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Busy Signal: Effects of Mobile Device Usage on Pedestrian Encounters


Abstract and Figures

Mobile communication technology plays an increasingly pervasive role in everyday life. This study examined one aspect of this role, specifically, the effects of mobile device use on the micro-interactions of pedestrians as they approached and passed a confederate. Over 400 participants were observed in a 2 (group: mobile device vs. control) × 3 [condition: look-only (L); look and smile (LS); look, smile, and greeting (LSG)] factorial design study measuring participants’ looks, smiles, nods, and greetings toward the confederates. Log-linear analyses of the dependent measures provided qualified support for the predicted decreased responsiveness from mobile device users. Specifically, a group by condition interaction on smiles showed that significantly fewer mobile device users than controls smiled at the confederates in the LSG condition. In addition, a group by sex of participant interaction on greetings indicated that significantly fewer female mobile device users offered greetings than males and females in the other conditions. The processes potentially mediating these effects are discussed and the broader influence of mobile devices on the micro-interactions of pedestrians is considered.
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Busy Signal: Effects of Mobile Device Usage
on Pedestrian Encounters
Miles L. Patterson Vanessa M. Lammers Mark E. Tubbs
Published online: 11 April 2014
ÓSpringer Science+Business Media New York 2014
Abstract Mobile communication technology plays an increasingly pervasive role in
everyday life. This study examined one aspect of this role, specifically, the effects of
mobile device use on the micro-interactions of pedestrians as they approached and passed a
confederate. Over 400 participants were observed in a 2 (group: mobile device vs. con-
trol) 93 [condition: look-only (L); look and smile (LS); look, smile, and greeting (LSG)]
factorial design study measuring participants’ looks, smiles, nods, and greetings toward the
confederates. Log-linear analyses of the dependent measures provided qualified support for
the predicted decreased responsiveness from mobile device users. Specifically, a
group by condition interaction on smiles showed that significantly fewer mobile device
users than controls smiled at the confederates in the LSG condition. In addition, a
group by sex of participant interaction on greetings indicated that significantly fewer
female mobile device users offered greetings than males and females in the other condi-
tions. The processes potentially mediating these effects are discussed and the broader
influence of mobile devices on the micro-interactions of pedestrians is considered.
Keywords Mobile devices Nonverbal communication Pedestrian behavior
In the few decades since their introduction, mobile communication devices have become
an increasingly common, and important, part of everyday life. For example, it is estimated
A version of this paper was presented at the annual convention of the Midwestern Psychological Associ-
ation, Chicago, May 2013.
M. L. Patterson (&)V. M. Lammers M. E. Tubbs
Department of Psychology, University of Missouri-St. Louis, One University Blvd., St. Louis,
MO 63121-4400, USA
J Nonverbal Behav (2014) 38:313–324
DOI 10.1007/s10919-014-0182-4
that there are over 5 billion wireless service subscribers worldwide (ITU 2010), with 321
million subscribers in the United States alone (Cellular Telecommunications Industry
Association 2012). A recent Pew Research Center poll across the world found that, in 18 of
21 countries polled, three-quarters of respondents reported having a cell phone (Pew
Research Center 2013). The practical advantages to these mobile technologies are many.
Quick and easy contact with distant others is possible without dependence on landlines.
Mobile devices provide a sense of increased security in case of emergencies. Various
messaging options provide a way of communicating without disturbing ongoing activities.
And, of course, mobile devices can provide easy access to the power of the Internet. In
spite of these considerable benefits, there are also problems related to some circumstances
of mobile device use.
One problem receiving considerable research attention is the effect of cell phone use on
driving safety. Specifically, cell phone use while driving increases reaction times to
changing traffic conditions (Beede and Kass 2006; Strayer and Drews 2004; Strayer et al.
2003). In addition, drivers who talk on a cell phone have a threefold increase in traffic
violations (Beede and Kass 2006) and a fourfold increase in accident rates (Redelmeier and
Tibshirani 1997). In fact, the level of impairment caused by talking on a cell phone while
driving is comparable to being intoxicated at a blood alcohol level of .08 (Strayer et al.
2006). Furthermore, it appears that these deficits are not the product of peripheral inter-
ference from simply holding a phone. Rather, these effects are the product of attentional
deficits from the cognitive demands of a phone conversation (Caird et al. 2008; Strayer and
Johnston 2001).
Although mobile devices now serve a variety of functions, their most frequent uses are
for phone calls and texting (Pew Research Center 2013). This kind of remote contact with
others creates what Gergen (2002) describes as absent–presence, that is, a continuous
presence of family, friends, colleagues, and others who are physically absent. It is not
surprising that such absent-engagement decreases attention to, and involvement with, in-
person conversational partners (Bugeja 2005; Glaser 2007; Hills et al. 2009). But such in-
person conversations, or what Goffman (1963) terms focused interactions (i.e., interactions
focused around a conversation), are not the only form of face-to-face interactions. Goffman
(1963) emphasized that we also interact in the absence of a conversation by making
nonverbal adjustments to the close presence of others—unfocused interactions. Thus, for
example, as people stand in line at a checkout counter, choose a seat in a half-filled
doctor’s waiting room, or enter an occupied elevator, they engage in subtle, and often brief,
interactions with nearby others.
The present study examined the effects of mobile device use in one type of unfocused
interaction, specifically, the encounters between pedestrian strangers passing one another
on the sidewalk. Although these mundane interactions may seem trivial, they are ubiqui-
tous occurrences that reflect social norms, signal attitudes toward in-group and out-group
others, and even prime subsequent social judgments and behavior (Patterson 2008).
Passing Encounters
According to Goffman (1963, pp. 83–88), a common pattern in these brief micro-inter-
actions is civil inattention. Presumably, this occurs when people initiate a brief glance to
recognize the presence of the approaching pedestrian, followed by looking away to respect
the individual’s privacy. Goffman (1963, p. 84) proposed that the transition from glancing
to gaze avoidance occurs when an approaching stranger reaches an approximate eight-foot
separation. Across four studies, Cary (1978) found, however, that pedestrians did not lower
314 J Nonverbal Behav (2014) 38:313–324
their heads and avert gaze as they closely approached and passed one another. To pursue
this issue further, we examined these micro-interactions between pedestrians in a series of
studies. In samples from an urban area in the Midwest, we found that, as confederates
initiated greater involvement toward an approaching pedestrian [i.e., from avoid, to look,
to look and smile (LS)], glances, smiles, nods, and greetings toward the confederates
increased (Patterson and Tubbs 2005; Patterson et al. 2002).
An important qualification to this pattern was observed in a cross-cultural study com-
paring pedestrians in Japan and the United States. Specifically, in Japan, smiles, nods, and
greetings were very rare (a range of 1–2 %) and unaffected by the confederates’ behavior,
compared to the US (a range of 9–25 %) where reactions were similar to those in the
earlier studies (Patterson et al. 2007). Thus, for our US samples, there was a consistent
pattern of increased confederate involvement precipitating greater participant involvement
(i.e., reciprocity) in the form of increased glances, smiles, nods, and greetings. In addition,
however, sex of participant and sex of confederate also affected response patterns. For
example, in two of the studies, female confederates received more glances than male
confederates (Patterson et al. 2002,2007). In one study, this was qualified by a sex of
confederate by sex of participant interaction, with more glances toward opposite-sex
confederates than toward same-sex confederates (Patterson et al. 2002).
Present Study
The purpose of the present study was to examine the effects of mobile device use on the
micro-interactions of pedestrians passing one another on the sidewalk. It seems clear that
the absent engagement of mobile device use adversely affects involvement with in-person
conversational partners (Bugeja 2005; Glaser 2007; Hills et al. 2009), but do these dis-
tracting effects extend to the brief passing encounters of pedestrians? First, we hypothe-
sized that the earlier condition main effect (Patterson et al. 2002,2007; Patterson and
Tubbs 2005) will be replicated. That is, increased confederate involvement will precipitate
more glances, smiles, nods, and greetings from participants. Our second, and more
important, hypothesis was that participants using mobile devices will be less responsive
than control participants. That is, there will be a group main effect, with mobile device
participants displaying lower involvement than control participants, reflected in some
combination of decreased glances, nods, smiles, and greetings.
We were also interested in a research question involving gender of participants and
confederates. Given our earlier results of participant and confederate gender affecting
patterns of glancing (Patterson et al. 2002,2007), does the gender of either participants or
confederates interact with mobile device use in affecting participants’ responses?
Design and Participants
The experiment employed a 2 (group: mobile device vs. control) 93 [condition: look-only
(L); LS; look, smile, and greeting (LSG)] design.
The greeting component was a simple
In the earlier pedestrian studies, the condition manipulations included avoid, look-only, and look and
smile. We removed the avoid condition in this study because we were concerned that the base rate of
responding might be too low, especially among mobile device participants. Thus, we intensified the high
J Nonverbal Behav (2014) 38:313–324 315
‘Hi’’ as the confederate initiated a LS. The mobile device participants had to be using their
mobile devices in some fashion (talking on the phone, using the key board, or reading the
screen) and not simply holding it in their hands. A total of 481 pedestrians walking alone
were observed as they approached and passed a confederate. There were 56 participants
dropped from the analysis due to procedural errors or observers’ problems with seeing the
participants clearly. This left a total of 425 participants with 206 men, 217 women, and 2
without gender identification included. Because we did not select for gender, but only for
solitary pedestrians who met the requirements identified in the ‘Procedure’ section, the
gender distribution in the 6 (2 93) cells of the experiment was free to vary. This resulted
in 120 males and 91 females in the control condition and 126 females and 86 males in
mobile device condition. The sample appeared to be approximately 60 % Caucasian and/or
Hispanic, 25 % African-American, 10 % Asian, and 5 % other/undetermined. Over 80 %
of the pedestrians appeared to be in the 18–30 age range.
The experiment was conducted on two sidewalks and an enclosed walkway on an urban
university campus in St. Louis. The chosen sidewalks were on level terrain and straight.
This allowed unobstructed vision to identify approaching participants. Trials were run on
the sidewalks during daylight hours when the weather was not too cold and there was no
precipitation. Some of the trials on the enclosed walkway were run when the weather did
not permit the outside trials. Times immediately around class changes were avoided
because pedestrian traffic levels were too high.
Eleven college-age students (six males and five females) served as both confederates and
observers in the experiment. The students were trained in the confederate role and prac-
ticed the conditions on one another before data were collected. The second author also
monitored several early trials for each confederate/observer. The basic format required the
confederates to initiate a look, LS, or LSG to the oncoming pedestrian at the start of an
approximate twelve-foot passing zone.
In order to make sure that each participant had a comparable opportunity to notice and
react to the confederate, a number of restrictions were placed on the potential participants.
These restrictions included the following circumstances: (a) the sidewalk had to be
uncrowded with no more than a few people in the oncoming traffic; (b) the participant had
to be walking alone on the right side of the sidewalk; (c) there had to be a gap of at least
30–40 ft between the participant and the person walking in front of him/her (i.e., in order
for the participant to have a clear view of the approaching confederate); (d) the participant
could not have just turned the corner on to the sidewalk; (e) participants could not be
involved in other activities while walking (wearing headphones, smoking, reading, eating,
carrying heavy or awkward objects); (f) participants could not be running or obviously
disabled; and (g) participants could not be wearing sunglasses because it was too difficult
to monitor their gaze direction. In addition, participants could not be someone the con-
federate knew or someone who had been observed previously.
Footnote 1 continued
confederate–involvement condition by adding a greeting in order to promote greater participant
316 J Nonverbal Behav (2014) 38:313–324
Each confederate ran the six conditions in a block randomized order. The observers
could obviously identify mobile device versus control participants, but they were blind to
the condition manipulations. Confederates and observers were dressed casually, typical of
their age group. The confederate positioned him/herself at one end of a sidewalk, in a
location to identify a potential participant. The observer was stationed to the side of, and
physically separated from, the confederate. No attempt was made to select participants by
gender or age. That is, the first person meeting the requirements described in the previous
paragraph was approached. When the confederate started to move down the sidewalk, the
observer followed at approximately 30–40 ft behind the confederate. After the confederate
and observer passed the participant and reached the end of the sidewalk, they stopped in
separate locations and recorded their observations. Then they got ready for the next trial.
Confederates and observers were kept blind regarding the hypotheses.
Response Measures
The observer’s data sheet contained items on the time of day, location, temperature,
weather, race and sex of participant, and approximate age of participant (18–30, 31–40,
41–50, 51–60, and over 61). The participant’s reactions toward the confederate in the
passing zone (12–0 ft) were recorded on the following dimensions: (a) glance, (b) nod,
(c) smile, and (d) a verbal greeting. In operational terms, a glance was defined as visually
focusing on the confederate in the passing zone. This was usually very brief and typically
involved a slight, but noticeable, head turn in the direction of the confederate. A head nod
was defined as down and up vertical head movement while glancing at the confederate. A
smile was defined as a noticeable upward turn of the corners of the mouth while glancing at
the confederate. A verbal greeting was defined as a verbalization directed toward the
confederate. On each of the measures, reactions were scored as present, absent, or
uncertain. The confederate and the observer scored each behavior on every trial.
In earlier studies, inter-rater reliabilities, based on Kappa (Cohen 1960) and computed
on the judgments of the confederates and observers, ranged from approximately .60–.95
(Patterson et al. 2002,2007; Patterson and Tubbs 2005). In general, the Kappas were lower
in the present study: glances =.56, nods =.39, smiles =.58, and greetings =.72.
Because the Kappa for nods was too low, nods were dropped from the analyses.
Log-Linear Analyses
Because the effects of multiple categorical variables were examined, log-linear analyses
were employed for analyzing effects on the separate response dimensions. Specifically, a
simultaneous entry procedure was conducted on SPSS, with the relevant variables entered
in a single step (see Howell 2010, pp. 629–659). Partial v
in the log-linear analysis tests
the significance of the relationships between predictor variables and the dependent
One factor that may have contributed to the lower Kappas was that the reaction times of mobile device
participants might have been a little slower than those in the control condition. As a result, the observers
may have noticed late reactions that the confederates did not see as the participant was passing. Because this
was not a problem with greetings, as confederates could still hear the comment even if they did not see it, the
Kappa for greetings was higher.
J Nonverbal Behav (2014) 38:313–324 317
measures. Specific comparison tests in log-linear analyses are typically made in terms of
odds ratios, that is, the ratios of two conditional probabilities, or odds, for a dichotomous
outcome. Odds ratios can assume any value between 0 and ?and are not affected
by marginal frequencies. Consequently, odds ratios are useful measures of effect sizes
(Howell 2010, pp. 629–659). It should also be noted that a significant partial v
that the odds ratio is significantly different from 1.0.
Although we were also interested in the potential higher-level interactions involving
condition, group, sex of participant, and sex of confederate, the expected cell frequencies
for smiles and greetings in the three-way and four-way combinations were too small to
meet the minimal requirements for log-linear analyses. The higher base rate for partici-
pants’ glances did permit analyses of three-way interactions. Overall, 45 % of all partic-
ipants glanced at the confederates, but only 13 % smiled and 12 % offered a greeting.
Consequently, the main and interaction effects of condition and group (mobile device vs.
control) are reported first and, later, the separate main and interaction effects involving sex
of participant and sex of confederate are reported.
Condition Effects
There were significant condition main effects on participants’ (1) glances, v
N=425) =50.69, p\.0001; (2) smiles, v
(2, N=423) =21.72, p\.0001; (3)
greetings, v
(2, N=423) =57.51, p\.0001. These effects are clearly seen in Fig. 1
where the percentages of responses on each of the dependent measures are displayed as a
function of condition. In general, participants in the L and LS conditions did not differ in
their responses. But participants in the LSG condition had much higher levels of glances
(odds ratio =2.23/.51 =4.37), smiles (odds ratio =.32/.08 =4.00), and greetings (odds
ratio =.42/.04 =10.50) than in the other two conditions. Thus, there was clear support
for the first hypothesis that increased involvement from the confederate would increase
participants’ responsiveness.
Mobile Device Effects
Although there were no significant main effects of group (mobile device vs. control) on any
of the response measures, there was a marginally significant group effect on greetings,
(1, N=425) =3.31, p\.07). That is, a higher percentage of control participants
(15 %) than mobile device participants (9 %) greeted the confederates (odds ratio =.17/
.10 =1.70), consistent with the second hypothesis. There were also three interaction
effects indicating conditional group effects. First, a significant group 9sex of participant
interaction effect on greetings (v
(1, N=423) =4.82, p\.03) qualified the marginal
group main effect on greetings. Specifically, males and females in the control condition
and males in the mobile device condition initiated more greetings (15 %) than did females
in the mobile device condition (6 %) (odds ratio =.17/.06 =2.83). Thus, the marginal
group difference in greeting was mainly a product of fewer greetings from females using
mobile devices.
For example, if six out of ten participants glanced at the confederate in condition A, the odds of glancing
would be 3/2 (six glancing/four not glancing). If two out of ten participants in condition B glanced at the
confederate, the odds of glancing would be 1/4 (two glancing/eight not glancing). The odds ratio of glancing
in condition A versus B would 1.5/.25 =6. That is, the odds of participants in condition A glancing would
be 6 times the odds of participants in condition B glancing.
318 J Nonverbal Behav (2014) 38:313–324
Second, there was a group 9condition interaction effect on smiles (v
N=425) =13.23, p\.002). Figure 2shows that the proportion of smiles from mobile
device participants remained low and constant across conditions, whereas the proportion
of smiles from control participants increased dramatically from the L and LS conditions
to the LSG condition. Specifically, in the LSG condition, control participants smiled
much more at the confederates (34 %) than did mobile device participants (14 %) (odds
ratio =.53/.17 =3.12) Although there was a higher percentage of control participants
(77 %) than mobile device participants (62 %) who glanced at the confederates in the
LSG condition (v
(1, N=139) =3.85, p=.05) (odds ratio =3.31/1.59 =2.08), it is
unlikely that this difference alone could explain the much larger difference in smiles. To
determine if the higher percentage of smiles from control participants was merely a
product of their glancing more frequently and, consequently, having more opportunity to
smile at the confederates, a separate comparison was made on only those participants
who glanced at the confederates in the LSG condition. The group effect remained, with a
significantly higher proportion of smiles (v
(1, N=96) =5.04, p\.03) from control
participants (45 %) than from mobile device participants (23 %) (odds ratio =.83/
.30 =2.77). Thus, the condition 9group interaction effect on smiles was not simply the
product of fewer mobile device participants than control participants glancing at the
Finally, there was a significant group 9sex of participant 9sex of confederate
interaction effect on glances (v
(1, N=423) =4.98, p\.03). One way of describing
the three-way interaction is that the largest difference in participants’ patterns of
glancing at same-sex or opposite-sex confederates occurred in the mobile device con-
dition, with a much higher percentage of male participants (69 %) glancing at female
confederates than at male confederates (34 %) (odds ratio =2.21/.52 =4.25). In
contrast, males in the control condition glanced at comparable rates at both female
(52 %) and male confederates (51 %) (odds ratio =1.09/1.04 =1.05). The glance
percentages across the group 9sex of participant 9sex of confederate cells are shown
in Table 1.
Fig. 1 Percent of responses on the dependent measures as a function of condition
J Nonverbal Behav (2014) 38:313–324 319
Sex of Participant and Sex of Confederate Effects
First, male participants (52 %) glanced significantly more at confederates (v
N=423) =4.89, p\.03) than female participants (38 %) (odds ratio =1.08/
.64 =1.69) did. In addition, participants glanced significantly more (v
N=423) =5.70, p\.02) at female confederates (50 %) than at male confederates
(39 %) (odds ratio =.99/.63 =1.57). These two main effects were qualified, however, by
the group 9sex of participant 9sex of confederate described in the last section and
shown in Table 1. In other words, the sex of participant and sex of confederate main effects
on glancing were disproportionately a product of male mobile device users’ high per-
centage of glancing at female confederates. In addition, participants also smiled signifi-
cantly more (v
(1, N=425) =18.62, p\.0001) at female confederates (19 %) than at
male confederates (5 %) (odds ratio =.23/.10 =2.30).
The results of this experiment indicated that greater confederate involvement increased
participants’ glances, smiles, and greetings toward the confederates, replicating the general
pattern from the earlier pedestrian studies (Patterson et al. 2002,2007; Patterson and Tubbs
2005) and supporting the first hypothesis. The condition effect, as seen in Fig. 1, was solely
Fig. 2 Percentage of smiles as a function of condition and group (control vs. mobile device)
Table 1 Percent of glances as a
function of condition, sex of
participant, and sex of
Male participants Female
Control Mobile
Control Mobile
Male confederates 51 34 30 34 39
Female confederates 52 69 47 39 50
Mean 52 38 45
320 J Nonverbal Behav (2014) 38:313–324
the product of participants’ greater responsiveness in the LSG condition, compared to the L
and LS conditions. The primary purpose of the study, however, was to examine how
mobile devices affect these brief occasions of pedestrians passing one another on the
Mobile Device Use
First, the marginally-significant trend for mobile device users making fewer greetings than
did control participants was qualified by a group 9sex of participant interaction, with
females on mobile devices having the smallest percentage of greetings. There are, at least,
two possible reasons for this effect. Perhaps, females are more dependent on their mobile
devices (Jenaro et al. 2007) and, consequently, are less sensitive to their social environ-
ments when using them than males are. In turn, this distraction might decrease the prob-
ability of noticing the greeting and reciprocating it. Of course, even if one’s attention is
directed toward a mobile device, a verbal greeting might still be heard, whereas a smile
might not be noticed.
An alternative explanation is that females might be more cautious around strangers than
males are. And for those females who are particularly concerned about these settings, a
mobile device might be used as a tactic or ‘‘prop’’ for avoiding contact. For example, Geser
(2004) suggests that, when a person is alone in a public setting, a mobile device serves as a
kind of symbolic bodyguard to signal that there is still virtual contact with another person.
This suggestion is consistent with Gergen’s (2002) notion of mobile devices allowing an
absent–presence of family, friends, and acquaintances who are not physically present. In
contrast, for control participants, without such a prop, there may be more social pressure to
reciprocate a greeting. As a result, females in the control condition reciprocated greetings
at a level comparable to males.
Next, the group 9condition interaction on smiles, seen in Fig. 2, shows that the per-
centages of smiles from mobile device participants were low and level across condition. In
contrast, smiles from control participants increased dramatically from the LS condition to
the LSG condition. Thus, smiling, the most common sign of friendliness and openness to
others (Fridlund 1994), was much lower among mobile device participants than among
control participants in the LSG condition. So, what might account for these differences? In
another area where mobile devices are a distraction—driving performance—it appears that
the increased cognitive demand from a cell phone conversation affects attention to the
environment (Caird et al. 2008; Strayer and Johnston 2001). Thus, the interference occurs
in processing the information relevant for making driving adjustments. That is, objects that
would normally be appreciated while driving go unnoticed during a cell phone conver-
sation (Maples et al. 2008). Recollection of hazards (Charlton 2009), traffic signals
(Strayer and Johnston 2001) and billboards (Strayer et al. 2003) was significantly lower
among drivers using both handheld and hands-free cell phones—even when subjects look
directly at such items—suggesting an effect on a cognitive level above the visual-sensory
In the present study, the lower proportion of smiles from mobile device users than from
controls was evident even among those who glanced at the confederates. Thus, like the
drivers using cell phones who looked directly at potential dangers, but failed to react,
mobile device users who glanced at the smiling and greeting confederate reciprocated
smiles much less frequently than did control participants. Normally, the simple and rapid
perception of others’ behavior is sufficient to trigger reciprocity, or behavioral mimicry,
without conscious awareness (Lakin 2006,2013), especially with a behavior as simple as a
J Nonverbal Behav (2014) 38:313–324 321
smile. Presumably, this ‘‘perception–behavior expressway’’ was selected over the course of
evolution because it was adaptive for our species (Dijksterhuis and Bargh 2001). Thus, the
cognitive demand from mobile device use seems to be disrupting the otherwise automatic
perception–behavior link, leading to fewer smiles.
Finally, the group 9sex of participant 9sex of confederate interaction on glances
qualified the main effects of (1) male participants glancing more at confederates than
female participants did and (2) female confederates receiving more glances than male
confederates did. The latter effect is consistent with results of earlier studies showing more
glances at female confederates than at male confederates (Patterson et al. 2002,2007). The
three-way interaction in the present study was primarily the result of the contrast between
male mobile device participants glancing much more frequently at female confederates
(M=69 %) than at male confederates (M=34 %), compared to the other contrasts
across mobile device and control participants. Like the possibility that female mobile
device participants may have used their mobile devices strategically to limit the recipro-
cation of greetings, male mobile device participants may have used their mobile devices to
cover for increased glancing at female confederates. Alternatively, perhaps the increased
cognitive demand from attention to their mobile devices reduced the normal inhibition to
avoid being too obvious in glancing at a passing female.
Finally, some mention should be made of the higher incidence of females among mobile
device users in this study, and their corresponding lower incidence among controls,
compared to males. This was not a random sample of all pedestrians on campus, but only
solitary pedestrians on the sidewalks during low traffic periods who also met the
requirements for potential participants. Nevertheless, in this sample of solitary pedestrians,
females were more frequent users of mobile devices. This is consistent with a study of
university students in Spain that found almost twice as many females as males identified as
heavy cell phone users (Jenaro et al. 2007). If our results are representative of a more
general pattern of greater mobile device use among females than males, then more research
is needed to determine the reasons for this difference. And how might such a difference
affect patterns of nonverbal interaction in other kinds of unfocused interactions?
Overview and Limitations
The present study was an attempt to examine how the use of mobile devices affects routine
contacts with others in unfocused interactions, specifically, in the brief micro-interactions
of pedestrians approaching and passing one another on the sidewalk. Although the
hypothesis that mobile device use would adversely affect participants’ responsiveness
across levels of confederate involvement was not directly supported, interaction effects
pointed to more nuanced impacts of mobile devices on social behavior. For example, the
reciprocity common in smiling to a confederate’s LSG seems to be disrupted by mobile
device use, even among those who glance back at the confederates. In addition, the
interaction effects involving mobile device, sex of participant, and sex of confederate
suggest that mobile devices may be conditionally used in a strategic fashion to manage
these very brief exchanges with strangers. Thus, mobile devices may not simply be dis-
tractions in unfocused interactions, but also props for managing these brief contacts with
Although the present study examined the effects of mobile device use in only one kind
of brief unfocused interaction, there is a growing recognition that mobile device use
negatively affects the quality of a wide range of face-to-face interactions (Bugeja 2005). In
spite of the extensive and powerful benefits of mobile technology, more research is needed
322 J Nonverbal Behav (2014) 38:313–324
on how mobile devices affect our face-to-face contacts with others. For example, does
increased time spent on mobile devices decrease interpersonal sensitivity in face-to-face
contacts and adversely affect the quality of and satisfaction with interactions?
There are, of course, important limitations to the present study that deserve mention.
First, the study was run on a Midwestern urban university campus and the majority of
participants were students. It should be noted, however, that, in an earlier experiment,
conducted both on campus and on downtown sidewalks, we did not find significant dif-
ferences between the reactions of students and downtown pedestrians (Patterson et al.
2007). Nevertheless, it is likely that there are regional differences in reactions across the
US and across culture. For example, in our study comparing US and Japanese patterns,
there were so few Japanese pedestrians greeting or smiling and nodding at strangers
(Patterson et al. 2007) that any mobile device effect would be difficult to detect.
Although there were more than 400 participants in the present study, the categorical
nature of the data and the relatively low base rates of smiling and greeting prevented a
more complete analysis of higher-level interactions involving sex of participants and sex of
confederates. And there were too few minority participants across conditions to permit an
analysis of race or ethnicity. Given these circumstances, a much larger sample is desirable
and different settings and regions should also be studied. Conducting a field study like this
one, however, is very time consuming, with assistants spending most of their time waiting
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... Talking on a cell phone introduces a form of cognitive load (i.e., consuming attentional resources; Strayer & Johnston, 2001;Walker, Lanthier, Risko, & Kinstone, 2012); thus, how individuals look at other social agents while talking on a cell phone can provide some clues as to how cognitive load might influence social looking. For example, Patterson, Lammers, and Tubbs (2014) examined the influence of cell phone use on social looking behaviors in an authentic social context and found that participants using cell phones looked toward the oncoming pedestrian but failed to react to them (e.g., smile or nod), which the authors suggest was due to the cell phone use reducing attentional resources. Additionally, cell phone users have also been shown to exhibit inattentional blindness to other social agents. ...
... Although both of these studies suggest that cognitive load can modulate social looking, neither experimentally manipulated load nor had a direct measure of gaze (i.e., eye tracking). In the present investigation, we directly manipulate cognitive load in an authentic pedestrian crossing paradigm (similar to Patterson et al., 2014) while individuals' eye movements were monitored via a mobile eye-tracker. ...
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The effect of cognitive load on social attention was examined across three experiments in a live pedestrian passing scenario (Experiments 1 and 2) and with the same scenario presented as a video (Experiment 3). In all three experiments, the load was manipulated using an auditory 2-back task. While the participant was wearing a mobile eye-tracker, the participant's fixation behavior toward a confederate was recorded and analyzed based on temporal proximity from the confederate (near or far) and the specific regions of the confederate being observed (i.e., head or body). In Experiment 1 we demonstrated an effect of cognitive load such that there was a lower proportion of fixations and time spent fixating toward the confederate in the load condition. A similar pattern of results was found in Experiment 2 when a within-subject design was used. In Experiment 3, which employed a less authentic social situation (i.e., video), a similar effect of cognitive load was observed. Collectively, these results suggest attentional resources play a central role in social attentional behaviors in both authentic (real-world) and less authentic (video recorded) situations.
... In particular, cell phone use reduces the quality of our direct face-to-face contacts (Bilton 2013;Bugeja 2005). That is, cell phones can interrupt and divert users from ongoing face-to-face interactions and reduce opportunities for impromptu contacts with others in public settings ( Patterson et al. 2014). ...
... However, the importance of observational studies has been stressed as a way to validate laboratory paradigms using real-world settings (Lewandowski and Strohmetz, 2009). More broadly, careful observation of trivial and mundane acts can reveal interesting and generalizable facts about emotion and social relationships (Patterson, 2008;Patterson et al., 2014), and can perhaps benefit public policy (Coxon et al., 2010). ...
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When someone holds a door for us we often respond with a verbal "thanks." But given such a trivial favor, our feelings can vary considerably depending on how the door is held. Studies have shown that verbal thanking increases in relation to door-holding effort. However, it is unclear how such a favor can lead to verbal thanks in addition to reciprocal help. We examined how holding a door in an effortful or non-effortful manner relates to verbal thanking and reciprocal helping. We measured: (1) whether participants verbally thanked the experimenter, (2) whether they agreed to help another person by taking a survey, and (3) whether they helped pick up objects (pens) that the door-holder subsequently dropped. Participants in the effortful condition were more likely to offer verbal thanks, to help pick up the pens, and to walk a greater distance to pick them up. Participants who thanked the door-holder, however, were not more likely to provide help.
... The implications of digital technology in human interaction are examined further in Patterson et al. (2014) timely exploration of the effects of mobile device use on pedestrian encounters. Complementing recent survey reports of an increase in the number of physical accidents associated with cellphone-using adults who report bumping into another person or an object while talking or texting on their phone (Pew 2014), these researchers demonstrate the social implications of cell-use in an experimental field setting. ...
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This Special Issue of the Journal of Nonverbal Behavior explores nonverbal issues in the digital age, with an emphasis how digital technologies provide researchers with new tools for exploring nonverbal components of interpersonal interactions in digital environments. In addition to showing a range of exciting applications, the featured articles also touch upon the challenges of new digital strategies.
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This article discusses a new systems model of dyadic nonverbal interaction. The model builds on earlier theories by integrating partners’ parallel sending and receiving nonverbal processes into a broader, dynamic ecological system. It does so in two ways. First, it moves the level of description beyond the individual level to the coordination of both partners’ contributions to the interaction. Second, it recognizes that the relationships between (a) individuals’ characteristics and processes and (b) the social ecology of the interaction setting are reciprocal and best analyzed at the systems level. Thus, the systems model attempts to describe and explain the dynamic interplay among individual, dyadic, and environmental processes in nonverbal interactions. The potential utility and the limitations of the systems model are discussed and the implications for future research considered. Although the systems model is focused explicitly on face-to-face nonverbal communication, it has considerable relevance for digital communication. Specifically, this model provides a useful framework for examining the social effects of mobile device use and as a template for studying human–robot interactions.
This chapter discusses the functional relation between perception and behavior. It presents a general perspective on perception and action along with elaborating the direct relation between perception and behavior and specifically on one consequence of this relation—namely, the imitation. The chapter describes the core concepts of social perception. Furthermore, the chapter examines all three forms of social perception that lead directly to corresponding overt behavioral tendencies. The cognitive approach that has dominated psychology for over 30 years has changed psychology's perspective on perception. Certainly, perception is essential for us to comprehend our environment but that does not mean that this understanding is an end in itself. The chapter concludes with a discussion on the perception-behavior link from a functional perspective. In specific, perception provides an understanding of the world. Social perception refers to the activation of a perceptual representation, which generally has a direct effect on social behavior. Perceptual inputs are translated automatically into corresponding behavioral outputs.