Making Sense by Making Sentient: Effectance Motivation Increases Anthropomorphism

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People commonly anthropomorphize nonhuman agents, imbuing everything from computers to pets to gods with humanlike capacities and mental experiences. Although widely observed, the determinants of anthropomorphism are poorly understood and rarely investigated. We propose that people anthropomorphize, in part, to satisfy effectance motivation-the basic and chronic motivation to attain mastery of one's environment. Five studies demonstrated that increasing effectance motivation by manipulating the perceived unpredictability of a nonhuman agent or by increasing the incentives for mastery increases anthropomorphism. Neuroimaging data demonstrated that the neural correlates of this process are similar to those engaged when mentalizing other humans. A final study demonstrated that anthropomorphizing a stimulus makes it appear more predictable and understandable, suggesting that anthropomorphism satisfies effectance motivation. Anthropomorphizing nonhuman agents seems to satisfy the basic motivation to make sense of an otherwise uncertain environment.
Journal of Personality and Social Psychology
Making Sense by Making Sentient: Effectance Motivation Increases
Adam Waytz, Carey K. Morewedge, Nicholas Epley, George Monteleone, Jia-Hong Gao, and John
T. Cacioppo
Online First Publication, July 12, 2010. doi: 10.1037/a0020240
Waytz, A., Morewedge, C. K., Epley, N., Monteleone, G., Gao, J.-H., & Cacioppo, J. T. (2010, July
12). Making Sense by Making Sentient: Effectance Motivation Increases Anthropomorphism.
Journal of Personality and Social Psychology. Advance online publication. doi: 10.1037/a0020240
Making Sense by Making Sentient:
Effectance Motivation Increases Anthropomorphism
Adam Waytz
Harvard University
Carey K. Morewedge
Carnegie Mellon University
Nicholas Epley, George Monteleone, Jia-Hong Gao, and John T. Cacioppo
University of Chicago
People commonly anthropomorphize nonhuman agents, imbuing everything from computers to pets to
gods with humanlike capacities and mental experiences. Although widely observed, the determinants of
anthropomorphism are poorly understood and rarely investigated. We propose that people anthropomor-
phize, in part, to satisfy effectance motivation—the basic and chronic motivation to attain mastery of
one’s environment. Five studies demonstrated that increasing effectance motivation by manipulating the
perceived unpredictability of a nonhuman agent or by increasing the incentives for mastery increases
anthropomorphism. Neuroimaging data demonstrated that the neural correlates of this process are similar
to those engaged when mentalizing other humans. A final study demonstrated that anthropomorphizing
a stimulus makes it appear more predictable and understandable, suggesting that anthropomorphism
satisfies effectance motivation. Anthropomorphizing nonhuman agents seems to satisfy the basic moti-
vation to make sense of an otherwise uncertain environment.
Keywords: anthropomorphism, social cognition, motivation, mind attribution, unpredictability
There is an universal tendency among mankind to conceive all
beings like themselves, and to transfer to every object, those
qualities, with which they are familiarly acquainted, and of which
they are intimately conscious. . . . No wonder, then, that mankind,
being placed in such an absolute ignorance of causes, and being at
the same time so anxious concerning their future fortune, should
immediately acknowledge a dependence on invisible powers, pos-
sessed of sentiment and intelligence. The unknown causes, which
continually employ their thought, appearing always in the same
aspect, are all apprehended to be of the same kind or species. Nor
is it long before we ascribe to them thought and reason and
passion, and sometimes even the limbs and figures of men, in order
to bring them nearer to a resemblance with ourselves.
—David Hume (1757/1957, p. xix)
Hume was neither the first nor the last to note the seemingly
chronic tendency for people to see humanlike agents in their
environment. Xenophanes (6th century B.C.E., as cited in Lesher,
1992) first named this tendency anthropomorphism (anthropos)
when describing the striking similarity between religious believers
and depictions of the gods they worshiped, with African gods
invariably being dark-haired and dark-eyed and Greek gods light-
skinned and blue-eyed, even joking that cows would invariably
worship cowlike gods. Feuerbach (1873/2004) and Freud (1930/
1989) similarly argued that anthropomorphism played a critical
role in religious belief, a claim present in many current psycho-
logical theories of religion (Barrett & Keil, 1996; Guthrie, 1993).
Beyond religion, anthropomorphism has been observed in expla-
nations of agents ranging from weather patterns (Hard, 2004) to
physical objects (Heider & Simmel, 1944) to technological agents
(Kiesler & Goetz, 2002) to nonhuman animals (Darwin, 1872/
2002; R. W. Mitchell, Thompson, & Miles, 1997). Anthropomor-
phism is used by marketers to sell everything from car insurance to
peanuts (Aaker, 1997; Aggarwal & McGill, 2007; Biel, 2000), by
engineers to design technological agents that are user friendly
(Breazeal, 2003; Duffy, 2003), and by computer scientists to
identify nonhuman robots that might serve as sources of social
connection to alleviate loneliness and depression (Kanamori, Su-
zuki, & Tanaka, 2002). Many ethical debates about the treatment
of other agents hinge on arguments about the presence or absence
of humanlike mental states, from environmental concern
(Morewedge & Clear, 2008; E. O. Wilson, 2006) to animal rights
Adam Waytz, Department of Psychology, Harvard University; Carey K.
Morewedge, Department of Social and Decision Sciences, Carnegie Mellon
University; Nicholas Epley, George Monteleone, and John T. Cacioppo, De-
partment of Psychology, University of Chicago; Jia-Hong Gao, Departments
of Radiology, Psychiatry, and Behavioral Neuroscience, Brain Research Im-
aging Center, University of Chicago.
This research was funded by the Templeton Foundation and the Booth
School of Business, as well as a dissertation fellowship from the Institute
for Quantitative Research in the Social Sciences at Harvard University and
a John Parker Scholarship from the Graduate Society of Fellows at Harvard
University to Carey Morewedge. We thank Ashley Angulo, Tatjana Aue,
Jonathan Cachat, Benjamin Converse, Natasha Davis, Jean Decety, Nicole
Donders, Diana Greene, J. S. Irick, Mina Kang, Jasmine Kwong, Jennifer
Lee, Entzu Lin, Carlos Lozano, Robert Lyons, Catherine Norris, Kelsey
Tupper, and Tor Wager for assistance with conducting research and anal-
Correspondence concerning this article should be addressed to Adam
Waytz, Harvard University, Department of Psychology, Northwest Science
Building Suite 290.02, 52 Oxford Street, Cambridge, MA 02138. E-mail:
Journal of Personality and Social Psychology © 2010 American Psychological Association
2010, Vol. ●●, No. , 000– 000 0022-3514/10/$12.00 DOI: 10.1037/a0020240
(R. W. Mitchell et al., 1997; Singer, 1975) to the treatment of
unborn human fetuses (Dennett, 1987).
Although both important and widespread, psychological re-
search has generally addressed either the ease with which people
anthropomorphize or the accuracy of anthropomorphic inferences.
Largely unaddressed are the psychological determinants that ex-
plain and predict variability in the tendency to anthropomorphize.
This research examined one potentially important determinant of
anthropomorphism—the basic motivation to be an effective and
competent social agent (White, 1959).
What Is Anthropomorphism?
Anthropomorphism represents a process of inductive inference
whereby people imbue the real or imagined behavior of other
agents with humanlike characteristics, motivations, intentions, or
underlying mental states (for a review, see Epley et al., 2007). This
may occur by attributing humanlike physical features to another
agent, as Xenophanes suggested, but more commonly by attribut-
ing mental states perceived to be uniquely human to other agents
(experience and agency according to Gray, Gray, & Wegner,
2007—more loosely called “thought and reason and passion,” by
Hume, 1757/1957, p. xix). Existing research suggests that people
intuitively consider higher order mental capacities to be uniquely
human. These include secondary emotions such as hope and guilt
that implicate higher order mental process of self-awareness and
prospection (Demoulin et al., 2004) and also traits that require
higher order cognition and emotion such as analytic, imaginative,
and insecure (Haslam, Bain, Douge, Lee, & Bastian, 2005;
Haslam, Kashima, Loughnan, Shi, & Suitner, 2008).
These lay theories of personhood are consistent with philosoph-
ical definitions that also consistently focus on higher order mental
states. One of the earliest definitions comes from Boethius (6th
century), who defined person as “an individual substance of a
rational nature” (quoted in Farah & Heberlein, 2007, p. 37). Locke
(1841/1997) likewise defined person as “an intelligent being that
has reason and reflection, and can consider itself the same thinking
being in different times and places” (quoted in Farah & Heberlein,
2007, p. 37), and modern-day philosophical definitions have noted
metarepresentation, unique conscious experience (Dennett, 1978),
moral consideration, and enhanced memory (Kagan, 2004) to be
essential properties of humanness. If anthropomorphism entails
attributing humanlike attributes to nonhuman agents, then these
intuitive and philosophical definitions of personhood all suggest
that anthropomorphism should be operationalized as the attribution
of humanlike mental states—a mind—to nonhuman agents (Epley
et al., 2007). We therefore used this operationalization in the
present research.
Variance in Anthropomorphism
Anthropomorphism is so easily recognized in so many domains
of everyday life that it is easy to overstate its strength, as Hume
(1757/1957) did by using terms like universal, all, and every.
Hume was not alone in this respect, as anthropomorphism has
historically been treated as a relatively invariant and automatic
psychological process that itself requires little explanation (Guth-
rie, 1993; R. W. Mitchell et al., 1997). The bulk of existing
research on anthropomorphism has therefore investigated either
the extent to which people anthropomorphize particular nonhuman
agents or its accuracy in describing the actual capacities of these
agents (Cheney & Seyfarth, 1990; Hauser, 2000; Morgan, 1894;
see Kwan & Fiske, 2008). A moment’s reflection, however, makes
it clear that some agents are anthropomorphized more than others
(Morewedge, Preston, & Wegner, 2007), some cultures seem more
prone to anthropomorphism than others (Asquith, 1986), children
are generally more likely to anthropomorphize than adults (Carey,
1985), and some situations increase the tendency to anthropomor-
phize compared to others (Epley, Akalis, Waytz, & Cacioppo,
2008; Norenzayan, Hansen, & Cady, 2008). Anthropomorphism is
not an invariant feature of everyday life to be taken for granted but
rather a wide-ranging and variable psychological process to be
Because anthropomorphism involves perceiving humanlike
states in other agents, it is also relevant when people make infer-
ences about other humans. Indeed, the inverse of anthropomor-
phism—treating other humans as animals or objects through de-
humanization—is a process that may engender private antipathy
toward other humans as well as aggression and violence (Bandura,
Underwood, & Fromson, 1975; Haslam, 2006). Understanding
why people anthropomorphize nonhuman agents may therefore
provide insight into the process by which people attribute mental
states and capacities to other humans as well.
Theoretical Determinants of Anthropomorphism
At least three major constructs appear to account for variability
in anthropomorphism: knowledge elicited by the agent being per-
ceived, the perceiver’s motivation for social connection, and the
perceiver’s motivation to be an effective and competent social
agent (Epley et al., 2007). Existing research provides support for
the first two constructs, whereas the link between anthropomor-
phism and effectance motivation has yet to be directly tested. The
present research served as that direct test. We briefly review
evidence suggesting elicited agent knowledge and sociality are
determinants of anthropomorphism and then turn our attention to
providing theoretical and empirical support for the influence of
effectance motivation on anthropomorphism.
Elicited Agent Knowledge
Anthropomorphism represents a process of inductive inference
about nonhuman agents and is guided by the basic properties of
knowledge acquisition, activation, and application that guide in-
ductive inferences more generally (Higgins, 1996). Factors that
alter the acquisition, activation, and application of knowledge
about the self or humans in general when considering nonhuman
agents also influence anthropomorphism. Young children, who
first develop a concept of self and of humans and only later
develop more sophisticated understandings of other agents, ram-
pantly anthropomorphize when reasoning about nonhuman ani-
mals (Carey, 1985; Inagaki & Hatano, 1987). Because the self is so
readily available and richly elaborated early in development, it
Although anthropomorphic beliefs exist on a continuum—from weaker
metaphorical forms to stronger explicit endorsements—the influence of
effectance on anthropomorphism should not differ between these forms
(see Epley, Waytz, & Cacioppo, 2007, for a review).
provides a highly accessible knowledge structure for reasoning
about lesser known stimuli. This early anthropomorphic tendency,
like self-centered biases in judgment more generally, subsides only
when people learn more about nonhuman agents through direct
experience or culture (e.g., Carey, 1985; Medin & Atran, 2004).
Anthropomorphism also increases for stimuli that bear a mor-
phological similarity to humans and therefore increase the acces-
sibility of egocentric or homocentric knowledge (e.g., Eddy, Gal-
lup, & Povinelli, 1993; Johnson, Slaughter, & Carey, 1998;
Morewedge et al., 2007). People are likely to project their own
beliefs and desires anthropomorphically onto stimuli that look
humanlike in their observable characteristics and movements
(Guthrie, 1993; Morewedge et al., 2007), just as people are likely
to project their beliefs and desires egocentrically onto people who
appear similar to the self (Ames, 2004; Epley, Keysar, Van Boven,
& Gilovich, 2004). Because the self often serves as the default
concept for reasoning about unfamiliar agents (e.g., Davis, Hoch,
& Ragsdale, 1986; Meltzoff, 2007; Nickerson, 1999), anthropo-
morphism is likely to result when reasoning about unfamiliar
Sociality Motivation
Sociality motivation encompasses the basic need to affiliate
with others and maintain a sense of belonging or relational con-
nection (Baumeister & Leary, 1995). Anthropomorphism may
operate as an attempt to satisfy this motivation by representing
nonhuman agents as sources of humanlike social connection. Con-
sistent with this possibility, chronically lonely individuals exhibit
a propensity to anthropomorphize pets (Epley, Waytz, Akalis, &
Cacioppo, 2008), technological gadgets (Epley, Akalis, et al.,
2008), and celestial bodies (Waytz, Cacioppo, & Epley, 2007).
People with insecure attachments to other people (i.e., individuals
who fear rejection from close others) report stronger personal
relationships with God (Kirkpatrick & Shaver, 1990). Finally,
experimentally inducing loneliness increases belief in commonly
anthropomorphized supernatural agents (e.g., God) and leads peo-
ple to describe their pets as more humanlike (Epley, Akalis, et al.,
2008). When lacking connection to other humans, people construct
sources of connection by creating humanlike agents out of nonhu-
Effectance Motivation
We suggest that anthropomorphism is also determined by effec-
tance motivation—the basic motivation to be an effective and
competent social agent (White, 1959). Effectance motivation en-
tails a desire for understanding, predictability, and control over
one’s environment. Anthropomorphism may serve to satisfy effec-
tance motivation because knowledge about the self and about
humans more generally is readily accessible and richly represented
in a way that may confer a strong sense of understanding, predict-
ability, and control over nonhuman agents (Gallese & Goldman,
1998; Meltzoff, 2007; Nickerson, 1999). Just as people reason
egocentrically when trying to understand other people, so too may
people readily use self-knowledge when trying to understand,
explain, and predict the behavior of nonhuman agents. When
reflecting on his classic study of object motion (Heider & Simmel,
1944), for example, Heider (1958/1964) noted the sense of order
provided by the projection of humanlike beliefs and desires toward
nonhuman entities:
As long as the pattern of events shown in the film is perceived in
terms of movements as such, it presents a chaos of juxtaposed items.
When, however, the geometrical figures assume personal character-
istics so that their movements are perceived in terms of motives and
sentiments, a unified structure appears. . . . But motives and senti-
ments are psychological entities. . . . They are “mentalistic concepts,”
so-called intervening variables that bring order into the array of
behavior mediating them. (pp. 31–32)
Being motivated to explain or understand an agent’s behavior may
therefore increase the tendency to anthropomorphize that agent.
Theoretical discussion from various domains including com-
puter science and artificial intelligence (Kiesler & Goetz, 2002; J.
McCarthy, 1983), religion (Guthrie, 1993), linguistics (Lakoff &
Johnson, 1980), philosophy (Dennett, 1987), and marketing (Agg-
arwal & McGill, 2007) has argued for such an association between
effectance and anthropomorphism based on anecdotal evidence.
No existing experimental research, however, has directly tested the
extent to which people anthropomorphize nonhuman agents in an
attempt to explain, understand, and predict their behavior. Some
supportive evidence for our hypothesis, however, comes from
behavioral attribution research. The desire for understanding, pre-
dictability, and control has long been recognized as motivating
people to explain their own and others’ behavior. Indeed, a moti-
vation to understand the causal forces active in the environment
appears to drive behavioral explanation (Kelley, 1967; Lombrozo,
2006). Observers tend to make dispositional attributions (Jones &
Nisbett, 1971), in part, to explain and understand others’ behavior
in terms of stable, internal, and thus predictable factors (Hamilton
& Sherman, 1996). For example, increasing a person’s need for
understanding, predictability, or control increases the likelihood
that he or she will explain others’ behavior using dispositional
attributions (Pittman & Pittman, 1980). Circumstances that require
a person to predict and understand another person’s future behav-
ior, such as expecting a future interaction, also increase the ten-
dency to explain that person’s behavior using dispositional attri-
butions (Berscheid, Graziano, Monson, & Dermer, 1976; Miller,
Norman, & Wright, 1978).
Dispositional attributions and anthropomorphic attributions dif-
fer in many respects, most notably in that dispositional attributions
need not involve any uniquely human properties at all. One may
perceive a dog to be active, a computer sluggish, or a God
powerful without attributing any uniquely human properties—
particularly uniquely human mental capacities—to any of these
agents. However, both dispositional and anthropomorphic attribu-
tions ascribe internal causality to other agents (Uleman, 2005).
Perceivers’ tendency to make dispositional attributions toward
other human agents when effectance motivation increases suggests
that effectance motivation may also engender anthropomorphic
perceptions of nonhuman agents.
Of course, the stimulus in question must at least be capable of eliciting
a humanlike representation for anthropomorphism to occur (Epley et al.,
2007)—if the stimulus elicits some alternate, more applicable concept, then
effectance motivation may stimulate sense making through other means.
Behavioral Attribution of Nonhuman Targets
Just as with human agents, situations that evoke the motivation for
mastery should prompt attributions of internal, comprehensible prop-
erties—mental states such as intentions and desires—toward nonhu-
man agents. Empirical evidence for this idea, however, is scarce. One
set of experiments demonstrated that people are more likely to judge
both negative and unexpected events as having been caused by
intentional agents (Morewedge, 2009). These experiments, however,
did not directly measure whether people are more inclined to attribute
intentions to nonhuman entities that violated their expectations or
caused them harm. In another relevant experiment, participants who
were denied control over a set of animate marbles attributed more
intentional agency to their behavior than those given control over
the marbles (Barrett & Johnson, 2003). Participants’ description of the
marbles, coded for anthropomorphic language, was the sole measure
of attributions of intentional agency. Because this measure captures
only the frequency of anthropomorphic terms spoken, it does not
reveal whether those lacking control over the marbles simply had
more to say about their marbles or had more marble-related thoughts
in general.
In the closest test of the current hypothesis (Epley, Waytz, et al.,
2008), individuals completed a measure of desire for control (Burger
relatively unpredictable, as rated by an independent population. Par-
ticipants rated the extent to which each dog possessed a number of
anthropomorphic traits (e.g., a conscious will) and its similarity to
other life-forms such as human beings and bacteria. Participants rated
the relatively unpredictable dog more anthropomorphically than the
predictable dog, and individuals with particularly high chronic control
needs rated both dogs more anthropomorphically compared to indi-
viduals low in desire for control.
Although consistent with our hypothesis, this study is inconclu-
sive. Because separate groups of people made judgments of the
dogs’ predictability and of the dogs’ anthropomorphic attributes,
the causal influence of predictability on anthropomorphism is
unclear. Because the study included only measures related to
anthropomorphic attributions, it is also unclear whether the unpre-
dictable dog elicited only increased anthropomorphic attributions
or increased dispositional attributions (including nonanthropomor-
phic traits) more generally. Furthermore, the study did not manip-
ulate predictability independent of the stimulus, leaving ambiguity
as to whether some idiosyncratic property unrelated to effectance
differed between the two dogs (e.g., attractiveness) that produced
differences in anthropomorphism.
The Present Research
We examined the relationship between effectance motivation
and anthropomorphism in two different ways— by investigating
whether increasing factors related to effectance motivation in-
creases anthropomorphism (Studies 1–5) and by investigating
whether anthropomorphism satisfies effectance motivation by in-
creasing a sense of understanding and predictability (Study 6). We
operationalized effectance motivation in Studies 1– 4 in terms of
its most commonly cited determinants, uncertainty and unpredict-
ability. For instance, Berlyne (1950), a seminal figure in the study
of motivation, identified uncertainty as the primary determinant of
infants’ motivation to master their environment, stating that effec-
tance motivation “may be one that all stimuli originally evoke, but
which disappears (becomes habituated) as the organism becomes
familiar with them” (p. 72). Kagan (1972) likewise deemed un-
certainty reduction a fundamental motive. Fiske’s (2004) identifi-
cation of social psychology’s five core motives distinguishes be-
tween understanding and controlling but identifies uncertainty as a
critical threat to both of these motivational forces.
Research across various domains in social psychology has also
demonstrated that the experience of uncertainty and unpredictabil-
ity stimulates attempts to regain control and mastery (Berlyne,
1962; Festinger, 1954; Pittman & D’Agostino, 1989; Plaks &
Stecher, 2007; Sorrentino, Smithson, Hodson, Roney, & Walker,
2003; Weary & Edwards, 1996; Whitson & Galinsky, 2008).
Operationalizing effectance motivation in terms of stimulus uncer-
tainty and unpredictability is therefore an appropriate and effective
method for stimulating this motivation for mastery. We manipu-
lated effectance motivation more directly in Study 5 by incentiv-
izing people to make accurate predictions about an agent’s future
behavior, thereby increasing the motivation for mastery over the
stimulus. Across all five studies, we predicted that increasing
factors related to effectance motivation would increase the ten-
dency to anthropomorphize nonhuman agents, and in a final ex-
periment (Study 6), we tested whether anthropomorphism is capa-
ble of satisfying this motivation.
Study 1: Unpredictable Computers
Anyone whose computer hard drive has crashed can recall an
immediate feeling of frustration followed by the sense that one’s
computer has a mind of its own and needs to be coaxed into
behaving properly. Indeed, a majority of people verbally scold
(79%) and curse (73%) their computer when it fails to comply with
their intentions (Luczak, Roetting, & Schmidt, 2003). Study 1
examined the relationship between technology malfunction and
anthropomorphism, to assess whether people perceive computers
that behave unexpectedly as humanlike. Because expectancy-
violating behavior of this type should elicit effectance motivation,
increases in expectancy violation should increase anthropomor-
Participants in two samples rated how frequently they experi-
enced problems with their computers or software and the extent to
which they perceived their computers to have minds of their own
or their own beliefs and desires. We predicted that the more
frequently participants’ computers malfunctioned, the more they
should perceive their computers to possess minds of their own,
beliefs, and desires. Although correlational in nature, this is the
first study we know of to directly assess within a single population
the relationship between stimuli that should evoke a need for
control and understanding and specifically anthropomorphic attri-
Participants. Sample A included 49 undergraduate students
(25 women) who volunteered to complete a brief survey. Sample
B included 63 undergraduate students (36 women) who completed
a brief survey in exchange for a candy bar.
Procedure. After describing the computer they used most
often and how many hours a week they used the computer,
participants in Sample A rated the extent to which their computer
appeared to “have a mind of its own” by drawing an X through a
112-mm continuous line marked with endpoints Does not appear
to have a mind of its own and Definitely appears to have a mind of
its own. Participants in Sample B rated the extent to which their
computer appeared “to behave as if it has its own beliefs and
desires” by drawing an X through a 112-mm continuous line
marked with endpoints Does not at all appear to behave as if it has
its own beliefs and desires and Definitely appears to behave as if
it has its own beliefs and desires. Participants in both samples rated
how often they had problems with the computer or its software on
a scale of identical length, marked with endpoints Never/Very
infrequently (0) and Very frequently (112). Question order was
As predicted, the more frequently participants’ computers mal-
functioned, the more likely participants in Sample A were to report
their computers to appear to have minds of their own, r(47) !.52,
and the more likely participants in Sample B were to
report that their computers behaved as if they had their own beliefs
and desires, r(61) !.34, p!.007. Interestingly, female partici-
pants in Sample A were more likely to perceive their computer to
have a mind of its own (M!47.3, SD !33.7) than were male
participants (M!21.3, SD !22.6), F(1, 47) !10.03, p!.003,
!.18. Because this gender difference was unexpected and is
not replicated in any other study reported in this article, we do not
discuss it further. No other effects were significant.
Participants in two samples were more likely to perceive their
computers to have minds, beliefs, and desires when their comput-
ers frequently malfunctioned. Most people expect their computers
to function properly, and thus, malfunctions are unexpected. These
findings therefore suggest that the more individuals experienced
their technological possessions operating unpredictably, the more
they anthropomorphized them. Although consistent with our hy-
potheses, computer malfunctioning is likely correlated with other
factors that could have created the correlation with the anthropo-
morphic items, such as negativity of the behavior, length of own-
ership, or expertise with one’s computer. To avoid the alternative
interpretations inherent in such correlational designs, we manipu-
lated the determinants of effectance motivation in Studies 2–5
while holding the stimulus being evaluated constant.
Study 2: Unpredictable Gadgets
Study 2 sought to demonstrate a causal relationship between
effectance motivation and anthropomorphism by asking people to
evaluate unfamiliar technological gadgets. Half of these gadgets
were described as behaving predictably, the other half as behaving
unpredictably. Participants then rated the gadgets on anthropomor-
phic and positive nonanthropomorphic measures. Previous studies
have linked liking to mental state attribution (Koda & Maes, 1996;
Kozak, Marsh, & Wegner, 2006; Leyens et al., 2003). Including
both types of measures allowed us to dissociate anthropomorphism
from dispositional attribution and positive evaluation more gener-
ally. Because unpredictable and unexpected behavior activates the
motivation to understand and explain the behavior (Weiner, 1985),
we predicted that participants would anthropomorphize gadgets
described as unpredictable more than gadgets described as predict-
Participants. Thirty-two people (15 women, M
years, SD !2.93) drawn from a university population participated
in exchange for $6.
Procedure. Participants rated 30 robotic gadgets in one of two
conditions (randomly assigned). The name of each gadget ap-
peared alongside a brief description and a rule as to how it
operated. The rules were designed to make the gadget appear either
predictable or unpredictable. Order was held constant across con-
ditions. Gadget predictability was counterbalanced: Participants in
one condition (“replicate A”) read descriptions and rules suggest-
ing that all of the odd-numbered gadgets (1, 3, 5, . . . , 29) operated
in an unpredictable manner. Participants in the other condition
(“replicate B”) read descriptions and rules suggesting that all of the
even-numbered gadgets (2, 4, 6, . . . , 30) operated in an unpre-
dictable manner.
Upon entering the laboratory for an experiment about “evaluat-
ing gadgets,” participants sat in individual cubicles to complete a
computerized questionnaire that began with the following instruc-
Today we would like you to evaluate a variety of technological
devices. Prototypes of these robotic devices currently exist in robotics
laboratories around the country and although they are not yet ready for
mass production, most of these devices will be available for nation-
wide consumer purchase by holiday season in 2007. Currently, de-
velopers are looking for consumer feedback, and your responses to
these devices will be valuable.
For now, we would like you to read a very brief description of how
each device works. More specifically, each product operates by a
“rule” and you will be given a brief description of what this “rule” is.
Some of these rules are straightforward, and some are not. After
reading about each device’s rule, you will be asked to rate the device
on a variety of measures. The measures on which you rate these
devices should be taken literally not figuratively.
Instructions to take the measures literally were intended to test a
stronger form of anthropomorphism as opposed to a metaphoric or
figurative version. Participants viewed each gadget one at a time.
All gadgets resembled existing products or products in develop-
ment. For example, “Clocky—Clocky is an alarm clock that looks
like a furry animal, and operates in a way that makes it difficult to
repeatedly press snooze in the morning.”
Following this short description, information depicted the prod-
uct as operating within a user’s control (i.e., predictably), such as
“You can program Clocky so that when you press snooze, it runs
away from you or you can program it so that when you press
snooze, it will jump on top of you,” or as operating outside the
user’s control (i.e., unpredictably), such as “When you press
snooze, Clocky either runs away from you, or it jumps on top of
you” (for descriptions of all stimuli, see Appendix A).
Controlling for gender, r(46) !.49, p".001.
Following the presentation of each gadget, participants reported
the extent to which they could control that device on a 7-point
scale, 1 (Not at all)to7(Very much). This measure served both as
a manipulation check and as a measure of perceived control over
each gadget. To assess anthropomorphism, participants then re-
ported the extent to which they believed the gadget appeared to
have “a mind of its own,” have “intentions, free will, conscious-
ness,” and appeared to experience emotions on the same scale.
Finally, participants rated each gadget on a number of positive
nonanthropomorphic measures, including the extent to which they
considered it attractive, efficient, and strong, on the same scale.
The experimenter then thanked, debriefed, and paid participants.
We first computed mean item ratings for all unpredictable
gadgets and for all predictable gadgets by averaging across each
set of gadgets. We then averaged these mean item ratings to attain
an overall anthropomorphism composite rating for both unpredict-
able and predictable gadgets ($!.93 and .94, respectively). We
used the same procedure for the nonanthropomorphic items to
create an overall composite (for unpredictable and predictable
gadgets, $!.70 and .75, respectively). Because we did not design
the nonanthropomorphic items to measure any single coherent
construct and because of their lower reliability as a composite
measure, we also analyzed these items individually.
The manipulation of gadget predictability appeared to be effec-
tive. Participants indicated that they would be less able to control
the gadgets when they were described as unpredictable (M!2.85,
SD !1.02) than when they were described as predictable (M!
5.11, SD !1.98), paired t(31) !9.33, p".0001, d!1.56.
As expected, participants anthropomorphized the unpredictable
gadgets (M!2.02, SD !1.15) more than the predictable gadgets
(M!1.69, SD !0.86), paired t(31) !3.47, p!.002, d!1.03.
Treating the nonanthropomorphic items as a composite revealed a
significant effect in the opposite direction, such that participants
rated the unpredictable gadgets more negatively on the nonanthro-
pomorphic items (M!2.87, SD !0.95) than the predictable
gadgets (M!3.33, SD !1.06), paired t(31) !4.30, p".001,
d!1.08. A 2 (replicate: A or B) %2 (description: predictable vs.
unpredictable) %2 (rating: anthropomorphic vs. nonanthropomor-
phic) mixed-model analysis of variance (ANOVA) yielded a sig-
nificant main effect for rating, qualified by a Description %Rating
interaction, F(1, 30) !31.09, p".0001, #
!.51. A closer
inspection of the individual nonanthropomorphic items revealed
that participants rated unpredictable gadgets to be less attractive
(M!2.77, SD !1.11) than predictable gadgets (M!3.44, SD !
1.41), paired t(31) !4.36, p".0001, d!1.15, and they rated
unpredictable gadgets to be less efficient (M!3.34, SD !1.09)
than predictable gadgets (M!4.11, SD !1.36), paired t(31) !
4.99, p".0001, d!1.31. Ratings of strength between predictable
and unpredictable gadgets did not differ significantly ( p!.63).
These findings suggest that increased anthropomorphism for the
unpredictable gadgets did not result simply from increased posi-
tivity or a general increase in dispositional attribution toward
unpredictable gadgets but instead increased the tendency, as Hume
(1757/1957) would have suggested, to “ascribe to them thought
and reason and passion” (p. xix). Because this experiment manip-
ulated stimulus predictability independent of the stimulus itself,
the results also demonstrate that unpredictability alone (rather than
some other correlated feature of the stimulus) can stimulate an-
One alternative interpretation, however, is that people may think
of other humans as inherently unpredictable, and the findings of
Study 2 could therefore result from semantic priming rather than
effectance motivation. If humans are thought of as being inherently
unpredictable, then describing a nonhuman as unpredictable could
activate thoughts about human attributes or characteristics. To
examine whether this association between human and unpredict-
ability exists, we conducted a posttest with 39 participants drawn
from the same university population as Study 2. These participants
answered two questions—(a) “Which type of movement is more
typical of a human: predictable movement or unpredictable move-
ment (movement that could occur in one of two ways at random)?”
and (b) “If a technological device were to behave in an unpredict-
able fashion or in a predictable fashion, which movement would
remind you most of a human?”—with the response options pre-
dictable, unpredictable, or neither. Twenty-four people (62%)
responded predictable to the first question, significantly more than
what would have been expected by chance, &
(2, N!39) !15.85,
p".0001. Fifteen people responded predictable, 17 responded
unpredictable, and seven responded neither to the second question,
revealing no significant difference between these options, &
N!39) !4.31, p!.12. This result suggests that an alternative
interpretation based on simple associative priming is unlikely.
Humans are no doubt capable of spontaneous and unpredictable
behavior, but there was no evidence for an association between
humanness and the type of unpredictability manipulated in Study
2. Studies 4 and 5 addressed this alternative explanation directly.
Before turning to these studies, we first examined the effect of
unpredictability on a more implicit measure of anthropomor-
phism—neural activation—in Study 3.
Study 3: Neuroimaging Anthropomorphism
The first two studies demonstrated that the unpredictability of a
stimulus increases anthropomorphism. However, the self-report
nature of the dependent variables did not distinguish between
whether participants were actually attributing humanlike minds to
nonhuman agents or simply using mind as a metaphoric descrip-
tion of their behavior. Study 3 extended this research using func-
tional magnetic resonance imaging (fMRI) to investigate the na-
ture of the information processing operations that may be triggered
using procedures virtually identical to Study 2. If people engage in
a strong form of anthropomorphism (i.e., truly attributing human
mental states to nonhuman agents; Epley et al., 2007), then men-
talizing nonhuman agents should correspond to increased activa-
tion in a network of brain regions involved in mentalizing other
human agents. If, however, people are instead responding to stim-
An initial analysis that included gadget (the particular gadget being
evaluated) as a factor revealed no meaningful effect for this factor. There-
fore, the analyses reported involve collapsing over all predictable gadgets
and collapsing over all unpredictable gadgets.
uli in a metaphoric sense and are instead responding, for instance,
by thinking about the predictability or unpredictability of the
stimuli, then a different network of brain regions should be in-
volved. Using neuroimaging should also help to determine the
neural correlates of anthropomorphism that existing research has
yet to identify definitively.
Existing research has suggested that tasks roughly related to
anthropomorphism activate brain regions involved in social cog-
nition, but these regions have varied widely across studies. This is
largely because of the lack of systematic attention to this topic and
because existing research has not employed tasks that constitute a
clear test of anthropomorphism itself. For instance, one study
demonstrated that motor areas of the brain hypothesized to be the
mirror neuron system (a system theoretically implicated in the
simulation of action) were equally active both when individuals
observed a robot’s actions and when they observed humans per-
forming those same actions (Gazzola, Rizzolatti, Wicker, & Key-
sers, 2007). Simulating action, however, does not involve mental
state inference and may or may not be directly related to anthro-
pomorphism. Another study investigated anthropomorphism by
assessing the neural correlates of dispositional attributions toward
objects (Harris & Fiske, 2008). Dispositional attributions are nec-
essary but insufficient for anthropomorphism because they do not
automatically imply uniquely human attributes, such as mental
states. Aggressiveness, for instance, is a dispositional trait that is
not perceived to be uniquely human. Nor are the amygdala and
superior temporal sulcus (STS), the neural regions examined in
that study of attribution, regions essential to the process of men-
talizing that operationally defines anthropomorphism. Instead,
these regions may index the processing of animate or living agents
rather than purely anthropomorphized agents (Blakemore et al.,
2003; Wheatley, Milleville, & Martin, 2007).
Other studies have demonstrated that the STS, an area that
reliably responds to biological motion versus nonbiological motion
(Allison, Puce, & McCarthy, 2000), and areas involved in theory
of mind (e.g., the temporoparietal junction and the medial prefron-
tal cortex [MPFC]) and processing emotions (the amygdala) are
more active when people observe animated shapes and characters
engaged in social or intentional motion compared to nonsocial,
random, or mechanical motion (Castelli, Happe´, Frith, & Frith,
2000; Martin & Weisberg, 2003; Pelphrey, Morris, & McCarthy,
2004; see also Heberlein & Adolphs, 2004). Another study dem-
onstrated that merely imagining a set of moving shapes to be
animate revealed increased activation of these regions commonly
involved in social cognition (Wheatley et al., 2007). This research
provides information about the perception of nonhuman stimuli,
but it constitutes a relatively insufficient test of anthropomor-
phism’s neural correlates because animacy and motion— even
biological or social motion—are not uniquely human features.
Attributing humanlike mental states to nonhuman agents is the
essence of anthropomorphism, and neural activity should show
evidence of that basic mentalizing process. We designed the
present experiment to refine and extend existing contributions and
to provide further understanding of anthropomorphism’s neural
The region that appears most centrally involved in mentalizing
is the MPFC. Over the past 15 years, one of cognitive neuro-
science’s most consistent findings has been that tasks that explic-
itly involve considering the mind of another person rely on a small
set of brain regions that includes the MPFC (Amodio & Frith,
2006; C. D. Frith & Frith, 1999; U. Frith & Frith, 2003; Gallagher
et al., 2000; Gallagher, Jack, Roepstorff, & Frith, 2002). Research
has also implicated this region in reasoning about people versus
objects (J. P. Mitchell, Heatherton, & Macrae, 2002) and assessing
an agent’s mental characteristics versus its physical characteristics
(J. P. Mitchell, Banaji, & Macrae, 2005). One study, in fact,
demonstrated the MPFC as preferentially active when participants
played a competitive game against a person compared to when
they played against a computer (Rilling, Sanfey, Aronson, Nys-
trom, & Cohen, 2004). Given existing findings on the role of the
MPFC, we hypothesize that if unpredictability increases anthro-
pomorphism via mentalizing, then evaluating unpredictable gad-
gets should produce greater activation in the MPFC.
One alternative hypothesis is that unpredictability merely in-
creases perceived biological and social motion. That is, unpredict-
able gadgets may increase animism rather than anthropomorphism
via mental state attribution. If this hypothesis is correct, then the
STS should be preferentially active when participants evaluate
unpredictable versus predictable gadgets because prior research
has implicated this region in the processing of biological and social
motion (Allison et al., 2000; Gre`zes et al., 2001).
A second alternative hypothesis is that unpredictable gadgets
simply increase thinking about the unpredictability of the stimulus.
Cognitive neuroscience research has operationalized unpredict-
ability in a multitude of ways ranging from expectancy violation to
temporal unpredictability of sound to infrequency of events and
has therefore identified a variety of regions implicated in process-
ing unpredictability. These regions include the amygdala (Herry et
al., 2007), the inferior parietal lobule (G. McCarthy, Luby, Gore,
& Goldman-Rakic, 1997), and the intraparietal sulcus (Dreher &
Grafman, 2003). If evaluating unpredictable gadgets simply in-
creases attentiveness to unpredictability rather than anthropomor-
phism, then only these regions that process unpredictability should
be preferentially active when evaluating predictable versus unpre-
dictable gadgets. We used fMRI to test these hypotheses, conduct-
ing a whole-brain contrast for unpredictable 'predictable trials, a
consequent connectivity analysis, and a weighted analysis in which
we assessed correspondence between neural activation and behav-
ioral measures of anthropomorphism.
Participants. Twenty-three healthy right-handed volunteers
(13 women, M
!23.39 years, SD !7.09) participated in the
experiment in exchange for $15 to $25 per hour of participation.
Procedure. Just before entering the scanner, participants read
about the 30 gadgets used in Study 2 as well as two additional
gadgets (an animatronic punching bag and a specialized drinking
straw), 32 gadgets in total. Gadgets always appeared in the same
order, and approximately half of the participants received unpre-
dictable descriptions of Gadgets 1– 8 and 17–24 and predictable
descriptions of Gadgets 9 –16 and 25–32, whereas the remaining
participants received descriptions in the opposite order. Gadgets of
each type, predictable or unpredictable, appeared on a correspond-
ing color background, either yellow or green (also counterbalanced
approximately equally). Participants studied the gadgets and then
completed a quiz in which they indicated whether each gadget was
described as predictable or unpredictable. Participants proceeded
to the next portion of the experiment only once they scored 100%
correct on the quiz.
Participants then completed an abridged practice run of the
primary experimental task on a computer outside the scanner. In
this task, participants viewed two blocks of slides, one pertaining
to the unpredictable gadgets and one pertaining to the predictable
gadgets. Each slide contained the question “To what extent does
[the gadget] have a mind of its own?”, and participants responded
using the keyboard number keys corresponding to a 5-point scale,
Not at all (1) to Very much (5). After this practice run, participants
began the fMRI portion of the experiment, from which we col-
lected data.
In the scanner, each session consisted of eight blocks with four
baseline blocks— each showing a static cross (48 s)—preceding
four rating blocks pertaining to unpredictable or predictable gad-
gets. These blocks corresponded to the order of gadget descriptions
from the prescanning task. Each rating block contained eight slides
(each slide !6 s) and prior to each rating block, the word ready
appeared on the appropriately colored background to indicate the
subsequent block type (see Figure 1). As in the practice run, each
slide contained a question asking participants the extent to which
a particular gadget has a mind. Participants had 6 s per gadget to
answer this question using the same 5-point scale and selecting a
number on a five-button response box. If participants did not
answer within the 6-s time frame, their rating was not recorded
(occurring in only 1.77% of all trials).
Participants viewed stimuli while being scanned in a 3T GE
Signa Scanner. The scanner recorded high-resolution anatomical
T1-weighted spoiled gradient-recalled images for each participant
in 124 1.5-mm sagittal slices with 6° flip angle and a 24-cm field
of view (FOV). We acquired functional images using a gradient-
echo spiral-in/out pulse sequence (Glover & Law, 2001) with 40
contiguous 4.2-mm coronal slices separated by 0.5-mm gaps, with
slices collected in an interleaved order spanning the whole brain
(repetition time !3 s, echo time !28 ms, flip angle !84°,
FOV !24 cm; 64 %64 matrix size, fat suppressed). The total time
for each functional scan was 6.6 min (see Figure 1) that yielded 64
data points for resting state and 32 data points each for unpredict-
able and predictable gadgets, respectively.
We performed image processing using AFNI software. Prepro-
cessing included motion correction, temporal smoothing using a
3-point Hamming window, spatial smoothing using a 5-mm full
width at half maximum (FWHM) Gaussian filter, and spatial
normalization to isometric 3-mm
voxels in the UCLA ICBM 452
T1 template provided by AFNI software. We estimated BOLD
responses using the general linear model and the AFNI program
3dDeconvolve (Ward, 2001). We modeled the expected hemody-
namic response by convolving a gamma-variate waveform with
stimulus-timing information for unpredictable and predictable
items and performed a within-participants regression against time-
series data to yield beta coefficients for each condition. We entered
voxelwise beta contrasts (unpredictable 'predictable) into a one-
sample ttest between participants (df !22). A cluster analysis
followed using a voxelwise threshold of p".025, t(22) !2.67; a
voxel connection radius of 5.2 mm; and a volume of 1,539 (L (57
voxels), resulting in a corrected whole-brain alpha of .05. Values
are based on a representative median value chosen from all voxels
in the cluster, and cluster parameters were determined using a
Monte Carlo simulation (10,000 iterations, FWHM !5 mm).
We also performed a functional connectivity analysis to deter-
mine regions of the brain showing correlated patterns of activity.
Functional connectivity refers to an undirected association be-
tween multiple fMRI time series (Wager, Hernandez, Jonides, &
Lindquist, 2007) and can supplement contrast analyses of fMRI
data to determine connections between brain regions. Functional
connectivity can be applied at different levels, from regions of
interest to clusters of voxels (Wager et al., 2007). The present
analysis involved functional connectivity at the voxelwise level,
using the cluster result from the whole-brain voxelwise ttest as a
seed and correlating each participant’s mean preprocessed time
series for the seed with each voxel’s time series as specified by
Wager et al. (2007). Because voxelwise connectivity can generate
false positives in identifying regions of association, we set thresh-
olds and cluster sizes based on Monte Carlo simulations to provide
a whole-brain alpha criterion of p".05. An interaction regressor
was constructed by calculating the pairwise product of the seed
time series and a contrast matrix coding unpredictable and pre-
dictable stimuli as 1 and )1, respectively. A whole-brain voxel-
wise regression modeled three parameters: the correlation with the
seed region, the unpredictable–predictable contrast, and the inter-
action between the seed region and the contrast determined by the
product of the first two regressors (Heekeren, Marrett, Bandettini,
& Ungerleider, 2004). We converted Rvalues to Z*using Fisher’s
transformation, entered Z*values into a one-sample ttest across
Figure 1. Schematic representation of the experimental design from Study 3.
participants (df !22), and performed a cluster analysis as de-
scribed above, voxelwise, p".00001, t(22) !6.690, cluster size
243 (L, corrected $".001. The results of this cluster analysis
revealed functional associations with the seed that were neuroana-
tomically interpretable and neurobiologically meaningful. Specif-
ically, prior research that we address in our discussion has asso-
ciated these anatomical regions with sociocognitive processes
related to mentalizing (Buckner & Carroll, 2007; Legrand & Ruby,
2009; Schilbach, Eickhoff, Rotarska-Jagiela, Fink, & Vogeley,
2008; but see Smith et al., 2009, for an alternative view).
Behavioral data. Anthropomorphism— operationalized as
ratings of the extent to which a gadget had a mind of its own— did
not vary as a function of block (for either unpredictable blocks or
predictable blocks; ps'.25) Therefore, we created composite
indexes by averaging participants’ anthropomorphism ratings for
all gadgets described as unpredictable and for all gadgets described
as predictable. Consistent with the findings of Study 2, participants
anthropomorphized the gadgets significantly more when they were
described as unpredictable (M!2.73, SD !1.35) than when they
were described as predictable (M!1.58, SD !0.64), paired
t(22) !4.20, p".0001, d!1.36.
fMRI data. A whole-brain contrast on the imaging results
comparing unpredictable gadgets to predictable gadgets yielded a
statistically significant cluster 2,646 (L in size. This cluster falls
within the ventromedial prefrontal cortex (vMPFC) and anterior
cingulate cortex (ACC), spanning the medial frontal gyrus (MFG)
and the orbitofrontal cortex, with a center of mass at Talairach
Coordinates 3, 40, )1 (Talairach & Tournoux, 1988), and a mean
t!3.79 across voxels in the cluster (see Figure 2). The vMPFC,
a region involved in inferring others’ mental states, is the only
significant region of activation that this contrast identified. This
finding supports our hypothesis that differences in stimulus pre-
dictability— holding all other properties of the stimulus constant—
are sufficient to produce differences in anthropomorphism.
It is possible that differences in activation reflect differences in
difficulty between evaluating both types of gadgets—all descrip-
tions of unpredictable gadgets include the term unpredictable
may facilitate relatively easier and less effortful heuristic respond-
ing to the measure of anthropomorphism. This account is unlikely,
however, given that research has identified the anterior dorsolat-
eral prefrontal cortex activation as associated with working mem-
ory (as an index of cognitive effort; D’Esposito et al., 1995) and
the anterior cingulate as indicative of task difficulty (e.g., Barch et
al., 1997), neither of which showed increased activation for eval-
uations of predictable gadgets in the current study (even at a more
liberal threshold, p".05, uncorrected). Furthermore, the absence
of increased activation at this threshold in the visual cortex sug-
gests that manipulating predictability did not meaningfully influ-
ence visual processing toward these gadgets.
If the identified cluster indexes anthropomorphism, then it
should correlate with other brain regions involved in mentalizing.
To identify correlated brain regions, we conducted a functional
connectivity analysis. This analysis, based on the vMPFC cluster
seed, yielded significant correlated activity for the simple correla-
tion parameter as well as the interaction parameter (as specified
above) in the following regions: the vMPFC and anterior cingulate
regions extending superior to the MFG, the medial cingulate gyrus
and posterior cingulate, medial and bilateral precuneus, bilateral
middle and superior temporal gyri extending to the inferior parietal
lobules in the region of the temporoparietal junction, bilateral
parahippocampal gyri, and bilateral middle occipital gyri (see
Figure 2 for regions and center of mass for each cluster in the
connectivity analysis; see Table 1 for a summary of these results).
This network of regions resembles a circuit strongly implicated in
the corresponding processes of self-projection (Buckner & Carroll,
2007), mentalizing (Legrand & Ruby, 2009; Spreng, Mar, & Kim,
2009), and social cognition more generally (Schilbach et al.,
2008), as would be expected if participants were anthropomorphiz-
ing unpredictable gadgets.
Weighted analysis. Finally, we examined the relationship be-
tween the behavioral ratings and the neuroimaging results by
performing an analysis that incorporated self-report anthropomor-
phism ratings into the fMRI model. We first standardized self-
reported anthropomorphism ratings within participants. Four par-
Figure 2. Results from the whole-brain voxelwise ttest and correlation analysis from Study 3. A: Results from
the whole-brain voxelwise ttest comparing the unpredictable–predictable contrast values to zero across
participants ( p".025, corrected), showing the sole significant cluster in medial prefrontal cortex (MPFC).
Results are shown in the Talairach Atlas grid. The peak tvalue for the cluster was 3.79. B: Results of a
correlation analysis using A as a seed. Functional magnetic resonance imaging time-series data were averaged
across the MPFC cluster and correlated voxelwise within participants. Pearson Rvalues were converted to Z*
using Fisher’s Ztransformation, then compared to zero using a one-sample ttest ( p".0000001, corrected).
ticipants showed no variance in their anthropomorphism ratings
(all items were rated as 1) and were thus excluded from the
analysis. We then recreated the block-design time series using
these zscores, providing an expected neural response function that
represented the ratings for each stimulus item. If the ratings did not
covary with the fMRI data from the scanner, the model would fit
poorly the fMRI data and the weighted model. However, this
analysis revealed significant activity for the unpredictable 'pre-
dictable contrast in MPFC p".01, volume !432 (l, threshold
t(18) !2.88. The strongest activity was in the same region of the
MPFC found in the primary analysis.
As predicted, participants were more likely to attribute mind to
gadgets described as unpredictable than to gadgets described as
predictable. More important, the neuroimaging results reveal that
evaluating the mental capacity of unpredictable gadgets is associ-
ated with relative increases in fMRI activity in an area centered in
the vMPFC and ACC. A weighted analysis demonstrated a corre-
spondence between this area of the MPFC and participants’ ex-
plicit anthropomorphism ratings. These findings suggest that per-
ceiving an agent as having a mind of its own may not be mere
The region identified raises the possibility that perceiving the
unpredictable gadgets involves thinking about these agents as legiti-
mately humanlike or selflike. Although the region of the MPFC we
identified is involved in a variety of processes, this region has been
previously shown to be critically involved a variety of sociocognitive
processes (Amodio & Frith, 2006), including egocentric mentalizing
about similar others (Jenkins, Macrae, & Mitchell, 2008; J. P. Mitch-
ell, Macrae, & Banaji, 2006). The present study’s findings allow for
the possibility that individuals are engaging in egocentric mentalizing
when anthropomorphizing these technological agents, whereas our
results are inconsistent with alternative hypotheses—specifically, that
the experimental manipulations simply increases neural activity asso-
ciated with perceiving animacy and biological motion or with per-
ceiving unpredictability. The areas we detected have been observed in
prior studies of mentalizing, whereas research on processing biolog-
ical motion has identified the STS (Allison et al., 2000) and research
on processing unpredictability has identified multiple regions includ-
ing the amygdala (Herry et al., 2007), the inferior parietal lobule (G.
McCarthy et al., 1997), and the intraparietal sulcus (Dreher & Graf-
man, 2003). None of these areas were observed in the current exper-
iment, even at a liberal threshold ( p".05, uncorrected). Moreover,
no study to our knowledge has yet implicated the vMPFC region we
identified to be activated by processing differences in predictability
per se, whereas a broad literature points to this region as involved in
social cognition processes such as mentalizing (see Amodio & Frith,
2006; U. Frith & Frith, 2003; J. P. Mitchell, 2009).
Because the vMPFC is implicated in various other functions
ranging from prediction error to reward processing to outcome
monitoring, the results of the connectivity analysis provided a
stronger test of the anthropomorphism hypothesis. If participants
were especially likely to think about the mental states of the
gadgets when they were described as unpredictable, then, based on
prior research, we hypothesized not only that the vMPFC would be
activated but that this activation would be functionally connected
to activation in the precuneus and posterior cingulate. If partici-
Table 1
Results of Connectivity Analysis
Region Brodmann’s areas Voxels X Y Z Median dMedian Z*
L/R medial prefrontal cortex 9, 10, 11, 32 2,003 2 42 2 1.77 .61
L/R posterior cingulate 7, 18, 19, 30, 31 686 2 )53 28 1.52 .43
L/R precuneus
L/R cuneus
L/R lingual gyrus
L/R calcarine gyrus
R superior temporal gyrus 19, 21, 22, 37, 39 208 55 )57 8 1.50 .39
R middle temporal gyrus 21 72 55 )13 )9 1.53 .45
L postcentral gyrus 1, 3 44 26 )42 69 1.47 .36
L middle temporal gyrus 19, 22, 37, 39 37 42 36 16 1.46 .38
L superior temporal gyrus
L superior temporal gyrus 13, 41 37 42 36 16 1.53 .43
L Rolandic operculum
R medial temporal pole 21 23 48 1 )22 1.51 .40
R Postcentral Gyrus 1, 2, 3 22 51 )17 51 1.47 .35
R superior temporal gyrus 41 19 57 )20 9 1.51 .39
L middle occipital gyrus 19 17 )38 )79 21 1.50 .39
R Heschl’s gyrus 13 15 41 )18 15 1.45 .43
R insula
R Rolandic operculum
L superior temporal gyrus 22 12 )48 0 1 1.50 .37
L temporal pole
L middle frontal gyrus 9 12 )22 39 31 1.44 .48
Note. Regions resulting from the correlation analysis using the functionally determined medial prefrontal cortex cluster as a seed, including nearest
Brodmann’s areas, cluster size, Talairach coordinates for each cluster’s center of mass, and the mean Z*value averaged across all voxels in the cluster.
Clusters were determined by calculating correlations to the seed within participants, performing Fisher’s Rto Ztransform, and comparing Z*values to zero
across participants with a voxelwise threshold of p".0000001. L !left; R !right.
pants were more likely to think about the animism or unpredict-
ability of the gadgets when they were described as unpredictable,
this network of activation should not be observed. Our results
permitted us to reject the hypotheses that our manipulation merely
influenced animism or processing unpredictability. The pattern of
activation from the seed region extends from the prefrontal cortex
to the parietal lobe, encompassing areas including the bilateral
precuneus and posterior cingulate. This circuit resembles a set of
regions termed the default network of the brain (Raichle et al.,
2001), identified as the network active when the brain is at a
baseline or resting state. This network appears associated with
self-projection involved in egocentric perspective taking (Buckner
& Carroll, 2007), and other work has noted this network’s specific
involvement in mentalizing or theory of mind (Legrand & Ruby,
2009; Spreng et al., 2009). One recent meta-analaysis of research
on this network “demonstrates a remarkable overlap between the
brain regions typically involved in social cognitive processes and
the ‘default system’” (Schilbach et al., 2008, p. 457). The conver-
gence of these findings suggests the possibility that the network of
regions identified in the connectivity analysis reflects preferential
mentalizing toward the unpredictable gadgets, supporting our
broader hypothesis that unpredictability increases anthropomor-
Study 4: An Unpredictable Robot
Study 4 provided an alternate manipulation of unpredictability
to examine the effects of predictability at encoding on anthropo-
morphism. Participants interacted with a computerized robot that
behaved predictably or unpredictably. Specifically, participants
asked a robot 10 yes-or-no questions through a computer interface.
The robot responded in either a relatively predictable fashion or an
unpredictable fashion. Participants then evaluated the robot on
anthropomorphic and nonanthropomorphic traits and reported how
predictable the robot seemed, how much they understood it, and
how much they liked it. We expected participants would anthro-
pomorphize the robot more when it behaved unpredictably (i.e.,
responding yes 50% of the time and no 50% of the time) than when
it behaved more predictably (i.e., responding yes or responding no
80% of the time).
Participants. Fifty-five visitors to the Museum of Science and
Industry in Chicago, IL (24 women, M
!34.89 years, SD !
12.32), received their choice of a small gift in exchange for
Procedure. Participants in each condition sat at a computer
where they interacted with the “operating system of the Asimo
robot” by asking Asimo 10 yes-or-no questions about anything
they wanted to know. Unbeknownst to participants, Asimo ran-
domly responded yes or no to their questions in one of three
specified proportions. In the unpredictable condition, Asimo re-
sponded yes to five of the questions and no to five of the questions
in a random fashion. In the predictable-yes condition, Asimo
responded yes to eight of the questions and no to two of the
questions. In the predictable-no condition, Asimo responded yes to
two of the questions and no to eight of the questions. Participants
asked each question one at a time, and Asimo appeared on the
screen after each question with an answer.
When finished asking questions, participants evaluated Asimo.
Participants first completed a manipulation check by reporting the
extent to which they thought Asimo was predictable, on a 5-point
scale, Not at all (1) to Extremely (5). To assess anthropomorphism,
participants reported the extent to which they thought Asimo
appeared to have a mind of its own, intentions, free will, con-
sciousness, desires, beliefs, and the ability to experience emotions
on the same 5-point scale. As in Study 2, participants also reported
the extent to which Asimo appeared to possess positive nonan-
thropomorphic traits, namely, attractiveness, efficiency, and
strength, on identical 5-point scales. Participants also reported the
extent to which they felt like they could understand Asimo’s
thought process and the extent to which they liked Asimo on the
same 5-point scales. Upon completing the experiment, participants
were thanked, debriefed, and compensated.
We computed composite scores for both anthropomorphic items
($!.82) and nonanthropomorphic items ($!.30). Because the
intercorrelation between the nonanthropomorphic items was insuf-
ficient to justify a composite, we only analyzed these items indi-
vidually. It is unclear whether the lower intercorrelation of these
nonanthropomorphic items reveals something systematic about
this participant sample or procedure or whether it stemmed from
the generally lower intercorrelation between this set of items
observed in all of the experiments reported in this article. These
nonanthropomorphic items, after all, were chosen because they
were unrelated to anthropomorphism, not because they were re-
lated to each other.
As hypothesized, planned orthogonal contrasts showed that par-
ticipants in the unpredictable (2) condition perceived Asimo as
more predictable than did participants in the predictable-yes ()1)
and predictable-no ()1) conditions, t(52) !2.87, p".01, r!.37
(for all means in this experiment, see Table 2). Interestingly and
unexpectedly, post hoc tests (Tukey’s honestly significant differ-
ence [HSD]) revealed that participants in the predictable-no con-
dition perceived Asimo to be more predictable than participants in
the predictable-yes condition ( p".05). Although we can only
speculate, this difference may have resulted from the different
questions asked between the conditions and the appropriateness of
Asimo’s responses to these questions (i.e., the no responses may
have been more correct to participants’ questions in the
predictable-no condition).
More important, a one-way ANOVA measuring the extent to
which participants attributed anthropomorphic qualities to Asimo
also revealed significant differences between conditions, F(2,
52) !4.50, p!.02, #
!.15. Planned orthogonal contrasts
revealed that participants in the unpredictable condition (2) were
more likely to attribute anthropomorphic qualities to Asimo than
were participants in the predictable-yes ()1) and predictable-no
()1) conditions, t(52) !2.57, p".015, r!.34. Anthropomor-
phism did not differ significantly between the two predictable
conditions (Tukey’s HSD, p!.31) but varied with the predict-
ability ratings as our theory would suggest, with participants in the
predictable-yes condition directionally anthropomorphizing more
than those in the predictable-no condition. Consistent with the
pattern of the predictability findings, post hoc tests (Tukey’s HSD)
indicated that anthropomorphic attributions differed significantly
between the unpredictable and predictable-no conditions ( p"
.015). The unpredictable and predictable-yes conditions did not
differ significantly ( p!.33).
We next performed ANOVAs on attributions of nonanthropo-
morphic traits, for which we had no predictions. No significant
differences between conditions emerged on attributions of effi-
ciency and strength (Fs"1.00), but a marginally significant
difference of attractiveness emerged, F(2, 52) !2.68, p!.08,
!.09. Post hoc tests (Tukey’s HSD) revealed that participants
found Asimo marginally less attractive in the predictable-yes con-
dition compared to the predictable-no condition ( p!.064). There
were no other significant differences between conditions. These
findings again suggest that stimulus unpredictability increases
anthropomorphism specifically rather than altering the attribution
of dispositional attributes more generally.
The findings from this study also make it clear that anthropo-
morphism and liking of an object may be relatively independent
and depend on the object being evaluated. No significant between-
condition differences emerged for liking of Asimo (F"1.00),
suggesting that manipulating predictability uniquely affected an-
thropomorphism independent of positivity toward Asimo. Some-
what unexpectedly, there were also no significant between-
condition differences in understanding of Asimo (F"1.00).
Understanding was negligibly correlated with predictability,
r(53) !.038, p'.78, suggesting that participants either did not
construe this question as expected (perhaps construing it as a
measure of whether they understood the programming of the
computer software itself) or that understanding in this context is
simply distinct from predictability.
Unpredictability increased anthropomorphism in Study 4, as it
did in Studies 1–3. Participants were more likely to perceive a
robot to be a thinking, desiring, intentional, emotional agent when
it responded in a relatively unpredictable manner. These results
extend those of Studies 1–3 by demonstrating a causal link be-
tween stimulus unpredictability and anthropomorphism. Although
the first three studies were consistent with our predictions that
effectance motivation increases anthropomorphism, none of them
conclusively demonstrated that the results stemmed from effec-
tance motivation per se, rather than stemming from some purely
cognitive process or a simple association. One hallmark of moti-
vation is that it is guided by one’s current goals and the incentive
for rewards versus punishments. We therefore manipulated effec-
tance motivation directly in Study 5 by increasing participants’
incentives for being an effective and competent social agent and
then measuring the extent to which participants anthropomor-
phized nonhuman agents.
Study 5: Motivating Predictability
In this study, participants watched videos of an unfamiliar robot.
We increased effectance motivation for some participants by ask-
ing them to predict what the robot would do next and paying them
for each correct answer. Other participants were not as motivated
because they were not asked predict the robot’s behavior nor were
they paid to do so. This manipulation should have increased
directly participants’ motivation for understanding, explaining,
and predicting an agent, the hallmarks of effectance motivation.
We therefore predicted that participants incentivized to predict the
robot’s behavior would be more likely to anthropomorphize it than
participants who had no incentive and were not asked to predict the
robot’s behavior.
Participants. Sixty-three people from a university population
(28 women, M
!20.83 years, SD !2.30) received $4 to $10
for participating based on their condition and the accuracy of their
Procedure. Participants evaluated a robot, R1, displayed on a
computer. Participants first familiarized themselves with R1 and
watched six brief videos of R1 edited to stop before its action
concluded (i.e., putting dishes away, attempting to turn on a
broken vacuum cleaner, picking up blocks, taking a beer from the
refrigerator, clearing dishes from a table, and being struck by a
person). After the first portion of each video, a statement of the
two possible outcomes of R1’s action appeared (e.g., “R1 will
EITHER put the dishes in the drawers OR it will put the dishes on
the counter,” “R1 will EITHER strike back OR it will retract,”
etc.). Participants randomly assigned to the control condition sim-
ply saw each statement. Participants randomly assigned to the
motivated condition predicted which of the two actions R1 would
perform at the end of each video and were told beforehand that
they would receive $1 for each correct prediction. The experimen-
tal condition involved both prediction and a monetary incentive for
accuracy to ensure that this manipulation would motivate rather
than merely instruct participants to predict the robot’s behavior.
Table 2
Average Responses by Robot Condition (Study 4)
Dependent measures
Robot condition
Predictable-yes Predictable-no Unpredictable
Predictability (manipulation check) 2.88 1.41 3.79 1.08 2.42 0.84
Anthropomorphism 1.57 0.65 1.26 0.33 1.87 0.80
Attractiveness 1.88 1.17 2.95 1.68 2.53 1.22
Efficiency 3.29 1.45 2.84 1.38 2.89 1.10
Strength 2.76 1.25 3.11 1.59 2.63 0.96
All participants then evaluated R1. To assess anthropomor-
phism, participants reported the extent to which they believed R1
had a mind of its own, intentions, desires; was conscious; and
could experience emotions on 7-point scales, Not at all (1) to Very
much (7). Interspersed within these items were nonanthropomor-
phic measures, including the extent to which they considered R1
good-looking, useful, durable, efficient, and strong, rated on iden-
tical scales. As a manipulation check, participants reported how
much they “care about predicting what R1 will do in these videos”
on an identical scale. Finally, participants saw the second portion
of each video, including its outcome. They were then thanked,
debriefed, and paid $4 (plus $1 for every correct prediction in the
motivated condition).
To analyze participants’ responses, we first averaged ratings of
the five anthropomorphism items to attain an overall anthropomor-
phism composite ($!.82). We used the same procedure for the
nonanthropomorphic items to create an overall composite ($!
.66). Because this nonanthropomorphic composite was once again
of only moderate reliability, rendering the composite variable more
difficult to interpret, we also analyzed each of these items indi-
vidually, as in Studies 2 and 4.
The motivation manipulation appeared to be effective. Partici-
pants in the motivated condition indicated that they cared more
about predicting R1’s actions (M!4.96, SD !1.48) than did
participants in the control condition (M!4.22, SD !1.49),
t(61) !1.96, p!.055, d!.50. Also as predicted, participants in
the motivated condition anthropomorphized R1 (M!2.20, SD !
1.28) more than did participants in the control condition (M!
1.65, SD !0.72), t(61) !2.18, p".035, d!.56.
Treating the nonanthropomorphic items as a composite revealed
no significant difference in ratings between the motivated condi-
tion (M!4.49, SD !0.80) and the control condition (M!4.15,
SD !0.93), t(61) !1.51, p!.14, d!.39. A 2 (condition:
motivated vs. control) %2 (composite: anthropomorphic vs. non-
anthropomorphic) mixed-model ANOVA on these composite mea-
sures revealed two significant main effects, but no significant
interaction. Participants produced higher ratings on the nonanthro-
pomorphism composite (M!4.29, SD !0.89) than on the
anthropomorphism composite (M!1.88, SD !1.02), F(1, 61) !
25.74, p".0001, #
!.81, and participants in the motivated
condition (M!3.35, SD !0.86) evaluated R1 higher on both
composites than those in the control condition (M!2.90, SD !
0.63), F(1, 61) !5.69, p!.02, #
!.082. A closer inspection of
the individual nonanthropomorphic items revealed that R1 was
rated as marginally more efficient by participants in the motivated
condition (M!5.00, SD !1.39) than participants in the control
condition (M!4.38, SD !1.16), t(61) !1.93, p!.058, d!.49.
No significant differences emerged between conditions in how
good-looking ( p!.31), durable ( p!.17), useful ( p!.70), or
strong ( p!.70) participants rated R1 to be.
As predicted, participants who were motivated to predict a
nonhuman agent’s behavior anthropomorphized it more than par-
ticipants who were not explicitly motivated to do so. Participants’
motivational state did not significantly affect overall ratings on
nonanthropomorphic traits, although no significant interaction
emerged. This pattern in the nonanthropomorphic ratings was
driven primarily by participants’ ratings of R1’s efficiency, a
finding we did not observe in the preceding two studies and one we
are therefore reluctant to speculate about in detail. There was no
meaningful effect of how good-looking R1 was rated to be, unlike
Studies 2 and 4, in which ratings of attractiveness were influenced
in opposite or orthogonal directions by our experimental manipu-
lation of effectance motivation. These results build on the preced-
ing experiments by manipulating effectance motivation in partic-
ipants directly while holding the behavior of the agent constant.
Not only is a stimulus that activates effectance motivation espe-
cially likely to be anthropomorphized but so too is a person who is
especially motivated to understand and predict a nonhuman agent
especially likely to anthropomorphize that agent.
Study 6: Anthropomorphizing Enhances Effectance
Anthropomorphism appears to arise, in part, from effectance
motivation. If this is the case, then not only should increasing the
factors central to effectance motivation increase anthropomor-
phism (as demonstrated by Studies 1–5) but also anthropomor-
phism should satisfy effectance motivation. Just as eating food
satisfies hunger, anthropomorphism should satiate this motivation
for mastery and make an agent seem more predictable and under-
standable. Although theoretical speculation has suggested that
anthropomorphism is functionally adaptive in providing this sense
of efficacy (Dennett, 1987; Epley et al., 2007; Humphrey, 1983;
Mithen, 1996), no experiment to our knowledge has examined this
hypothesis directly. We therefore designed Study 6 to do so.
Participants in Study 6 wrote brief essays about four different
stimuli—a small dog, a humanlike robot, a mobile alarm clock
that contains some humanlike features, and basic geometric
shapes. Each participant received instructions to anthropomor-
phize two of the target stimuli and to treat the other two target
stimuli objectively. After each essay, participants rated the
extent to which they understood the agent and felt they could
predict its future behavior. We predicted that participants would
rate the agents they were asked to anthropomorphize as more
understandable and predictable than the agents they were asked
to describe objectively.
Participants. Forty-two
people from a university population
(18 women, 3 unidentified, M
!21.71 years, SD !6.47)
received $4 for their participation.
Procedure. Participants evaluated four stimuli in one of two
conditions. The four stimuli appeared in the same order in both
conditions (dog, robot, alarm clock, shapes). Participants randomly
assigned to replicate A were asked to anthropomorphize the dog
and alarm clock but to describe the robot and the shapes objec-
tively. Participants randomly assigned to replicate B were asked to
describe the dog and alarm clock objectively but to anthropomor-
phize the robot and the shapes.
Data from three participants were excluded because of a computer
error that failed to record their responses in full.
Each participant watched videos of the four stimuli, wrote a
brief essay about each stimulus, and evaluated each stimulus on a
laboratory computer in a private cubicle. The first stimulus eval-
uated was a small spotted puppy that played with a larger dog
(instructions informed participants to focus on the small puppy
only). The second stimulus evaluated was Kismet, a robot de-
signed to facilitate interactions between robots and humans. The
video showed Kismet generating affective responses toward an
experimenter. The third stimulus evaluated was Clocky, a mobile
alarm clock with wheels and a simple facelike appearance that runs
away when the alarm sounds. The video showed Clocky beeping
loudly, spinning, and rolling across a floor surface. The final
stimulus evaluated was an animated set of shapes similar to those
presented in Heider and Simmel’s (1944) classic study of sponta-
neous attribution toward the behavior of objects. Each video was
under 1 min in duration (M!30.09 s, SD !11.40).
Participants watched the video of each stimulus three times and
received instructions either to anthropomorphize or to treat the
stimulus objectively as a behaviorist would (see Appendix B for all
instructions). Participants received reminders of these instructions
before the onset of each video and directly before evaluating each
stimulus on two measures to assess perceived efficacy: the extent
to which participants felt they understood the stimulus and the
extent to which they felt capable of predicting its future behavior
because understanding and predictability are hallmarks of effec-
tance (White, 1959). Participants made evaluations on 11-point
scales, Not at all (0) to Very much (10). After making these
evaluations, participants were debriefed, compensated, and dis-
Compared to the prior studies, this study included a much wider
variety of nonhuman agents, including biological agents (a dog),
technological agents (a robot and an alarm clock), and animated
geometric shapes. A multivariate ANOVA demonstrated that this
variety produced significant variability in the average ratings of per-
ceived understanding and predictability, F(3, 39) !7.02, p!.001,
!.35, with some agents rated as easier to understand and predict
(the shapes and dog) than others (the robot and clock; M
!1.97; M
!4.61, SD
!2.09; M
!2.68; M
!5.44, SD
!2.53). We therefore
standardized participants’ responses for each agent and averaged
ratings of perceived understanding and predictability for each agent
!.72, r
!.41, r
!.60, r
!.71, all ps".01) to
create a composite effectance score for each one. We then averaged
these scores for the two agents participants were instructed to anthro-
pomorphize and the two they were instructed to treat objectively.
A 2 (instructions: anthropomorphic vs. objective) %2 (replicate:
A vs. B) repeated-measures ANOVA revealed only a significant
main effect for instructions, F(1, 40) !4.46, p".05, #
Participants perceived greater efficacy with the agents they were
instructed to describe anthropomorphically (M!0.13, SD !0.70)
than with the agents they were instructed to describe objectively
(M!)0.13, SD !0.79). There was no significant main effect of
replicate or interaction (Fs"2.30, ps'.14), suggesting that the
effect of instructions did not depend on the specific agents partic-
ipants were describing.
Studies 1–5 suggest that people anthropomorphize at least partly
to satisfy their basic motivation for understanding and efficacy.
The results of Study 6 suggest that anthropomorphism may indeed
satisfy effectance motivation. Although we did not measure effec-
tance motivation directly, participants reported greater understand-
ing and predictability for stimuli they were told to anthropomor-
phize compared to those they were told to treat objectively.
Consistent with existing suggestions and theoretical predictions
(Dennett, 1987; Epley et al., 2007; Hebb, 1946; Heider, 1958/
1964), transforming these stimuli into humanlike entities provided
more understanding and predictability than construing the stimuli
as what they actually were: nonhuman biological, robotic, mechan-
ical, and animated entities.
General Discussion
The concept of anthropomorphism first arose in a philosopher’s
critique of religion. Centuries later, it remains a central topic of
discussion across an increasingly diverse range of scholars (Atran
& Norenzayan, 2004; Barrett, 2000; Cohen, Hill, Shariff, & Rozin,
2008; Feuerbach, 1873/2004; Freud, 1930/1989; Guthrie, 1993;
Kirkpatrick, 1998; Serpell, 2003; D. S. Wilson, 2002). The ten-
dency to perceive humanlike agency in the environment is not,
however, limited to supernatural agents and can extend to targets
spanning the alphabetical spectrum from alarm clocks (Epley,
Akalis, et al., 2008) to zebras (Sapolsky, 1994). Understanding
anthropomorphism is not simply an attempt to understand how
people understand this diversity of agents in their everyday lives
but is also an attempt to understand the psychological processes
that enable people to attribute humanlike capacities to other agents.
This research examined whether effectance motivation—the
basic motivation to be an effective social agent that entails main-
taining a sense of predictability, control, and understanding over
one’s environment—serves as one possible determinant of anthro-
pomorphism. We recognize that effectance motivation stimulates a
variety of strategies for explanation, prediction, and sense making,
and the current research demonstrates that anthropomorphism is
one of the strategies employed when attempting to maintain mas-
tery with nonhuman stimuli. The first four studies demonstrated
that unpredictability (either through naturally occurring variability
or through experimental manipulation) and the motivation for
predictability increase the tendency to anthropomorphize nonhu-
man agents. Study 1 demonstrated that everyday instances of
unpredictability in a nonhuman agent, namely, one’s computer, are
associated with anthropomorphic inferences about that agent. Study 2
demonstrated the causal link between unpredictability and anthropo-
morphism by demonstrating that people were more likely anthropo-
morphize gadgets described as unpredictable than the same gadgets
described as predictable. This pattern of results was specific to an-
thropomorphic attribution and not to general dispositional attribution.
Study 3 suggests that the neural bases of anthropomorphism triggered
by effectance motivation are similar to those involved in simulating
the minds of other humans. These findings provide additional support
for our hypotheses using a measure that does not rely solely on
self-report and also provides insight into the underlying neural corre-
lates of anthropomorphism. Study 4 demonstrated that people are
more likely to anthropomorphize nonhuman stimuli that behave rel-
atively unpredictably in an interaction.
Studies 2 and 4 also demonstrated that liking a stimulus is not a
necessary condition for anthropomorphizing it. This point is im-
portant because prior research has demonstrated a positive corre-
lation between liking of an agent and attribution of mind to that
agent (Kozak et al., 2006; McPherson-Frantz & Janoff-Bulman,
2000). Other studies have demonstrated that people enjoy inter-
acting more with anthropomorphic technology compared to more
mechanomorphic technology (Burgoon et al., 2000; Koda & Maes,
1996). It is interesting that the present studies did not replicate this
pattern. In fact, Study 2 demonstrated precisely the opposite pat-
tern such that participants anthropomorphized the unpredictable
gadgets more than the predictable gadgets but liked them less. We
think these findings highlight the importance of an agent’s func-
tionality in determining the relationship between mind perception
and liking. For example, when interacting with a tool or object
with specific functionality (such as the gadgets in Study 2), indi-
viduals may dislike an agent that operates with a humanlike mind
of its own. When interacting with agents that lack a singular or
particular functionality, such as with one’s pets or one’s friend,
liking and mind perception may be positively correlated. Existing
research has not definitively explained the relationship between
mind perception, liking, and functionality, and this topic bears
further investigation.
Study 5 demonstrated that manipulating the motivation to pre-
dict the behavior of a stimulus increases anthropomorphism. In-
centivizing participants to make accurate predictions about a ro-
bot’s behavior increased anthropomorphism of the robot, even
though the robot’s behavior was held constant across conditions.
Study 5 thus rules out the possibility that unpredictability increases
anthropomorphism only by cuing or priming humanness. It does
not, of course, rule out the possibility that these associations play
some role in the process of anthropomorphism.
Finally, Study 6 demonstrated that anthropomorphizing an un-
predictable agent actually satisfies effectance motivation. In this
study, stimuli that participants were led to anthropomorphize were
rated as being more understandable and predictable than objects
they were instructed not to anthropomorphize. Not only do factors
that increase effectance motivation increase anthropomorphism
but anthropomorphizing seems to satisfy this motivation as well.
We believe these studies have interesting implications for at least
six areas of future research, three more broadly concerned with
effectance motivation and three more specific to the phenomenon of
anthropomorphism. The following sections describe these topics.
A Unifying Concept of Effectance Motivation
The present studies operationalize effectance as the motivation
to attain control, predictability, and understanding, and to reduce
uncertainty, unpredictability, and randomness. We believe that this
broad conceptualization usefully ties together a number of related
factors that researchers have traditionally considered in isolation.
Research on the need for meaning, uncertainty reduction, and
sense making, as well as that on individual differences in causal
uncertainty (Weary & Edwards, 1996), need for closure (Webster
& Kruglanski, 1994), desire for control (Burger & Cooper, 1979),
locus of control (Rotter, 1966), and tolerance for ambiguity (Nor-
ton, 1975), suggests a common motivation to be an effective agent
in one’s environment.
Our conceptualization of effectance motivation unites these
related tendencies and, we believe, contributes to a growing psy-
chological literature on how people make sense of the world and
attain a sense of competence. Our research demonstrates that
anthropomorphizing a nonhuman agent is one way of satisfying
effectance motivation, but it is certainly not the only way. Recent
research has demonstrated that experiencing a loss of control or a
violation of one’s expectancies increases the tendency to see
meaningful patterns in random information (Whitson & Galinsky,
2008), seek external sources of control such as God (Kay, Gau-
cher, Napier, Callan, & Laurin, 2008), and affirm one’s moral
beliefs to reattain meaning (Proulx & Heine, 2008). We believe
that considering the commonalities across these seemingly distinct
research findings may create a more coherent picture of human
judgment and experience and that it is a critical and interesting
mission for future research to determine whether the factors that
contribute to understanding and competence are distinct or substi-
tutable for one another.
Effectance Motivation and the Primacy of Mental
State Attribution
Our research is consistent with existing research on person
perception suggesting that effectance motivation increases attribu-
tional processing of others’ behavior (see Pittman, 1998, for re-
view). Explanations of others’ behavior typically focus on personal
causality (Gilbert & Malone, 1995) because these dispositional
factors are seen as more stable, more predictable, and easier to
control (Pittman & Pittman, 1980; Wortman, 1976). Our research
suggests that others’ mental states may be one particularly impor-
tant element of attributions of personal causality that effectance
motivation increases. Heider (1958/1964) described attributions of
personal causality as explaining behavior in terms of one’s under-
lying intentions, compared to “impersonal causality” that explains
behavior “involving persons but not intentions” (p. 101). Because
this important intentional– unintentional distinction became sub-
sumed almost immediately by the person–situation distinction in
attribution research (see Malle, 1999), only a few experiments
have investigated or demonstrated a preferential focus on inten-
tionality in explaining behavior (Malle & Knobe, 1997;
Morewedge, 2009; Rosset, 2008). Other factors that evoke effec-
tance motivation, such as deprivation of control or the expectation
of interaction with an agent, may likewise increase mental state
attribution and anthropomorphism.
Anthropomorphism and Efficacy in
Human–Technology Interaction
In 2008, the United States Patent and Trademark Office granted
157,772 patents for technological inventions, a 7% increase from
10 years previous (U. S. Patent and Trademark Office, 2009). As
technology advances while the population continues to age, people
may find it difficult to interact effectively with the numerous
gadgets and machines they must use in their work and everyday
lives. Anthropomorphism may be one way to cope with this
increasingly technological environment, and the findings of Study
6 demonstrate that anthropomorphism can provide a sense of
efficacy in interactions with technology. Although this study mea-
sured perceived efficacy rather than actual efficacy, a series of
other findings suggests that anthropomorphism increases engage-
ment with technology that can enable more effective interactions.
For instance, anthropomorphic computer interfaces are more en-
gaging for users (Koda & Maes, 1996; Wexelblat, 1998), elicit
greater attention from users (Nass, Moon, Fogg, Reeves, & Dryer,
1995), and appear more credible in decision-making tasks (Bur-
goon et al., 2000). Anthropomorphizing a computer may also
reduce anxiety about interacting with technical agents (Luczak et
al., 2003). These findings suggest that stimulating individuals to
anthropomorphize technology can facilitate effective interaction
and provide real efficacy.
Anthropomorphism and Moral Consideration
Perhaps the most fundamental consequence of anthropomor-
phism is its implication for moral agency. Kant (1785/2005) ex-
plicated this intuition most clearly when he argued that “every
rational being exists as an end in himself and not merely as a
means to be arbitrarily used by this or that will. . . . rational beings
are called persons inasmuch as their nature already marks them out
as ends in themselves” (quoted in Farah & Heberlein, 2007, p. 37).
Consistent with this argument, people report that it is less accept-
able to harm nonhuman entities that they perceive to have minds
(Gray et al., 2007). Individuals chronically high in the tendency to
anthropomorphize also judge harm committed toward a computer,
a motorcycle, or even a bed of flowers to be more morally
reprehensible (Waytz, Cacioppo, & Epley, 2010). Endangered
animal species that individuals care most about protecting also are
those that have familiar attributes and demonstrate high similarity
to humans (Kellert, 1996). Anthropomorphism grants an entity the
capacity for feeling pain and pleasure, thus creating moral concern.
So too does anthropomorphism grant nonhuman agents responsi-
bility for their actions, responsibility that may then justify the
delegation of punishment or credit to the agent (Ashman & Win-
stanley, 2007; Gray et al., 2007; Hinds, Roberts, & Jones, 2004).
Understanding the basic mechanisms that enable or disable the
attribution of humanlike states to other agents is critical, we
believe, for understanding when nonhumans are treated as moral
agents and when they are not.
Anthropomorphism and Well-Being
Just as anthropomorphizing provides benefit to the target stim-
ulus in granting it moral status, anthropomorphizing may benefit
the perceiver of the stimulus. If anthropomorphism enhances effi-
cacy, then it may also contribute to individuals’ physical and
mental health. Experiencing a loss of mastery and control over
one’s environment can lead to depression (Benassi, Sweeney, &
Dufour, 1988), anxiety (Molinari & Khanna, 1981), and an overall
pattern of learned helplessness (Petersen, Maier, & Seligmann,
1995). Anthropomorphism may help counteract these conse-
quences by providing a sense of understanding, predictability, and
One final implication of this research is that understanding the
causes and consequences of humanizing a nonhuman may provide
insight into the inverse process of dehumanizing other people.
Psychological discussion on this topic has typically depicted the
function of dehumanization as licensing aggression and immoral
behavior toward the dehumanized target (Bandura, 2002; Bandura
et al., 1975). Dehumanization may operate passively as well, in
that individuals may simply fail to see others as essentially hu-
manlike. Just as the present research demonstrates entities that
prompt a desire for understanding and explanation as eliciting
humanization, entities that fail to engage a desire for understanding
or effective interaction may also diminish the attribution of mind.
People may perceive individuals who behave in a rote, predict-
able, or seemingly inert manner as mindless automatons (Haslam,
2006; Loughnan & Haslam, 2007). Targets with whom one is
unlikely to interact, such as outgroup members (Harris & Fiske,
2006; Leyens et al., 2003), minority group members (Marcu &
Chryssochoou, 2005), refugees (Esses, Veenvliet, Hodson, & Mihic,
2008), or individuals on the verge of death (Osofsky, Bandura, &
Zimbardo, 2005; Schulman-Green, 2003), are more likely targets
of dehumanization as well. Because interaction is unlikely with
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(Appendices follow)
Appendix A
Gadget Descriptions and Rules (Study 2)
Clocky is an alarm clock that looks like a furry animal, and
operates in a way that makes it difficult to repeatedly press
snooze in the morning.
A. You can program Clocky so that when you press snooze, it
runs away from you or you can program it so that when you press
snooze, it will jump on top of you.
B. When you press snooze, Clocky either runs away from you,
or it jumps on top of you. Its response to pressing snooze is
unpredictable in this way.
Moodpod is an MP3 player that plays songs in different moods
based on your bodily state (measured through galvanic skin
A. You can program Moodpod so that when your body ex-
presses a sad mood, Moodpod will play sad songs (congruent with
that mood), or you can program it so that when your body ex-
presses a sad mood, Moodpod will play happy songs (to make your
mood more positive).
B. When Moodpod detects your body expressing a sad mood, it
either plays sad songs (congruent with that mood), or it plays
happy songs (to make your mood more positive). The affective
valence of the music it plays in response to detecting a sad mood
is unpredictable.
Emotoboard is a computer keyboard used primarily for indi-
viduals writing e-mails at work. It assesses—through sensors
on each key—the impact of one’s keystrokes, the speed with
which one is typing, and physiological arousal. Emotoboard
then conveys to a typist when his or her typing style might be
conveying a message that is too hostile or aggressive.
A. You can program Emotoboard such that it will automatically
change words and sentence structure to convey a more pleasant
tone when it perceives your typing style as hostile; or you can
program it to simply flash a warning message on the screen to
indicate that your typing style is too hostile.
B. When Emotoboard senses your typing style is hostile, it will
either automatically change words and sentence structures in order
to convey a more pleasant tone; or it will simply flash a warning
message on the screen to indicate that your typing style is too
hostile. It responds to hostility in an unpredictable manner.
Attention Goggles
Attention Goggles are glasses that detect where your visual
attention is directed, and report feedback about the environ-
ment into attached headphones.
A. You can program Attention Goggles such that they will
provide feedback about the most potentially dangerous (avoidable)
aspects in the field where one’s attention is directed; or you can
program them such that they will provide feedback about the most
potentially pleasurable (approachable) aspects in the field where
one’s attention is directed.
B. When Attention Goggles are triggered, they will either pro-
vide feedback about the most potentially dangerous (avoidable)
aspects in the field where one’s attention is directed or they will
provide feedback about the most potentially pleasurable (ap-
proachable) aspects in the field where one’s attention is directed.
Which type of environmental stimuli they provide feedback about
is unpredictable.
RestVest is a vest that you wear to release muscle tension. It
can detect muscle tightness in your back and works to relieve
those particular areas.
A. You can program RestVest so that when it senses tension it
massages that particular area, or you can program it to remain
stationary and heat the particular area.
B. When RestVest senses tension in a particular area it either
massages that area, or it remains stationary and heats the particular
area. Its response to sensing tension is unpredictable.
Breathalyzer Phone
Breathalyzer Phone is a cell phone that can detect through a
breath test when you are intoxicated.
A. You can program Breathalyzer Phone so that when it detects
a high blood alcohol level (BAL), it alerts you on the screen, or
you can program it so when it detects a high BAL, it locks up your
phone so that you do not call others while intoxicated.
B. When Breathalyzer Phone detects a high blood alcohol level
(BAL) it either alerts you on the screen or it locks up your phone
so that you do not call others while intoxicated. Its response to
detecting a high BAL is not always predictable.
(Appendices continue)
WeatherToaster is a toaster that also prints out the current
weather outside onto your toast.
A. You can program WeatherToaster so that when the weather
is cloudy, it prints out the word “CLOUDY” on the toast, or you
can program it so that it prints out the shape of a cloud on the toast.
B. When you make toast, and the weather is cloudy, Weather-
Toaster either prints out the word “CLOUDY” on the toast or it
prints out the shape of a cloud on the toast. Which printout it
displays, the word or the image, is not predictable.
Snowsneakers are tennis shoes that help people walk through
snow using sensors on the bottom of each shoe.
A. You can program Snowsneakers so that when they sense
snow, they heat up to melt the snow, or you can program them so
that they shoot out tiny claws to help provide traction on the snow.
B. When Snowsneakers sense snow, they either heat up to melt
the snow or they shoot out tiny claws to help provide traction on
the snow. Which function they perform when detecting snow is not
Voicetype is a device attached to a computer that you speak
into. Voicetype simultaneously converts speech into type in a
word processing program.
A. You can program Voicetype so that when it notices a gram-
matical mistake, it provides a suggestion; or you can program it so
that when it notices a grammatical mistake, it stops typing until
you verbally make a correction.
B. When Voicetype notices a grammatical mistake, it either
provides a suggestion or it stops typing until you verbally make a
correction. Its response to grammatical mistakes is not predictable.
EnFocus Camera
EnFocus is a camera that virtually eliminates blurriness in
A. You can program EnFocus so that when it detects blurriness
in an image, it readjusts the aperture automatically to take a clear
photo or you can program it so that when it detects blurriness, it
closes its lens and will not take a photo until manually readjusted.
B. When EnFocus detects blurriness in an image, it either
readjusts the aperture automatically to take a clear photos or it
closes its lens and will not take a photo until manually readjusted.
Its response to blurriness is unpredictable.
DangerMouse is a computer mouse (that looks like a real
mouse) for children that prevents them from accessing dan-
gerous or explicit material on a computer or on the Internet.
A. You can program DangerMouse so that when the cursor is
placed on a potentially dangerous element (piece of e-mail, soft-
ware download, hyperlink, etc.) it freezes up (does not allow the
cursor to click), or you can program it to “run away” and auto-
matically move the cursor to the corner of the computer screen.
B. When the cursor is placed on a potentially dangerous element
(piece of e-mail, software download, hyperlink, etc.) Danger-
Mouse either freezes up (does not allow the cursor to click), or it
“runs away” and automatically moves the cursor to the corner of
the computer screen. Which of these functions is performed when
a dangerous element is approached is not predictable.
Sound Princess
The Sound Princess is a device that can be attached to a toilet to
eliminate potentially embarrassing “toilet noises” so that, in pub-
lic restrooms, surrounding patrons do not hear these noises.
A. You can program the Sound Princess to mask toilet noises by
producing white noise, or you can program it to reduce toilet
noises by simulating the sound of repeated toilet flushes.
B. When triggered, the Sound Princess masks toilet noises either
by producing white noise, or by simulating the sound of repeated
toilet flushes. The sound produced by this device is unpredictable.
Heart-Healthy Watch
The Heart-Healthy Watch is a normal time-telling watch that
also alerts you to indicators of heart disease.
A. You can program the Heart-Healthy Watch to indicate when
your blood pressure reaches a dangerously high level, or you can
program it to indicate when your pulse reaches a dangerously high
B. When the Heart-Healthy Watch is triggered it either indicates
when your blood pressure reaches a dangerously high level, or it
indicates when your pulse reaches a dangerously high level. Which
process it indexes and provides feedback on is unpredictable.
Sensorazor is an electric face razor that can detect and adjust
to changes in someone’s facial architecture.
A. You can program Sensorazor to stop shaving (turn off) when
it detects an area of sensitive skin, or you can program it to employ
softer blades when it detects an area of sensitive skin.
(Appendices continue)
B. When Sensorazor detects an area of sensitive skin, it either
stops shaving (turns off) or it employs softer blades. Which oper-
ation occurs when Sensorazor detects sensitive skin is unpredict-
Pillow Mate
Pillow Mate is a robotic pillow shaped like the torso of a
A. You can program Pillow Mate so that when you squeeze it,
it hugs you or you can program it so that when you squeeze it, it
curls into a ball.
B. When Pillow Mate is squeezed, it either hugs you or it curls
into a ball. It is unpredictable in this way.
Childsafe DVD Player
The Childsafe DVD Player is a DVD player that censors
explicit or R-rated portions of particular movies or programs.
A. You can program the Childsafe DVD Player to blur out
images in particularly explicit scenes or you can program it to omit
particularly explicit scenes altogether.
B. When explicit scenes are identified, the Childsafe DVD
Player either blurs out images in these scenes or it omits the scenes
altogether. Which of these operations occurs is not predictable.
Sportalert Monitor
Sportalert is a monitor attached to your television that notifies
you when a sports contest on another channel has reached a
critical point (e.g., when a football team is closing in on a
A. You can program Sportalert so that when it identifies a
critical point in a sports contest, it flashes a message on the channel
that you are currently watching, or starts playing that sports contest
(on a miniature screen) within the screen one is currently watching.
B. When Sportalert identifies a critical point in a sports contest,
it either flashes a message on the channel that you are currently
watching or it starts playing that sports contest (on a miniature
screen) within the screen one is currently watching. Which of these
functions occurs when a critical point in a sports contest is iden-
tified is unpredictable.
Reminder Ring
Reminder Ring is a ring that reminds you of daily appoint-
ments (e.g., meetings, times to take a medication, etc.).
A. You can program Reminder Ring to display a message on its
small LCD screen when an appointment comes up, or you can
program it to flash a light when an appointment comes up.
B. When an appointment comes up, Reminder Ring either
displays a message on its small LCD screen or it flashes a light
when an appointment comes up. Which notification occurs when
an appointment comes up is not predictable.
CleverCharger is a battery charger used in order to prevent
overcharging batteries.
A. You can program CleverCharger so that when it is done
charging a battery, it will beep loudly (and keep charging), or you
can program it to automatically stop charging when it is done
charging a battery.
B. When CleverCharger is done charging a battery it either
beeps loudly (and keeps charging) or it automatically stops charg-
ing. It is unpredictable as to which of these operations occur when
CleverCharger is done charging.
Street Mutt
Street Mutt is a robotic dog.
A. You can program Street Mutt to whimper when it is ap-
proached by humans, or you can program it to bark loudly when it
is approached by humans.
B. When Street Mutt is approached by humans, it either whimpers
or it barks loudly. Its response to being approached is unpredictable.
Ecopod is a functional recyclable bin for cans and plastic bottles.
A. You can program Ecopod so that it sorts and stores cans/
bottles in a compact manner, or you can program it to immediately
crush cans/bottles when they are deposited.
B. When cans and bottles are deposited into Ecopod, it either
sorts and stores them or it immediately crushes cans/bottles. How
it handles deposits is unpredictable.
CogMask is a mask worn to bed that subliminally flashes
information before your eyes to promote memory consolida-
tion and learning during sleep.
A. You can program CogMask to flash vocabulary words and
definitions or you can program CogMask to flash historical facts.
B. When CogMask is activated it either flashes vocabulary
words and definitions, or it flashes historical facts. Which type of
information it displays is unpredictable.
(Appendices continue)
No-Harm Scissors
No-Harm Scissors are scissors made to reduce the harm in
carrying scissors upward.
A. You can program No-Harm Scissors so the blade automati-
cally softens when being carried upside down, or you can program
No-Harm scissors so that the blade automatically retracts when
being carried upside down.
B. When No-Harm Scissors are carried upside down, either the
blade automatically softens or the blade automatically retracts.
How the scissors respond to being carried upside down is not
Detector Headset
Detector Headset is a combination glasses and headphones that
indicate when a conversation partner is lying while speaking.
A. You can program the Detector Headset so that when it
identifies a lie, it reinterprets the partner’s untruthful statements
into the headphones, or you can program it to send an alert signal
to the headphones.
B. When the Detector Headset identifies a lie, it either reinter-
prets the partner’s untruthful statements into the headphones, or it
sends an alert signal to the headphones. Which of these operations
the headset performs when detecting a lie is not predictable.
Ultimo Headphones
Ultimo Headphones are the latest in noise-reduction head-
A. You can program Ultimo Headphones so that when you enter
noisier environments, they reduce surrounding ambient noise, or you
can program them so that when you enter noisier environments they
will increase the volume of the audio in the headphones.
B. When you enter noisier environments, Ultimo Headphones
either reduce surrounding ambient noise, or they increase the volume
of the audio in the headphones. Which of these functions they perform
when encountering a noisy environment is unpredictable.
Supershade is an innovative parasol for sunbathing.
A. You can program Supershade to convert sunlight into the
ideal spectral band for tanning your skin, or you can program it to
convert sunlight into energy to power personal electronic devices.
B. When sunlight contacts Supershade it either converts sunlight
into the ideal spectral band for tanning your skin, or it converts
sunlight into energy to power your personal electronic devices.
Which of these responses is triggered when sunlight contacts
Supershade is unpredictable.
Pure Air
Pure Air is an air purifier that has particular settings (e.g.,
humid air or dry air) for people with specific allergies and
respiratory problems.
A. You can program Pure Air so that when it detects unhealthy
air, it humidifies the room, or you can program it so that when it
detects unhealthy air, it provides dry air throughout the room.
B. When unhealthy air is detected, Pure Air either humidifies or
it provides dry air throughout the room. Which of these functions
it performs after detecting unhealthy air is not easily predictable.
Handletek is a basketball with various functions that allows
basketball players to work on dribbling techniques and han-
dling the ball.
A. You can program Handletek so when it is activated, it
becomes bouncier than a normal basketball (more difficult to
corral), or you can program it so that when it is activated, it
becomes stickier to the palm of the dribbler (easier to grip).
B. When Handletek is on, it either becomes bouncier than a
normal basketball (more difficult to corral) or it becomes sticky to
the palm of the dribbler (easier to grip). What the ball does when
activated is unpredictable.
Auto Detective Pen
The Auto Detective Pen is a pen that scans your surroundings
to detect unknown wireless signals.
A. You can program the Auto Detective pen to alert you by
flashing a light when it detects a wireless signal, or you can program
it to magnetically point toward a wireless signal when it is detected.
B. When it detects a wireless signal, the Auto Detective pen
either alerts you by flashing a light or it magnetically points toward
the signal. Which of these functions it performs when detecting a
wireless signal is not predictable.
IonKids System
IonKids System is a PDA device with a separate wristwatch. Strap
the watch to the kid’s wrist (or more appropriately, ankle) and it will
send an alert when your kids get too far away from you.
A. You can program IonKids to alert your kids when they are
too far away from you, or you can program IonKids to alert you
when they are too far away from you.
B. When your kids get too far away from you, IonKids either
alerts your kids or IonKids alerts you. This device is unpredictable
with regard to the person it alerts.
(Appendices continue)
Appendix B
Anthropomorphic and Behaviorist Instructions (Study 6)
Anthropomorphic Description
You will now watch a video of two dogs. We would like you
to focus on the little dog with black spots, named Gizmo. When
watching Gizmo, we want you try to get inside of its mind and
think about it in the same way you would think about other
people. We want you to anthropomorphize Gizmo to see it as
humanlike, and to treat it as if it had humanlike traits, emotions,
and intentions. Watch its behavior closely and try to think about
it as if it was a person interacting with another person. When
you are done watching the video three times, we will have you
write a story about what Gizmo was doing in these videos, again
trying to describe the dog’s behavior as if it was a human.
Behaviorist Description
You will now watch a video of two dogs. We would like you
to focus on the little dog with black spots, named Gizmo. When
watching Gizmo, we want you to remain detached and think
only about the observable behaviors this dog is performing and
think about it as you might think about any other unfamiliar
animal. We want you to focus on the dog’s observable behavior
and think about it only in terms of the specific behaviors and
actions you can actually see. Treat it as an animal interacting
with another animal. Watch its behavior closely and try to
remain objective. When you are done watching the video three
times, we will have you write a story about what the dog was
doing in these videos, again trying to describe the dog’s behav-
ior as objectively as you can.
Anthropomorphic Description
You will now watch a video of Kismet, a robot developed at
MIT. When watching Kismet, we want you try to get inside of its
mind and think about it in the same way you would think about
other people. We want you to anthropomorphize Kismet to see it
as humanlike, and to treat it as if it had humanlike traits, emotions,
and intentions. Watch its behavior closely and try to think about it
as if it was a person. When you are done watching the video three
times, we will have you write a story about what Kismet was doing
in these videos, again trying to describe the robot’s behavior as if
it was a human.
Behaviorist Description
You will now watch a video of Kismet, a robot developed at
MIT. When watching Kismet, we want you to remain detached and
think only about the observable behaviors this robot is performing
and think about it as you might think about any other unfamiliar
machine. We want you to focus on the robot’s observable behavior
and think about it only in terms of the specific behaviors and
actions you can actually see. Treat it as a machine. Watch its
behavior closely and try to remain objective. When you are done
watching the video three times, we will have you write a story
about what Kismet was doing in these videos, again trying to
describe the robot’s behavior as objectively as you can.
Anthropomorphic Description
You will now watch a video of Clocky, a moving alarm clock.
When watching Clocky, we want you try to get inside of its mind
and think about it in the same way you would think about other
people. We want you to anthropomorphize Clocky to see it as
humanlike, and to treat it as if it had humanlike traits, emotions,
and intentions. Watch its behavior closely and try to think about it
as if it was a person. When you are done watching the video three
times, we will have you write a story about what Clocky was doing
in these videos, again trying to describe the gadget’s behavior as if
it was a human.
Behaviorist Description
You will now watch a video of Clocky, a moving alarm clock.
When watching Clocky, we want you to remain detached and think
only about the observable behaviors it is performing and think
about it as you might think about any other unfamiliar gadget. We
want you to focus on the gadget’s observable behavior and think
about it only in terms of the specific behaviors and actions you can
actually see. Treat it as a gadget. Watch its behavior closely and try
to remain objective. When you are done watching the video three
times, we will have you write a story about what Clocky was doing
in these videos, again trying to describe the gadget’s behavior as
objectively as you can.
Anthropomorphic Description
You will now watch a video of shapes. When watching these
shapes, we want you try to get inside of their minds and think
about them in the same way you would think about other people.
We want you to anthropomorphize these shapes to see them as
humanlike, and to treat them as if they had humanlike traits,
emotions, and intentions. Watch their behavior closely and try to
think about them as if they were people. When you are done
watching the video three times, we will have you write a story
about what these shapes were doing in these videos, again trying
to describe the shapes’ behavior as if they were humans.
(Appendices continue)
Behaviorist Description
You will now watch a video of shapes. When watching these
shapes, we want you to remain detached and think only about the
observable behaviors they are performing and think about them as
you might think about any other unfamiliar objects. We want you
to focus on the shapes’ observable behavior and think about them
only in terms of the specific behaviors and actions you can actually
see. Treat them as objects. Watch their behavior closely and try to
remain objective. When you are done watching the video three
times, we will have you write a story about what these shapes were
doing in these videos, again trying to describe the shapes’ behavior
as objectively as you can.
Received September 2, 2008
Revision received April 5, 2010
Accepted April 14, 2010 "
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    Anthropomorphism is generally defined as the attribution of human-like characteristics to social robots and other non-human objects. We argue that different researchers have different interpretations of this concept, leading to measuring instruments that focus on different subsets of human-like characteristics. In the current paper, we discuss these different interpretations and explore a new method for measuring anthropomorphism, based on the Rasch model. The aim of the current work is to map anthropomorphism as a range of human-like characteristics on a one-dimensional scale. The scale’s validity and sensitivity were tested by comparing it with two available measuring instruments and by comparing people’s responses to different types of agents. In three studies, we explored whether the Rasch model is suitable for measuring anthropomorphism. Despite some limitations, results showed that the Rasch model can successfully be applied to the measurement of anthropomorphism. Implications for future work on anthropomorphism of social robots are discussed.
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    Previous research suggests that how people conceive of minds depends on the culture in which they live, both in determining how they interact with other human minds and how they infer the unseen minds of gods. We use exploratory factor analysis to compare how people from different societies with distinct models of human minds and different religious traditions perceive the minds of humans and gods. In two North American samples (American adults, N = 186; Canadian students, N = 202), we replicated a previously found two‐factor agency/experience structure for both human and divine minds, but in Fijian samples (Indigenous iTaukei Fijians, N = 77; Fijians of Indian descent, N = 214; total N = 679) we found a three‐factor structure, with the additional containing items related to social relationships. Further, Fijians’ responses revealed a different three‐factor structure for human minds and gods’ minds. We used these factors as dimensions in the conception of minds to predict (a) expectations about human and divine tendencies towards punishment and reward; and (b) conception of gods as more embodied (an extension of experience) or more able to know people's thoughts (an extension of agency). We found variation in how these factors predict conceptions of agents across groups, indicating further theory is needed to explain how culturally generated concepts of mind lead to other sorts of social inferences. We conclude that mind perception is shaped by culturally defined social expectations and recommend further work in different cultural contexts to examine the interplay between culture and social cognition.
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    Anthropomorphism, the attribution of distinctively human mental characteristics to nonhuman animals and objects, illustrates the human propensity for extending social cognition beyond typical social targets. Yet, its processing components remain challenging to study because they are typically all engaged simultaneously. Across one pilot study and one focal study, we tested three rare people with basolateral amygdala lesions to dissociate two specific processing components: those triggered by attention to social cues (e.g., seeing a face) and those triggered by endogenous semantic knowledge (e.g., imbuing a machine with animacy). A pilot study demonstrated that, like neurologically intact control group participants, the three amygdala-damaged participants produced anthropomorphic descriptions for highly socially salient stimuli but not for stimuli lacking clear social cues. A focal study found that the three amygdala participants could anthropomorphize animate and living entities normally, but anthropomorphized inanimate stimuli less than control participants. Yet, amygdala participants could anthropomorphize across all stimuli when explicitly questioned, demonstrating that the ability to make social attributions as such is intact. Our findings suggest that the amygdala contributes to how we anthropomorphize stimuli that are not explicitly social.
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    Modern neurosurgical concepts call for not only "seeing" but also for "localizing" structures in three-dimensional space in relationship to each other. Hence there is a need for a reference system. This book aims to put this notion into practice by means of anatomical and MRI sections with the same stereotaxic orientation. The purpose is to display the fundamental distribution of structures in three-dimensional space and their spatial evolution within the brain as a whole, while facilitating their identification; to make comparative studies of cortico-subcortical lesions possible on a basis of an equivalent reference system; to exploit the anatomo-functional data such as those furnished by SEEG in epilepsy and to enable the localization of special regions such as the SMA in three-dimensional space; and to apply the anatomical correlations of this reference system to neurophysiological investigations lacking sufficient anatomical back-up (including PET scan).
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    To communicate effectively, people must have a reasonably accurate idea about what specific other people know. An obvious starting point for building a model of what another knows is what one oneself knows, or thinks one knows. This article reviews evidence that people impute their own knowledge to others and that, although this serves them well in general, they often do so uncritically, with the result of erroneously assuming that other people have the same knowledge. Overimputation of one's own knowledge can contribute to communication difficulties. Corrective approaches are considered. A conceptualization of where own-knowledge imputation fits in the process of developing models of other people's knowledge is proposed.
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    In this paper we argue that attachment theory, as developed by John Bowlby and refined and extended by a host of other psychological researchers, offers a potentially powerful theoretical framework for the psychology of religion. A wide range of research findings concerning such topics as images of God, conversion, and prayer can be conceptually integrated within this framework. An exploratory investigation was conducted of the relationship between individual differences in respondents' childhood attachments to their parents and their adult religious beliefs and involvement. A sample of 213 respondents to a newspaper survey on love completed a follow-up mail survey concerning their religious beliefs and family backgrounds. Multiple regression analyses revealed that certain aspects of adult religiosity, particularly beliefs about God and having a personal relationship with God, can be predicted from the interaction of childhood attachment classification and parental religiousness. Respondents who classified their childhood relationships with their mothers as avoidant (one of two insecure patterns of attachment) were more religious as adults, according to several measures, than were those classifying their childhood relationships as secure or anxious/ambivalent; however, this pattern held only when the parents were reported as having been relatively nonreligious. Respondents in the avoidant category also reported significantly higher rates of sudden religious conversions during both adolescence and adulthood, irrespective of parental religiosity. These results suggest that God and religion may function in a compensatory role for people with a history of avoidant attachment; that is, God may serve as a substitute attachment figure.
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    Emotion scientists often distinguish those emotions that are encountered universally, even among animals ( “primary emotions”), from those experienced by human beings ( “secondary emotions”). No attempt, however, has ever been made to capture the lay conception about this distinction and to find the criteria on which the distinction is based. The first study presented in this paper was conducted in three countries involving four languages, so as to allow for cross‐cultural comparisons. Results showed a remarkable convergence. People from all samples not only differentiated between “uniquely human” and “non‐uniquely human” emotions on a continuum, but they did so on the same basis as the one used by emotion scientists to distinguish between “primary” and “secondary” emotions. Study 2 focused on the implicit use of such a distinction. When confronted with a human (animal) context, participants reacted faster to secondary (vs primary) emotions. The implications of the human uniqueness of some emotions within the social and interpersonal contexts are discussed.