory (Lipps,
1903), and contemporary models of motor cognition
(Dijksterhuis & Bargh, 2001; Prinz, 1997). The com-
mon thread uniting these theories is that when observ-
ing targets experiencing an internal state, perceivers en-
gage many of the cognitive and somatic processes they
would engage while experiencing those states them-
selves (Preston & de Waal, 2002).1A link between the
perception of others’ states and the evocation of simi-
lar states in one’s self is supported by various data, in-
cluding demonstrations that perceivers often adopt the
bodily postures (Chartrand & Bargh, 1999), facial ex-
pressions (Dimberg, Thunberg, & Elmehed, 2000), au-
tonomic arousal (Vaughan & Lanzetta, 1980), and self-
reported emotional states (Neumann & Strack, 2000)
of targets.
In the last 15 years, neuroscience research has
famously supported the idea of perception-action
matching by identifying brain regions that demon-
strate properties consistent with experience sharing.
The common feature of these regions is that they
become engaged both when perceivers experience
an internal state themselves and when they observe
targets experiencing those states. The specific regions
demonstrating such shared activity for both self and
other experiences depend on the type of internal
state being shared (Decety & Jackson, 2004; Zaki &
Ochsner, 2011b). For example, when both executing
and observing motor acts, perceivers engage the so-
called mirror neuron system, encompassing premotor,
inferior frontal, and inferior parietal cortex (Iacoboni,
2009; Rizzolatti & Craighero, 2004; Rizzolatti &
Sinigaglia, 2010). When experiencing and observing
nonpainful touch, perceivers engage somatosensory
cortex (Keysers, Kaas, & Gazzola, 2010; Keysers
et al., 2004). When experiencing pain and observing
(or knowing that) targets (are) in pain, perceivers also
engage somatosensory cortex (Avenanti, Bueti, Galati,
& Aglioti, 2005) and additionally recruit activity in
regions related to the interoceptive and affective com-
ponents of pain, including anterior insula and anterior
1In many ways, models of perception-action matching borrow
from the more general idea of “embodied cognition,” which posits
that concepts related to physical states (including, presumably, those
of other people) are processed through sensory and motor repre-
sentations (Barsalou, 2008; Decety, 1996; Kosslyn, Thompson, &
Alpert, 1997; Niedenthal, Barsalou, Ric, & Krauth-Gruber, 2005; E.
R. Smith & Collins, 2009).
cingulate cortex (Jackson, Meltzoff, & Decety, 2005;
Morrison, Lloyd, di Pellegrino, & Roberts, 2004;
Ochsner et al., 2008; Singer et al., 2004). The insula
also is engaged both when perceivers feel disgust and
observe it in others (Jabbi, Swart, & Keysers, 2007;
Wicker et al., 2003), consistent with this region’s role
in processing information from the viscera (Craig,
2009; Lamm & Singer, 2010). Recent data suggest
that even the hippocampus and posterior medial
frontal cortex exhibit overlapping engagement during
action observation and imitation (Mukamel, Ekstrom,
Kaplan, Iacoboni, & Fried, 2010). For simplicity,
hereafter we refer to all brain regions demonstrating
this property as experience sharing systems (ESS),
with the understanding that this is a functional defi-
nition and not one based on specific cytoarchitectonic
properties or patterns of anatomical connectivity.
The general overlap between self and other expe-
rience instantiated in the ESS has generated a great
deal of excitement, for at least two reasons. First, as
noted earlier, the ESS has been put forward as the
likely neural basis of perception-action matching. This
claim is plausible and well supported, especially given
demonstrations that overlapping neural activity in the
ESS often correlates with self-report and online mea-
sures of experience sharing (Pfeifer, Iacoboni, Mazz-
iotta, & Dapretto, 2008; Singer et al., 2004). Second,
evidence about the neural bases of experience shar-
ing has led to several claims that such sharing is the
primary mechanism underlying interpersonal under-
standing (Gallese & Goldman, 1998; Gallese, Keysers,
& Rizzolatti, 2004). This argument aligns much less
well with existing data than the first. On one hand,
it is true that shared experience provides a parsimo-
nious, efficient way for perceivers to learn from and
learn about others’ motor intentions, affective states,
and attitudes (Dijksterhuis & Bargh, 2001; Niedenthal,
Barsalou, Winkielman, Krauth-Gruber, & Ric, 2005;
Prinz, 1997; Schippers, Gazzola, Goebel, & Keysers,
2009; Schippers, Roebroeck, Renken, Nanetti, & Key-
sers, 2010). On the other hand, such sharing is a much
less likely mediator of interpersonal understanding in
other situations. This is because targets’ “higher level”
intentions and beliefs often are translated only am-
biguously into motor or somatic states. For example,
the identical motor program of pushing someone could
signify the very different intentions of starting a fight
or saving an inattentive commuter from an oncoming
bus (Jacob & Jeannerod, 2005).
The utility of experience sharing becomes even blur-
rier when one considers that nonverbal expressions of
intentions, beliefs, and emotions often fail to match
the states targets are actually experiencing. In many
cases, targets with no interest in being understood—
because they are lying to, competing with, or at-
tempting to produce a specific impression in targets—
intentionally dissociate their nonverbal expressions
162
THE REINTEGRATION OF ACCURACY
from their internal states (Ansfield, 2007; Ekman,
Friesen, & O’Sullivan, 1988; Ybarra et al., 2010). The
situation improves only slightly for more forthcoming
targets: Even when earnestly expressing themselves,
these targets often produce ambiguous nonverbal cues.
This is especially prevalent in the domain of emotional
expression. Although Ekman and colleagues famously
demonstrated the ease with which posed facial expres-
sions of canonical emotions are recognized (Ekman,
Sorenson, & Friesen, 1969), such expressions rarely
occur outside artificial contexts such as emotion studies
and tourist photos: Even while experiencing powerful
emotions, targets in naturalistic settings often produce
subtle cues that leave perceivers puzzling over what
those targets are feeling (Fernandez-Dols, Carrera, &
Russell, 2002; Russell, Bachorowski, & Fernandez-
Dols, 2003; Zaki, Bolger, & Ochsner, 2009). In such
situations (and they are common), it is unclear how per-
ceivers’ use of experience sharing—by itself—could
guide insightful inferences about target states.
Mental State Attribution
What is a perceiver to do, given the limitations of
experience sharing? One alternative is to rely on con-
textually defined semantic information about targets’
likely states, which is based on what perceivers know
of the target and situation they are observing. By way of
illustration, consider encountering a friend you know,
who is grieving the recent death of a family member.
This friend displays a neutral facial expression and oc-
casionally even smiles at you while talking about the
funeral and events since. If you were to make a judg-
ment about his or her feelings based solely on sharing
the internal states implied by these expressions, you
may decide that your friend actually feels fine. How-
ever, it is more likely that your knowledge about your
friend’s situation will influence your judgments and
lead you to the (probably correct) hypothesis that your
friend’s outward appearance belies a more negative in-
ternal experience.
Perceivers commonly create and test such hypothe-
ses about targets’ internal states. Although these hy-
potheses may be generated quickly and easily, they can
typically be represented explicitly and propositionally
within awareness as a perceiver effortfully deliberates
about a target. We refer to this form of mind perception
as mental state attribution2to describe the process by
which perceivers tie together multiple strands of infor-
mation in the service of nuanced, flexible inferences
2This type of inference has also been described in developmental
and clinical research as, “theory of mind” or “mentalizing” (Baron-
Cohen, Leslie, & Frith, 1985; Flavell, 1999; Leslie, Friedman, &
German, 2004; Saxe et al., 2004), whereas inferences about more
stable traits—as opposed to phasic internal states—are often grouped
under the heading “person perception.” We believe these terms re-
fer to a highly overlapping set of computations and hence use one
unifying term to refer to all of them.
about targets’ states and dispositions (Castelli, Happe,
Frith, & Frith, 2000; Kelley, 1973; Saxe et al., 2004).
Like experience sharing, mental state attribution
also has a relatively stable neural signature, involving
multiple brain regions that support inferences about in-
tentional states. Cognitive neuroscience research over
the last 15 years has borrowed a number of paradigms
from developmental and clinical traditions to study
mental state attribution, usually by asking perceivers
to draw inferences about the beliefs, knowledge, in-
tentions, and emotions of others based on written vi-
gnettes, pictures, or cartoons. In a typical study, brain
activity is compared between two conditions in which
perceivers make judgments that differ only in their so-
cial or mental state content: For example, drawing in-
ferences about the traits and states of intentional agents
(e.g., “How dependable is Tracy?” “Is Kenneth’s be-
lief up to date?”), as opposed to inanimate objects
that nonetheless have similar characteristics (“How de-
pendable is Tracy’s computer?” “Is Kenneth’s photo-
graph up to date?”).
Regardless of the type of judgment being made
about others or the medium in which target cues
are presented, such comparisons produce a strikingly
consistent pattern of activation in a network that in-
cludes: Medial prefrontal cortex, temporoparietal junc-
tion, posterior cingulate cortex, and temporal poles. We
refer to this set of regions as the mental state attribu-
tion system (MSAS), again with the understanding that
this categorization is somewhat loose and functional
(for more descriptions of the MSAS and its functions,
see Baron-Cohen et al., 1999; Castelli, Frith, Happe,
& Frith, 2002; Fletcher et al., 1995; Goel, Grafman,
Sadato, & Hallett, 1995; J. P. Mitchell, Heatherton, &
Macrae, 2002; Ochsner et al., 2004; Olsson & Ochsner,
2008; Peelen, Atkinson, & Vuilleumier, 2010; Saxe &
Kanwisher, 2003). Notably, many regions within the
MSAS have been tied to humans’ more general ability
to “project” themselves into distal scenarios or points
of view (including the past, future, and uncertain or
counterfactual concepts, as well as targets’ nonobserv-
able mental states; see Buckner, Andrews-Hanna, &
Schacter, 2008; J. P. Mitchell, 2009b; Schacter, Addis,
& Buckner, 2007; Spreng, Mar, & Kim, 2009). This
putative role for the MSAS—in lifting oneself out of
the cognitive “here and now” and simulating past and
future perspectives and states—is intriguing because it
is complementary to the assumed functional role of the
ESS in providing a basis for vicariously experiencing
the motor, sensory, and visceral states of targets. All
this being said, it is important to note that the specific
computations carried out by MSAS regions remain to
be precisely specified, and whatever they turn out to
be, they also play functional roles in behaviors not ob-
viously related to mind perception (Corbetta, Patel, &
Shulman, 2008; Daw, Niv, & Dayan, 2005; for more on
this, see Amodio & Frith, 2006; Cavanna & Trimble,
163
ZAKI AND OCHSNER
2006; J. P. Mitchell, 2008a, 2009a; Olsson & Ochsner,
2008; Saxe, 2006; Saxe et al., 2004; Zaki & Ochsner,
2011b).
A Tale of Two Systems
Experience sharing and mental state attribution are
functional cousins, serving the intimately related goals
of sharing and appraising targets’ internal states. As
such, one might expect them to work together often in
guiding mind perception. This makes the lack of family
resemblance in these processes—between either their
behavioral and neural correlates or the research pro-
grams that have explored them—all the more striking.
Extant data have supported a picture of these mind
perception processes as surprisingly dissociable, in at
least two ways.
First, experience sharing and mental state attribu-
tion differ in the level of effort they seem to require, as
reflected both in these processes’ developmental tra-
jectory and in the circumstances during which they
are engaged. Developmentally, mental state attribu-
tion comes online concurrently with executive func-
tions such as response inhibition (Carlson & Moses,
2001), and much later than behavioral signs of experi-
ence sharing (Flavell, 1999; Meltzoff & Decety, 2003;
Meltzoff & Moore, 1977; Wellman, Cross, & Wat-
son, 2001). Mental state attribution is most common
when perceivers are given an incentive to make accu-
rate or defensible judgments (Devine, Plant, Amodio,
Harmon-Jones, & Vance, 2002; Kunda, 1990; Tetlock
& Kim, 1987) and when they have the time and at-
tentional firepower necessary to perform the necessary
mental state calculus (Gilbert, Pelham, & Krull, 1989;
Kruglanski & Freund, 1983). By contrast, sharing of
targets’ motor and emotional states often occurs out-
side of awareness (Dijksterhuis & Bargh, 2001; Neu-
mann & Strack, 2000), and regions within the ESS—
but not the MSAS—are engaged even when perceivers’
attention to social targets is limited (Chong, Williams,
Cunnington, & Mattingley, 2008; Spunt & Lieberman,
2011).
Second, as readers may have noticed, the brain re-
gions making up the ESS and the MSAS are almost
completely nonoverlapping. This dissociation holds up
under meta-analytic scrutiny: Studies engaging one
system rarely engage the other concurrently (Gob-
bini, Koralek, Bryan, Montgomery, & Haxby, 2007;
van Overwalle & Baetens, 2009). Even more strik-
ingly, the situations and task parameters that engage
the MSAS often dampen activity in the ESS, and vice
versa, leading to suggestions that these neural systems
sometimes “compete” with each other for the guid-
ance of behavior. For example, Brass, Ruby, and Spen-
gler (2009) demonstrated that when participants were
asked to refrain from imitating targets’ movements,
they demonstrated reduced engagement in the ESS and
concurrently engaged areas within the MSAS. In an-
other study, we (Zaki, Hennigan, Weber, & Ochsner,
2010) presented participants with nonverbal emotional
expressions of targets combined with sentences de-
scribing the situational contexts to which targets were
putatively reacting. In some cases, these two types
of information suggested incongruent affective states
(e.g., a happy-looking target paired with a contextual
sentence stating that the target’s dog just died). Per-
ceivers then were asked to judge how they believed
targets felt. As perceivers relied more on target nonver-
bal behavior when making judgments, they increased
engagement of their ESS and dampened engagement of
the MSAS; the opposite pattern emerged as perceivers
relied more on contextual cues.
The impressive dissociations between the cogni-
tive and neural signatures of experience sharing and
mental state attribution have sometimes motivated an
“either/or” approach to mind perception, in which re-
searchers focus on one process while ignoring—or dis-
missing the importance of—the other. As we see next,
this view proves an ill fit for existing and emerging data
(Apperly, 2008; J. P. Mitchell, 2005), and reintegrating
accuracy into the study of mind perception provides
a way to move past such assumptions in favor of po-
tentially richer questions about how mind perception
operates.
Accuracy Returns
As just described, a process-oriented approach to
mind perception has been both generative—in its abil-
ity to produce robust, replicable findings and relatively
lean theoretical accounts of processes—and potentially
limiting—in its tendency to isolate the study of sin-
gle processes and ignore (or remain agnostic about)
their relationship to behavioral outcomes such as ac-
curacy. If process- and accuracy-related research pro-
grams are to make mutually beneficial contact, how-
ever, the question naturally arises as to what has
become of accuracy research while the process-
oriented approach has dominated research on mind per-
ception, and on social cognition more broadly. After
suffering a 25-year dry spell following the critiques
of Cronbach and others, interpersonal accuracy re-
search has regrouped and grown steadily over recent
decades. Existing reviews provide excellent and de-
tailed descriptions of current approaches to accuracy
research (Funder, 1995; Ickes, 1997; Jussim, 2005;
Kenny & Albright, 1987; Swann, 1984). As such,
we describe only four of these very briefly, with the
goal of laying out the basics of the most common
approaches so that in later sections we can explore
their integration with process-oriented work. The first
three of these accuracy types (pragmatic, realistic, and
164
THE REINTEGRATION OF ACCURACY
componential) focus inferences about dispositions,
whereas the fourth (empathic) directly addresses mind
perception.
Pragmatic Accuracy
One approach to accuracy has overcome problems
in measurement and validity by circumventing them
entirely. Instead of attempting to establish a crite-
rion representing the “true” state or trait of targets
and measuring interpersonal accuracy as the ability
of perceivers’ inferences to approach that truth, the
pragmatic approach focuses on the utility of social
inferences for negotiating social relationships (Fiske,
1992, 1993; Jussim, 1991; E. R. Smith & Collins, 2009;
Swann, 1984). Following Mischel’s interactionist ap-
proach to personality, if targets’ behavior varies stably
across situations, then a perceiver’s accuracy for a tar-
get need not (and potentially cannot) encompass all
of that target’s behavior. Rather, perceivers need only
predict behavior relevant to domains in which they in-
teract with a target (e.g., to successfully interact with
John, his students need to accurately assess how hard
a grader he is but not how much he loves banjo mu-
sic). There is evidence that perceivers do achieve such
“circumscribed accuracy” (Swann, 1984) and that they
constrain their predictions about targets to situations
relevant to their basis for judgment (Idson & Mischel,
2001; Noordewier & Stapel, 2009).
Realistic Accuracy
In contrast to pragmatic accuracy, the realistic ac-
curacy approach takes as a starting point the idea that
targets’ dispositions (e.g., a target’s level of extraver-
sion) are real: that is, they exist independently of target
and perceiver opinions. Thus, accuracy can be studied
using methods used for construct validation by first
identifying traits that are consensually perceived by
others, predictive of behavior, and stable across time,
and then examining how accurate perceivers are in per-
ceiving such traits (Funder, 1995).
Componential Accuracy
Componential accuracy is embodied by Kenny and
colleagues’ work (Kenny & Albright, 1987; Malloy &
Kenny, 2006), which statistically disentangles multiple
potential sources of interpersonal consensus in a man-
ner similar to that suggested by Cronbach’s original
critiques. Especially interesting for current purposes,
component models provide behavioral cues about the
processes that perceivers employ in drawing inferences
about targets (e.g., projection or stereotyping). Unlike
the realistic approach, however, component models re-
main agnostic about targets’ “real” traits and instead
focus on the mix of sources that influence interpersonal
judgments.
Empathic Accuracy
Pragmatic, realistic, and componential approaches
to accuracy all share a focus on perceivers’ ability
to gauge targets’ stable dispositions, but often, per-
ceivers are more concerned with a target’s transient
states (How does Frank feel right now? Is Jenna
flirting with me?). Work on empathic accuracy ex-
amines interpersonal consensus about such states us-
ing paradigms in which perceivers’ rating of targets’
thoughts and feelings are compared with targets’ re-
ports on their own states (Ickes, 1997; Ickes, Stin-
son, Bissonnette, & Garcia, 1990; Levenson & Ruef,
1992). Such paradigms have been used to examine the
situational and individual-difference predictors of ac-
curacy for emotions (Klein & Hodges, 2001; Pickett,
Gardner, & Knowles, 2004; Simpson, Ickes, & Black-
stone, 1995; Simpson, Orina, & Ickes, 2003; Stinson &
Ickes, 1992). Similar approaches in nonverbal behavior
also have examined the types of emotional cues (e.g.,
visual, prosodic) that predict accuracy, and in which
circumstances they do so (Costanzo & Archer, 1989;
Hall & Schmid Mast, 2007; Nowicki & Duke, 1994;
Rosenthal, Hall, DiMatteo, Rogers, & Archer, 1979;
Russell et al., 2003).
Where Do We Go From Here? Integrating
Process and Accuracy
The process- and accuracy-oriented approaches de-
scribed here ostensibly study two sides of the same
social cognitive coin: How people go about attempting
to understand each other and how well their attempts
fare. As such, the relative lack of crosstalk between
these approaches is at least somewhat surprising. In
some cases, this disconnect may stem from percep-
tions that processes and accuracy are orthogonal and
do not impact each other in lawful ways. If this were
true, it would be unclear how single studies or research
programs could meaningfully tie these phenomena to-
gether. In other cases, researchers may believe that
processes and accuracy are related but that the struc-
ture of these relationships is obvious (e.g., the more
a perceiver applies a given process, the more accu-
rate their inferences will be), and therefore the explicit
study of their connection offers little new insight about
mind perception. If this were true, it would be un-
clear why examining process–accuracy relationships is
a meaningful endeavor. In this section we address the
first of these issues (how processes and outcomes can
be brought together). In the third section we address
the second (why bringing these phenomena together is
important to the future of mind perception research).
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ZAKI AND OCHSNER
A Framework for Integration
A Social Cognitive Neuroscience Approach
In building a framework for integrating research on
process and accuracy, it should be highlighted that we
adopt a social cognitive neuroscience (SCN) approach
(Lieberman, 2007; J. P. Mitchell, 2006; Ochsner, 2007;
Ochsner & Lieberman, 2001) that combines the theory
and methods of cognitive neuroscience and social psy-
chology. Two key ideas are embodied by this approach.
The first idea is that behavior can be explained usefully
at multiple levels of analysis, including (a) the social
level describing behaviors and experience in their in-
terpersonal context, (b) the cognitive level specifying
underlying information processing mechanisms, and
(c) the neural level specifying the neural systems that
implement these processes. Whereas social psychol-
ogy traditionally has been concerned primarily with
the first two of these levels, and cognitive neuroscience
primarily concerned with the latter two, SCN (or social
neuroscience more broadly; see Cacioppo & Bernston,
1992) seeks to connect all three.
The second idea concerns the way in which we draw
inferences about the relationships between these lev-
els. In any given experiment we can manipulate and/or
measure variables at the social (e.g., accurate vs. inac-
curate inference) and neural (e.g., activity in specific
brain systems) levels. By contrast, cognitive processes
such as experience sharing and mental state attribution
cannot be directly measured; their operation must be
inferred from patterns we observe in social- and neural-
level variables. For this reason, the SCN approach em-
phasizes the use of converging evidence from the so-
cial and neural levels to triangulate on psychological
processes. This can provide greater leverage for testing
psychological theories than approaches that emphasize
only the social and cognitive, or cognitive and neural
levels, respectively.
Applying SCN to Process-Accuracy Relationships
The SCN approach can be used to build a frame-
work that uses behavioral (including self-report) and
neuroscientific measures to draw inferences about how
interpersonal accuracy is related to the underlying pro-
cesses of experience sharing and mental state attribu-
tion. Although no one technique taken alone is suffi-
cient to document the presence of a given cognitive
process—or its effect on accuracy—the hope is that
combining data from varying levels of analysis will
afford us more traction in examining mind perception
in a way that speaks to multiple domains of research.
Figure 1 illustrates this framework, using experi-
ence sharing as an example process. Cognitive/process-
level phenomena—for example, a perceiver’s de-
ployment of experience sharing—are impossible to
observe directly. Even defining them requires wading
into murky phenomenological terrain (e.g., “How can
we really know what emotion a target is experienc-
ing?”). However, each of these phenomena involves
multiple measurable variables at the social/behavioral
and neural levels. By way of comparison, consider that
the study of affective experience (the leftmost phe-
nomenon in Figure 1) has been well served by seek-
ing out converging evidence from self-report, neural
activity, and other domains (Barrett, 2009; Barrett,
Mesquita, Ochsner, & Gross, 2007; Kober et al., 2008;
Lindquist & Barrett, 2008). Similarly, we believe that
the operation of mind perception processes and their
effect on accuracy can be inferred best from the many
measurable signs that these cognitive-level phenomena
leave in their wake. These include activity in relevant
neural systems, convergence between targets’ and per-
ceivers’ reports of their own affect (a social/behavioral
level sign of experience sharing), convergence between
targets’ and perceivers’ reports on targets’ affect (a so-
cial/behavioral level sign of interpersonal accuracy),
and correlations between target and perceiver neural or
physiological activity over time (Marci & Orr, 2006;
Schippers et al., 2009; Stephens, Silbert, & Hasson,
2010; Vaughan & Lanzetta, 1980). It is important to
note that connections between phenomena can also be
effectively interrogated by sampling evidence at multi-
ple levels of observation. For example, a strong case for
Figure 1. A framework for combining neural and behavioral observations to gain traction on how and when mind
perception processes produce accurate inferences. Note. ESS =experience sharing systems.
166
THE REINTEGRATION OF ACCURACY
the idea that experience sharing contributes to interper-
sonal accuracy can be made by marshalling evidence
that these phenomena are related at behavioral, experi-
ential, and neural levels (Cacioppo & Bernston, 1992;
Ochsner & Lieberman, 2001).
Modeling Context Dependency
As Lieberman (2005) pointed out, if a social psy-
chologist was stranded on a desert island and given the
choice of one idea to bring with her, a likely candi-
date would be “the power of the situation.” The SCN
approach expands this “situationalist” focus to neuro-
science research as well. Does Jack’s high score on a
conscientiousness scale mean he won’t be found play-
ing air guitar while standing precariously on a barstool
this weekend? Will viewing a surprised face engage
participants’ amygdala or not? The likely answer to
these (and myriad other questions) is, It depends on
the contexts in which behaviors and brain activity are
embedded. Our own work and our examination of oth-
ers’ work suggest that process-outcome relationships
in mind perception are no exception to this trend. That
is, asking whether experience sharing or mental state
attribution (or any other mind perception process) pro-
duces accuracy is a conceptual nonstarter. Instead, re-
searchers should focus on when these processes pro-
duce accuracy. This requires “zooming out” from a
focus on processes or accuracy in isolation, to achieve
a broader focus on contextual factors that determine
their connection to each other.
Figure 2 displays such a broad view of accuracy,
with an emphasis on the many moving parts that deter-
mine the course of a mind perception episode. In this
model, both the cues produced by a target and those
received by a perceiver constrain (a) the likelihood that
a perceiver will engage a given mind perception pro-
cess, and (b) the effect that process can be expected
to have on accuracy. Zooming out also makes clear
that neither cognitive processes nor accuracy are mind
perception’s endgame. Both of these phenomena serve
the more “downstream” goal of supporting adaptive
social behavior and fostering positive social ties. Re-
searchers often labor under the assumption that mind
perception processes and interpersonal accuracy allow
perceivers to “navigate,” “maneuver,” or otherwise lo-
comote adeptly through their social world. However,
there are likely cases in which a given process, or even
accuracy, are not social interaction’s power steering.
Processes and accuracy sometimes have no effect on
social well-being, and can even reduce perceivers’ abil-
ity to connect fruitfully with others. Our approach em-
phasizes the need to model situational factors that de-
termine when a process (or accuracy) helps—and when
it harms—perceivers’ social interactions.
Thus, central to this model is a focus on two ways
in which the relationship between cognitive processes
and later outcomes are mediated by other factors. First,
there is an emphasis on understanding the situational
factors that determine whether a given process will pro-
duce accurate inferences. Second, there is an emphasis
on understanding how accuracy itself can serve as a
mediator between the use of a mind perception process
and adaptive social outcomes.
It is worth noting that connections between mind
perception phenomena are bidirectional and that the
left–right direction in Figure 2 is not a timeline de-
marcating sequentially completed steps. Perceivers do
not encounter targets, deploy a mind perception pro-
cess, draw an (in)accurate inference, produce a so-
cially (mal)adaptive behavior, and call it a day. In-
stead, social interactions are dynamic and dense with
feedback loops (Freeman & Ambady, 2011; Kunda
& Thagard, 1996). For example, learning that she is
Figure 2. An illustration of some of the many contextual factors constraining the relationship between mind perception phenomena.
167
ZAKI AND OCHSNER
mistaken or has behaved inappropriately, a perceiver
may alter the way she engages during subsequent
interactions with a target. Perceivers can shift their
mind perception in multiple ways, including chang-
ing the content that they focus on when employing
mental state attribution (such as the level at which
they construe a target’s behavior; see Spunt, Satpute,
& Lieberman, 2010; Vallacher & Wegner, 1987), or
altogether changing the process they employ in per-
ceiving that target (e.g., “turning up” one’s shared ex-
perience through perspective taking; see Batson, 1991;
Lamm, Batson, & Decety, 2007). These adaptations
can, in turn, serve perceivers’ goals of better under-
standing targets (Eyal & Epley, 2010). Such feedback
processes are not limited to perceivers’ information
processing; targets also may change the social cues
they produce to clarify their internal states after learn-
ing that a perceiver has misjudged them (Swann & Ely,
1984; Swann, Hixon, Stein-Seroussi, & Gilbert, 1990),
or (even more interesting) may change their self-view
to match a perceiver’s initially erroneous judgment of
them (Turner, 1991). The role of such feedback loops
in mind perception is an emerging topic of enormous
interest. However, because relatively little is known
about how such feedback works—and to constrain
the scope of this article—we focus predominantly on
“left to right” relationships between the phenomena in
Figure 2.
What Does This Buy Us? The (Basic and
Applied) Fruits of Integration
Thus far, we have outlined a framework for inte-
grating processes and accuracy in the study of mind
perception. We now broaden our focus to suggest ways
in which this integrative approach can foster novel in-
sights and influence the way observations are made
in behavioral, neuroscientific, and clinical mind per-
ception research. We describe separately the potential
advances that could be made for our understanding
of mind perception in each of these domains, but we
emphasize at the outset that a major advantage of in-
tegrating the study of processes and accuracy emerges
specifically from bridging data gleaned from different
approaches (e.g., neural and clinical) that too often are
isolated from each other.
Specifically, we discuss five advantages an integra-
tion of process and accuracy have over the study of ei-
ther in isolation: (a) overturning incorrect but pervasive
assumptions about mind perception, (b) delineating the
situation-specific relationships between processes and
accuracy, (c) tying data about information processing
more directly to evidence about social well-being, (d)
drawing parallels between mind perception and other
domains of cognition, and (e) mapping the domains
in which social cognitive abnormalities in psychiatric
disorders lead to functional impairments.
As previously mentioned, the majority of exam-
ples we discuss focus on mind perception accuracy for
transient affective states (i.e., empathic accuracy) and
not on inferences about other aspects of individuals,
such as their stable dispositions. Beyond the pragmatic
limitations of space, this narrowing of scope is mo-
tivated by two principled factors. First, empathic ac-
curacy paradigms enjoy some psychometric strengths
that obviate some of the early concerns leveled against
accuracy research more generally. Historically, accu-
racy has been calculated as the simple difference be-
tween a perceiver’s rating of a target state or trait
relative to some criterion measure (e.g., the target’s
self-report). This approach is intended to index ac-
curacy about the overall level of some attribute, has
been common to many forms of accuracy research,
and has been roundly criticized as statistically prob-
lematic (see earlier and see Cronbach, 1955; Kenny,
1991). By contrast, contemporary empathic accuracy
paradigms record perceivers’ inferences, as well as tar-
gets’ self-report, at multiple timepoints. This allows
researchers to compute a measure of accuracy for the
time-varying dynamics of a target’s state, not just the
overall average level of that state. For example, one
can calculate the correlation between a perceiver’s in-
ferences about a target’s affect and a target’s self-rating
as they both vary across time. The resulting correla-
tions reflect target–perceiver agreement about when a
target felt relatively negative. It is important to note that
such correlations are independent of the overall level
of some state that perceivers infer that targets are expe-
riencing and that targets assign to themselves. As such,
dynamic measures are robust to many of the biases—
including stereotype application, assumed similarity,
or scale usage tendencies—that can inflate empathic
accuracy measures defined as the difference between
two global assessments.
Second, for reasons explained above (see Where
Do We Go From Here?), we are especially interested
in connecting measures of cognitive processes such as
shared experience and mental state attribution to mea-
sures/correlates of interpersonal accuracy at the behav-
ioral and neuroscientific levels. Because almost all neu-
roscience research on process use concerns the sharing
and inferring of transient states (beliefs, emotions, in-
tentions), and not static traits, empathic accuracy is a
natural candidate for exploring process-accuracy rela-
tionships across multiple levels of analysis. This does
not mean that this measure of accuracy is the only one
that can be used to examine the connections we de-
scribe next. In fact, it is our hope that future researchers
will be motivated to explore similar connections—
at both the neural and behavioral levels—between
mind perception processes and other forms of
accuracy.
168
THE REINTEGRATION OF ACCURACY
Contributions to Behavioral Approaches
Perceivers’ Mind Perception Abilities: Half Full or
Half Empty?
Imagine an extraterrestrial preparing to make first
contact with Earth. In advance of meeting humans,
she decides to do research on our behavior, taps into
PsycINFO, and reads all the work she can find on
mind perception. She is disappointed to learn that peo-
ple are, by and large, inept at understanding each other:
They ascribe traits to each other based on demonstra-
bly nondiagnostic behaviors (Gilbert & Malone, 1995;
Jones & Harris, 1967); erroneously impute their knowl-
edge, beliefs, and preferences onto others (Epley et al.,
2004; Gilovich, Medvec, & Savitsky, 2000; Gilovich,
Savitsky, & Medvec, 1998; Ross, Greene, & House,
1977); and carelessly apply stereotypes (Fiske, 1998).
They can correct these mistakes, but only through
slow, labor-intensive application of rules, and in the
absence of such efforts, they “default” to error and
bias (Devine, 1989; Devine et al., 2002; Gilbert, Pinel,
Wilson, Blumberg, & Wheatley, 1989).
Why has existing research equipped our interplan-
etary traveler with such underwhelming expectations?
Because accuracy is hard to define, and the faults
and foibles of mind perception have proved compara-
tively easier to uncover and eye-catching to boot, the
process-oriented approach tends toward a potentially
disheartening view of mind perceivers as “faulty com-
puters,” running mind perception software that is of-
ten situation inappropriate (Higgins & Bargh, 1987;
Krueger & Funder, 2004). This outlook gains further
momentum from its contact with a historically popular
view of decision making, which suggests that people—
instead of optimizing their decisions based on all the
information available to them—rely on simple judg-
mental heuristics that lead them toward a host of in-
correct decisions about the outside world (Kahneman
& Tversky, 1996; Tversky & Kahneman, 1974), the
sources of their own behaviors and abilities (Nisbett
& Wilson, 1977), and even the nature of those behav-
iors and abilities (Kruger, 1999; Kruger & Dunning,
1999).3
Is such a pessimistic attitude about mind percep-
tion warranted? Granted, perceivers can be induced
into committing lawful, compelling errors when judg-
ing targets’ states and traits. However, studies of these
effects often use statistically perfect judgments as a
criterion against which to define social cognitive error
(e.g., completely discounting nondiagnostic informa-
tion when making preference judgments, see Jones &
Harris, 1967). Further, studies of social cognitive er-
ror typically use highly superficial and nonnaturalistic
tasks that—in a manner analogous to visual illusions—
3Nonetheless, heuristics offer efficient decisions that are often as
accurate—and sometimes more accurate—than exhaustive problem-
solving strategies (Gigerenzer, 1999; Rieskamp & Otto, 2006).
are designed to create the errors they document
(Funder, 1987) and may overlook the ways these errors
actually reflect generally adaptive processing. These
factors result in a “half-empty glass” view of perceivers
who are seen in light of their errors and not their ac-
complishments (Krueger & Funder, 2004).
Traditionally, accuracy research has taken a much
different tack: Comparing judgments made in more
naturalistic settings (i.e., judgments made about ac-
tual social targets on the basis of complex behavior)
to targets’ own self-perceptions. Further, accuracy in
these studies is typically measured against a baseline
of chance, as opposed to an assumption of perfect (and
often perfectly rational) performance. This approach
produces a more optimistic view of mind perceivers,
who are shown to excel in a number of ways. First,
perceivers largely agree with each other and establish
impressive consensus about the states and traits of so-
cial targets, even when they have access only to “thin
slices” of target behavior (Ambady & Rosenthal, 1992,
1993; but see also Ames, Kammrath, Suppes, & Bolger,
2010), or impoverished target cues (North, Todorov, &
Osherson, 2010; Zaki, Bolger, et al., 2009). Second,
perceivers’ inferences are impressive predictors of tar-
get behavior (Funder, 1991; Moskowitz & Schwarz,
1982), even decades after perceivers’ from their ini-
tial impressions (Nave, Sherman, Funder, Hampson,
& Goldberg, 2010). Third, perceivers achieve consen-
sus with targets themselves (Ickes, 1997; Kenny &
Albright, 1987; Levenson & Ruef, 1992; Zaki, Bolger,
& Ochsner, 2008). Finally, emerging evidence suggests
that, at least in certain situations, perceivers have a
well-calibrated understanding of when they are likely
to be accurate, versus inaccurate, about target states
(Kelly & Metcalfe, in press).
An integration of processes and accuracy can com-
bine the strengths of each of these approaches in bal-
ancing views about perceivers’ skills in understanding
targets. Specifically, not only can we document that
perceivers fare pretty well in their endeavors (the con-
clusion of extant accuracy research) or that a given
process sometimes fails them (the conclusion of extant
process research), but we can more specifically chart
the landscape of process-accuracy relationships to de-
scribe when a given process supports accuracy. We turn
to this issue now.
Processes’ Situation-Specific Utility
When humans are equipped with more than one tool
for completing a single task, a few questions naturally
arise. One could ask which tool is better for completing
that task (e.g., “Should I use the hammer or screwdriver
for hanging this painting?”). However, a deeper ques-
tion may be, Why would nature equip us with more
than one tool for a single task? The answer is often
that what appears to be a single task (e.g., hanging a
painting), upon closer inspection, ends up splintering
169
ZAKI AND OCHSNER
into multiple, independent tasks each suited to differ-
ent tools (e.g., hanging frames held up by screws vs.
nails).
Perceivers’ repertoire of mind perception processes
suggests just such a state of affairs: Shared experience,
mental state attribution, and other processes likely ex-
ist in tandem because they each support accurate inter-
personal understanding under differing circumstances.
This perspective can clear up confusion in the extant
literature on the process-accuracy relationship.
Consider the age-old search for the “good per-
ceiver.” Popular intuition has long held that some
individuals—specifically, those who tend to share oth-
ers’ experiences— also should be adept at understand-
ing those experiences (Allport & Allport, 1921). How-
ever, attempts to relate accuracy and experience sharing
have fared surprisingly poorly (Hall, 1979; Ickes et al.,
1990; Levenson & Ruef, 1992), leading modern accu-
racy research to largely abandon the search for “good
perceivers” (Ickes et al., 2000). The model described
in the Modeling Context Dependency section suggests
a way out of this counterintuitive null finding: Lawful
features of social situations may determine when expe-
rience sharing will come in handy to mind perceivers.
Following this logic, we recently tested the idea
that a critical feature of a perceiver’s situation is the
type of target they encounter. Our premise was that
mind perception, as a fundamentally interpersonal pro-
cess, should depend both on a perceiver’s tendencies
to deploy specific kinds of processes—such as expe-
rience sharing—and features of targets that influence
how easily they can be perceived—such as their trait
levels of emotional expressivity (Gross & John, 1997;
Zaki, Bolger, et al., 2009). Specifically, we predicted
that perceivers high on the tendency to use experience
sharing would be more accurate in judging the emo-
tions of a target to the extent that the target sends strong
expressive signals to their internal emotional state that
the perceivers could share. In line with this prediction,
we found that perceivers’ trait-level experience sharing
predicted accuracy as a function of a target’s tendency
to be emotionally expressive (Zaki et al., 2008). Find-
ings like this both flesh out situation-specific utility
of mind perception processes and highlight the impor-
tance of moving beyond the “perceiver-centric” view
that has dominated process-oriented research for half
a century.
Linking Processes to Social Well-Being and
Dysfunction
Whereas the proximal goal of mind perception pro-
cesses is forming an accurate impression of targets,
the ultimate goal of such processes is to allow per-
ceivers to function adaptively in the social world, by
forming and maintaining social bonds with others.
Positive social relationships are a central human need
(Baumeister & Leary, 1995) that provides both psycho-
logical and physical protection against environmental
stressors (Bolger & Eckenrode, 1991; Cohen, Doyle,
Skoner, Rabin, & Gwaltney, 1997; Hawkley, Burleson,
Berntson, & Cacioppo, 2003). Indeed, discussions of
mind perception often begin with the idea that pro-
cesses such as experience sharing and mental state at-
tribution are important because those processes support
adaptive social behavior.
But do they? Perhaps only sometimes. On one hand,
individual differences in experience sharing sometimes
are associated with adaptive social behaviors, such
as cooperation, altruism (Eisenberg & Miller, 1987;
Johnson, 1975), or some measures of social skills (Bai-
ley, Henry, & Von Hippel, 2008; Pfeifer et al., 2008;
Riggio, Tucker, & Coffaro, 1989), and individual dif-
ferences in the tendency to employ mental state attri-
bution may track with accommodating behavior dur-
ing conflict in close relationships (Arriaga & Rusbult,
1998; Long & Andrews, 1990). On the other hand,
some studies have found no relationship between these
processes and social skills, adjustment, or integration
(Cliffordson, 2002; McWirther, Besett-Alesch, Horib-
ata, & Gat, 2002).
The integrative approach we advocate suggests a
novel prediction: Accuracy may serve as a “middle-
man” mediating the relationship between deployment
of a process and its ultimate adaptive goal of promoting
social well-being. Support for this comes from the find-
ing that interpersonal accuracy (especially concerning
transient states such as thoughts and emotions) pre-
dicts adaptive relationship behavior, such as skillful
social support (Verhofstadt, Buysse, Ickes, Davis, &
Devoldre, 2008); lower relationship abuse (Clements,
Holtzworth-Munroe, Schweinle, & Ickes, 2007); and
social adjustment in adolescents (Edwards, Manstead,
& MacDonald, 1984; Gleason, Jensen-Campbell, &
Ickes, 2009; Spence, 1987), college students (Carton,
Kessler, & Pape, 1999; Zaki & Ochsner, 2011a), and
adults (Bartz et al., 2010).
Of course, the idea that accuracy supports adaptive
social function is nothing new (for a review of nearly
100 studies on this topic, see Hall, Andrzejewski, &
Yopchick, 2009) and was, in fact, part of the impe-
tus for the original accuracy movement described ear-
lier. Today, well-known theories incorporate decoding
of others’ states into larger constructs, such as emo-
tional intelligence (Lopes, Grewal, Kadis, Gall, & Sa-
lovey, 2006; Mayer, DiPaolo, & Salovey, 1990; Mayer,
Salovey, & Caruso, 2008; Mayer, Salovey, Caruso,
& Sitarenios, 2001) or affective social competence
(Halberstadt, Denham, & Dunsmore, 2001). However,
these frameworks often view accuracy as a precur-
sor, or “lower level, fundamental skill” (Mayer et al.,
2008, p. 506) that combines with other skills such as
affect management and communication ability to pro-
duce a higher level set of abilities that support social
function.
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THE REINTEGRATION OF ACCURACY
In contrast, mind perception researchers view accu-
racy itself as a complex outcome dependent on a suite
of flexibly deployed cognitive processes. As such, re-
search tying health and well-being to accuracy often
makes little contact with work on the cognitive pro-
cesses that produce accuracy. This is unfortunate, given
that social cognitive work has begun to suggest that
empathic accuracy does not always promote adaptive
behavior. In fact, in some cases accuracy can be mal-
adaptive, as when perceivers correctly intuit a target’s
negative or relationship-damaging thoughts and feel-
ings (Simpson et al., 1995; Simpson et al., 2003) or
when accuracy prevents the application of (presum-
ably adaptive) positive biases in self-perception that
are characteristic of most individuals (Taylor & Brown,
1988).
Integrating processes and outcomes suggests ways
to address these seemingly conflicting relationships be-
tween mind perception processes, accuracy, and adap-
tive social behavior by reframing the questions we ask.
Instead of asking whether a given mind perception pro-
cess promotes adaptive behavior, we might ask when
its use is adaptive by virtue of producing accurate in-
ferences, and when does that process motivate adaptive
behaviors irrespective of (or even by reducing) accu-
racy?
Following a social cognitive neuroscience ap-
proach, both behavioral and neural correlates of mind
perception processes and accuracy can be brought to
bear in answering such questions, and testing models
in which accuracy mediates the relationship between
mind perception processes and social function. The use
of brain activity to predict outcomes in the field is only
now taking hold (cf. the “brain as predictor” model em-
ployed by Berkman, Falk, & Lieberman, 2011; Falk,
Berkman, Mann, Harrison, & Lieberman, 2010) and
stands to make headway in linking information pro-
cessing to adaptive behavior in the social domain.
Contributions to Neuroscientific Approaches
Fleshing Out Single-Process Models
Dissociations between the neural systems support-
ing experience sharing and mental state attribution
have prompted a curious debate among neuroscien-
tists about which system is primarily responsible for
mind perceivers’ abilities (Apperly, 2008). Some, fol-
lowing so-called simulation theory (Heal, 1996), have
cited the role of the ESS as evidence that shared expe-
rience is the royal road to interpersonal understanding
(e.g., Gallese & Goldman, 1998; Gallese et al., 2004).
Others, following the tongue-twistingly named “theory
theory” (Gopnik & Wellman, 1992), argue that mental
state attribution, supported by the MSAS, is central to
understanding targets (e.g., Saxe, 2005).
We believed such theories lack two features critical
to forming a more complete theory of mind percep-
tion’s neural bases. First, in focusing on single neural
systems, it is easy to forget that processing the complex
social cues perceivers most often encounter in daily life
typically draws on both the MSAS and ESS (among
other regions). Second, MSAS and ESS-based theories
of interpersonal understanding have proceeded largely
in the absence of direct evidence about whether either
neural system supports accuracy about actual social
targets. This is because tasks engaging the ESS rarely
require perceivers to infer targets’ internal states, and
tasks tapping the MSAS typically employ extremely
easy, simplified social tasks that produce ceiling ef-
fects. In each case, the ability to directly measure the
neural systems supporting accurate, as opposed to in-
accurate, social inferences is limited at best (Zaki &
Ochsner, 2009). We now discuss each of these weak-
nesses in the literature—and how we believe they can
be overcome—in turn.
Moving from single to multiple-process models.
In our view, nominating single processes or neural sys-
tems as supporting interpersonal understanding likely
reflects a lack of contact between cognitive neurosci-
entific and behavioral approaches to mind perception.
A major aim of cognitive neuroscience is using brain
activity as a guide for interpreting multiple cognitive
processes as either distinct (based on separable neural
circuitry) or functionally related (based on overlapping
neural circuitry; see Henson, 2005). This approach has
helped to resolve a number debates about cognition, for
example, providing evidence that visual imagery and
visual perception are highly similar (Kosslyn et al.,
1997), or that declarative and procedural memory are
not (Buckner et al., 1995; Schacter, 1997).
However, such an approach also encourages re-
searchers to emphasize single patches of neural real
estate and to focus on tasks that excite their particular
neural neighborhood. For example, a researcher exam-
ining the role of the ESS may be more interested in
tasks tapping the ESS, such as perceivers’ sharing of
targets motor intentions, disgust, or pain, and may pay
less attention to false belief tasks that do not engage the
ESS. Similarly, such a researcher may pay more atten-
tion to data demonstrating that damage to the ESS im-
pairs emotion perception (Adolphs, Damasio, Tranel,
Cooper, & Damasio, 2000; Shamay-Tsoory, Tomer,
Berger, Goldsher, & Aharon-Peretz, 2005) than to sim-
ilar lesion data suggesting that the MSAS is necessary
for making judgments about many forms of beliefs and
intentions (Shamay-Tsoory, Aharon-Peretz, & Perry,
2009).
Nonetheless, the fact that neural systems can be
dissociated does not imply that they are necessarily
or even usually dissociated during social inferences,
especially those based on the kinds of complex social
information that we encounter in everyday situations
(Keysers & Gazzola, 2007; Shamay-Tsoory, 2010;
171
ZAKI AND OCHSNER
Singer, 2006; Uddin, Iacoboni, Lange, & Keenan,
2007; Zaki & Ochsner, 2009). Consistent with this,
both the ESS and MSAS are concurrently engaged by
“naturalistic” mind perception tasks, such as viewing
videos of complex social cues (Brass, Schmitt, Spen-
gler, & Gergely, 2007; de Lange, Spronk, Willems,
Toni, & Bekkering, 2008; Spunt et al., 2010; Wolf,
Dziobek, & Heekeren, 2010).
The second weakness in the cognitive neuroscience
literature on mind perception is the absence of direct
data on the neural sources of accuracy, which renders
any claim about the neural bases of interpersonal un-
derstanding more speculative than is typically recog-
nized. In some ways, this is similar to the state of mem-
ory research in the mid-1990s. At that time, a few key
brain regions had been linked to memory performance
through lesion studies, and in the first wave of neu-
roimaging studies of memory these same regions were
engaged during encoding tasks. The extent to which
any given region was critical for successful encoding
was not yet known, however, because the issue had yet
to be assessed directly. This changed with the advent
of the subsequent memory paradigm, in which brain
activity during a given encoding trial was related to
veridical recollection of a memorandum on a later test.
This approach confirmed that activity in the medial
temporal lobe and inferior frontal gyrus was related
to later performance, cemented their functional impor-
tance to memory formation, and provided a critical
methodological tool for probing the neural correlates
of memory more generally (Brewer, Zhao, Desmond,
Glover, & Gabrieli, 1998; Wagner et al., 1998).
We believe that integrating a focus on accuracy into
neuroimaging studies of mind perception can play a
similar role, by providing novel, direct evidence about
the role of the ESS and MSAS in producing interper-
sonal accuracy. To take a step in that direction, we
identified brain regions the activity of which increased
as perceivers became more accurate about the emotions
expressed by targets shown on videotapes talking about
autobiographical emotional experiences. This analysis
revealed that activity in several regions within both the
MSAS and ESS (especially the putative mirror neuron
system engaged by observing and performing actions;
see Zaki, Weber, Bolger, & Ochsner, 2009) predicted
accuracy. Dovetailing with this initial finding, a sub-
sequent study demonstrated that functional coupling
between target brain activity and perceiver brain activ-
ity in both ESS and MSAS predicts perceivers’ com-
prehension of stories told by targets (Stephens et al.,
2010).
This type of data is critical in that it can help move
past the prevalent yet ultimately unproductive debate
as to whether interpersonal understanding is supported
by the MSAS or ESS, by demonstrating that both
systems—and, it stands to reason, their related cog-
nitive processes—support accuracy.
The suggestion here is that future work could move
toward asking more nuanced questions about when a
given system is most important to fostering accuracy.
Again, consider memory research, where the neural
bases of successful encoding have been shown to differ
critically depending on the type of information being
encoded (e.g., social vs. nonsocial; see Macrae, Moran,
Heatherton, Banfield, & Kelley, 2004; J. P. Mitchell,
Macrae, & Banaji, 2004). In like fashion, the MSAS
and ESS may turn out to be variably useful in produc-
ing accurate inferences, depending on types of social
cues perceivers encounter and the inferences they are
asked to make. Specifically—although direct evidence
is still lacking—extant data suggest that the MSAS
may support accurate inferences about complex, con-
textualized internal states (i.e., those that require un-
derstanding the source of a belief or emotion), whereas
the ESS may support accuracy about states with more
prominent bodily components, such as disgust or pain
(Keysers & Gazzola, 2009; Lamm & Singer, 2010;
Saarela et al., 2007; Zaki, Davis, & Ochsner, 2011;
Zaki et al., 2010). Future work should investigate such
possibilities.
Drawing Parallels With Other Phenomena
As previously described, a major aim of cognitive
neuroscience is to “carve nature at its joints” by using
imaging data to inform questions about the distinct
or overlapping processing systems underlying various
behaviors. In this regard, imaging data have proven to
be particular useful in addressing particular kinds of
questions about processing mechanisms. One question
ideally suited for imaging data is whether two different
behavioral phenomena depend on common or distinct
processing systems. By determining whether the two
behaviors recruit similar or different neural systems
one gains purchase on this question.
In the last decade, this approach has been applied
to the study of social cognition to demonstrate that
encountering, drawing inferences about, and respond-
ing to social information recruits brain regions largely
distinct from those supporting processing of nonsocial
information. Consider the example of cognitive con-
trol. Tasks requiring the engagement of control pro-
cesses such as response inhibition or working mem-
ory engage lateral prefrontal and cingulate regions
but seldom if ever activate regions within the MSAS
(Botvinick, Braver, Barch, Carter, & Cohen, 2001;
Wager, Jonides, & Reading, 2004). In fact, cogni-
tively demanding tasks deactivate several MSAS re-
gions, and activity in regions associated with social
and nonsocial task types demonstrates negative, re-
ciprocal relationships (Drevets & Raichle, 1998; Fox
et al., 2005). At first these findings were taken to mean
that thinking about animate agents requires a discrete
form of information processing unique to the social
domain (J. P. Mitchell, 2008b) and that cognition and
172
THE REINTEGRATION OF ACCURACY
socioemotional processes might be antagonistic to one
another (Drevets & Raichle, 1998). More recent mod-
els have focused on the specific computations that may
differentiate the demands of social versus nonsocial
inference (Buckner & Carroll, 2007; J. P. Mitchell,
2009b).
Although useful for clarifying the boundaries be-
tween social and nonsocial cognition, a strong focus on
dissociating their underlying processing systems may
miss ways in which the two kinds of systems collabo-
rate in everyday social interactions that demand more
than one kind of process come into play. This bears on
the previous discussion of cognitive control. Behav-
ioral work suggests that mind perception—and specif-
ically mental state attribution—is highly demanding,
and succeeding in it depends on the availability of ex-
ecutive control resources (Apperly & Butterfill, 2009;
Carlson & Moses, 2001). At first blush, this finding
seems to clash with evidence that executive control
and social cognition rely on different neural systems.
This confusion is partially cleared up, however, when
we consider that neuroimaging studies of mind per-
ception often employ social tasks with limited or no
executive demands (e.g., passive viewing of social tar-
gets), and when they do employ more difficult social
tasks (e.g., some studies of mental state attribution),
cognitive control demands are typically equated across
the critical mind perception and baseline control con-
ditions so as to isolate the neural correlates of mental
state attribution.
As such, distinctions between neural systems in-
volved in social and nonsocial information processing
may not reflect “deep” dissociations between the com-
putations underlying these phenomena. Instead, they
may reflect the simple fact that, to date, extant work has
largely focused on a particular question: Is mind per-
ception different from other cognitive abilities? This
question is addressed by attempting to isolate neural
systems preferentially engaged by the presentation of,
and judgments about, social cues. Typically, this is ac-
complished by stripping away the complexities of ev-
eryday social interaction to devise tasks simple enough
that they depend most critically on only the specific
mind perception process(es) of interest in a given study.
Asking a different question—for example, What
do social and nonsocial information processing have
in common?— suggests focusing on tasks that more
closely match the complex processing demands of “ev-
eryday” social cognition where cognitive control pro-
cesses might be important. For example, targets often
produce unclear or contradictory feedback about their
internal states, which perceivers must sort through or
choose between in order to be accurate. In such cases,
drawing accurate interpersonal inferences requires ad-
judication between multiple sources of social informa-
tion (e.g., a target who looks sad but sounds happy). It
is likely that these requirements functionally resemble
other forms of response conflict that engage executive
control centers in the brain. Studies of the neural cor-
relates of perceiving conflicting social cues bear out
this parallel by showing engagement of domain gen-
eral control systems (Decety & Chaminade, 2003; R.
L. Mitchell, 2006; Wittfoth et al., 2009) that interact
with regions in the ESS and MSAS to guide attention to
the cues that perceivers find most relevant to deciding
how targets feel (Zaki et al., 2010).
Thus, although the neural systems involved in mind
perception and nonsocial cognition can be dissociated,
exploring their common reliance on domain-general
control systems can illuminate some of their similari-
ties as well. Future work employing naturalistic social
tasks in combination with measures of accuracy may
serve to further characterize the links between mind
perception processes and other cognitive abilities.
Contributions to Clinical Approaches
Finally, an integration of processes and accuracy
has the potential to reframe thinking about clinical dis-
orders characterized by social cognitive deficits. As
with the study of healthy perceivers, this approach al-
lows for a shift away from viewing these disorders as
representing disruptions of single processes that invari-
ably cause social symptoms and toward a focus on (a)
seeing disorders as arising from abnormal profiles of
function in multiple processes and their interrelation-
ships and (b) examining the situation-specific effects
of these processing abnormalities on social symptoms.
Here we consider autism spectrum disorders (ASD) as
an example case.
Processes and Accuracy in Autism
Individuals with ASD famously fail to engage in
typical forms of interpersonal interactions (Lord et al.,
1997; Lord, Rutter, & Le Couteur, 1994; Wing &
Gould, 1979) and to normatively deploy mental state
attribution and experience sharing or engage the neu-
ral systems underlying these processes (Baron-Cohen
et al., 1985; Dapretto et al., 2006; Kennedy, Red-
cay, & Courchesne, 2006; Oberman, Ramachandran,
& Pineda, 2008; Rogers, Hepburn, Stackhouse, &
Wehner, 2003). The observed covariance between a
specific kind of processing dysfunction (e.g., experi-
ence sharing) and abnormal social function in ASD
is sometimes used as evidence that abnormalities
in single mind perception processes underlie social
deficits in autism (Baron-Cohen, 1994; Oberman &
Ramachandran, 2007). On this view, abnormalities in
either mental state attribution or experience sharing
lead, more or less directly, to the complex social symp-
toms evinced by ASD.
Although processes such as those supporting expe-
rience sharing no doubt play some role in ASD, single
173
ZAKI AND OCHSNER
process models fail to square with several lines of evi-
dence (Happe, Ronald, & Plomin, 2006). For example,
not all studies of ASD document problems in mind per-
ception tasks or their neural bases (Bird et al., 2010;
Bowler, 1992; Castelli, 2005; Fan, Decety, Yang, Liu,
& Cheng, 2010). Further, the few studies attempting
to directly link deficits in mind perception processes
with social symptom severity have yielded inconsis-
tent results (Dapretto et al., 2006; Fombonne, Siddons,
Achard, Frith, & Happe, 1994; Lombardo, Barnes,
Wheelwright, & Baron-Cohen, 2007; Rogers et al.,
2003; Tager-Flusberg, 2007). What’s more, interven-
tions aimed at encouraging the use of mind perception
processes (e.g., training in recognizing photographed
emotional faces) often produce improvements on these
tasks without causing any parallel improvements in
clinically assessed social deficits (Gevers, Clifford,
Mager, & Boer, 2006; Golan & Baron-Cohen, 2006;
Hadwin, Baron-Cohen, Howlin, & Hill, 1996, 1997;
Ozonoff & Miller, 1995). These disparities underscore
the gap between successfully deploying a particular
cognitive process and successfully interacting with oth-
ers.
The model we are advocating here suggests that two
conceptual shifts could better link information process-
ing to social function in ASD. First is the proposition
that adaptive social functioning depends on the simul-
taneous and concerted use of multiple processes to
support accurate understanding of the complex social
cues perceivers typically encounter. Extant tasks used
to assess impairments in ASD (such as motor imitation
or emotion identification using pictures) are ill suited
to capturing such deficits, because they are aimed at
assessing the deployment of single processes using
highly simplified stimuli. Second, abnormalities in the
operation of mind perception processes likely interact
with features of a situation (e.g., the target to which a
perceiver is paying attention), affecting social function
more in some situations and less so or not at all in
others. As such, moving beyond a “perceiver-centric”
take on mind perception could allow for mapping the
specific contextual domains in which the processing
deficits characterizing ASD are most damaging to pa-
tients.
Although there are only three extant studies of em-
pathic accuracy in ASD, they provide promising initial
support for the more nuanced view we are advocat-
ing here. For example, individuals with ASD demon-
strate more consistent impairments in empathic accu-
racy than in simpler, more canonical theory of mind
tasks (Demurie, De Corel, & Roeyers, 2011; Roeyers,
Buysse, Ponnet, & Pichal, 2001). Second, the one study
examining contextual effects on accuracy deficits sug-
gests that ASD individuals’ social cognitive problems
are indeed selective and related to the types of cues they
encounter. Specifically, ASD status predicted reduced
accuracy when perceivers observed unstructured inter-
actions between targets but not when they observed
targets interviewing each other in a structured format
(asking each other questions, such as “What do you
like to do in your spare time?”; see Ponnet, Buysse,
Roeyers, & De Clercq, 2008). These data suggest that
examining context-dependent deficits in accuracy can
help researchers map the domains in which individu-
als with ASD are likely to be more or less impaired
and how these impairments evolve out of mind per-
ception processes that support greater or lesser accu-
racy. Such an approach also suggests potential novel
interventions based not on erasing cognitive deficits
but rather on placing individuals in contexts/situations
where those cognitive deficits are less likely to
matter.
Consider anecdotal reports (Grandin & Barron,
2004) and empirical studies (Baron-Cohen, 2009;
Baron-Cohen, Richler, Bisarya, Gurunathan, & Wheel-
wright, 2003) suggesting that individuals with ASD of-
ten compensate for mind perception deficits by using
systemized rules to elaboratively “work out” the likely
experiences of social targets. Such a compensatory
strategy could be most useful when targets are produc-
ing clear and structured cues about their internal states
(as in the interview condition previously described)
of the type produced by emotionally expressive tar-
gets (Zaki et al., 2008; Zaki, Bolger, et al., 2009).
This suggests a form of intervention in which care-
givers and family members of individuals with ASD
could restructure their behavior to provide clear, read-
able cues about their internal states, thereby rendering
the information-processing issues inherent to ASD less
debilitating.
Although speculative, ideas like this one highlight
a broader point: Integrating measures of mind percep-
tion processes, accuracy, and adaptive functioning can
produce novel predictions about the domains in which
information-processing deficits lead to clinical dys-
function, and suggest situation-specific interventions
to alleviate such dysfunction.
Conclusions
Mind perception research has a long and some-
times rocky past. The early years were defined by
the hunt for accuracy. This hunt never captured its
quarry, which led researchers to focus almost exclu-
sively on the information-processing steps underpin-
ning social cognition—independent of accuracy. This
shift has been enormously successful in characterizing
the separable mechanisms through which perceivers
try to understand targets, and recent work identifying
neural signatures of these processes has made the en-
deavor even more compelling.
We have argued that mind perception research—
in focusing almost exclusively on process—has
174
THE REINTEGRATION OF ACCURACY
often chosen to ignore its past, and as a consequence
has been limited in some important ways. The major-
ity of current mind perception research is concerned
with determining whether and when a given cognitive
processes is in play—and neural correlates of these
processes are—in many cases without reference to
whether or to what extent one’s resulting understanding
of another person is accurate. Or put another way, we
know a lot about what processes people engage when
they try to make sense of others minds but less about
what determines whether they are accurate. This mat-
ters because in actual social encounters, a perceiver’s
goal is not simply to draw any old inference about
their social partners but (usually) to draw an accurate
one.
There seem to be two main reasons that the pro-
cess approach has failed to make contact with the ac-
curacy approach. First, researchers may believe that
accuracy is too difficult to quantify. Luckily, this con-
cern is outdated. Following a 25-year hibernation,
accuracy research has surged, producing a novel and
varied approaches to quantifying accuracy and identi-
fying its predictors. Second, even if process-oriented
researchers believe that accuracy can be quantified,
they may not believe doing so is relevant to their work.
Here, we hope to have shown that this view may be
shortsighted insofar as integration can provide several
novel insights, predictions, and lines of research. Fol-
lowing the framework previously outlined, the strength
of this approach comes from directly linking (either
behavioral or neural) signs that a process has been
deployed with signs that a perceiver has accurately de-
coded a target’s internal states, and in turn relating both
of these constructs to adaptive outcomes in the social
world. Critical to all of these connections is identifying
first the contextual boundaries that determine when a
process supports accuracy and, second, when accuracy
predicts social well-being.
In elaborating this approach, we have purposefully
constrained our focus to two processes—mental state
attribution and experience sharing—and on one form
of accuracy—the ability to correctly judge a target’s
dynamic emotional experience—because we consid-
ered a limited focus to be necessary for fully fleshing
out and illustrating the value of a process-accuracy
integration in a single article. However, we hope that
future work will port this approach to the study of other
processes, such as stereotyping and projection (Hoch,
1987; Judd & Park, 1993; Jussim, 1991; Neyer, Banse,
& Asendorpf, 1999); other forms of accuracy, such as
predictions of one’s own future experiences (Gilbert
et al., 1998; Kermer, Driver-Linn, Wilson, & Gilbert,
2006; Wilson, Wheatley, Meyers, Gilbert, & Axsom,
2000); and other forms of adaptive behavior, such as
successful negotiation, cooperation, and accurate pre-
diction about others’ actions (Coricelli & Nagel, 2009;
Galinsky, Maddux, Gilin, & White, 2008; Hampton,
Bossaerts, & O’Doherty, 2008; Valdesolo, Ouyang, &
DeSteno, 2010).
Overall, we believe that this approach can reframe
current data concerning mind perception processes,
prompt richer questions about how people understand
each other, and suggest new ways of testing these ques-
tions. The sorts of changes motivated by this approach
will vary depending on the phenomenon being stud-
ied and level of analysis being employed. In some
cases, questions will be refined from the categorical
(“Which process leads to accuracy?”) to the condi-
tional (“When will this process produce accuracy?). In
other cases, neuroscientists could shift away from char-
acterizing single systems (“What processing steps are
instantiated in the MSAS or ESS?”) and toward more
holistically viewing these systems’ role in naturalistic
situations (“How do the MSAS and ESS interact with
each other and with other systems—like those involve
in domain general cognitive control—to produce ac-
curate inferences?”). Finally, in clinical settings, this
could mean moving beyond characterizing psychiatric
disorders as arising from deficits in single processes
(“Do individuals with ASD fail to normatively em-
ploy mental state inference?”) to examining profiles of
information-processing abnormalities across multiple
processes in a broader social-cognitive context (“Un-
der which situations will failing to employ mental state
attribution most affect an ASD individual’s abilities to
interact with others, and is there a way to attenuate this
effect?”).
Let us end by saying that in no way do we wish
to suggest that the current, process-focused approach
dominating social cognition should be replaced by the
one described here. That would be as counterproduc-
tive as the screeching halt of accuracy research half a
century ago. Instead, we are making the simple point
that—as so often is the case when two approaches pro-
vide different angles on the same, complex question—
joining forces can be beneficial to everyone.
Note
Address correspondence to Jamil Zaki, Department
of Psychology, Harvard University, Northwest Science
Building, 52 Oxford Street, Cambridge, MA 02138.
E-mail: zaki@wjh.harvard.edu
References
Adolphs, R., Damasio, H., Tranel, D., Cooper, G., & Damasio, A. R.
(2000). A role for somatosensory cortices in the visual recog-
nition of emotion as revealed by three-dimensional lesion map-
ping. Journal of Neuroscience, 20, 2683–2690.
Allport, F., & Allport, G. (1921). Personality traits: Their classi-
fication and measurement. Journal of Abnormal and Social
Psychology, 16, 6–40.
175
ZAKI AND OCHSNER
Ambady, N., & Rosenthal, R. (1992). Thin slices of expressive be-
havior as predictors of interpersonal consequences: A meta-
analysis. Psychological Bulletin, 111, 256–274.
Ambady, N., & Rosenthal, R. (1993). Half a minute: Predicting
teacher evaluations from thin slices of nonverbal behavior and
physical attractiveness. Journal of Personality and Social Psy-
chology, 64, 431–441.
Ames, D. R. (2004). Inside the mind reader’s tool kit: Projection and
stereotyping in mental state inference. Journal of Personality
and Social Psychology, 87, 340–353.
Ames, D., Kammrath, L., Suppes, A., & Bolger, N. (2010). Not so
fast: The (not-quite-complete) dissociation between accuracy
and confidence in thin slice impressions. Personality and Social
Psychology Bulletin, 36, 264–277.
Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The
medial frontal cortex and social cognition. Nature Reviews.
Neuroscience, 7, 268–277.
Ansfield, M. E. (2007). Smiling when distressed: When a smile is a
frown turned upside down. Personality and Social Psychology
Bulletin, 33, 763–775.
Apperly, I. A. (2008). Beyond simulation-theory and theory-theory:
Why social cognitive neuroscience should use its own concepts
to study “theory of mind.” Cognition, 107(1), 266–283.
Apperly, I. A., & Butterfill, S. A. (2009). Do humans have two
systems to track beliefs and belief-like states? Psychological
Review, 116, 953–970.
Arriaga, X., & Rusbult, K. (1998). Standing in my partner’s shoes:
Partner perspective taking and reactions to accommodative
dilemmas. Personalityand Social Psychology Bulletin, 24, 927–
948.
Avenanti,A., Bueti, D., Galati, G., & Aglioti, S. M. (2005). Transcra-
nial magnetic stimulation highlights the sensorimotor side of
empathy for pain. Nature Reviews. Neuroscience, 8, 955–960.
Bailey, P. E., Henry, J. D., & Von Hippel, W. (2008). Empathy and
social functioning in late adulthood. Aging & Mental Health,
12, 499–503.
Bandura, A. (1978). The self system in reciprocal determinism.
American Psychologist, 33, 344–358.
Baron-Cohen, S. (1994). Mindblindness. Cambridge, MA: MIT
Press.
Baron-Cohen, S. (2009). Autism: The empathizing-systemizing (E-
S) theory. Annals of the New York Academy of Sciences, 1156,
68–80.
Baron-Cohen, S., Leslie, A. M., & Frith, U. (1985). Does the autistic
child have a “theory of mind”? Cognition, 21(1), 37–46.
Baron-Cohen, S., Richler, J., Bisarya, D., Gurunathan, N., & Wheel-
wright, S. (2003). The systemizing quotient: an investigation of
adults with Asperger syndrome or high-functioning autism, and
normal sex differences. Philosophical Transactions of the Royal
Society of London. Series B, Biological Sciences, 358(1430),
361–374.
Baron-Cohen, S., Ring, H. A., Wheelwright, S., Bullmore, E. T.,
Brammer, M. J., Simmons, A., et al. (1999). Social intelligence
in the normal and autistic brain: An fMRI study. The European
Journal of Neuroscience, 11, 1891–1898.
Barrett, L. F. (2009). The future of psychology: Connecting mind to
brain. Perspectives on Psychological Science, 4, 326–339.
Barrett, L. F., Mesquita, B., Ochsner, K. N., & Gross, J. J. (2007).
The experience of emotion. Annual Review of Psychology, 58,
373–403.
Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psy-
chology, 59, 617–645.
Bartz, J., Zaki, J., Bolger, N., Hollander, E., Ludwig, N.,
Kolevzon, A., et al. (2010). Oxytocin selectively improves em-
pathic accuracy in less socially proficient individuals. Psycho-
logical Science, 21(10), 1426–1428.
Batson, C. D. (1991). The Altruism Question: Toward A Social-
psychological Answer: Lawrence Erlbaum.
Baumeister, R. F., & Leary, M. R. (1995). The need to belong:
Desire for interpersonal attachments as a fundamental human
motivation. Psychological Bulletin, 117, 497–529.
Berkman, E. T., Falk, E. B., & Lieberman, M. D. (2011). In the
trenches of real-world self-control: Neural correlates of break-
ing the link between craving and smoking. Psychological Sci-
ence, 22(4), 498–506.
Bird, G., Silani, G., Brindley, R., White, S., Frith, U., & Singer, T.
(2010). Empathic brain responses in insula are modulated by
levels of alexithymia but not autism. Brain, 133(Pt. 5), 1515–
1525.
Bolger, N., & Eckenrode, J. (1991). Social relationships, person-
ality, and anxiety during a major stressful event. Journal of
Personality & Social Psychology, 61, 440–449.
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., &
Cohen, J. D. (2001). Conflict monitoring and cognitive control.
Psychological Review, 108, 624–652.
Bowler, D. (1992). Theory of mind in Asperger’s syndrome. Journal
of Child Psychology and Psychiatry, 33, 877–893.
Brass, M., Ruby, P., & Spengler, S. (2009). Inhibition of imitative
behaviour and social cognition. Philosophical Transactions of
the Royal Society of London. Series B, Biological Sciences,
364(1528), 2359–2367.
Brass, M., Schmitt, R. M., Spengler, S., & Gergely, G. (2007). In-
vestigating action understanding: inferential processes versus
action simulation. Current Biology, 17, 2117–2121.
Brewer, J. B., Zhao, Z., Desmond, J. E., Glover, G. H., & Gabrieli,
J. D. (1998). Making memories: Brain activity that predicts
how well visual experience will be remembered. Science, 281,
1185–1187.
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008).
The brain’s default network: anatomy, function, and relevance
to disease. Annals of the New York Academy of Sciences, 1124,
1–38.
Buckner, R. L., & Carroll, D. C. (2007). Self-projection and the
brain. Trends in Cognitive Sciences, 11(2), 49–57.
Buckner, R. L., Petersen, S. E., Ojemann, J. G., Miezin, F. M., Squire,
L. R., & Raichle, M. E. (1995). Functional anatomical studies
of explicit and implicit memory retrieval tasks. The Journal of
Neuroscience, 15(1, Pt. 1), 12–29.
Cacioppo, J. T., & Bernston, G. (1992). Social psychologi-
cal contributions to the decade of the brain: Doctrine of
multilevel analysis. The American Psychologist, 47, 1019–
1028.
Carlson, S. M., & Moses, L. J. (2001). Individual differences in
inhibitory control and children’s theory of mind. Child Devel-
opment, 72, 1032–1053.
Carton, J., Kessler, E., & Pape, C. (1999). Nonverbal decoding skills
and relationship to well-being in adults. Journal of Nonverbal
Behavior, 23(1), 91–100.
Castelli, F. (2005). Understanding emotions from standardized facial
expressions in autism and normal development. Autism, 9, 428–
449.
Castelli, F.,Frith, C., Happe, F., & Frith, U. (2002). Autism, Asperger
syndrome and brain mechanisms for the attribution of mental
states to animated shapes. Brain, 125(Pt. 8), 1839–1849.
Castelli, F., Happe, F., Frith, U., & Frith, C. (2000). Movement and
mind: A functional imaging study of perception and interpre-
tation of complex intentional movement patterns. Neuroimage,
12, 314–325.
Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: a review
of its functional anatomy and behavioural correlates. Brain,
129(Pt. 3), 564–583.
Chaiken, S., & Trope, Y. (Eds.). (1999). Dual process theories in
social psychology.NewYork,NY:Guilford.
Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect:
the perception-behavior link and social interaction. Journal of
Personality and Social Psychology, 76, 893–910.
176
THE REINTEGRATION OF ACCURACY
Chong, T. T., Williams, M. A., Cunnington, R., & Mattingley, J.
B. (2008). Selective attention modulates inferior frontal gyrus
activity during action observation. Neuroimage, 40(1), 298–
307.
Clements, K., Holtzworth-Munroe, A., Schweinle, W., & Ickes, W.
(2007). Empathic accuracy of intimate partners in violent versus
nonviolent relationships. Personal Relationships, 14, 369–388.
Cliffordson, C. (2002). The hierarchical structure of empathy: Di-
mensional organization and relations to social functioning.
Scandinavian Journal of Psychology, 43(1), 49–59.
Cohen, S., Doyle, W. J., Skoner, D. P., Rabin, B. S., & Gwaltney, J.
M., Jr. (1997). Social ties and susceptibility to the common cold.
Journal of the American Medical Association, 277, 1940–1944.
Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reorienting
system of the human brain: From environment to theory of
mind. Neuron, 58, 306–324.
Coricelli, G., & Nagel, R. (2009). Neural correlates of depth of
strategic reasoning in medial prefrontal cortex. Proceedings
of the National Academy of Sciences of the United States of
America, 106, 9163–9168.
Costanzo, M., & Archer, D. (1989). Interperting the expressive be-
havior of others: The Interpersonal Perception Task. Journal of
Nonverbal Behavior, 13, 225–245.
Craig, A. D. (2009). How do you feel—now? The anterior insula
and human awareness. Nature Reviews. Neuroscience, 10(1),
59–70.
Cronbach, L. (1955). Processes affecting scores on “understanding
of others” and “assumed similarity.Psychological Bulletin, 52,
177–193.
Dapretto, M., Davies, M. S., Pfeifer, J. H., Scott, A. A., Sigman,
M., Bookheimer, S. Y., & Iacoboni, M. (2006). Understanding
emotions in others: Mirror neuron dysfunction in children with
autism spectrum disorders. Nature Reviews.Neuroscience, 9(1),
28–30.
Daw, N. D., Niv, Y., & Dayan, P. (2005). Uncertainty-based compe-
tition between prefrontal and dorsolateral striatal systems for
behavioral control. Nature Reviews. Neuroscience, 8, 1704–
1711.
Decety, J. (1996). Do imagined and executed actions share the same
neural substrate? Brain Research. Cognitive Brain Research, 3,
87–93.
Decety, J., & Chaminade, T. (2003). Neural correlates of feeling
sympathy. Neuropsychologia, 41, 127–138.
Decety, J., & Jackson, P. L. (2004). The functional architecture of
human empathy. Behavioral and Cognitive Neuroscience Re-
views, 3, 71–100.
de Lange, F. P., Spronk, M., Willems, R. M., Toni, I., & Bekkering,
H. (2008). Complementary systems for understanding action
intentions. Current Biology, 18, 454–457.
Demurie, E., De Corel, M., & Roeyers, H. (2011). Empathic accu-
racy in adolescents with autism spectrum disorders and adoles-
cents with attention-deficit/hyperactivity disorder. Research in
Autism Spectrum Disorders, 5(1), 126–134.
Devine, P. (1989). Stereotypes and prejudice: Their automatic and
controlled components. Journal of Personality and Social Psy-
chology, 56(1), 5–18.
Devine, P. G., Plant, E. A., Amodio, D. M., Harmon-Jones, E., &
Vance, S. L. (2002). The regulation of explicit and implicit
race bias: The role of motivations to respond without prejudice.
Journal of Personality and Social Psychology, 82, 835–848.
Dijksterhuis, A., & Bargh, J. (2001). The perception-behavior Ex-
pressway: Automatic effects of social perception on social be-
havior. Advances in Experimental Social Psychology, 33, 1–40.
Dimberg, U., Thunberg, M., & Elmehed, K. (2000). Unconscious
facial reactions to emotional facial expressions. Psychological
Science, 11(1), 86–89.
Drevets, W., & Raichle, M. E. (1998). Reciprocal suppression of
regional cerebral blood flow during emotional versus higher
cognitive processes: Implications for interactions between emo-
tiona and cognition. Cognition and Emotion, 12, 353–385.
Dymond, R. (1949). A scale for the measurement of empathic ability.
Journal of Consulting Psychology, 13, 127–133.
Edwards, R., Manstead, A., & MacDonald, C. J. (1984). The re-
lationship between children’s sociometric status and ability to
recognize facial expressions of emotion. European Journal of
Social Psychology, 14, 235–238.
Eisenberg, N., & Miller, P. A. (1987). The relation of empathy to
prosocial and related behaviors. Psychological Bulletin, 101(1),
91–119.
Ekman, P., Friesen, W., & O’Sullivan,M. (1988). Smiles when lying.
Journal of Personality and Social Psychology, 54, 414–420.
Ekman, P., Sorenson, E. R., & Friesen, W. V. (1969). Pan-cultural
elements in facial displays of emotion. Science, 164(875), 86–
88.
Epley, N., Keysar, B., Van Boven, L., & Gilovich, T. (2004). Perspec-
tive taking as egocentric anchoring and adjustment. Journal of
Personality and Social Psychology, 87(3), 327–339.
Epley, N., & Waytz, A. (2009). Mind perception. In S. Fiske, D.
Gilbert & G. Lindzey (Eds.), The handbook of social psychology
(5th ed.; pp. 498–541). New York, NY: Oxford University Press.
Eyal, T., & Epley, N. (2010). How to seem telepathic: Enabling
mind reading by matching construal. Psychological Science,
21, 700–705.
Falk, E. B., Berkman, E. T., Mann, T., Harrison, B., & Lieberman,
M. D. (2010). Predicting persuasion-induced behavior change
from the brain. The Journal of Neuroscience, 30, 8421–8424.
Fan, Y. T., Decety, J., Yang, C. Y., Liu, J. L., & Cheng, Y. (2010).
Unbroken mirror neurons in autism spectrum disorders. Journal
of Child Psychology and Psychiatry, 51, 981–988.
Fernandez-Dols, J. M., Carrera, P., & Russell, J. A. (2002). Are facial
displays social? Situational influences in the attribution of emo-
tion to facial expressions. The Spanish Journal of Psychology,
5, 119–124.
Fiske, S. T.(1992). Thinking is for doing: Portraits of social cognition
from Daguerreotype to laserphoto. Journal of Personality and
Social Psychology, 63, 877–889.
Fiske, S. T. (1993). Social cognition and social perception. Annual
Review of Psychology, 44, 155–194.
Fiske, S. T. (1998). Stereotyping, prejudice, and discrimination. In
D. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook
of social psychology (4th ed., pp. 357–414). New York, NY:
McGraw Hill.
Fiske, S., & Taylor, S. F. (2007). Social cognition, from brains to
culture. New York, NY: McGraw Hill.
Flavell, J. (1999). Cognitive development: Children’s knowledge
about other minds. Annual Review of Psychology, 50, 21–45.
Fletcher, P. C., Happe, F., Frith, U., Baker, S. C., Dolan, R. J.,
Frackowiak, R. S., & Frith, C. D. (1995). Other minds in the
brain: A functional imaging study of “theory of mind” in story
comprehension. Cognition, 57, 109–128.
Fombonne, E., Siddons, F., Achard, S., Frith, U., & Happe, F. (1994).
Adaptive behaviour and theory of mind in autism. European
Child and Adolescent Psychiatry, 3, 176–186.
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen,
D., & Raichle, M. E. (2005). The human brain is intrinsically or-
ganized into dynamic, anticorrelated functional networks. Pro-
ceedings of the National Academy of Sciences of the United
States of America, 102, 9673–9678.
Freeman, J. B., & Ambady, N. (2011). A dynamic interactive theory
of person construal. Psychological Review, 118, 247–279.
Funder, D. (1987). Errors and mistakes: Evaluating the accuracy of
social judgments. Psychological Bulletin, 101(1), 75–90.
Funder, D. (1991). Global traits: A neo-Allportian approach to per-
sonality. Psychological Science, 2(1), 31–39.
Funder, D. C. (1995). On the accuracy of personality judgment: A
realistic approach. Psychological Review, 102, 652–670.
177
ZAKI AND OCHSNER
Gage, N., & Cronbach, L. (1955). Conceptual and methodological
problems in interpersonal perception. Psychological Review,
62, 411–422.
Gage, N. L., Leavitt, G., & Cronbach, L. (1956). The intermediary
key in the analysis of interpersonal perception. Psychological
Bulletin, 53, 258–266.
Galinsky, A. D., Maddux, W. W., Gilin, D., & White, J. B. (2008).
Why it pays to get inside the head of your opponent: The differ-
ential effects of perspective taking and empathy in negotiations.
Psychological Science, 19, 378–384.
Gallese, V., & Goldman, A. (1998). Mirror neurons and the simu-
lation theory of mind-reading. Trends in Cognitive Sciences, 2,
493–501.
Gallese, V., Keysers, C., & Rizzolatti, G. (2004). A unifying view of
the basis of social cognition. Trends in Cognitive Sciences, 8,
396–403.
Gevers, C., Clifford, P., Mager, M., & Boer, F. (2006). Brief report:
A theory-of-mind-based social-cognition training program for
school-aged children with pervasive developmental disorders:
an open study of its effectiveness. Journal of Autism and De-
velopmental Disorders, 36, 567–571.
Gigerenzer, G. (1999). Fast and frugal heuristics. In G. Gigerenzer
& P. M. Todd (Eds.), Simple heuristics that make us smart
(pp. 3–34). New York, NY: Oxford.
Gilbert, D. (1998). Ordinary personology. In D. Gilbert, S. T. Fiske,
& G. Lindzey (Eds.), The handbook of social psychology (4th
ed., pp. 89–150). New York, NY: McGraw Hill.
Gilbert, D. T., & Malone, P. S. (1995). The correspondence bias.
Psychological Bulletin, 117(1), 21–38.
Gilbert, D., Pelham, B., & Krull, D. (1989). On cognitive busyness:
When person perceivers meet persons perceived. Journal of
Personality & Social Psychology, 54, 733–740.
Gilbert, D. T., Pinel, E. C., Wilson, T. D., Blumberg, S. J., & Wheat-
ley, T. P. (1998). Immune neglect: a source of durability bias
in affective forecasting. Journal of Personality and Social Psy-
chology, 75, 617–638.
Gilovich, T., Medvec, V. H., & Savitsky, K. (2000). The spotlight
effect in social judgment: An egocentric bias in estimates of
the salience of one’s own actions and appearance. Journal of
Personality and Social Psychology, 78, 211–222.
Gleason, K., Jensen-Campbell, L., & Ickes, W. (2009). The role
of empathic accuracy in adolescents’ peer relations and ad-
justment. Personality and Social Psychology Bulletin, 35(8),
991–1011.
Gobbini, M. I., Koralek, A. C., Bryan, R. E., Montgomery, K. J., &
Haxby, J. V. (2007). Two takes on the social brain: a comparison
of theory of mind tasks. Journal of Cognitive Neuroscience, 19,
1803–1814.
Goel, V., Grafman, J., Sadato, N., & Hallett, M. (1995). Modeling
other minds. Neuroreport, 6, 1741–1746.
Golan, O., & Baron-Cohen, S. (2006). Systemizing empathy: Teach-
ing adults with Asperger syndrome or high-functioning autism
to recognize complex emotions using interactive multimedia.
Developmental Psychopathology, 18, 591–617.
Gopnik, A., & Wellman, H. (1992). Why the child’s theory of mind
really is a theory. Mind and Language, 7(1-2), 145–171.
Grandin, T., & Barron, S. (2004). Unwritten rules of social relation-
ships. New York, NY: Future Horizons.
Gross, J., & John, O. P. (1997). Revealing feelings: Facets of emo-
tional expressivity in self-reports, peer ratings, and behavior.
Journal of Personality and Social Psychology, 72, 435–448.
Hadwin, J., Baron-Cohen, S., Howlin, P., & Hill, K. (1996). Can
we teach children with autism to understand emotion, belief, or
pretense? Development and Psychopathology, 8, 345–365.
Hadwin, J., Baron-Cohen, S., Howlin, P., & Hill, K. (1997). Does
teaching theory of mind have an effect on the ability to develop
conversation in children with autism? Journal of Autism and
Developmental Disorders, 27, 519–537.
Halberstadt, A., Denham, S., & Dunsmore, J. (2001). Affective social
competence. Social Development, 10(1), 79–119.
Hall, J. A. (1979). Gender, gender roles, and nonverbal communica-
tion skills. In R. Rosenthal (Ed.), Skills in nonverbal communi-
cation (pp. 32–67). Cambridge, MA: Oelgeschlager, Gunn, &
Hain.
Hall, J. A., Andrzejewski, S., & Yopchick, J. (2009). Psychosocial
correlates of interpersonal sensitivity: A meta-analysis. Journal
of Nonverbal Behavior, 33, 149–180.
Hall, J. A., & Schmid Mast, M. (2007). Sources of accuracy in the
empathic accuracy paradigm. Emotion, 7, 438–446.
Hampton, A. N., Bossaerts, P., & O’Doherty, J. P. (2008). Neural
correlates of mentalizing-related computations during strategic
interactions in humans. Proceedings of the National Academy
of Sciences of the United States of America, 105, 6741–6746.
Happe, F., Ronald, A., & Plomin, R. (2006). Time to give up on a
single explanation for autism. Nature Reviews. Neuroscience,
9, 1218–1220.
Hawkley, L. C., Burleson, M. H., Berntson, G. G., & Cacioppo, J.
T. (2003). Loneliness in everyday life: Cardiovascular activity,
psychosocial context, and health behaviors. Journal of Person-
ality and Social Psychology, 85(1), 105–120.
Heal, J. (1996). Simulaton, theory, and content. In P. Carruthers &
P. Smith (Eds.), Theories of theories of mind. Cambridge, UK:
Cambridge University Press.
Heider, F. (1958). The psychology of interpersonal relations.New
York, NY: Wiley.
Henson, R. (2005). What can functional neuroimaging tell the ex-
perimental psychologist? The Quarterly Journal of Experi-
mental Psychology. A. Human Experimental Psychology, 58,
193–233.
Higgins, E. T., & Bargh, J. A. (1987). Social cognition and social
perception. Annual Review of Psychology, 38, 369–425.
Hoch, S. (1987). Perceived consensus and predictive accuracy: The
pros and cons of projection. Journal of Personality and Social
Psychology, 53, 221–234.
Iacoboni, M. (2009). Imitation, empathy, and mirror neurons. Annual
Review of Psychology, 60, 653–670.
Ickes, W. (1997). Empathic accuracy. New York, NY: Guilford.
Ickes, W., Buysse, A., Pham, H., Rivers, K., Erickson, J., Hancock,
M., et al. (2000). On the difficulty of distinguishing “good”
and “poor” perceivers: A social relations analysis of empathic
accuracy data. Personal Relationships, 7, 219–234.
Ickes, W., Stinson, L., Bissonnette, V., & Garcia, S. (1990). Natural-
istic social cognition: Empathic accuracy in mixed-sex dyads.
Journal of Personality & Social Psychology, 59, 730–742.
Idson, L. C., & Mischel, W. (2001). The personality of familiar
and significant people: The lay perceiver as a social-cognitive
theorist. Journal of Personality and Social Psychology, 80, 585–
596.
Jabbi, M., Swart, M., & Keysers, C. (2007). Empathy for positive
and negative emotions in the gustatory cortex. Neuroimage, 34,
1744–1753.
Jackson, P. L., Meltzoff, A. N., & Decety, J. (2005). How do we
perceive the pain of others? A window into the neural processes
involved in empathy. Neuroimage, 24, 771–779.
Jacob, P., & Jeannerod, M. (2005). The motor theory of social cog-
nition: A critique. Trends in Cognitive Sciences, 9(1), 21–25.
Johnson, D. (1975). Cooperativeness and social perspective tak-
ing. Journal of Personality and Social Psychology, 31, 241–
244.
Jones, E., & Harris, V. (1967). The attribution of attitudes. Journ al
of Experimental and Social Psychology, 3(1), 1–24.
Judd, C. M., & Park, B. (1993). Definition and assessment of accu-
racy in social stereotypes. Psychological Review, 100(1), 109–
128.
Jussim, L. (1991). Social perception and social reality: A reflection-
construction model. Psychological Review, 98, 54–73.
178
THE REINTEGRATION OF ACCURACY
Jussim, L. (2005). Accuracy: Criticisms, controversies, criteria, and
cognitive processes. Advances in Experimental Social Psychol-
ogy, 37, 1–93.
Kahneman, D., & Tversky, A. (1996). On the reality of cognitive
illusions. Psychological Review, 103, 582–591.
Kelley, H. (1973). The process of causal attribution. The American
Psychologist, 28, 107–128.
Kelly, K., & Metcalfe, J. (in press). Metacognition of emotional face
recognition. Emotion.
Kennedy,D. P., Redcay, E., & Courchesne, E. (2006). Failing to deac-
tivate: Resting functional abnormalities in autism. Proceedings
of the National Academy of Sciences of the United States of
America, 103, 8275–8280.
Kenny, D. A. (1991). A general model of consensus and accu-
racy in interpersonal perception. Psychological Review, 98,
155–163.
Kenny, D. A., & Albright, L. (1987). Accuracy in interpersonal
perception: A social relations analysis. Psychological Bulletin,
102, 390–402.
Kermer, D. A., Driver-Linn, E., Wilson, T. D., & Gilbert, D. T.
(2006). Loss aversion is an affective forecasting error. Psycho-
logical Science, 17, 649–653.
Keysers, C., & Gazzola, V. (2007). Integrating simulation and theory
of mind: From self to social cognition. Trends in Cognitive
Sciences, 11, 194–196.
Keysers, C., & Gazzola, V. (2009). Expanding the mirror: vicarious
activity for actions, emotions, and sensations. Current Opinions
in Neurobiology, 19, 666–671.
Keysers, C., Kaas, J. H., & Gazzola, V. (2010). Somatosensa-
tion in social perception. Nature Reviews. Neuroscience, 11,
417–428.
Keysers, C., Wicker, B., Gazzola, V., Anton, J. L., Fogassi, L., &
Gallese, V. (2004). A touching sight: SII/PV activation during
the observation and experience of touch. Neuron, 42, 335–346.
Klein, K., & Hodges, S. (2001). Gender differences, motivation and
empathic accuracy: When it pays to understand. Personality
and Social Psychology Bulletin, 27, 720–730.
Kober, H., Barrett, L. F., Joseph, J., Bliss-Moreau, E., Lindquist,
K., & Wager, T. D. (2008). Functional grouping and cortical-
subcortical interactions in emotion: a meta-analysis of neu-
roimaging studies. Neuroimage, 42, 998–1031.
Kosslyn, S. M., Thompson, W. L., & Alpert, N. M. (1997). Neural
systems shared by visual imagery and visual perception: A
positron emission tomography study. Neuroimage, 6, 320–334.
Krueger, J. I., & Funder, D. C. (2004). Towards a balanced social
psychology: Causes, consequences, and cures for the problem-
seeking approach to social behavior and cognition. The Behav-
ioral and Brain Sciences, 27, 313–327.
Kruger, J. (1999). Lake Wobegon be gone! The “below-average
effect” and the egocentric nature of comparative ability judg-
ments. Journal of Personality and Social Psychology, 77, 221–
232.
Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it:
how difficulties in recognizing one’s own incompetence lead
to inflated self-assessments. Journal of Personality and Social
Psychology, 77, 1121–1134.
Kruglanski, A. W. (1989). The psychology of being “right”: The
problem of accuracy in social perception and cognition. Psy-
chological Bulletin, 106, 395–409.
Kruglanski, A. W., & Freund, Y. (1983). The freezing and unfreez-
ing of lay-inferences: Effects on impressional primacy, ethnic
stereotyping, and numerical anchoring. Journal of Experimen-
tal and Social Psychology, 19, 448–468.
Kunda, Z. (1990). The case for motivated reasoning. Psychological
Bulletin, 108, 480–498.
Kunda, Z., & Thagard, P. (1996). Forming impressions from stereo-
types, traits, and behaviors: A parallel-constraint-satisfaction
theory. Psychological Review, 103, 284–308.
Lamm, C., Batson, C. D., & Decety, J. (2007). The neural substrate
of human empathy: effects of perspective-taking and cognitive
appraisal. J Cogn Neurosci, 19(1), 42–58.
Lamm, C., & Singer, T. (2010). The role of anterior insular cortex in
social emotions. Brain Structure & Function, 214, 579–591.
Leslie, A. M., Friedman, O., & German, T. P. (2004). Core mech-
anisms in “theory of mind.” Trends in Cognitive Sciences, 8,
528–533.
Levenson, R. W., & Ruef, A. M. (1992). Empathy: A physiological
substrate. Journal of Personality & Social Psychology, 63, 234–
246.
Lieberman, M. D. (2005). Principles, processes, and puzzles of so-
cial cognition: An introduction for the special issue on social
cognitive neuroscience. Neuroimage, 28, 745–756.
Lieberman, M. D. (2007). Social cognitive neuroscience: A review
of core processes. Annual Review of Psychology, 58, 259–289.
Lindquist, K. A., & Barrett, L. F. (2008). Constructing emotion: The
experience of fear as a conceptual act. Psychological Science,
19, 898–903.
Lipps, T. (1903). Einf¨
uhlung, innere Nachahmung und Or-
ganempfindung [Empathy, imagination and internal organ sen-
sation]. Archiv f¨
ur die gesamte Psychologie, 1, 465–519.
Lombardo, M. V., Barnes, J. L., Wheelwright, S. J., & Baron-Cohen,
S. (2007). Self-referential cognition and empathy in autism.
PLoS ONE, 2, e883.
Long, E., & Andrews, D. (1990). Perspective taking as a predic-
tor of marital adjustment. Journal of Personality and Social
Psychology, 59(1), 126–131.
Lopes, P. N., Grewal, D., Kadis, J., Gall, M., & Salovey, P. (2006).
Evidence that emotional intelligence is related to job perfor-
mance and affect and attitudes at work. Psicothema, 18(Suppl.),
132–138.
Lord, C., Pickles, A., McLennan, J., Rutter, M., Bregman, J., Fol-
stein, S., et al. (1997). Diagnosing autism: Analyses of data
from the Autism Diagnostic Interview. Journal of Autism and
Developmental Disorders, 27, 501–517.
Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic
Interview–Revised: A revised version of a diagnostic interview
for caregivers of individuals with possible pervasive develop-
mental disorders. Journal of Autism and Developmental Disor-
ders, 24, 659–685.
Macrae, C. N., & Bodenhausen, G. V. (2000). Social cognition:
Thinking categorically about others. Annual Review of Psy-
chology, 51, 93–120.
Macrae, C. N., Moran, J. M., Heatherton, T. F., Banfield, J. F., & Kel-
ley, W. M. (2004). Medial prefrontal activity predicts memory
for self. Cerebral Cortex, 14, 647–654.
Malloy, T., & Kenny, D. (2006). The Social Relations Model: An
integrative method for personality research. Journal of Person-
ality, 54(1), 199–225.
Marci, C. D., & Orr, S. P. (2006). The effect of emotional distance
on psychophysiologic concordance and perceived empathy be-
tween patient and interviewer. Applied Psychophysiology and
Biofeedback, 31, 115–128.
Mayer, J. D., DiPaolo, M., & Salovey, P. (1990). Perceiving affective
content in ambiguous visual stimuli: A component of emotional
intelligence. Journal of Personality Assessment, 54, 772–781.
Mayer, J. D., Salovey, P., & Caruso, D. R. (2008). Emotional intel-
ligence: New ability or eclectic traits? The American Psychol-
ogist, 63, 503–517.
Mayer, J. D., Salovey, P., Caruso, D. R., & Sitarenios, G. (2001).
Emotional intelligence as a standard intelligence. Emotion, 1,
232–242.
McWirther, B., Besett-Alesch, T., Horibata, J., & Gat, I. (2002).
Loneliness in high risk adolescents: The role of coping, self-
esteem, and empathy. Journal of Youth Studies, 5(1), 69–84.
Meltzoff, A. N., & Decety, J. (2003). What imitation tells us about
social cognition: A rapprochement between developmental
179
ZAKI AND OCHSNER
psychology and cognitive neuroscience. Philosophical Trans-
actions of the Royal Society of London. Series B, Biological
Sciences, 358, 491–500.
Meltzoff, A. N., & Moore, M. K. (1977). Imitation of facial and
manual gestures by human neonates. Science, 198(4312), 75–
78.
Mischel, W. (1968). Personality and assessment.NewYork,NY:
Wiley.
Mischel, W. (1973). Towards a cognitive social learning reconcep-
tualization of personality. Psychological Review, 8, 252–283.
Mischel, W., & Shoda, Y. (1995). A cognitive-affective system the-
ory of personality: reconceptualizing situations, dispositions,
dynamics, and invariance in personality structure. Psychologi-
cal Review, 102, 246–268.
Mitchell, J. P. (2005). The false dichotomy between simulation and
theory–theory: The argument’s error. Trends in Cognitive Sci-
ences, 9, 363–364.
Mitchell, J. P. (2006). Mentalizing and Marr: An information pro-
cessing approach to the study of social cognition. Brain Re-
search, 1079(1), 66–75.
Mitchell, J. P. (2008a). Activity in right temporo-parietal junction is
not selective for theory-of-mind. CerebralCortex, 18, 262–271.
Mitchell, J. P. (2008b). Contributions of functional neuroimaging to
the study of social cognition. Current Directions in Psycholog-
ical Science, 17, 142–146.
Mitchell, J. P. (2009a). Inferences about mental states. Philosoph-
ical Transactions of the Royal Society of London. Series B,
Biological Sciences, 364, 1309–1316.
Mitchell, J. P. (2009b). Social psychology as a natural kind. Trends
in Cognitive Sciences, 13, 246–251.
Mitchell, J. P., Heatherton, T. F., & Macrae, C. N. (2002). Distinct
neural systems subserve person and object knowledge. Pro-
ceedings of the National Academy of Sciences of the United
States of America, 99, 15238–15243.
Mitchell, J. P., Macrae, C. N., & Banaji, M. R. (2004). Encoding-
specific effects of social cognition on the neural correlates of
subsequent memory. The Journal of Neuroscience, 24, 4912–
4917.
Mitchell, R. L. (2006). How does the brain mediate interpretation of
incongruent auditory emotions? The neural response to prosody
in the presence of conflicting lexico-semantic cues. The Euro-
pean Journal of Neuroscience, 24, 3611–3618.
Morrison, I., Lloyd, D., di Pellegrino, G., & Roberts, N. (2004).
Vicarious responses to pain in anterior cingulate cortex: Is em-
pathy a multisensory issue? Cognitive, Affective & Behavioral
Neuroscience, 4, 270–278.
Moskowitz, D., & Schwarz, J. (1982). Validity comparison of behav-
ior counts and ratings by knowledgeable informants. Journal of
Personality and Social Psychology, 42, 518–528.
Mukamel, R., Ekstrom, A. D., Kaplan, J., Iacoboni, M., & Fried,
I. (2010). Single-neuron responses in humans during ex-
ecution and observation of actions. Current Biology, 20,
750–756.
Nave, C., Sherman, R., Funder, D., Hampson, S., & Goldberg, L.
(2010). On the contextual independence of personality: Teach-
ers’ assessments predict directly observed behavior after four
decades. Social Psychological and Personality Science, 1, 327–
334.
Neisser, U. (1967). Cognitive psychology. New York, NY: Appleton-
Century-Crofts.
Neumann, R., & Strack, F.(2000). “Mood contagion”: The automatic
transfer of mood between persons. Journal of Personality and
Social Psychology, 79, 211–223.
Neyer, F., Banse, R., & Asendorpf, J. (1999). The role of projec-
tion and empathic accuracy in dyadic perception between older
twins. Journal of Social and Personal Relationships, 16, 419–
442.
Niedenthal, P., Barsalou, L. W., Ric, F., & Krauth-Gruber, S. (2005).
Embodiment in the acquisition and use of emotion knowledge.
In L. Feldman Barrett, P. Niedenthal, & P. Winkielman (Eds.),
Emotion and consciousness (pp. 21–50). New York, NY: Guil-
ford.
Niedenthal, P. M., Barsalou, L. W., Winkielman, P., Krauth-Gruber,
S., & Ric, F.(2005). Embodiment in attitudes, social perception,
and emotion. Personality and Social Psychology Review, 9,
184–211.
Nisbett, R., & Wilson, T. (1977). Telling more than we can know:
Verbal reports on mental processes. Psychological Review, 84,
231–259.
Noordewier, M. K., & Stapel, D. A. (2009). Judging the unexpected:
Disconfirmation of situation-specific expectancies. European
Journal of Social Psychology, 39, 944–956.
North, M., Todorov, A., & Osherson, D. (2010). Inferring the prefer-
ences of others from spontaneous, low-emotional facial expres-
sions. Journal of Experimental and Social Psychology, 13(3),
279–301.
Nowicki, S., & Duke, M. (1994). Individual differences in the non-
verbal communcation of affect: The Diagnostic Analysis of
Nonverbal Accuracy Scale. Journal of Nonverbal Behavior, 18,
9–35.
Oberman, L. M., & Ramachandran, V. S. (2007). The simulating so-
cial mind: The role of the mirror neuron system and simulation
in the social and communicative deficits of autism spectrum
disorders. Psychological Bulletin, 133, 310–327.
Oberman, L. M., Ramachandran, V. S., & Pineda, J. A. (2008).
Modulation of mu suppression in children with autism spec-
trum disorders in response to familiar or unfamiliar stimuli:
The mirror neuron hypothesis. Neuropsychologia, 46, 1558–
1565.
Ochsner, K. (2007). Social cognitive neuroscience: Historical devel-
opment, core principles, and future promise. In A. Kruglanksi
& E. T. Higgins (Eds.), Social psychology: A handbook of basic
principles (pp. 39–66). New York, NY: Guilford.
Ochsner, K. N., Knierim, K., Ludlow, D. H., Hanelin, J., Ramachan-
dran, T., Glover, G., et al. (2004). Reflecting upon feelings:
An fMRI study of neural systems supporting the attribution of
emotion to self and other. Journal of Cognitive Neuroscience,
16, 1746–1772.
Ochsner, K. N., & Lieberman, M. D. (2001). The emergence of
social cognitive neuroscience. The American Psychologist, 56,
717–734.
Ochsner, K. N., Zaki, J., Hanelin, J., Ludlow, D. H., Knierim, K.,
Ramachandran, T., et al. (2008). Your pain or mine? Common
and distinct neural systems supporting the perception of pain
in self and others. Social Cognitive Affective Neuroscience, 3,
144–160.
Olsson, A., & Ochsner, K. N. (2008). The role of social cognition in
emotion. Trends in Cognitive Sciences, 12(2), 65–71.
Ozonoff, S., & Miller, J. N. (1995). Teaching theory of mind: A
new approach to social skills training for individuals with
autism. Journal of Autism and Developmental Disorders, 25,
415–433.
Peelen, M. V., Atkinson, A. P., & Vuilleumier, P. (2010). Supramodal
representations of perceived emotions in the human brain. The
Journal of Neuroscience, 30, 10127–10134.
Pfeifer, J. H., Iacoboni, M., Mazziotta, J. C., & Dapretto, M. (2008).
Mirroring others’ emotions relates to empathy and interpersonal
competence in children. Neuroimage, 39, 2076–2085.
Pickett, C. L., Gardner, W. L., & Knowles, M. (2004). Getting a cue:
The need to belong and enhanced sensitivity to social cues.
Personality & Social Psychology Bulletin, 30, 1095–1107.
Ponnet, K., Buysse, A., Roeyers, H., & De Clercq, A. (2008). Mind-
reading in young adults with ASD: Does structure matter? Jour-
nal of Autism and Developmental Disorders, 38, 905–918.
180
THE REINTEGRATION OF ACCURACY
Preston, S. D., & de Waal, F. B. (2002). Empathy: Its ultimate and
proximate bases. The Behavioral and Brain Sciences, 25(1),
1–20.
Prinz, W. (1997). Perception and action planning. European Journal
of Cognitive Psychology, 9, 129–154.
Rieskamp, J., & Otto, P. E. (2006). SSL: A theory of how people
learn to select strategies. Journal of Experimental and Social
Psychology General, 135, 207–236.
Riggio, R., Tucker, J., & Coffaro, D. (1989). Social skills and empa-
thy. Personality & Individual Differences, 10(1), 93–99.
Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system.
Annual Review of Neuroscience, 27, 169–192.
Rizzolatti, G., & Sinigaglia, C. (2010). The functional role of the
parieto-frontal mirror circuit: Interpretations and misinterpre-
tations. Nature Reviews. Neuroscience, 11, 264–274.
Roeyers, H., Buysse, A., Ponnet, K., & Pichal, B. (2001). Advancing
advanced mind-reading tests: Empathic accuracy in adults with
a pervasive developmental disorder. Journal of Child Psychol-
ogy and Psychiatry, 42, 271–278.
Rogers, S. J., Hepburn, S. L., Stackhouse, T., & Wehner, E. (2003).
Imitation performance in toddlers with autism and those with
other developmental disorders. Journal of Child Psychology
and Psychiatry, 44, 763–781.
Rosenthal, R., Hall, J. A., DiMatteo, M., Rogers, P., & Archer, D.
(1979). Sensitivity to nonverbal communication: The PONS
Tes t . Baltimore, MD: Johns Hopkins University Press.
Ross, L., Greene, D., & House, P. (1977). The false consensus ef-
fect: An egocentric bias in social perception and attribution
processes. J Exp Soc Psychol, 13(3), 279–301.
Russell, J. A., Bachorowski, J. A., & Fernandez-Dols, J. M. (2003).
Facial and vocal expressions of emotion. Annual Review of
Psychology, 54, 329–349.
Saarela, M. V., Hlushchuk, Y., Williams, A. C., Schurmann, M.,
Kalso, E., & Hari, R. (2007). The compassionate brain: Humans
detect intensity of pain from another’s face. Cerebral Cortex,
17, 230–237.
Saxe, R. (2005). Against simulation: The argument from error.
Trends in Cognitive Sciences, 9, 174–179.
Saxe, R. (2006). Uniquely human social cognition. Current Opinions
in Neurobiology, 16, 235–239.
Saxe, R., Carey, S., & Kanwisher, N. (2004). Understanding other
minds: Linking developmental psychology and functional neu-
roimaging. Annual Review of Psychology, 55, 87–124.
Saxe, R., & Kanwisher, N. (2003). People thinking about thinking
people. The role of the temporo-parietal junction in “theory of
mind.” Neuroimage, 19, 1835–1842.
Schacter, D. L. (1997). Searching for memory: The brain, the mind,
and the past. New York, NY: Basic Books.
Schacter, D. L., Addis, D. R., & Buckner,R. L. (2007). Remembering
the past to imagine the future: the prospective brain. Nature
Reviews Neurosciences, 8(9), 657–661.
Schippers, M. B., Gazzola, V., Goebel, R., & Keysers, C. (2009).
Playing charades in the fMRI: Are mirror and/or mentalizing
areas involved in gestural communication? PLoS One, 4(8),
e6801.
Schippers, M. B., Roebroeck, A., Renken, R., Nanetti, L., & Key-
sers, C. (2010). Mapping the information flow from one brain
to another during gestural communication. Proceedings of the
National Academy of Sciences of the United States of America,
107, 9388–9393.
Schneider, D., Hastorf, A., & Ellsworth, P. (1979). Person percep-
tion. Princeton, NJ: Wiley.
Shamay-Tsoory, S. G. (2010). The neural bases for empathy. The
Neuroscientist, 17, 18–24.
Shamay-Tsoory, S. G., Aharon-Peretz, J., & Perry, D. (2009). Two
systems for empathy: A double dissociation between emotional
and cognitive empathy in inferior frontal gyrus versus ventro-
medial prefrontal lesions. Brain, 132(Pt. 3), 617–627.
Shamay-Tsoory, S. G., Tomer, R., Berger, B. D., Goldsher, D., &
Aharon-Peretz, J. (2005). Impaired “affective theory of mind”
is associated with right ventromedial prefrontal damage. Cog-
nitive and Behavioral Neurology, 18(1), 55–67.
Simpson, J. A., Ickes, W., & Blackstone, T. (1995). When the head
protects the heart: Empathic accuracy in dating relationships.
Journal of Personality & Social Psychology, 69, 629–641.
Simpson, J. A., Orina, M. M., & Ickes, W. (2003). When accu-
racy hurts, and when it helps: A test of the empathic accuracy
model in marital interactions. Journal of Personality & Social
Psychology, 85, 881–893.
Singer, T. (2006). The neuronal basis and ontogeny of empathy and
mind reading: review of literature and implications for future
research. Neuroscience and Biobehavioral Reviews, 30, 855–
863.
Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J.,
& Frith, C. D. (2004). Empathy for pain involves the affec-
tive but not sensory components of pain. Science, 303, 1157–
1162.
Smith, A. (2002). The theory of moral sentiments. Cambridge, UK:
Cambridge University Press. (Original work published 1790)
Smith, E., & DeCoster, J. (2000). Dual-process models in social and
cognitive psychology: Conceptual integration and links to un-
derlying memory systems. Personality and Social Psychology
Review, 4, 108–131.
Smith, E. R., & Collins, E. C. (2009). Contextualizing person percep-
tion: Distributed social cognition. Psychological Review, 116,
343–364.
Spence, S. (1987). The relationship between social-cognitive skills
and peer sociometric status. British Journal of Developmental
Psychology, 5(4), 347–356.
Spreng, R. N., Mar, R. A., & Kim, A. S. (2009). The common
neural basis of autobiographical memory, prospection, navi-
gation, theory of mind, and the default mode: a quantitative
meta-analysis. Journal of Cognitive Neuroscience, 21(3), 489–
510.
Spunt, R. P., & Lieberman, M. D. (2011, January). The busy social
brain: An fMRI study of cognitive load during action observa-
tion. Paper presented at the Society for Personality and Social
Psychology, San Antonio, TX.
Spunt, R. P., Satpute, A. B., & Lieberman, M. D. (2010). Identify-
ing the what, why, and how of an observed action: An fMRI
study of mentalizing and mechanizing during action observa-
tion. Journal of Cognitive Neuroscience, 23(1), 63–74.
Stephens, G. J., Silbert, L. J., & Hasson, U. (2010). Speaker–listener
neural coupling underlies successful communication. Proceed-
ings of the National Academy of Sciences of the United States
of America, 107, 14425–14430.
Stinson, L., & Ickes, W. (1992). Empathic accuracy in the interac-
tions of male friends versus male strangers. Journal of Person-
ality & Social Psychology, 62, 787–797.
Swann, W. B., Jr. (1984). Quest for accuracy in person perception:
A matter of pragmatics. Psychological Review, 91, 457–477.
Swann, W. B., Jr., & Ely, R. J. (1984). A battle of wills: Self-
verification versus behavioral confirmation. Journal of Person-
ality and Social Psychology, 46, 1287–1302.
Swann, W. B., Jr., Hixon, J. G., Stein-Seroussi, A., & Gilbert, D. T.
(1990). The fleeting gleam of praise: Cognitive processes un-
derlying behavioral reactions to self-relevant feedback. Journal
of Personality and Social Psychology, 59(1), 17–26.
Taft, R. (1955). The ability to judge people. Psychological Bulletin,
52(1), 1–22.
Tager-Flusberg, H. (2007). Evaluating the theory-of-mind hypothesis
in autism. Current Directions in Psychological Science, 16,
311–315.
Taylor, S. E., & Brown, J. D. (1988). Illusion and well-being: A so-
cial psychological perspective on mental health. Psychological
Bulletin, 103, 193–210.
181
ZAKI AND OCHSNER
Tetlock, P. E., & Kim, J. I. (1987). Accountability and judgment
processes in a personality prediction task. Journal of Personality
and Social Psychology, 52, 700–709.
Turner, J. (1991). Social influence. Pacific Grove, CA: Brooks/Cole.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty:
Heuristics and biases. Science, 185, 1124–1131.
Uddin, L. Q., Iacoboni, M., Lange, C., & Keenan, J. P. (2007). The
self and social cognition: The role of cortical midline struc-
tures and mirror neurons. Trends in Cognitive Sciences, 11,
153–157.
Valdesolo, P., Ouyang, J., & DeSteno, D. (2010). The rhythm of
joint action: Synchrony promotes cooperative ability. Journal
of Experimental and Social Psychology, 46, 693–695.
Vallacher, R., & Wegner, D. (1987). What do people think they’re do-
ing? Action identification and human behavior. Psychological
Review, 94(1), 3–15.
van Overwalle, F., & Baetens, K. (2009). Understanding others’
actions and goals by mirror and mentalizing systems: A meta-
analysis. Neuroimage, 48, 564–584.
Vaughan, K. B., & Lanzetta, J. T. (1980). Vicarious instigation and
conditioning of facial expressive and autonomic responses to a
model’s expressive display of pain. Journal of Personality and
Social Psychology, 38, 909–923.
Verhofstadt, L. L., Buysse, A., Ickes, W., Davis, M., & Devoldre,
I. (2008). Support provision in marriage: the role of emo-
tional similarity and empathic accuracy. Emotion, 8, 792–
802.
Wager, T. D., Jonides, J., & Reading, S. (2004). Neuroimaging
studies of shifting attention: A meta-analysis. Neuroimage, 22,
1679–1693.
Wagner, A. D., Schacter, D. L., Rotte, M., Koutstaal, W., Maril,
A., Dale, A. M., et al. (1998). Building memories: Remember-
ing and forgetting of verbal experiences as predicted by brain
activity. Science, 281, 1188–1191.
Wellman, H. M., Cross, D., & Watson, J. (2001). Meta-analysis of
theory-of-mind development: The truth about false belief. Child
Development, 72, 655–684.
Wicker, B., Keysers, C., Plailly, J., Royet, J. P., Gallese, V., & Riz-
zolatti, G. (2003). Both of us disgusted in My insula: The com-
mon neural basis of seeing and feeling disgust. Neuron, 40,
655–664.
Wilson, T. D., Wheatley, T., Meyers, J. M., Gilbert, D. T., & Axsom,
D. (2000). Focalism: A source of durability bias in affective
forecasting. Journal of Personality and Social Psychology, 78,
821–836.
Wing, L., & Gould, J. (1979). Severe impairments of social inter-
action and associated abnormalities in children: Epidemiology
and classification. Journal of Autism and Developmental Dis-
orders, 9(1), 11–29.
Wittfoth, M., Schr¨
oder, C., Schardt, D. M., Dengler, R., Heinze, H.
J., & Kotz, S. A. (2009). On emotional conflict: Interference
resolution of happy and angry prosody reveals valence-specific
effects. Cerebral Cortex, 20, 383–392.
Wolf, I., Dziobek, I., & Heekeren, H. R. (2010). Neural correlates of
social cognition in naturalistic settings: A model-free analysis
approach. Neuroimage, 49(1), 894–904.
Ybarra, O., Keller, M., Chan, E., Garcia, S., Sanchez-Burks, J., Rios
Morrison, K., et al. (2010). Being unpredictable: Friend or foe
matters. Social Psychological and Personality Science, 1,259
267.
Zaki, J., Bolger, N., & Ochsner, K. (2008). It takes two: The inter-
personal nature of empathic accuracy. Psychological Science,
19, 399–404.
Zaki, J., Bolger, N., & Ochsner, K. (2009). Unpacking the informa-
tional bases of empathic accuracy. Emotion, 9, 478–487.
Zaki, J., Davis, J. I., & Ochsner, K. (2011). The role of interoceptive
cortex in emotion and empathy. Manuscript in preparation.
Zaki, J., Hennigan, K., Weber, J., & Ochsner, K. N. (2010). Social
cognitive conflict resolution: Contributions of domain-general
and domain-specific neural systems. The Journal of Neuro-
science, 30, 8481–8488.
Zaki, J., & Ochsner, K. (2009). The need for a cognitiveneuroscience
of naturalistic social cognition. Annals of the New York Academy
of Sciences, 1167, 16–30.
Zaki, J., & Ochsner, K. (2011a). Empathic accuracy, but not self-
reported empathy, predicts social functioning in college stu-
dents. Manuscript in preparation.
Zaki, J., & Ochsner, K. (2011b). You, me, and my brain: Self and
other representations in social cognitive neuroscience. In A.
Todorov, S. T. Fiske, & D. Prentice (Eds.), Social neuroscience:
Toward understanding the underpinnings of the social mind.
New York, NY: Oxford University Press.
Zaki, J., Weber, J., Bolger, N., & Ochsner, K. (2009). The neu-
ral bases of empathic accuracy. Proceedings of the National
Academy of Sciences of the United States of America, 106,
11382–11387.
182
Psychological Inquiry, 22: 183–186, 2011
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ISSN: 1047-840X print / 1532-7965 online
DOI: 10.1080/1047840X.2011.573767
COMMENTARIES
Interacting Minds: A Framework for Combining Process- and
Accuracy-Oriented Social Cognitive Research
Bahador Bahrami
UCL Institute of Cognitive Neuroscience, University College London, London, England; Institute of Anthropology,
Archaeology, Linguistics, Aarhus University, Aarhus, Denmark; and Centre of Functionally Integrative
Neuroscience, Aarhus University Hospital, Aarhus, Denmark
Chris D. Frith
Institute of Anthropology, Archaeology, Linguistics, Aarhus University, Aarhus, Denmark; Centre of Functionally
Integrative Neuroscience, Aarhus University Hospital, Aarhus, Denmark; and Wellcome Trust Centre for
Neuroimaging, University College London, London, England
Zaki and Ochsner underscore the importance of
combining two different viewpoints in social cogni-
tion: a currently popular, process-oriented approach
that is concerned with how we come to understand
others’ mental states. The main insights from this mind
perception research come from the ideas of theory of
mind and understanding by simulation. Another, rather
older approach focused on what enables human agents
to understand others’ mental states better. The latter ap-
proach is motivated by the assumption that there must
be some survival value in higher accuracy and reliabil-
ity of our internal mental models of others’ intentions
and beliefs. Accuracy research has been thwarted, Zaki
and Ochsner argue, by a lack of principled theoreti-
cal approach leading to research programs plagued by
a plethora of scattered and often difficult-to-replicate
cross-correlations between multiple arbitrary depen-
dent variables.
Both approaches are admittedly concerned with so-
cial interaction. However, both also address social in-
teraction as yet another cognitive faculty serving the
isolated agent. For example, in a game of poker, the
accuracy approach may seek to understand who is bet-
ter at calling bluffs. The process approach, on the other
hand, may ask what happens in the poker player’s brain
that enables him to call the bluff and raise the stakes as
opposed to fold. But whether these findings will be es-
sentially different from the work of a psychophysicist
who investigates the object recognition that happens
when the player reads his cards is far from decided
(Frith & Frith, 2010).
Social interaction also allows agents to share infor-
mation and make decisions together. This function is
directly relevant to the accuracy and the process of so-
cial perception but is qualitatively different from what
helps our poker player win the game. Every social agent
is continuously sampling information and making
judgments and inferences about her or his surrounding
environment. These inferences are always corrupted
by (extrinsic environmental as well as intrinsic neural)
noise and therefore have limited reliability (Green &
Swets, 1966). By interacting with other social agents
who have obtained their own samples of information,
individuals can benefit from the increased reliability
of joint decisions (Nitzan & Paroush, 1985). Informa-
tion sharing through interaction can lead to emergent
forms of collective wisdom that could serve all mem-
bers of a group. Indeed, such collective wisdom that
arises from efficient near-optimal democratic decision-
making strategies are amply observed in social animals
such as bees and deer (Conradt & Roper, 2005, 2007;
Seeley & Visscher, 2004a, 2004b) and has been the
topic of much interest in humans (Surowiecki, 2004).
The research on collective wisdom asks what consti-
tutes efficient social interaction. We are often told that
“two heads are better than one.” But what are the min-
imum necessary or sufficient conditions for two heads
together to excel better than one in isolation? Are there
any limiting conditions to group benefit accrued by in-
teraction? Are there any conditions where interaction is
counterproductive and not advisable? These questions
rephrase the Zaki and Ochsner’s idea of “accuracy of
social perception” in the context of collective decision
making.
The brain is a highly efficient information integra-
tion device that adeptly integrates information from
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Figure 1. Leili and Majnun make joint decisions about where the ball landed.
(A) The ball’s trajectory. (B) Leili and Majnun’s individual decisions are based
their respective noisy perceptual representation conceptualized here as Gaussian
distributions. The figure was inspired by Ernst (2010).
multiple sensory channels. A prominent example of
such highly efficient integration is speech perception:
Noisy visual and auditory information are efficiently
and quickly combined almost as perfectly as math-
ematically possible (given their own inherent noise
level) to maximize understanding (Ernst & Bulthoff,
2004). One brain can combine information optimally
from multiple modalities. But can two different brains
integrate information about the same modality? If
yes, how does the integration happen? The answer to
this question entails rephrasing Zaki and Ochsner’s
process-oriented mind perception in terms of collective
decision making: What is the nature of the shared
information that contributes to making joint decisions?
It is interesting to note that these questions—which
relate critically to the accuracy limit and the process of
collective decisions—date back to (at least) the French
revolution (Condorcet, 1785). However, it is only re-
cently that they have been formally and rigorously ad-
dressed in humans (Bahrami et al., 2010; Sorkin, Hays,
& West, 2001). The conceptual basis of this new ap-
proach to collective decision making is described next
in the context of a game of tennis (see Figure 1).
Leili and Majnun—our two protagonists from here
on—are watching a game of tennis (Figure 1). The ball
has just landed very close to the line (Figure 1A, white
arrow), and they disagree about whether it landed in
or outside the court (Figure 1B). They will consult
each other and arrive at a joint decision about the ball,
and the game will then carry on. But let’s stop here
and consider the situation. Each observer’s visual per-
ception of what happened can be conceptualized as a
normal distribution1withamean(dLfor Leili and dM
for Majnun) and a standard deviation (σLand σM). The
mean identifies the observer’s decision about whether
the ball fell above (e.g., Leili) or below (e.g., Majnun)
the thick black line that marks the border of the ten-
nis court. The standard deviation of each distribution
quantifies the amount of noise in the observer’s sensory
representation and is inversely related to the observer’s
perceptual sensitivity. A reliable percept would there-
fore be characterized by a mean with large magnitude
(greater distance from the boundary, e.g., Majnun) and
a small standard deviation (sharper, less noisy distri-
bution, e.g., Leili).
This formulation is useful in many respects. First,
a large body of literature in sensory psychophysics
gives us reliable tools to empirically identify these pa-
rameters underlying the decisions made by Leili and
1This normal distribution could represent, for example, the ac-
tivity pattern of neurons in the observer’s primary visual cortex.
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COMMENTARIES
Majnun in isolation and for the group (Green & Swets,
1966). Second, and more important, one could use this
formulation to construct different models of collective
decision making (Bahrami et al., 2010; Sorkin et al.,
2001). Such models explicitly define a communication
strategy in terms of which parameters Leili and Maj-
nun share with each other and what decision rule they
employ to combine the communicated parameters. The
model takes (dL,dM) and (σL,σM)—which have been
empirically determined (see previously)—and makes
exact, specific predictions about the corresponding pa-
rameters for the joint performance.
By comparing the empirical and predicted parame-
ters of the group’s performance, one could distinguish
between alternative models of communication—thus
addressing the “process” question proposed by Zaki
and Ochsner (this issue). Once a winning model of
communication is identified, one could then explore
that model’s predictions for conditions under which
two heads are expected to do better than one and when
they are expected to do worse—thus addressing the
“accuracy” question proposed by Zaki and Ochsner
(this issue).
Take the simplest possible case: If all Leili and Ma-
jnun could communicate to each other was their dis-
agreement (i.e., that dL×dM<0), then their joint
sensitivity will be no better than the average of their
individual sensitivities (Bahrami et al., 2010). A num-
ber of studies have shown that groups of two or more
observers can achieve greater perceptual sensitivity
than their respective individual members in isolation
(Bahrami et al., 2010; Green & Swets, 1966; Sorkin
et al., 2001). Clearly the content of communication is
richer than just signifying disagreement.
On the other hand, what would happen if Leili and
Majnun were so good at communicating that each
could fully reconstruct the other’s internal sensory rep-
resentation? The normal distribution is fully described
by its mean and standard deviation. So, perfect commu-
nication would require Leili and Majnun to exchange
the mean and the standard deviation of their respective
internal normal distribution distinctly and accurately.
The group’s sensitivity, SLM (i.e., sharpness of the nor-
mal distribution) is then expected to be
SLM =S2
L+S2
M.(1)
It turns out that this is quite a tall order to fill for
collective decision making. Sorkin et al. (2001) and
Bahrami et al. (2010) have shown that group perfor-
mance falls markedly short of this expectation. That
complete and exact communication of sensory repre-
sentations between separate brains is not possible is
hardly surprising. Previous research in multisensory
perception has shown that this model is a good predic-
tor of how information from different sensory modali-
ties, such as touch and vision are combined within the
brain of the same observer (Alais & Burr, 2004; Ernst &
Banks, 2002). Communication between brains is not
as reliable or high-fidelity as communication within
the same brain. Neither “just disagreement” nor “full-
fledged communication” captures the characteristics of
human collective decision making. A middle ground
should be sought.
A useful clue about the likely strategy for collec-