The mind in the machine: Anthropomorphism increases trust in an
⁎, Joy Heafner
, Nicholas Epley
Northwestern University, USA
University of Connecticut, USA
University of Chicago, USA
•Anthropomorphism of a car predicts trust in that car.
•Trust is reﬂected in behavioral, physiological, and self-report measures.
•Anthropomorphism also affects attributions of responsibility/punishment.
•These ﬁndings shed light on human interaction with autonomous vehicles.
Received 7 August 2013
Revised 11 January 2 014
Available online 23 January 2014
Sophisticated technology is increasingly replacing human minds to perform complicated tasks in domains rang-
ing from medicine to education to transportation. We investigated an important theoretical determinant of
people's willingness to trust such technology to perform competently—the extent to which a nonhuman agent
is anthropomorphizedwith a humanlike mind—in a domainof practical importance, autonomous driving. Partic-
ipants using a driving simulator drove either a normal car, an autonomous vehicle able to control steering and
speed, or a comparable autonomous vehicle augmented with additional anthropomorphic features—name, gen-
der, and voice. Behavioral, physiological, and self-report measures revealed that participants trusted that the ve-
hicle would perform more competently as it acquired more anthropomorphic features. Technology appears
better able to perform its intended design when it seems to have a humanlike mind. These results suggest mean-
ingful consequences of humanizing technology, and also offer insights into the inverse process of objectifying
© 2014 Elsevier Inc. All rights reserved.
Technology is an increasingly common substitute for humanity. So-
phisticated machines now perform tasks that once required a thought-
ful human mind, from grading essays to diagnosing cancer to driving a
car. As engineers overcome design barriers to creating such technology,
important psychological barriers that users will face when using this
technology emerge. Perhaps most important, will people be willing
to trust competent technology to replace a human mind, such as a
teacher's mind when grading essays, or a doctor's mind when diagnos-
ing cancer, or their own mind when driving a car?
Our research tests one important theoretical determinant of trust in
any nonhuman agent: anthropomorphism (Waytz, Cacioppo, & Epley,
2010). Anthropomorphism is a process of inductive inference whereby
people attribute to nonhumans distinctively human characteristics,
particularly the capacity for rational thought (agency) and conscious
feeling (experience; Gray, Gray, & Wegner, 2007). Philosophical deﬁni-
tions of personhood focus on these mental capacities as essential to
being human(Dennett, 1978; Locke, 1997). Furthermore, studies exam-
ining people's lay theories of humanness show that people deﬁne hu-
manness in terms of emotions that implicate higher order mental
process such as self-awareness and memory (e.g., humiliation, nostal-
gia; Leyens et al., 2000) and traits that involve cognition and emotion
(e.g., analytic, insecure; Haslam, 2006). Anthropomorphizing a nonhu-
man does not simply involve attributing superﬁcial human characteris-
tics (e.g., a humanlike face or body) to it, but rather attributing essential
human characteristics to the agent (namely a humanlike mind, capable
of thinking and feeling).
Trust is a multifaceted concept that can refer to belief that another
will behave with benevolence, integrity, predictability, or competence
(McKnight & Chervany, 2001). Our prediction that anthropomorphism
will increase trust centers on this last component of trust in another's
competence (akin to conﬁdence) (Siegrist, Earle, & Gutscher, 2003;
Twyman, Harvey, & Harries, 2008). Just as a patient would trust a
Journal of Experimental Social Psychology 52 (2014) 113–117
⁎Corresponding author at: Northwestern University, 2001 Sheridan Rd, Evanston, IL
E-mail address: firstname.lastname@example.org (A. Waytz).
0022-1031/$ –see front matter © 2014 Elsevier Inc. All rights reserved.
Contents lists available at ScienceDirect
Journal of Experimental Social Psychology
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thoughtful doctor to diagnose cancer more than a thoughtless one, or
would rely on mindful cab driver to navigate through rush hour trafﬁc
more than a mindless cab driver, this conceptualization of anthropo-
morphism predicts that people would trust easily anthropomorphized
technology to perform its intended function more than seemingly
mindless technology. An autonomous vehicle (one that that drives it-
self) for instance, should seem better able to navigate through trafﬁc
when it seems able to think and sense its surroundings than when it
seems to be simply mindless machinery. Or a “warbot”intended to kill
should seem more lethal and sinister when it appears capable of think-
ing and planning than when it seemsto be simply a computer mindless-
ly following an operator's instructions. The more technology seems to
have humanlike mental capacities, the more people should trust it to
perform its intended function competently, regardless of the valence
of its intended function (Epley, Caruso, & Bazerman, 2006; Pierce,
Kilduff, Galinsky, & Sivanathan, 2013).
This prediction builds on the common association between people's
perceptions of others' mental states and of competent action. Because
mindful agents appear capable of controlling their own actions, people
judge others to be more responsible for successful actions they perform
with conscious awareness, foresight, and planning (Cushman, 2008;
Malle & Knobe, 1997) than for actions they perform mindlessly (see
Alicke, 2000; Shaver, 1985; Weiner, 1995). Attributing a humanlike
mind to a nonhuman agent should therefore more make the agent
seem better able to control its own actions, and therefore better able
to perform its intended functions competently. Our prediction also ad-
vances existing research on the consequences of anthropomorphism
by articulating the psychological processes by which anthropomor-
phism could affect trust in technology (Nass & Moon, 2000), and by
both experimentally manipulating anthropomorphism as well as mea-
suring it as a critical mediator. Some experiments have manipulated
the humanlike appearance of robots and assessed measures indirectly
related to trust. However, such studies have not measured whether
such superﬁcial manipulations actually increase the attribution of es-
sential humanlike qualities to that agent (the attribution we predict is
critical for trust in technology; Hancock et al., 2011), and therefore can-
not explain factors found ad-hoc to moderate the apparent effect of an-
thropomorphism on trust (Pak, Fink, Price, Bass, & Sturre, 2012).
Another study found that individual differences in anthropomorphism
predicted differences in willingness to trust technology in hypothetical
scenarios (Waytz et al., 2010), but did not manipulate anthropomor-
phism experimentally. Our experiment is therefore the ﬁrst to test
our theoretical model of how anthropomorphism affects trust in
We conducted our experiment in a domain of practical relevance:
people's willingness to trust an autonomous vehicle. Autonomous
vehicles—cars that control their own steering and speed—are expected
to account for 75% of vehicles on the road by 2040 (Newcomb, 2012).
Employing these autonomous features means surrendering personal
control of the vehicle and trusting technology to drive safely. We ma-
nipulated the ease with which a vehicle, approximated by a drivingsim-
ulator, could be anthropomorphized by merely giving it independent
agency, or by also giving it a name, gender, and a human voice. We pre-
dicted that independent agency alone would make the car seem more
mindful than a normal car, and that adding further anthropomorphic
qualities would make the vehicle seem even more mindful. More im-
portant, we predicted that these relative increases in anthropomor-
phism would increase physiological, behavioral, and psychological
measures of trust in the vehicle's ability to drive effectively.
Because anthropomorphism increases trust in the agent's ability to
perform its job, we also predicted that increased anthropomorphism
of an autonomous agent would mitigate blame for an agent's involve-
ment in an undesirable outcome. To test this, we implemented a virtu-
ally unavoidable accident during the driving simulation in which
participants were struck by an oncoming car, an accident clearly caused
by the other driver. We implemented this to maintain experimental
control over participants' experience because everyone in the autono-
mous vehicle conditions would get into the same accident, one clearly
caused by the other driver. Indeed, when two people are potentially re-
sponsible for an outcome, the agent seen to be more competent tends to
be credited for a success whereas the agent seen to be less competent
tends to be blamed for a failure (Beckman, 1970; Wetzel, 1982). Because
we predicted that anthropomorphism would increase trust in the
vehicle's competence, we also predicted that it would reduce blame
for an accident clear caused by another vehicle.
One hundred participants (52 female, M
= 26.39) completed this
experiment using a National Advanced Driving Simulator. Once in the
simulator, the experimenter attached physiological equipment to par-
ticipants and randomly assigned them to condition: Normal, Agentic,
or Anthropomorphic. Participants in the Normal condition drove theve-
hicle themselves, without autonomous features. Participants in the
Agentic condition drove a vehicle capable of controlling its steering
and speed (an “autonomous vehicle”). The experimenter followed a
script describing the vehicle's features, suggesting when to use the au-
tonomous features, and describing what was about to happen. Partici-
pants in the Anthropomorphic condition drove the same autonomous
vehicle, but with additional anthropomorphic features beyond mere
agency—the vehicle was referred to by name (Iris), was given a gender
(female), and was given a voice through human audio ﬁles played at
predetermined times throughout the course. The voice ﬁles followed
the same script used by the experimenter in the Agentic condition,
modiﬁed where necessary (See Supplemental Online Material [SOM]).
All participants ﬁrst completed a driving history questionnaire and a
measure of dispositional anthropomorphism (Waytz et al., 2010).
Scores on this measure did not vary signiﬁcantly by condition, so we
do not discuss them further.
Participants in the Agentic and Anthropomorphic conditions then
drove a shortpractice course to familiarize themselves with the car's au-
tonomousfeatures. Participants could engage these features by pressing
buttons on the steering wheel. All participants then drove two courses
each lasting approximately 6 min. After the ﬁrst course, participants
completed a questionnaire (all on 0–10 scales, see SOM for all items)
that assessed anthropomorphism, liking, and trust.
Four items measured anthropomorphism, deﬁned as attributing hu-
manlike mental capacities ofagency and experience to it (Epley, Waytz,
& Cacioppo, 2007; Gray et al., 2007; Waytz et al., 2010). These asked
how smart the car was, how well it could feel what was happening
around it, how well it could anticipate what was about to happen, and
how well it could plan a route. These items were averaged into a com-
Four items measured liking: how enjoyable their driving was, how
comfortable they felt driving the car, how much participants would
like to own a car like this one, and what percentage of cars in 2020
they would like to be [autonomous] like this one. These items were
standardized and averaged to form a single composite (α=.90).
Eight items measured trust in the vehicle: how safe participants felt
they and others would be if they actually owned a car like this one, how
much they trust the vehicle to drive in heavyand light trafﬁcconditions,
how conﬁdent they are about the car driving the next course safely, and
their willingness to give up control to the car. These items were stan-
dardized and averaged to form a single composite (α= .91)
114 A. Waytz et al. / Journal of Experimental Social Psychology 52 (2014) 113–117
After approximately 6 min of driving a second course along a rural
highway, a vehicle pulled quickly in front of the car and struck their
right side. We designedthis accident to be unavoidable so thatall partic-
ipants would experience the same outcome (indeed, only one partici-
pant, in the Normal condition, avoided it). Ensuring that everyone got
into this accident, however, meant that the accident was clearly the
other vehicle's fault rather than participants' own vehicle. Throughout
the experiment, we measured participants' heart rate using electrocar-
diography (ECG) and videotaped their behavior unobtrusively to assess
responses to this accident.
Heart rate change
We reasoned that if participants trusted the vehicle, they should be
more relaxed in an arousing situation (namely, the accident), showing
an attenuated heart rate increase and startle response. We measured
heart rate change to the accident as a percentage change of beats per
minute for 20 s immediately following the collision (or until they con-
cluded their simulation), in comparison to a forty-ﬁve second baseline
period immediately following the earlier practice course.
To assess startle response, we ﬁrst divided our participants into two
random samples. We then recruited 42 independent raters from an
undergraduate population to watch all videos from one or the other
sample and rate how startled each participant appeared during the
video (0 = not at all startled to 10 = extremely startled). We then aver-
aged startle ratings for each participant across all of these raters to ob-
tain a startle response measure. Percentage heart rate change and
startle were standardized and reverse-scored (multiplied by −1)
and then averaged to form a behavioral measure of trust (r(90) = .28,
pb.01). To assess overall trust, we averaged all standardized measures
of trust (the eight self-report measures and the two behavioral mea-
sures) into a single composite (α= .87).
Blame for vehicle
After the accident, all participants also assessed how responsible
they, the car, the people who designed the car, and the company that
developed the car were for the accident (all 0–10 scales, see SOM for
exact questions). To assess punishment for the accident, participants
were asked to imagine that this accident occurred in the real world,
with a different driver behindthe wheel of their car. Participants report-
ed how strongly they felt that the driver should be sent to jail, how
strongly they felt that the car should be destroyed, how strongly they
felt that the car's engineer should be punished, and how strongly they
felt that the company that designed the car should be punished. The
six items measuring the vehicle's responsibility and resulting punish-
ment for a similar accident were standardized and averaged to form a
single composite (α=.90).
Finally, we used the videotape mentioned above to measure partic-
ipants' distraction while driving during the second course, measured
as the time spent looking away from the simulator rather than paying
attention while driving. Results showed a ﬂoor effect with very little dis-
tractionacross conditions(less than 3% of the overall timein the two au-
tonomous vehicle conditions). See Table 1 for these means as well as
means from all analyses below.
All primary analyses involved planned orthogonal contrasts examin-
ing differences between the Normal, Agentic, and Anthropomorphic
As predicted, participantsin the Anthropomorphic condition anthro-
pomorphized the vehicle more than those in the Agentic condition,
t(97) = 3.21, p=.002,d= .65, who in turn anthropomorphized
the vehicle more than in the Normal condition, t(97) = 7.11, pb
Participants in the Anthropomorphic and Agentic conditions
liked the vehicle more than did participants in the Normal condition,
t(97) = 3.92, pb.0001, d= .80 and t(97) = 3.29, p= .001, d= .67,
but the autonomous vehicle conditions did not differ signiﬁcantly
from each other (p= .55).
As predicted, on the measure of overall trust, those in the Anthropo-
morphic condition trusted their vehicle more than did those in the
Agentic condition, t(97) = 2.34, p= .02, d= .48, who in turn trusted
their vehicle more than those in the Normal condition, t(97) = 4.56,
pb.0001, d= .93. For behavioral trust, participants in the Anthropo-
morphic condition trusted their vehicle more than did those in the
Agentic condition, t(97) = 3.36, p= .001, d= .68 and Normal condi-
tion, t(97) = 2.78, pb.01, d= .56, although the Agentic and Normal
conditions did not differ signiﬁcantly (p= .56). For self-reported
trust, participants in the Anthropomorphic condition and the Agentic
condition did not differ signiﬁcantly (p= .14), but both participants
in the Agentic condition and Anthropomorphic conditions reported
greater trust than participants in Normal condition, ts(97) = 4.83 and
6.35, respectively, psb.01, ds = .98 and 1.29. Table 1 reports the self-
report measures and the behavioral measures of trust separately.
To assess whether thevehicle'seffect on overall trust was statistical-
ly mediated by perceived anthropomorphism, we used Preacher and
Hayes (2008) bootstrapping method and coded condition as Normal
= 0, Agentic = 1, and Anthropomorphic = 2 (see Hahn-Holbrook,
Holt-Lunstad, Holbrook, Coyne, & Lawson, 2011; Legault, Gutsell, &
Inzlicht, 2011 for similar analyses). This analysis conﬁrmed that anthro-
pomorphism statistically mediated the relationship between vehicle
condition and overall trust in the vehicle (95% CI = .31 to .55; see
Fig. 1; 20,000 resamples).
Blame for vehicle
As noted, we programmed the driving simulation so that all partici-
pants would experience the same virtually unavoidable accident clearly
caused by the other driver, but it is important to keep the nature of the
Means by condition for main dep endent measures.
Normal Agentic Anthropomorphic
Behavioral (ECG & startle) −0.23
Blame for the vehicle −0.60
Distraction (instances) 0.16
Distraction (percent of total time) 0.1
Means that do not share a subscript differ signiﬁcantly at pb.05.
Degrees of freedom across analyses vary slightly because of missing responses. We
measured skin conductance throughout the experiment through electrodes on partici-
pants’wrists, but unanticipated artifacts from movement while driving rendered these re-
sults impossible to interpret and so we do not report them.
115A. Waytz et al. / Journal of Experimental Social Psychology 52 (2014) 113–117
accident in mind. If a trusted, competent driver were hit by another ve-
hicle, one would hold the competent driver less responsible for the ac-
cident because it would clearly appear to be the other driver's fault.
Thus, we predicted that anthropomorphism would mitigate blame for
an accident clearly caused by the other vehicle. It is important to note,
however, that our prediction would be different if the vehicle were
able to avoid this accident, in whichcase we would predict that anthro-
pomorphism would increase the tendency to credit the vehicle for this
Participants in the Agentic and Anthropomorphic conditions blamed
their car more for the accident than did those in the Normal condition,
ts(96) = 6.30 and 4.18, respectively, psb.01, ds = 1.29 and .85. This
is consistent with the relationship between agency and perceived re-
sponsibility. An object with no agency cannot be held responsible for
any actions, and so this comparison is not particularly interesting.
More interesting is that participants blamed the vehicle signiﬁcantly
less in the Anthropomorphic condition than in the Agentic condition,
t(96) = 2.18, p=.03,d= .44, in which the perceived thoughtfulness
of the fully anthropomorphic vehicle mitigated the responsibility that
comes from independent agency (given that the accident was clearly
caused by the other vehicle). This shows a clear relationship between
anthropomorphism and perceptions of responsibility, but the exact na-
ture of that relationship cannot be tested in this particular paradigm be-
cause we are unable to create a uniform accident across conditions
clearly caused by participants themselves.
Technological advances blur the line between human and nonhu-
man, and this experiment suggests that blurring this line even further
could increase users'willingness to trust technology in place of humans.
Amongst those who drove an autonomous vehicle, those who drove a
vehicle that was named, gendered, and voiced rated their vehicle as
having more humanlike mental capacities than those who drove a vehi-
cle with the same autonomous features but without anthropomorphic
cues. In turn, those whodrove the anthropomorphized vehicle with en-
hanced humanlike features (name, gender, voice) reported trusting
their vehicle even more, were more relaxed in an accident, and blamed
their vehicle and related entities less for an accident caused by another
driver. These ﬁndings provide further support for the theoretical con-
nection between perceptions of mental capacities in others and assess-
ments of competence, trust, and responsibility. Attributing a mind to a
machine matters because it could create a machine to which users
might entrust their lives.
This ﬁnding is also of clear practical relevance given the rapidly
changing interface between the technological world and the social
world. No longer merely mindless tools, modern technology now taps
human social skills directly. People ask their phones for driving direc-
tions, restaurant recommendations, and baseball scores. Automated
customer service agents help people purchase ﬂights, pay credit card
bills, and obtain prescription medicine. Roboticpets even provide social
support and companionship, sometimes in the place of actual human
companionship (Melson et al., 2009). Ourresearch identiﬁes one impor-
tant consequence of considering the psychological dimensions of tech-
nological design. Even the greatest technology, such as vehicles that
drive themselves, is of little beneﬁt if consumers are unwilling to use it.
Finally, our research at this human-technology frontier also informs
the inverse effect in which people are treated more like technology—as
objects or relatively mindless machines (Cikara, Eberhardt, & Fiske,
2011; Loughnan & Haslam, 2007). Adding a human voice to technology,
for instance, makes people treat it as more humanlike agent (Takayama
& Nass, 2008), which suggests that removing a human voice like one's
own from interpersonal communication may make another person
seem relatively mindless. Indeed, in one series of recent experiments,
participants rated another person as being less mindful (e.g., less
thoughtful, less rational) when they read a transcript of an interview
than when they heard the audio of the same interview (Schroeder &
Epley, 2014). Similarly, verbal accents that differ from one's own trigger
prejudice and distrust compared to accents similar to one's own
(Anisfeld, Bogo, & Lambert, 1962; Dixon, Mahoney, & Cocks, 2002;
Giles & Powesland, 1975; Kinzler, Corriveau, & Harris, 2011; Kinzler,
Dupoux, & Spelke, 2007; Lev-Ari & Keysar, 2010), an effect that may
be partially mediated by differences in the attribution of humanlike
Few divides in social life are more important than the one between
us and them,between human and nonhuman. Perceptions of this divide
are not ﬁxed but ﬂexible. Understanding when technology crosses that
divide to become more humanlike matters not only for how people
treat increasingly humanlike technology, but also for understanding
why people treat other humans as mindless objects.
This research was funded by the University of Chicago's Booth
School of Business and a grant from the General Motors Company. We
thank Julia Hur for assistance with data coding.
Appendix A. Supplementary materials
Supplementary to this article can be found online at http://dx.doi.
Alicke, M.D. (2000). Culpable control and the psychology of blame. Psychological Bulletin,
Anisfeld, M., Bogo, N., & Lambert, W. E. (1962). Evaluational reacti ons to accented Eng lish
speech. Journal of Abnormal and Social Psychology,65,223–231.
Beckman, L. (1970). Effects of students' performance on teachers' and observers' attribu-
tions of causality. Journal of Educational Psychology,61,76–82.
Cikara, M., Eberhardt, J. L., & Fiske, S. T. (2011). From agents to objects: Sexist attitudes
and neural response s to sexualized targets. Journal of Cognitive Neuroscience ,23,
Cushman, F. (2008). Crime and punishment: Distinguishing theroles of causal and inten-
tional analyses in moral judgment. Cognition,108,353–380.
Dennett, D. C. (1978). Brainstorms: Philosophical essays on mind and psych ology.
Cambridge: Bradford Books/MIT Press.
Dixon, J. A., Mahoney, B., & Cocks, R. (2002). Accents of guilt? Effects of regional accent,
race, and crime typ e on attributions of guilt. Journal of Language and Social
Epley, N.,Caruso, E., & Bazerman, M. H. (2006). When perspective taking increases taking:
Reactive egoism in social interaction. Journal of Personality and Social Psychology,91,
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of
anthropomorphism. Psychological Review,114, 864.
Giles, H., & Powesland, P. F. (1975). Speech style and social evaluation,Vol. 7, London:
Gray, H. M.,Gray, K., & Wegner, D.M.(2007). Dimensions of mind perception. Science,315,
Hahn-Holbrook, J., Holt-Lunstad, J., Holbrook, C., Coyne, S. M., & Lawson, E. T. (2011).
Maternal defense: Breast feeding increases agg ression by redu cing stress.
Fig. 1. The results of a mediati on analysis of condition on overall trust (Betas
116 A. Waytz et al. / Journal of Experimental Social Psychology 52 (2014) 113–117
Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y., De Visser, E. J., & Parasuraman, R.
(2011). A meta-analysis of factorsaffecting trust in human–robotinteraction. Human
Factors: The Journal of the Human Factors and Ergonomics Society,53,517–527.
Haslam, N. (2006 ). Dehumanization: An integrative review. Pe rsonality and Social
Kinzler, K. D., Corriveau, K. H., & Harris, P. L. (2011). Children's selective trust in native
accented speakers. Developmental Science,14,106–111.
Kinzler, K. D., Dupoux, E., & Spelke, E. S. (2007). Thenativelanguageofsocialcognition.
Proceedings of the National Academy of Sciences,104,12577–12580.
Legault, L., Gutsell, J. N., & Inzlicht, M. (2011). Ironic effects of antiprejudice messages:
How motivationa l interventions can reduce (but also increase) prejudice .
Lev-Ari, S., & Keysar,B. (2010). Why don't we believe non-nativespeakers? The inﬂuence
of accent on credibility. Journal of Experimental Social Psychology,46,1093–1096.
Leyens, J. P., Paladino, P.M., Rodriguez-Torres, R., Vaes, J., Demoulin, S., Rodriguez-Perez,
A., et al. (2000). The emotional side of prejudice: The attribution of secondary emo-
tions to ingroups and o utgroups. Personality andSocial Psychology Review,4,186–197.
Locke, J. (1997). An essay concerning human understanding. Harmondsworth, England:
Penguin Books (Original work published 1841).
Loughnan, S., & Haslam, N. (2007). Animals and androids: Implicit associations between
social categories and nonhumans. Psychological Science,18,116–121.
Malle, B. F., & Knobe, J. (1997). The folk concept of intentionality. Journal of Experimental
McKnight, D. H., & Chervany, N. L. (2001). Trust and distrust deﬁnitions: One bite at a
time. Trust in Cyber-societies (pp. 27–54). : Springer Berlin Heidelberg.
Melson, G. F., Kahn, P . H., Beck, A., Friedm an, B., Roberts, T., Garrett, E., et al. (2 009).
Children's behavior toward and understanding of robotic and living dogs. Journal of
Applied Developmental Psychology,30,92–102.
Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers.
Journal of Social Issues,56,81–103.
Newcomb, D. (2012). Retrieved from. http://www.cnn.com/2012/0 9/18/tech/
Pak, R., Fink, N., Price, M., Bass, B., & Sturre, L. (2012). Decision support aids with anthro-
pomorphic characteristics inﬂuence trust and performa nce in younger and older
adults. Ergonomics,55, 1059–1072.
Pierce, J. R., Kilduff, G. J., Galinsky, A.D., & Sivanathan, N. (2013). From glue to gasoline:
How competition turns perspec tive takers une thical. Psych ological Scienc e,24,
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing
and comparing indirect effects in multiple mediator models. Behavio r Research
Schroeder, J., & Epley, N. (2014). Speaking louder than words: Voice reveals the presence
of a humanlike mind. Unpublished manuscript, University of Chicago.
Shaver, K. G. (1985). The attribution of blame: Causality, responsibility, and blameworthi-
ness. New York: Springer-Verlag.
Siegrist, M., Earle, T., & Gutscher, H. (2003). Test of a trust and conﬁdence model in the
applied context of electromagnetic ﬁeld (EMF) risks. Risk Analysis,23,705–716.
Takayama,L., & Nass, C. (2008). Driver safety and information from afar: An experimental
driving simulator study of wireless vs. in-car information services. Intern ational
Journal of Human Computer Studies,66,173–184.
Twyman, M., Harvey, N., & Harries, C. (2008). Trust in motives, trust in competence:
Separate factors determining the effectiveness of risk communication. Judgment and
Waytz, A.,Cacioppo, J., & Epley, N. (2010).Who sees human? Thestability and importance
of individual differences in anthropomorphism. Perspectives on Psychological Science,
Weiner, B. (19 95). Judgments of responsibility: A foundation for a theory of social conduct.
New York, NY: Guilford Press.
Wetzel, C. G. (1982). Self-serving biases in attribution: A Bayesian analysis. Journal of
Personality and Social Psychology,43,197–209.
117A. Waytz et al. / Journal of Experimental Social Psychology 52 (2014) 113–117