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Does Smile Intensity in Photographs Really Predict Longevity? A Replication and Extension of Abel and Kruger (2010)

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Abstract

Abel and Kruger (2010) found that smile intensity, coded from photographs of professional baseball players who were active in the year 1952, predicted these players’ longevity. In the current investigation, we sought to replicate this result and to extend the initial analyses. We analyzed (a) a sample that was almost identical to the one from the original study using the same database and inclusion criteria (N=224), (b) a considerably larger non-overlapping sample consisting of other players from the same cohort (N=527), and (c) all players of the database (N=13,530 valid cases). Like Abel and Kruger (2010), we relied on categorical smile codings as indicators of positive affectivity, yet we supplemented these codings with subjective ratings of joy intensity and automatic codings of positive affectivity made by computer programs. In neither sample and for none of the three indicators, positive affectivity predicted mortality once birth year was controlled as a covariate.
Running head: SMILE INTENSITY AND LONGEVITY
1
Does Smile Intensity in Photographs Really Predict Longevity? A Replication and Extension
of Abel and Kruger (2010)
Michael Dufner and Martin Brümmer
University of Leipzig
Joanne M. Chung
Tilburg University
Pia M. Drewke
University of Leipzig
Christophe Blaison
Humboldt-Universität zu Berlin
Stefan C. Schmukle
University of Leipzig
Psychological Science, in press
September 7th 2017, preprint. This article may not exactly replicate the final version
published in the journal. It is not the copy of record.
Additional online material and analysis scripts can be retrieved from
https://osf.io/8y2ga/
Correspondence: Michael Dufner, Department of Psychology, University of Leipzig,
Neumarkt 9-19, 04109 Leipzig, Germany; e-mail: michael.dufner@uni-leipzig.de
SMILE INTENSITY AND LONGEVITY
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Abstract
Abel and Kruger (2010) found that smile intensity, coded from photographs of professional
baseball players who were active in the year 1952, predicted these players’ longevity. In the
current investigation, we sought to replicate this result and to extend the initial analyses. We
analyzed (a) a sample that was almost identical to the one from the original study using the
same database and inclusion criteria (N=224), (b) a considerably larger non-overlapping
sample consisting of other players from the same cohort (N=527), and (c) all players of the
database (N=13,530 valid cases). Like Abel and Kruger (2010), we relied on categorical smile
codings as indicators of positive affectivity, yet we supplemented these codings with
subjective ratings of joy intensity and automatic codings of positive affectivity made by
computer programs. In neither sample and for none of the three indicators, positive affectivity
predicted mortality once birth year was controlled as a covariate.
Keywords: emotion, affect, longevity, life outcomes, replication
SMILE INTENSITY AND LONGEVITY
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Does Smile Intensity in Photographs Really Predict Longevity? A Replication and Extension
of Abel and Kruger (2010)
Past research indicates that dispositional positive affectivity has a life-prolonging function
(for a review, see Diener & Chan, 2011). This effect has great theoretical and practical
importance. It corroborates the notion that positive affectivity should be regarded as not only
an outcome variable but also as a predictor of major life outcomes (Lyubomirsky, King, &
Diener, 2005). Moreover, it indicates that policy makers should consider interventions that
aim to increase positive affectivity. Furthermore, the effect strengthens personality
psychology’s standing as a discipline because identifying determinants of longevity is a task
of key societal interest (Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007).
Past studies on the topic have assessed positive affectivity by self-report (e.g., Blazer
& Hybels, 2004; Lyyra, Törmäkangas, Read, Rantanen, & Berg, 2006), informant-report
(Friedman et al., 1993), and content analysis of written text (e.g., Danner, Snowdon, &
Friesen, 2001; Pressman & Cohen, 2007). In most studies, however, the timing between
assessments of positive affectivity and mortality was short to medium (< 30 years; Diener &
Chan, 2011), which is not ideal, because low positive affectivity toward the end of life might
be a by-product of illness or physical degradation instead of a genuine predictor of mortality.
Only a few studies have investigated time lags of more than four decades, and the results have
been contradictory (Danner et al., 2001; Friedman et al., 1993).
In an exceptional study, Abel and Kruger (2010) investigated a sample of professional
athletes who were in their prime years of physical fitness and predicted mortality more than
five decades later. Specifically, they analyzed photographs of and personal information about
U.S. American professional baseball players who were active in the year 1952 and
investigated how long they lived up to the year 2009. The authors coded smiling behavior as a
proxy for positive affectivity. They did this by classifying smile intensity into three categories
(no smile, partial smile, and full smile) and investigated its effects on longevity using a Cox
SMILE INTENSITY AND LONGEVITY
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proportional hazards regression model. Baseball players who showed a full (or Duchenne)
smile (Ekman, Davidson, & Friesen, 1990) in the photograph were half as likely to die in any
given year than players who did not smile (hazard ratio of 0.50). The model included college
attendance, marital status, birth year, career length, age at debut year, and body mass index
(BMI) as covariates.
In the current research, we revisited the association between smile intensity and
longevity by replicating Abel and Kruger’s (2010) finding. We relied on the same database as
these authors and implemented the same procedures and statistical analyses. We separately
analyzed a subsample that was nearly identical to the one analyzed by Abel and Kruger
(2010), a non-overlapping subsample consisting of players from the same cohort, and the full
database. Like Abel and Kruger (2010), we relied on categorical smile codings as indicators
of positive affectivity, but we also supplemented these codings with subjective ratings of joy
intensity and automatic codings made by computer programs.
Method
The study was preregistered, and the central parts of the code for data analyses
(analyses based on human smile codings and analyses based on automatic codings from one
of three emotion recognition computer programs) were uploaded to the internet prior to data
collection. Preregistration files, the full data set, and the final code for the current analyses
can be downloaded from https://osf.io/8y2ga/.
Sample
We retrieved photographs of all baseball players from the website of the American
Baseball Register (http://www.baseball-reference.com/bullpen/Baseball_Register) on
September 17, 2015. A total of 18,437 players were listed in the database. For 903 of them, no
photograph was available, and therefore, we deleted the data for these players. We also
removed 26 cases from the data set for whom information on birth year was missing (and who
therefore could not be used in our main analyses). At this stage, the sample size was 17,508.
SMILE INTENSITY AND LONGEVITY
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Data Preparation
We retrieved the same variables as in the original study (death year, college
attendance, birth year, career length in the professional league, age at debut year, and BMI).1
To estimate effects on mortality, we computed two variables: survival status and age. Survival
status was determined by whether a player had died (= 1) or had not died (= 0) at the time
when we retrieved the data, which was indicated by whether there was a death year in the
retrieved data. The age variable indicated the age at which a player died (computed by
subtracting the birth year from the death year), or if that the player was still alive, how old the
he was at the time of the study (computed by subtracting the birth year from the current year
of 2015). When we investigated the distribution of this age variable in the sample of players
who were all still alive according to the American Baseball Register, we found that some
people had unrealistically high values (see Figure S1 in the online supplemental material). In
detail, cases in the distribution ranged up to 100 years, there were no cases between 101 and
125 years, but then there were 42 cases with values equal to or larger than 126 years. The
careers of all of these players had ended before 1915, and we deemed it most likely that
entries for the variable “death year” were missing for these players from early cohorts.
Accordingly, we removed these cases from the data set. Thus, the resulting sample size was
17,466.
Subsamples
In the original study by Abel and Kruger (2010), human codings were obtained for
players who were active in 1952, who started their careers before 1950 and who appeared to
be looking directly into the camera. There criteria were met for 230 players and for 196 of
them all covariates were available, yielding a sample size of 196. We aimed to achieve a
sample that was as similar as possible to the original one. Because the judgment of whether or
not a player is looking directly into the camera is somewhat vague, we contacted the original
authors to obtain access to the original sample. Unfortunately, however, the first author
SMILE INTENSITY AND LONGEVITY
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informed us that the original data are not available anymore (E. Abel, personal
communication February 21, 2017). Therefore, we applied of the above described criteria by
Abel and Kruger (2010) to the data downloaded from the database and had a research
assistant judge whether or not players are looking directly into the camera, yielding to a
sample size of 224 players. Co-variates were available for all cases. We will refer to this
sample as the 1952 sample.
Moreover, we sought to assemble a second subsample from the Baseball Register
sample that did not overlap with Abel and Kruger’s sample but that was nevertheless as
similar as possible. Again, we only included players who were looking into the camera. We
followed Simonsohn’s (2015) recommendation and aimed to obtain a sample size that was 2.5
times larger than the one used in the original study. Accordingly, approximately half of our
human-coding sample consisted of players who ended their career in 1951 (n = 50, all
players), 1950 (n = 56, all players), 1949 (n = 68, all players), 1948 (n = 65, all players), or
1947 (n = 28 randomly chosen players). The other half of the sample consisted of players who
debuted in 1953 (n = 67, all players), 1954 (n = 63, all players), 1955 (n = 65, all players),
1956 (n = 43, all players), or 1957 (n = 22 randomly chosen players). Thus, our second
subsample consisted of 527 players. We will refer to this sample as the non-overlapping
replication sample.
Human Codings
As in the original study, a group of five coders (four female, one male; age range: 20
to 35 years) coded the photographs in terms of smile intensity (0 = no smile, 1 = partial smile,
2 = full smile). The operational definitions for these categories were equivalent to the original
publication (partial smile = only contraction of the zygomatic major muscles; full smile =
movement contraction of both zygomatic and orbicularis oculi muscle). One of the coders (J.
M. C.) is a certified coder of the Facial Action Coding System (FACS; Ekman, Friesen, &
Hager, 2002). This person taught the other four coders how to distinguish between the three
SMILE INTENSITY AND LONGEVITY
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smile categories. Interrater agreement, was Kappa = .61 for both the cases from the 1952
subsample and for the cases from the non-overlapping replication subsample. (In the original
study, inter-rater agreement was Kappa = .63.) When raters disagreed, we selected the
category that was chosen most frequently. (It never happened that two categories were chosen
with equal frequency.) Of the 751 players, 300 (39.95%) showed no smile, 313 (41.68%)
showed a partial smile, and 138 (18.37%) showed a full smile.
In addition to these categorical smile codings, which directly matched the design by
Abel and Kruger (2010), another group of five raters (undergraduate students; four female,
one male; age range: 19 to 20 years) judged the joy intensity shown in the photographs on the
basis of their subjective perception (1 = does not show any joy to 5 = shows a lot of joy). The
same 751 players were analyzed as for the smile codings. Interrater agreement was high in
both the 1952 sample (ICC = .94) and in the non-overlapping replication sample (ICC = .94),
and the subjective joy intensity score averaged across observers was strongly correlated with
the categorical codings of smile intensity (Spearman rank-order correlation: r = .87, p < .001,
based on all 751 cases).
Automatic Codings
We relied on three different emotion-recognition computer programs to assess positive
affectivity. For theses analyses, we used the complete sample of 17,467 players. However, the
results of each program indicated that a number of photographs were uncodable. The number
of successfully coded photographs are presented separately for each program below.
First, we used the program FaceReader (Noldus, 2014) version 4.0.8. The program
provides continuous scores for happiness, six other emotions, and neutrality, each of which
can vary between 0 and 1 (with higher values indicating greater emotional intensity). The
program, which has rather strict criteria for identifying a photograph as codable, provided
codings for 2,613 cases (2,197 cases for which data on all variables that were relevant for our
main analyses were available). Second, we used the emotion-recognition software Emotion
SMILE INTENSITY AND LONGEVITY
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API (Microsoft Cognitive Services, 2016). The program provides probability scores that sum
to 1 across happiness, six other emotions, and neutrality. The program provided codings for
15,506 cases (13,531 cases for which data on all relevant variables were available). Third, we
used the Computer Expression Recognition Toolbox Version 5.1 (CERT; Littlewort et al.,
2011). This program provides a continuous score that indicates smile detection and
probability estimates for joy, six other emotions, and neutrality (totaling 1). The program also
provides activity scores for the three facial action units (AUs) involved in a full smile (AU6 =
“cheek raiser; AU7 = “lid tightener; and AU12 = “lip corner puller). Using CERT, we
were able to code 12,417 cases for the smile detection variable (10,652 cases for which data
for all relevant variables were available) and 12,419 cases for the remaining variables (10,654
cases for which data on all relevant variables were available).
In addition to treating the positive affectivity scores from the three programs as
continuous variables, we also computed dichotomized positive affectivity scores. In each
case, a value of 1 was given if positive affectivity predominated over all other emotions,
whereas a value of 0 was given if another emotion (or neutrality) predominated over positive
affectivity.
Results
Table S1 of the supporting online material shows descriptive statistics for all study
variables in the three samples and Table S2 shows the intercorrelations between these
variables in the full sample. As can be seen in Table S2, there were substantial correlations
between (a) automatic codings of positive affectivity and human codings of smile intensity
(values ranged from r = .70 to r = .86), (b) automatic codings of positive affectivity and
human subjective ratings of joy intensity (values ranged from r = .75 to r = .88, and (c)
automatic codings of positive affectivity from the different emotion-recognition programs
(values ranged from r = .62 to r = .75). Correlation coefficients for the activity of the AUs
SMILE INTENSITY AND LONGEVITY
9
with human codings and ratings of smile intensity varied across the three units and ranged
from r = .23 to r = .71.
Like Abel and Kruger (2010) we used Cox proportional hazards regression models to
address our main research question. Such models test the effects of categorical or continuous
predictor variables on an event variable (in our case mortality). A significant b value for a
given predictor indicates that this predictor is linked to mortality. A hazard ratio smaller than
1 means that mortality is less likely with increasing levels of the predictor and a hazard ratio
larger than 1 means that mortality is more likely with increasing levels of the predictor. In
each subsample and for each operationalization of positive affectivity, we first ran a model to
test the effect of positive affectivity on mortality without covariates and then we ran a second
model to test the effect of positive affectivity when controlling for the covariates. The results
of these main analyses are summarized in Tables 1 and 2.
First, we examined the 1952 subsample. As shown in Table 1, the results for the first
model without covariates revealed that mortality could not be predicted by smile intensity,
χ2(2) = 0.59, p = .746. The results for the second model with covariates revealed that birth
year (b = -.055, SE = .027, p = .044) was a negative predictor of mortality. Thus, players who
were born later had a reduced mortality risk. Yet, smile intensity again did not predict
mortality, Δχ²(2) = .513, p = .774. Neither partial smilers (b = .102, SE = .161, p = .529) nor
full smilers (b = .114, SE = .200, p = .570) were less likely to die than people who did not
smile. A visual display of these results can be found in Figure S2. When we included
subjective joy intensity ratings instead of categorical smile codings as the predictor, joy
intensity neither predicted mortality when it was entered as the sole predictor (b = .005, SE =
.061, p = .929) nor when it was entered in combination with the covariates (b = -.011, SE =
.062, p = .862; see Table S3 for complete model information). Similarly, automatic codings
neither predicted mortality when they were entered as sole predictors (see Table S4), nor
when they were entered in combination with the covariates (see Table 2).
SMILE INTENSITY AND LONGEVITY
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Table 1
Results of a Cox Regression Analysis Predicting Mortality by Human Smile Codings and Covariates
1952 SAMPLE
NON-OVERLAPPING REPLICATION SAMPLE
b
95% CI
b
SE
P
HR
95% CI
Model I: Without covariates
Contrast partial smile vs. non-smile
.063
[.78, 1.45]
.064
.109
.560
1.07
[.86, 1.32]
Contrast full smile vs. non-smile
.149
[.79, 1.71]
-.240
.146
.099
.79
[.59, 1.05]
Model II: With covariates
College
-.269
[.56, 1.04]
-.346
.107
.001
.71
[.57, .87]
Birth year
-.055
[.90, 1.00]
-.029
.009
.001
.97
[.95, .99]
Age at debut
-.028
[.91, 1.04]
-.003
.019
.863
1.00
[.96, 1.04]
Career length
-.040
[.91, 1.01]
-.017
.013
.188
.98
[.96, 1.01]
BMI
.042
[.95, 1.14]
.054
.037
.144
1.06
[.98, 1.14]
Contrast partial smile vs. non-smile
.102
[.81, 1.52]
.098
.112
.382
1.10
[.89, 1.37]
Contrast full smile vs. non-smile
.114
[.76, 1.66]
-.197
.148
.185
.82
[.61, 1.10]
Note. 1952 SAMPLE: N = 224; SE = standard error; HR = hazard ratio; CI = confidence interval; college attendance: 1 = yes, 0 = no; Model I statistics: χ2(2) = .59, p = .75; Model II
statistics: χ2(7) = 10.06, p = .19; the incremental effect of smile codings on mortality in Model II was not significant, Δχ²(2) = .51, p = .77; NON-OVERLAPPING REPLICATION
SAMPLE: N = 527; SE = standard error; HR = hazard ratio; CI = confidence interval; college attendance: 1 = yes, 0 = no; Model I statistics: χ2(2) = 4.43, p = .11; Model II statistics: χ2(7)
= 32.96, p < .001; the incremental effect of smile codings on mortality in Model II was not significant, Δχ²(2) = 4.23, p = .12.
SMILE INTENSITY AND LONGEVITY
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Table 2
Results of Separate Cox Regressions Predicting Mortality by Automatic Codings of Facial Displays of Positive Affectivity from Different Computer Programs (Controlling for Covariates)
1952 SAMPLE
NON-OVERLAPPING REPLICATION SAMPLE
FULL SAMPLE
N
b
SE
p
HR
95% CI
N
b
SE
p
HR
95% CI
N
B
SE
p
HR
95% CI
FaceReader
Happiness
82
.187
.344
.587
1.21
[.61,2.37]
160
-.371
.251
.139
.69
[.42,1.13]
2,197
-.170
.094
.070
0.84
[0.70,1.01]
Happiness
dichotomized
82
.146
.262
.576
1.16
[.69,1.93]
160
-.297
.211
.160
.74
[.49,1.12]
2,197
-.105
.074
.158
0.90
[0.78,1.04]
Microsoft Emotion
API
Happiness
223
.128
.165
.436
1.14
[.82,1.57]
522
-.057
.115
.623
.95
[.75,1.18]
13,530
.040
.034
.241
1.04
[0.97,1.11]
Happiness
dichotomized
223
.131
.144
.363
1.14
[.86,1.51]
522
-.017
.103
.871
.98
[.80,1.20]
13,530
.052
.030
.077
1.05
[0.99,1.12]
CERT
Smile detection
213
-.002
.016
.891
1.00
[.97,1.03]
480
-.004
.013
.737
1.00
[.97,1.02]
10,652
-.003
.004
.490
1.00
[0.99,1.00]
Joy
213
.173
.196
.379
1.19
[.81,1.75]
480
-.258
.158
.102
.77
[.57,1.05]
10,654
.000
.054
.994
1.00
[0.90,1.11]
Joy
dichotomized
213
.110
.152
.468
1.12
[.83,1.5]
480
-.160
.123
.192
.85
[.67,1.08]
10,654
-.001
.041
.971
1.00
[0.92,1.08]
AU6 (“cheek
raiser”)
213
.030
.131
.820
1.03
[.80,1.33]
480
-.090
.095
.345
.91
[.76,1.10]
10,654
.013
.029
.663
1.01
[0.96,1.07]
AU7 (“lid
tightener”)
213
.002
.343
.996
1.00
[.51,1.96]
480
.044
.230
.850
1.05
[.67,1.64]
10,654
.111
.059
.063
1.12
[0.99,1.25]
AU 12 (“lip
corner
puller”)
213
.060
.060
.323
1.06
[.94,1.19]
480
-.007
.045
.878
.99
[.91,1.09]
10,654
.005
.013
.714
1.00
[0.98,1.03]
Note. SE = standard error; HR = hazard ratio; CI = confidence interval; college attendance: 1 = yes, 0 = no.
SMILE INTENSITY AND LONGEVITY
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Second, we examined the non-overlapping replication sample. Table 1 shows that, as
in the 1952 subsample, smile codings did not predict mortality when they were included as
the sole predictors, χ2(2) = 4.427, p = .109). When smile intensity was entered in combination
with the covariates, mortality was significantly predicted by birth year (b = -.029, SE = .009, p
< .001) and college attendance (b = -.346, SE = .107, p < .001). However, smile intensity once
again did not predict mortality, Δχ²(2) = 4.232, p = .120. Neither partial smilers (b = .098, SE
= .112, p = .382) nor full smilers (b = -.197, SE = .148, p = .185) were less likely to die than
people who did not smile (see Figure S3 for a plot of the results). When we used subjective
joy intensity ratings as the predictor variable, they neither predicted mortality when they were
entered as the sole predictor (b = -.047, SE = .042, p = .264) nor when they were entered in
combination with the covariates (b = -.037, SE = .043, p = .390; see Table S3 for complete
model information). When we used automatic codings as predictors, happiness scores from
the FaceReader program were negative predictors of mortality (b = -.493, SE = .244, p = .043)
when they were entered as sole predictors, but no significant results were present for any of
the other automatic codings (see Table S4). When automatically coded variables were entered
in combinations with the covariates, none of them predicted mortality (see Table 2).
Third, we the analyzed the automatic codings of positive affectivity in the full sample.
When automatic codings of positive affectivity were included as the sole predictors, all of
them predicted mortality, and the same was true for activity codings of the three AUs (see
Table S4). However, in all cases the effect vanished once we controlled for the covariates (see
Table 2 for the main results and Tables S5 to S14 for full model information). This means that
automatic codings of positive affectivity did not predict mortality beyond the covariates.
We then explored which covariate was responsible for the initial associations between
automatic codings of positive affectivity and mortality in the full sample, and identified birth
year as the crucial covariate. Birth year was a consistent negative predictor of mortality in the
analyses based on the full sample, and at the same time, it was a negative correlate of positive
SMILE INTENSITY AND LONGEVITY
13
affectivity displays (see Table S2). Once we controlled for birth year as the sole covariate in
our models, the effects of positive affectivity and AU activity became nonsignificant (see
Tables S5 to S14). Thus, when we considered that it was uncommon for players from earlier
cohorts to smile in photographs (see Figure S4) and that these players also had a reduced life
expectancy (see Tables S5 to S14), smiling ceased to predict mortality.
Finally, we considered the possibility that smile intensity might be predictive of
mortality solely for specific birth cohorts. To do so, we relied on happiness scores from the
Microsoft Emotion API because this program had the least missing values and scores
correlated most strongly with human codings. We used happiness scores to predict mortality
separately for players who were born prior to 1869, who were born in any specific decade
between 1870 and 1989 (1870 to 1879, 1880 to 1889, etc.) and for players who were born
after 1970 (again, controlling for all covariates). In none of the analyses happiness was a
significant predictor of mortality (see Table S15).
Discussion
Why did the results from the 1952 sample differ from the results reported by Abel and
Kruger (2010)? Since we do not have access to the data from the original study, it is not
possible to answer this question with certainty. Due to the vagueness inherent in the selection
criteria, there were most likely slight differences between the original and the replication
sample, which might have led to divergent results. Furthermore, even though agreement
among the human coders was reasonably high in both the original and the replication study,
each assessment contains a degree of noise, or measurement error, which might also explain
the divergent results. The current coders were led by a certified FACS coder, inter-rater
agreement was acceptable, and the aggregate coding score correlated substantially with both
subjective ratings of joy intensity and automatic codings of positive emotionality. We can
thus reasonably conclude that the validity of the current codings was high, but that we are
unable to judge the validity of the codings from the original study. Importantly, we did not
SMILE INTENSITY AND LONGEVITY
14
only fail to detect the expected effect for the categorical smile codings, but also for subjective
ratings of joy intensity and automatic codings of positive emotionality.
A similar picture emerged from the analyses that were based on the other samples. The
non-overlapping replication sample was substantially larger than the sample of the original
study (N = 527 vs. N = 196), yet nevertheless categorical smile codings, subjective ratings of
joy intensity, and automatic codings of positive affectivity all failed to predict mortality when
covariates were controlled. In the analyses of the full sample, the N was up to more than 69
times larger than the one from the original study. Nevertheless, we again did not find evidence
for the premise that smiling has a life-prolonging effect.
When replication studies fail to reproduce an effect, critics often claim that hidden
moderators”, such as differences in time, culture, or sample composition between the original
and the replication study might account for the null effect (Stroebe & Strack, 2014; Van
Bavel, Mende-Siedlecki, Brady, & Reinero, 2016). However, it is particularly difficult to
blame such “hidden moderators” in the present case because the sample was drawn from the
same population as in the original study, and the cohort that was analyzed was virtually
identical. Therefore, in our view, the null results cannot be explained by unassessed
moderating factors. Instead, the finding reported by Abel and Kruger (2010) appears to be a
false positive result.
The current null results do not imply that dispositional positive affectivity is generally
unrelated to longevity. As we stated above, studies relying on different methodologies and
analyzing shorter time periods have reported positive links between positive affectivity and
longevity (Diener & Chan, 2011). However, the current results indicate that the degree to
which professional baseball players smile on a photograph taken during their career does not
contain any genuine information about whether they are still alive half a century later.
SMILE INTENSITY AND LONGEVITY
15
References
Abel, E. L., & Kruger, M. L. (2010). Smile intensity in photographs predicts longevity.
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Footnote
1 Marital status was used a covariate in the original but not in the current study because
systematic information on players’ marital status was lacking when we retrieved the data.
... It provides users with readings of six basic emotions (sadness, happiness, anger, fear, disgust, and surprise) on a scale from 0 (not present at all) to 1 (present at maximum intensity). This software has been used extensively in prior research as an indicator of emotion expression (e.g., Danner et al., 2014;Dufner et al., 2018;Lewinski, den Uyl, & Butler, 2014;Noordewier & van Dijk, 2018;Owada et al., 2018). We calculated the average value of each emotion over the 45-s time interval before participants saw the feedback (time 1) and during their reading of the feedback (time 2). ...
... Our research suggests that management research could benefit by considering a more objective evaluation of emotions through face-reading technology. Although this software has been used in psychology (Dufner et al., 2018), economics (Fiala & Noussair, 2017), behavioral finance (Akansu, Cicon, Ferris, & Sun, 2017), and consumer behavior (Hamelin, Moujahid, & Thaichon, 2017;Vergura & Luceri, 2018), ours is one of the first management studies of which we are aware to take advantage of it. We hope that other management researchers will adopt this objective tool to assess emotions. ...
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We investigate the effects of performance feedback on a subsequent performance task and the mediating role of sadness. Drawing on the appraisal tendency framework, we expect that negative performance feedback increases sadness, which then spills over to negatively affect performance on a future task. In addition, we expect two individual traits—feedback self-efficacy and grit—to moderate the relationship, such that the negative effect of sadness on subsequent task performance is weakened for individuals with high feedback self-efficacy and high grit. We use face-based emotion recognition software to capture emotion expression during the delivery of positive and negative feedback. We find support for our mediated moderation model in a sample of 96 participants. Theoretical and practical implications are discussed.
... It provides users with readings of six basic emotions (sadness, happiness, anger, fear, disgust, and surprise) on a scale from 0 (not present at all) to 1 (present at maximum intensity). This software has been used extensively in prior research as an indicator of emotion expression (e.g., Danner et al., 2014;Dufner et al., 2018;Lewinski, den Uyl, & Butler, 2014;Noordewier & van Dijk, 2018;Owada et al., 2018). We calculated the average value of each emotion over the 45-s time interval before participants saw the feedback (time 1) and during their reading of the feedback (time 2). ...
... Our research suggests that management research could benefit by considering a more objective evaluation of emotions through face-reading technology. Although this software has been used in psychology (Dufner et al., 2018), economics (Fiala & Noussair, 2017), behavioral finance (Akansu, Cicon, Ferris, & Sun, 2017), and consumer behavior (Hamelin, Moujahid, & Thaichon, 2017;Vergura & Luceri, 2018), ours is one of the first management studies of which we are aware to take advantage of it. We hope that other management researchers will adopt this objective tool to assess emotions. ...
... Using various forms of operationalization, several studies report associations of positive affect with a wealth of positive outcomes. For example, positive affect is positively associated with meaning in life [3], future life satisfaction [4], positive health behaviors [5], restful sleep [6], and longevity [7]; for reviews, see [8,9]; and [10]; see [5] as well as [11] for more nuanced approaches. ...
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Positive affect is associated with alleviating mental and physiological stress responses. As laughter is a common physiological operationalization of positive affect, we investigated whether the effects of experiencing a stressful event on stress symptoms is lessened by frequency and intensity of daily laughter. Using an intensive longitudinal design, we ambulatory assessed the self-reported experience of stressful events, stress symptoms and the frequency as well as the intensity of laughter in university students’ daily lives. Our hierarchical ecological momentary assessment data were analyzed with multilevel models. The results support the stress-buffering model of positive affect: We found that the frequency of laughter attenuated the association between stressful events and subsequent stress symptoms. The level of intensity of laughter, however, was found to have no significant effect. Future studies should use additional psychophysiological indicators of stress and straighten out the differential contributions of frequency and intensity of daily laughter.
... Additionally, naturally occurring smiles during structured interviews (among other PA indicators) were associated with a 22% decreased risk of heart disease over a 10-year follow-up (Davidson et al. 2010). Smiling in professional baseball player cards was not correlated with mortality in a recent study (Dufner et al. 2017) indicating a need to consider the source and meaning of smiles since it is likely that public photographs may represent something different (e.g., self-presentation preferences, agreeableness) than lab-based, experimentally manipulated, and naturally occurring smiles. Despite their limitations, these approaches are novel and provide convergent validity for self-report findings as well as plausible unique pathways to health (e.g., via differences in expression versus suppression, direct nerve stimulation to peripheral physiology via facial expression; Pressman & Cross in press). ...
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Positive affect (PA) is associated with better health across a wide range of physical health outcomes. This review reflects on why the study of PA is an essential component of our understanding of physical health and expands on pathways that connect these two variables. To encourage forward movement in this burgeoning research area, measurement and design issues in the study of PA and health are discussed, as are the connections between PA and a range of different health outcomes. Plausible biological, social, and behavioral pathways that allow for positive feelings to get under the skin and influence physical wellness are detailed and framed in the context of several theoretical models. Finally, new directions for the field and important methodological and interpretative considerations that are essential to moving this important research area forward are explored. Expected final online publication date for the Annual Review of Psychology Volume 70 is January 4, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... Empirical evidence indicates that the experience of positive emotions (e.g., positive affect and happiness) is associated with better health and increased longevity (Diener & Chan, 2011). To date, the long-term health-benefits of emotional expression have been examined solely in North America (Abel & Kruger, 2010;Danner, Snowdon, & Friesen, 2001;Dufner et al., 2018;Pressman & Cohen, 2012). These studies assumed that the use of emotional words in writing (i.e., published autobiographies and religious vows) or the facial expressions in photographs (i.e., profiles on a website) reflect a general readiness to express positive emotions which in turn have more positive effects. ...
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Prior research suggests that positive emotions contribute to health and longevity. However, evidence in East Asia, where emotional expression can be maladaptive to disrupt social harmony, remains sparse. We examined autobiographies of 243 Japanese male business leaders to determine whether emotional words used in writing was associated with longevity. In each autobiography, emotional words were identified and the percentages of emotional words were calculated separated by valence (positive versus negative). As a results, either positive or negative emotions did not predict longevity, after controlling for year of publication, year of birth, education, and self-reported illness in autobiography. Our findings did not find beneficial effects of the expression of positive emotions on longevity. Possible explanations for the null results were discussed.
... In longitudinal research high SWB was predictive in later years of people getting married, becoming parents, a lower likelihood of divorcing, and a lower probability of losing one's job or changing jobs (Luhmann, Lucas, Eid, & Diener, 2013), and these findings replicated across three nations (Germany, the U.K., and Australia). Smile intensity in photographs has been found to predict later marital quality and divorce (Harker & Keltner, 2001;Hertenstein, Hansel, Butts, & Nile, 2009), though one famous study on smile intensity predicting longevity among baseball players (Abel & Kruger, 2010) recently failed to replicate (Dufner et al., 2017). Furthermore, in natural settings happy people talk more when with others, and have more substantive conversations (Mehl, Vazire, Holleran, & Clark, 2010). ...
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Subjective well-being (SWB) is an extremely active area of research with about 170,000 articles and books published on the topic in the past 15 years. Methodological and theoretical advances have been notable in this period of time, with the increasing use of longitudinal and experimental designs allowing for a greater understanding of the predictors and outcomes that relate to SWB, along with the process that underlie these associations. In addition, theories about these processes have become more intricate, as findings reveal that many associations with SWB depend on people’s culture and values and the context in which they live. This review provides an overview of many major areas of research, including the measurement of SWB, the demographic and personality-based predictors of SWB, and process-oriented accounts of individual differences in SWB. In addition, because a major new focus in recent years has been the development of national accounts of subjective well-being, we also review attempts to use SWB measures to guide policy decisions.
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Smiling has been a topic of interest to psychologists for decades, with a myriad of studies tying this behavior to well-being. Despite this, we know surprisingly little about the nature of the connections between smiling and physical health. We review the literature connecting both naturally occurring smiles and experimentally manipulated smiles to physical health and health-relevant outcomes. This work is discussed in the context of existing affect and health-relevant theoretical models that help explain the connection between smiling and physical health including the facial feedback hypothesis, the undoing hypothesis, the generalized unsafety theory of stress, and polyvagal theory. We also describe a number of plausible pathways, some new and relatively untested, through which smiling may influence physical health such as trait or state positive affect, social relationships, stress buffering, and the oculocardiac reflex. Finally, we provide a discussion of possible future directions, including the importance of cultural variation and replication. Although this field is still in its infancy, the findings from both naturally occurring smile studies and experimentally manipulated smile studies consistently suggest that smiling may have a number of health-relevant benefits including beneficially impacting our physiology during acute stress, improved stress recovery, and reduced illness over time.
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Although the literature that connects positive affect (PA) to health has exploded over the last 20 years, the approach to studying this topic has remained simplistic. Specifically, researchers overwhelmingly rely on the principle that all PA is healthful, all of the time. Here, we review recent studies indicating that a more nuanced approach is valuable. In particular, we demonstrate that a more thoughtful approach to factors such as arousal, culture, timing, and measurement type results in a more complex picture of when PA is helpful and when it is not. Taking these issues into account also has implications for the types of mechanisms underlying these associations, as well as how other moderators might operate. Thus, we argue that considering these gradations will allow researchers to develop successful and theoretically based health interventions, untangle mixed findings, and enable a deeper understanding of the connection between PA and health.
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This study examined whether positive facial expressions in student identification photographs were connected with a health-relevant behavior: visits to a health care center in the last year for preventive and non-preventive (e.g. illness, injury) purposes. Identification photographs were coded for degree of smile. Smiling participants were more likely to have sought preventive care versus those not smiling in their photographs, but there was no difference in non-preventive (i.e. ill health) visits. This study shows for the first time that smiling in photographs may be related to healthy behavior and complements past work connecting smiling to positive psychosocial and health outcomes.
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There has been increasing criticism of the way psychologists conduct and analyze studies. These critiques as well as failures to replicate several high-profile studies have been used as justification to proclaim a "replication crisis" in psychology. Psychologists are encouraged to conduct more "exact" replications of published studies to assess the reproducibility of psychological research. This article argues that the alleged "crisis of replicability" is primarily due to an epistemological misunderstanding that emphasizes the phenomenon instead of its underlying mechanisms. As a consequence, a replicated phenomenon may not serve as a rigorous test of a theoretical hypothesis because identical operationalizations of variables in studies conducted at different times and with different subject populations might test different theoretical constructs. Therefore, we propose that for meaningful replications, attempts at reinstating the original circumstances are not sufficient. Instead, replicators must ascertain that conditions are realized that reflect the theoretical variable(s) manipulated (and/or measured) in the original study. © The Author(s) 2013.
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We present the Computer Expression Recognition Toolbox (CERT), a software tool for fully automatic real-time facial expression recognition, and officially release it for free academic use. CERT can automatically code the intensity of 19 different facial actions from the Facial Action Unit Coding System (FACS) and 6 different protoypical facial expressions. It also estimates the locations of 10 facial features as well as the 3-D orientation (yaw, pitch, roll) of the head. On a database of posed facial expressions, Extended Cohn-Kanade (CK+ (1)), CERT achieves an average recognition performance (probability of correctness on a two-alternative forced choice (2AFC) task between one positive and one negative example) of 90.1% when analyzing facial actions. On a spontaneous facial expression dataset, CERT achieves an accuracy of nearly 80%. In a standard dual core laptop, CERT can process 320 × 240 video images in real time at approximately 10 frames per second.
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This article introduces a new approach for evaluating replication results. It combines effect-size estimation with hypothesis testing, assessing the extent to which the replication results are consistent with an effect size big enough to have been detectable in the original study. The approach is demonstrated by examining replications of three well-known findings. Its benefits include the following: (a) differentiating "unsuccessful" replication attempts (i.e., studies yielding p > .05) that are too noisy from those that actively indicate the effect is undetectably different from zero, (b) "protecting" true findings from underpowered replications, and (c) arriving at intuitively compelling inferences in general and for the revisited replications in particular. © The Author(s) 2015.
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Seven types of evidence are reviewed that indicate that high subjective well-being (such as life satisfaction, absence of negative emotions, optimism, and positive emotions) causes better health and longevity. For example, prospective longitudinal studies of normal populations provide evidence that various types of subjective well-being such as positive affect predict health and longevity, controlling for health and socioeconomic status at baseline. Combined with experimental human and animal research, as well as naturalistic studies of changes of subjective well-being and physiological processes over time, the case that subjective well-being influences health and longevity in healthy populations is compelling. However, the claim that subjective well-being lengthens the lives of those with certain diseases such as cancer remains controversial. Positive feelings predict longevity and health beyond negative feelings. However, intensely aroused or manic positive affect may be detrimental to health. Issues such as causality, effect size, types of subjective well-being, and statistical controls are discussed.