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837981
research-article2019
PSSXXX10.1177/0956797619837981Plaisier et al.The Timing of Weight Perception
ASSOCIATION FOR
PSYCHOLOGICAL SCIENCE
Research Article
When Does One Decide How Heavy an
Object Feels While Picking It Up?
Psychological Science
2019, Vol. 30(6) 822­–829
© The Author(s) 2019
Article reuse guidelines:
sagepub.com/journals-permissions
https://doi.org/10.1177/0956797619837981
DOI: 10.1177/0956797619837981
www.psychologicalscience.org/PS
Myrthe A. Plaisier, Irene A. Kuling, Eli Brenner,
and Jeroen B. J. Smeets
Department of Human Movement Sciences, Vrije Universiteit Amsterdam
Abstract
When lifting an object, it takes time to decide how heavy it is. How does this weight judgment develop? To answer
this question, we examined when visual size information has to be present to induce a size-weight illusion. We found
that a short glimpse (200 ms) of size information is sufficient to induce a size-weight illusion. The illusion occurred not
only when the glimpse was before the onset of lifting but also when the object’s weight could already be felt. Only
glimpses more than 300 ms after the onset of lifting did not influence the judged weight. This suggests that it takes
about 300 ms to reach a perceptual decision about the weight.
Keywords
size-weight illusion, multisensory perception, time dependency, perceptual decision making
Received 6/22/18; Revision accepted 1/17/19
In order to make perceptual decisions about properties
in our environment, we combine sensory information
with expectations based on prior experience (Kersten,
Mamassian, & Yuille, 2004; Summerfield & de Lange,
2014). For instance, prior experience with one of an
object’s properties, such as its material or size, influences how heavy the object feels (Buckingham, 2014;
Buckingham, Cant, & Goodale, 2009; Buckingham &
Goodale, 2010a; de Brouwer, Smeets, & Plaisier, 2016;
Ellis & Lederman, 1998, 1999; Ross, 1969). The bestknown example of this is the size-weight illusion: a
large object is perceived to be lighter than a smaller
object of the same weight (for a recent review, see
Saccone & Chouinard, 2018). The size-weight illusion
is a robust effect that occurs even if the perceiver
knows that both objects have the same mass (Flournoy,
1894). It also occurs when heaviness is judged by pushing an object instead of lifting it (Plaisier & Smeets,
2012; Platkiewicz & Hayward, 2014), and it occurs when
size is felt instead of seen (Ellis & Lederman, 1993;
Plaisier & Smeets, 2015). As is the case with influences
of other priors, it is possible to alter the size-weight
illusion by training (Flanagan, Bittner, & Johansson,
2008). Size can affect perceived weight even if the
object is shown only prior to lifting (Buckingham &
Goodale, 2010b), suggesting that weight expectations
prior to lifting might play a role (but see Masin &
Crestoni, 1988, for counterevidence). Direct somatosensory information about an object’s weight becomes available as soon as the object loses contact with its supporting
surface. After more time passes, we reach a decision as
to how heavy the object feels. Our question is what is
the time course of this perceptual decision-making
process?
Prior to “liftoff,” the only information that one has
about an object’s mass are expectations based on what
it looks like and a statistical relation between its appearance and weight. After liftoff, the gravitational and inertial forces provide unambiguous sensory information
about the object’s mass. If seeing the size of the object
influences the judged weight because it provides an
expectation of the force required to achieve liftoff, it
should become much less effective as soon as expectations become irrelevant, that is, after the liftoff has
Corresponding Author:
Jeroen B. J. Smeets, Vrije Universiteit Amsterdam, Department of
Human Movement Sciences, Van der Boechorststraat 9, 1081 BT
Amsterdam, The Netherlands
E-mail: j.b.j.smeets@vu.nl
The Timing of Weight Perception
already occurred. If so, information about size presented
after liftoff should not influence the perceived weight.
Alternatively, if size information is considered throughout the judgment, there is no reason to expect the
moment of liftoff to have a special relevance, so presenting size information will remain effective until the decision has been made.
Perceptual decision making is usually studied in situations in which a choice needs to be made between
two alternatives (Shadlen & Kiani, 2013): a one-bit decision. Other judgments involve more alternatives, for
instance, three for judging the color of a traffic light or
four for judging the suit of a playing card. One can
interpret the number of bits of information as the number of binary decisions underlying the judgment (e.g.,
two bits for judging the suit of a playing card). Object
properties such as size or weight can vary on a continuous scale, so a judgment of such properties could
involve an infinite number of alternatives. However,
given the finite precision of such a judgment, one can
regard them as the outcome of a set of binary decisions,
with the number of decisions corresponding to the
relative precision expressed as bits of information (Fitts,
1954; Summerfield & de Lange, 2014).
The time needed for decisions that are more complex
than a binary decision is known to scale with the number of bits of information. For instance, choice reaction
times increase linearly with the number of bits of information processed (Hick, 1952; Hyman, 1953). Therefore, we can expect the time needed to reach a
perceptual decision on a continuous scale to increase
with the relative precision of the percept (expressed in
bits). Here, we monitored the process of judging an
object’s weight by varying the time at which visual
information about its size was made available during a
lifting action. Using three experiments that differed in
when participants were allowed to view the object they
were lifting and what happened after they lifted the
object, we determined the time course of when visual
size information can influence weight judgments.
Method
Participants
Ten participants (2 male; all right handed; age: M = 22
years, SD = 3) were recruited for Experiment 1. A second group of 10 participants (3 male; all right handed;
age: M = 28 years, SD = 3) was recruited for Experiment
2. A third group of 12 participants (6 male; 2 left
handed; age: M = 25 years, SD = 4) was recruited for
Experiment 3. Each participant only completed one
of the experiments. None of the participants was
aware of any relevant sensory or motor deficits. All
823
participants were naive as to the purpose of the experiments. They were treated in accordance with the local
ethical guidelines and gave informed consent prior to
participating. We used 10 participants on the basis of
earlier experience that this sample size allowed for an
easy detection of the illusion using the present stimuli
(Plaisier & Smeets, 2012). We included 2 more participants in Experiment 3 after observing the results of
Experiment 2. The study was part of a program that
was approved by the Scientific and Ethical Review
Board of the Faculty of Behavioural and Movement
Sciences of Vrije Universiteit Amsterdam.
Stimuli and setup
We used objects of two sizes: small (6 × 6 × 6 cm) and
large (6 × 6 × 9 cm; Fig. 1a). A plastic handle was
attached to the top of each object. We let participants
lift the objects by a handle so that they could not
deduce the size from the grip aperture when holding
the object. We made sure that wielding the object could
not provide information about its size (Amazeen &
Turvey, 1996; Kingma, van de Langenberg, & Beek,
2004) by connecting the handle to the object by a rotatable joint. In Experiment 1, we used two pairs of objects
(one pair of 260 g and one pair of 210 g, including the
handle); in Experiments 2 and 3, only the pair of objects
weighing 260 g was used. An infrared marker was
attached to the surface of each object at the center of
one side. Its position was tracked using an Optotrak
3020 system (Northern Digital, Waterloo, Ontario,
Canada). The objects were placed on a force sensor so
we could measure the lifting force (ATI Industrial Automation, Apex, NC; Nano17 F/T Sensor). The position
and force-sensor signals were sampled synchronously
at 500 Hz. Participants wore computer-controlled
PLATO visual-occlusion goggles (Translucent Technology, Toronto, Ontario, Canada).
Procedure
Participants were seated at a table with the occlusion
goggles closed. The experimenter placed an object in
front of the participant and indicated that he or she
could grasp the handle with the dominant hand. The
experimenter manually guided the participant’s hand
to the handle. Participants were instructed to wait while
holding the handle until an auditory go cue sounded.
At that moment, they were to lift the object straight up
without shaking or rotating it. In Experiments 1 and 2,
they subsequently placed it back on the table at a specific position. In Experiment 3, the experimenter
removed the object from the participant’s hand, so participants never moved the object down after lifting it.
Plaisier et al.
824
a
b
Experiment 1
No Vision
Force Sensor
Late Vision
c
Experiments 2 & 3
200-ms Vision
Continuous Vision
Fig. 1. Stimuli and procedure. Participants were asked to lift small and large objects (a) using handles connected to each object
by a hinge. Lifting objects in this way removed all haptic size cues. An infrared LED was attached to each object to track its
position. In Experiment 1, there were three conditions (b), which differed in the timing of when the occlusion goggles worn by
participants opened (the gray shading in the figure indicates when they were closed). In the two conditions in which the occlusion goggles opened, the object had to be placed on the square that corresponded to its size. Participants lifted the object off a
force sensor, allowing us to precisely determine the time of liftoff. In Experiment 2 (c), the procedure was largely the same as in
Experiment 1, except that the goggles opened for 200 ms at varying moments with respect to liftoff. The procedure for Experiment 3 (not illustrated) was the same as for Experiment 2, except that participants did not place the object back on the table.
If the object was to be placed on the table, participants
had to complete the whole movement within 3 s. Otherwise they had to reach maximum height within 2 s.
After completing each trial, participants were asked to
indicate the object’s weight using a method of free
magnitude estimation (Zwislocki & Goodman, 1980).
Participants performed 10 practice lifts to become
acquainted with the task prior to starting the main
experiment. Practice was performed with an object that
was not part of the stimulus set.
In Experiment 1, there were three conditions: no
vision, late vision, and continuous vision (Fig. 1b). In
the no-vision condition, the goggles remained closed
throughout the trial. In the late-vision condition, the
goggles opened as soon as the object was raised 5 mm
above the table surface. In the continuous-vision condition, the goggles opened roughly 0.5 s prior to the go
cue. This experiment consisted of three blocks of trials.
The first and third block each consisted of 20 no-vision
trials (5 per object). In these blocks, participants placed
the object on the table in front of them. In the second
block, participants performed a total of 80 late-vision
and continuous-vision trials (10 per object in each condition), which were randomly interleaved. During this
block of trials, drawings of a large and small square on
the table indicated on which side (left or right) to place
each object. Halfway through the block, these locations
were reversed. Participants placed the object at the
correct side on all trials so we could be sure that they
had taken note of the size of the object.
In Experiment 2 (Fig. 1c), the goggles opened for
200 ms during every trial. The moment at which the
goggles opened was varied with respect to the auditory
go cue. The goggles could open 200 ms prior to the go
cue or 100 ms, 400 ms, 700 ms, or 1,000 ms after the
go cue. Given the variability in response times, these
opening times resulted in a more or less uniform distribution of times of visual information relative to lift
The Timing of Weight Perception
onset throughout all phases of lifting. Each of the five
opening times was presented 10 times for both objects,
resulting in 100 trials per participant. Trials were performed in blocks, with one trial of each opening time
for each object randomly interleaved within each block
to ensure an even distribution of all opening times
throughout the experiment. Participants placed the
small object on the left and the large object on the right.
We did not switch left and right placement halfway
through, as in Experiment 1, because in this case the
goggles were always closed during this part of the trial.
Thus, participants could not see the drawings of the
small and large rectangles on the table and had to
remember where to place which object size. On average, participants did this correctly in 98.4% of the trials
(minimum individual trials correct was 92%).
Experiment 3 was identical to Experiment 2 except
that after lifting, participants did not place the object
back on the table but held it in the air until a second
auditory cue (2 s after the go cue) indicated that the
experimenter was going to remove it from their hand.
To ensure that participants noticed the size of the
object, we asked them to report whether it was a large
or a small object after giving their heaviness rating. On
average, participants judged the size correctly in 98.5%
of the trials (minimum was 93%).
Analysis
We first converted heaviness ratings into z scores for
each participant individually to be able to compare the
heaviness ratings across participants. To this end, we
took the heaviness ratings for all trials of an individual
participant and calculated the mean and standard deviation across all trials. To arrive at the z scores, we subtracted the mean from each heaviness rating and
divided the result by the standard deviation.
In Experiments 2 and 3, we determined the moment
of liftoff from the force-sensor signal with a method
that we adapted from the recommendation of Oostwoud
Wijdenes, Brenner, and Smeets (2014). We fitted a line
through the signal between 50% and 80% of the maximum force. We used this period because it was the
smoothest part of the force profile. We excluded a trial
if the R2 value of the fit was below .6 (this happened
in 1.8% of the trials in Experiment 2 and 3.1% of the
trials in Experiment 3). We took the intersection
between the fit line and a line at the level of no force
(the average of the last 100 samples during which there
was no object on the force sensor) as the moment of
liftoff. In the late-vision condition of Experiment 1, the
opening of the goggles happened 120 ms (betweenparticipants SD = 30) after the moment of liftoff that
was determined in this way.
825
In Experiment 1, we calculated a single illusion magnitude for each participant, object mass, and condition
by subtracting the z scores for the large object from
those for the small object of the same mass. We subsequently performed a repeated measures analysis of variance (ANOVA) on the illusion magnitude with object
mass and condition as factors. We followed this up with
post hoc paired-samples t tests to determine whether
the illusion magnitude differed between the conditions.
In all statistical tests, we considered p < .05 to be significant. In Experiments 2 and 3, we determined the time at which the visual size information was provided (“time of visual information”) for each trial as the difference between the time of liftoff and the center of the 200-ms time window during which the goggles were open. We subsequently transformed the heaviness ratings (expressed as z scores) to smooth functions of the time of visual information for each participant by calculating a Gaussian weighted average for each instant and object. The Gaussian function had a standard deviation of 50 ms and was shifted in steps of 1 ms. Within the range that we show in the figures, there was at least 1 data point within every 100-ms interval for each participant and object size. We calculated illusion magnitude as a function of time of visual size information for each participant by subtracting the heaviness rating function for the large object from that for the small object. In order to relate the heaviness ratings to the lifting movement, we determined three parameters in addition to the moment of liftoff: loading-phase onset, time of half height, and time of maximum height. We used the moment at which the loading force first exceeded 0.2 newton as the loading-phase onset. Time of half height and time of maximum height were determined in a straightforward manner from the Optotrak position signal. These two experiments were exploratory: No hypotheses are tested; 95% confidence intervals around the mean are provided as an indication of precision. Results In Experiment 1, we tested whether the size-weight illusion occurs if size information is provided only immediately after liftoff, when the decision process has just started. As was to be expected, we did not find an illusion in the no-vision condition, and we found a clear illusion with continuous vision. Limiting vision to the period after liftoff reduced the illusion to less than half of its magnitude with continuous vision (late-vision condition). A repeated measures ANOVA on the illusion magnitude showed a significant effect of condition, F(2, 18) = 12.18, p < .001, η2 = .575, no effect of object mass, and no interaction effects. Post hoc paired-samples t Plaisier et al. 826 a b Illusion Magnitude (z score) 1 c 0.8 0.6 0.4 0.2 0 –0.2 Late Vision Continuous Vision No Vision Loading –0.2 Half Height 0 0.2 Max Height 0.4 0.6 Time of Visual Information (s) Loading –0.2 Half Height 0 0.2 Max Height 0.4 0.6 Time of Visual Information (s) Fig. 2. Results (averaged across participants). For Experiment 1 (a), the illusion magnitude is shown for each of the three conditions. Data bars show means, circles indicate values for individual participants, and error bars show 95% confidence intervals. For Experiment 2 (b) and Experiment 3 (c), the illusion magnitude is shown as a function of the time when visual information was provided with respect to liftoff. The full illusion magnitude as found in the continuous-vision condition of Experiment 1 is indicated by the red dashed line. The gray horizontal bar indicates the loading phase, and the black dots indicate the moment at which the half height and maximum height were reached. Shaded bands indicate 95% confidence intervals. Note that in Experiment 3, the full illusion effect did not occur for very late presentations because participants did not place the object back on the table. tests with Bonferroni correction showed a significant difference between the no-vision and continuous-vision conditions, t(9) = 4.12, p = .008, and between the latevision and continuous-vision conditions, t(9) = 3.94, p = .010, but not between the late-vision and no-vision conditions, t(9) = 1.55, p = .47. Thus, the size-weight illusion decreased considerably when visual information about the object’s size was available only after the decision process had started, so much so that performance was statistically indistinguishable from having no visual size information. Although the illusion effects in the late-vision and the no-vision conditions were indistinguishable, we cannot conclude that visual size information was ignored from the moment of liftoff, when the decision-making process presumably started. The size of the illusion effect and its associated 95% confidence interval in Figure 2a leave the possibility open that the illusion did not disappear completely in the late-vision condition (despite the magnitude not being significantly different from that in the no-vision condition). It is possible that visual information influenced perceived weight even after liftoff up to a certain moment during the decisionmaking process. To test this hypothesis, we conducted a more detailed investigation of how visually presenting size information at different times during the decision process influences the judged weight. In Experiment 2, the goggles opened very briefly (200 ms) once every trial. Despite this very short presentation of visual size information, the illusion was strong. If visual size information was provided before liftoff, the participants in Experiment 2 were influenced by the short window of visual information to a similar extent as the participants in Experiment 1 were influenced by continuous vision of the object (Fig. 2b; curve slightly above the red dashed line). The illusion magnitude remained approximately the same when vision was provided up to 300 ms after liftoff. Visual size information thus influenced the perceived weight until well after the start of the decision-making process. The size-weight illusion was reliably lower than for the full illusion in Experiment 1 only when the visual size information was provided between 330 ms and 500 ms after liftoff (when the object had already reached more than half of its maximal height). Surprisingly, the illusion returned to its full magnitude when vision was provided around 600 ms after liftoff, at about the moment at which the maximum height was reached. At that moment, the object was being held more or less stationary in these trials, because participants were waiting for the visual information to appear in order to decide on which square they should place the object. Possibly, the start of the downward movement induced a reevaluation of the perceptual decision, which might The Timing of Weight Perception have been responsible for the illusion also occurring in this situation. In Experiment 3, we tested the robustness of our results and investigated the occurrence of the illusion when size information is provided very late without a new movement possibly tempting one to reevaluate the decision. To do so, we repeated Experiment 2 but without letting participants place the objects back on the table. They were instructed to lift the object and hold it in the air until the experimenter removed the object from their hand. In Experiment 3, we replicated the results of Experiment 2: The illusion decreased only when vision was provided well after liftoff (Fig. 2c). The illusion persisted for even slightly later moments of providing visual size information than in Experiment 2 (up to 400 ms after liftoff). In line with our explanation for the reoccurrence of the illusion in Experiment 2, the illusion did not return to its full magnitude when vision was provided later after liftoff. Discussion The size-weight illusion was markedly reduced when visual size information became available only after liftoff in Experiment 1 (Fig. 2a), suggesting that the use of prior information stopped when sensory input about weight became available. By providing only a short glimpse of visual information, we could determine the timing at which this reduction occurred more precisely in Experiments 2 and 3 (Figs. 2b and 2c). We found that the illusion did continue to occur for visual information that was provided briefly up to 400 ms after liftoff (Figs. 2b and 2c). We can thus conclude that information related to prior experience affected the decisions well after sensory input about weight became available and thus after the decision-making process had started. We can also conclude that the decision process took at least 330 ms and 400 ms in Experiments 2 and 3, respectively. At first glance, this interpretation of Experiments 2 and 3 might seem inconsistent with the results of Experiment 1. In the late-vision condition of Experiment 1, the illusion was considerably reduced when visual information was continuously available after the object had moved 5 mm upward, about 120 ms after liftoff. In Experiments 2 and 3, we found a full-strength illusion when visual information was provided briefly at that time. This difference is probably due to the fact that we did not control when participants determined the size of the objects in Experiment 1 as precisely as we did in Experiments 2 and 3. In the latter experiments, participants had to look at the objects during the brief exposure in order to know the size, while in Experiment 1, they could have looked at the object at any time after the goggles opened and knew that they could 827 do so. This could be why the average illusion effect in the late-vision condition of Experiment 1 was in between no effect and a full-strength illusion. In Experiment 2, the decision about weight appears to have been reached 70 ms earlier than in Experiment 3. We argued in the introduction that the time needed for a perceptual decision on a continuous scale depends on the precision of the percept. If the perceptual decision was indeed made more quickly in Experiment 2 than in Experiment 3, one would expect that the participants in Experiment 2 would have been less precise than those in Experiment 3. We therefore determined the precision for each participant on the basis of the variation of the responses for all trials for a single object in which the visual information was provided before liftoff. We indeed found that this coefficient was higher (less precise) in Experiment 2 (coefficient of variation = 0.15) than in Experiment 3 (coefficient of variation = 0.12). Our data show that it takes at least 330 ms to reach a decision on how heavy an object feels. We cannot exclude the possibility that the decision-making process was still in progress after 330 ms. On the other hand, one third of a second has been claimed to be the typical duration of embodied decisions (Ballard, Hayhoe, Pook, & Rao, 1997). Is 330 ms indeed a reasonable time for a perceptual decision of this precision? The observed values for the coefficient of variation in the perceptual judgments correspond to about three bits of information (Welford, 1960). If the decision-making process would indeed have finished at the moment visual information about size ceased to have an effect, the informationprocessing capacity would have been about 10 bits per second, which seems a reasonable value for human sensorimotor processing (Fitts, 1954; Welford, 1960). So it is likely that the time it took to reach a decision indeed coincided with the time that visual information had an effect after liftoff. We interpreted the fact that visual information affected weight perception for more than 300 ms after the haptic information became available as indicating that the indirect size information was combined with haptic information to judge heaviness even when it was presented considerably after direct weight information became available. One could argue that this is not necessarily the case: If tactile information were processed more than 300 ms slower than visual size information, the visual size information might have been available to the relevant parts of the brain before the haptic weight information. We consider this to be unlikely because tactile information is known to be processed within 100 ms to stabilize the grasp ( Johansson & Flanagan, 2009). It is known that the judged timing of signals can shift to some extent with repeated exposure when judging simultaneity (Sugita & Suzuki, 2003), but Plaisier et al. 828 it is also known that we do not correct for processingtime differences when using signals to control goaldirected movements (van Mierlo, Louw, Smeets, & Brenner, 2009), so we may also not adjust the timing for making judgments on the basis of lifting movements. Even if the timing of signals would be shifted, it is very unlikely that such a shift would influence our conclusions substantially, as reported shifts were less than 100 ms. Note that in the above-mentioned cue-combination studies, the temporal-integration window was also clearly less than 100 ms, so a sluggish temporal integration also cannot explain our finding that visual information presented 300 ms after liftoff affected heaviness ratings. There are two approaches to explain the size-weight illusion: a top-down and a bottom-up approach. The top-down approach involves expectations (Buckingham, 2014; Ross, 1969), quantified as anti-Bayesian (Brayanov & Smith, 2010) or Bayesian priors (Peters, Ma, & Shams, 2016). Our results are clearly in conflict with such explanations because the visual information that is supposed to set the prior was just as effective when it was presented after the haptic information. The results are in line with an explanation in terms of a bottom-up combination of a direct and an indirect cue (Anderson, 1970; Masin & Crestoni, 1988). For this approach, one needs to identify the indirect cue. One suggestion is that object density is this indirect cue (Wolf, Bergman Tiest, & Drewing, 2018). However, the size-weight illusion is equally strong for objects that differ in size but clearly not in amount of material and thus not in density (Plaisier & Smeets, 2015). So this explanation of the size-weight illusion is still lacking a convincing candidate for the indirect cue. In summary, our results show that perceptual decisions can be affected by prior knowledge that is invoked at a moment at which direct sensory information is already available. However, once a perceptual decision has been reached, prior knowledge does not lead to a reevaluation of the decision. Changes in direct sensory information, for instance due to a new motor action, could lead to reevaluation of the decision, in which recently invoked prior knowledge is also considered. Overall, this study provides a first account of the time course of the use of prior knowledge in making perceptual decisions on a continuous scale. Action Editor Philippe G. Schyns served as action editor for this article Author Contributions M. A. Plaisier and I. A. Kuling conceived the experiments and discussed the experimental design with E. Brenner and J. B. J. Smeets. M. A. Plaisier and I. A. Kuling performed the experiments. M. A. Plaisier analyzed the data and drafted the manuscript. All authors reviewed the manuscript and approved the final version for submission. ORCID iD Jeroen B. J. Smeets https://orcid.org/0000-0002-3794-0579 Declaration of Conflicting Interests The author(s) declared that there were no conflicts of interest with respect to the authorship or the publication of this article. Funding This research for Scientific M. A. Plaisier grant (12160) was supported by a Netherlands Organisation Research Veni grant (MaGW 451-12-040) to and by a Dutch Technology Foundation STW to J. B. J. Smeets. Open Practices Design and analysis plans for the experiments reported in this article were not formally preregistered. All data underlying Figure 2 have been made publicly available via the Open Science Framework and can be accessed at http://osf.io/6e9bv. All other data and materials for the experiments have not been made publicly available. References Amazeen, E. L., & Turvey, M. T. (1996). 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Perception & Psychophysics, 28, 28–38. 835147 research-article2019 PSSXXX10.1177/0956797619835147StavrovaLife Satisfaction and Mortality ASSOCIATION FOR PSYCHOLOGICAL SCIENCE Short Report Having a Happy Spouse Is Associated With Lowered Risk of Mortality Psychological Science 2019, Vol. 30(5) 798­–803 © The Author(s) 2019 Article reuse guidelines: sagepub.com/journals-permissions https://doi.org/10.1177/0956797619835147 DOI: 10.1177/0956797619835147 www.psychologicalscience.org/PS Olga Stavrova Department of Social Psychology, Tilburg University Abstract Studies have shown that individuals’ choice of a life partner predicts their life outcomes, from their relationship satisfaction to their career success. The present study examined whether the reach of one’s spouse extends even further, to the ultimate life outcome: mortality. A dyadic survival analysis using a representative sample of elderly couples (N = 4,374) followed for up to 8 years showed that a 1-standard-deviation-higher level of spousal life satisfaction was associated with a 13% lower mortality risk. This effect was robust to controlling for couples’ socioeconomic situation (e.g., household income), both partners’ sociodemographic characteristics, and baseline health. Exploratory mediation analyses pointed toward partner and actor physical activity as sequential mediators. These findings suggest that life satisfaction has not only intrapersonal but also interpersonal associations with longevity and contribute to the fields of epidemiology, positive psychology, and relationship research. Keywords life satisfaction, mortality, dyadic analyses, couples, open materials Received 8/2/18; Revision accepted 12/30/18 Research has consistently shown that life satisfaction is associated with longevity (for a review, see Diener & Chan, 2011). For example, meta-analyses of long-term prospective studies have shown that higher life satisfaction predicts lower risk of mortality over decades (Chida & Steptoe, 2008). Although this literature has demonstrated an intrapersonal effect of life satisfaction (i.e., an effect of an individual’s life satisfaction on that individual’s mortality), it is less clear whether life satisfaction has interpersonal effects as well. In particular, does an individual’s life satisfaction affect the mortality risk of his or her spouse? Epidemiological studies have demonstrated the importance of contextual characteristics (e.g., neighborhood characteristics; Bosma, Dike van de Mheen, Borsboom, & Mackenbach, 2001) for individuals’ longevity. Adopting the interpersonal perspective (Zayas, Shoda, & Ayduk, 2002), I propose that the characteristics (e.g., life satisfaction) of the people who are close to an individual can also make up that person’s context and, potentially, affect his or her life outcomes. For example, life satisfaction has been associated with healthy behaviors such as physical exercise (Kim, Kubzansky, Soo, & Boehm, 2017). Given that spouses tend to affect each other’s lifestyle ( Jackson, Steptoe, & Wardle, 2015), having a happy spouse might increase one’s likelihood of engaging in healthy behaviors. In addition, happiness has been associated with helping behavior (O’Malley & Andrews, 1983). Hence, having a happy partner might be related to experiencing support from that partner and, consequently, might improve one’s health and longevity. Indeed, a recent study found that spousal life satisfaction was associated with individuals’ self-rated health (Chopik & O’Brien, 2017), although such interpersonal effects were not detected for doctor-diagnosed chronic conditions (Chopik & O’Brien, 2017) or for inflammation markers (Uchino et al., 2018). None of the existing studies have explored whether spousal life satisfaction predicts individuals’ mortality. The present research examined this question using panel data of approximately 4,400 elderly couples in the United States. In addition, a set of Corresponding Author: Olga Stavrova, Department of Social Psychology, Tilburg University, Warandelaan 2, 5000 LE, Tilburg, The Netherlands E-mail: O.Stavrova@uvt.nl Life Satisfaction and Mortality exploratory mediation analyses tested the role of partner support as well as partner and actor physical activity as potential mechanisms for such an association. Finally, it is possible that the level of spousal life satisfaction per se matters much less than the extent to which it is similar to individuals’ own life satisfaction. A growing body of research has underscored the level of congruence between partners’ dispositional characteristics as an important factor for their relationship and life outcomes (Dyrenforth, Kashy, Donnellan, & Lucas, 2010). Therefore, in an additional set of analyses, I explored whether the level of actor-partner similarity in life satisfaction was associated with actor mortality. Method Participants The data for this study came from the Health and Retirement Study (HRS; http://hrsonline.isr.umich.edu/), a nationally representative panel study of American adults ages 50 and older and their spouses. It is sponsored by the National Institute on Aging (Grant No. NIA U01AG009740) and is conducted by the University of Michigan. HRS is particularly well suited for the present investigation because it collects data from both spouses. Starting in 2006, the study has included a measure of life satisfaction, as part of a self-report questionnaire that participants are asked to complete on their own and return by mail. For one half of the sample, life satisfaction was first measured in 2006, and for the other half, it was first measured in 2008. These data were combined into a baseline assessment. I selected participants who had a spouse or a live-in partner at baseline (95.7% of the participants who had a live-in partner were officially married).1 After I removed cases with missing values on key variables (actor life satisfaction, partner life satisfaction, survival time), the final sample consisted of 8,748 individuals (mean age at baseline = 67.17, SD = 9.75; 50.0% male). This sample size was large enough for even small effects to be detected with 80% power (at α = .05). Of the 4,374 couples, 99.5% were heterosexual. Data from participants who remained alive throughout the observation period (n = 6,643) or were lost to follow-up (n = 656) were censored.2 The data and materials for HRS can be accessed at its Website (http://hrsonline.isr.umich.edu/). The computer code for the analyses reported here can be accessed at the Open Science Framework (https://osf.io/geq9x/). Measures Life satisfaction. Life satisfaction was measured with the Satisfaction With Life Scale (Diener, Emmons, Larsen, & Griffin, 1985). This scale includes five items (e.g., “I am 799 satisfied with my life”). Because a 6-point response scale was used in 2006 and a 7-point response scale was used in 2008 (both scales ranged from strongly disagree to strongly agree), I rescaled the responses to range from 1 to 10.3 The scale had good reliability (2006 subsample: α = .89; 2008 subsample: α = .88). The analyses included both partner and actor life satisfaction. Mortality. The HRS data set included information on participants’ vital status (1 = deceased, 0 = alive) through December 2014. This information came from the National Death Index (Centers for Disease Control and Prevention, 2017), the spouse’s report, or both. Survival time was computed in months, starting from the month of the baseline interview and ending with death or censoring (in December 2014). Additional variables. Perceived partner support was measured by participants’ ratings of the extent to which their partners provided them with social support (seven items; e.g., “How much can you rely on [your partner] if you have a serious problem?” “How much can you open up to [your partner] if you need to talk about your worries?” “How much does [your partner] let you down when you are counting on him/her?”; all items are provided in the Supplemental Material available online). Responses were given on a 4-point scale (1 = a lot, 4 = not at all) and were recoded such that higher values reflected stronger support. Each person’s recoded responses were then averaged (2006 subsample: α = .82; 2008 subsample: α = .84). Actor and partner physical activity were assessed with two questions. Both partners indicated how often they engaged in vigorous activities (e.g., jogging, cycling, digging with a spade or shovel) and moderately energetic activities (e.g., gardening, cleaning the car, walking at a moderate pace, dancing). Responses to both questions were given on a 4-point scale (1 = more than once a week, 2 = once a week, 3 = one to three times a month, 4 = hardly ever or never) and were recoded such that higher values reflected higher frequency. The frequencies of vigorous and moderately energetic activity were related to each other (r = .36, p < .001), so I combined the responses to these two questions to form an indicator of physical activity. To make sure that any observed association between partner life satisfaction and actor mortality was not driven by an overlap with sociodemographic characteristics or baseline health (e.g., one spouse’s health problems might negatively affect both spouses’ life satisfaction and mortality), I included a range of control variables in the analyses. Specifically, I controlled for actor and partner self-rated health (1 = poor, 5 = excellent), as well as morbidity, measured with the number of doctor-diagnosed chronic conditions (hypertension, 800 diabetes, cancer, lung disease, coronary heart disease, stroke, arthritis, incontinence, psychiatric problems; although this list is not comprehensive, it covers major causes of death). Further control variables included actor gender (1 = male, 0 = female), actor and partner age at baseline, actor and partner ethnicity (1 = Caucasian, 0 = other), actor and partner education (1 = less than high school, 2 = general education diploma, 3 = high school diploma, 4 = some college, 5 = college and above), and baseline year (1 = 2008, 0 = 2006). Given that the household financial situation is likely to affect both partners’ life satisfaction and longevity, the analyses also included baseline household income (total annual household income in dollars, log transformed). To account for partner mortality, the analyses included a variable indicating whether the partner died during the observation period (1 = deceased, 0 = alive). Results Means and standard deviations of the variables, as well as their zero-order correlations, are provided in Table S1 in the Supplemental Material. During the observation period, 16.6% (n = 1,449) of the sample died. The survival time ranged from 2 to 104 months (8.67 years) and averaged 50.5 months (4.21 years). An examination of differences between survivors and decedents revealed that the latter were older, t(8746) = 33.57, p < .001; were more likely to be male, χ2(1, N = 8,748) = 191.90, p < .001; were less educated, t(8745) = 11.33, p < .001; and were less wealthy, t(2703) = 14.43, p < .001. They also were more likely to have chronic diseases, t(1955) = 20.66, p < .001; were less likely to engage in physical activity, t(8591) = 17.84, p < .001; and reported poorer self-rated health, t(1959) = 23.51, p < .001, and lower life satisfaction, t(1976) = 6.55, p < .001. Similarly, decedents’ spouses, compared with survivors’ spouses, were older, t(8746) = 24.53, p < .001; were less educated, t(2031) = 9.70, p < .001; reported more chronic conditions, t(8745) = 11.00, p < .001; reported a lower level of physical activity, t(8591) = 10.91, p < .001; and had poorer self-rated health, t(8741) = 8.13, p < .001. They also reported lower relationship satisfaction, t(1922) = 7.97, p < .001, and lower life satisfaction, t(1986) = 5.09, p < .001. Finally, decedents’ spouses were more likely than survivors’ spouses to die within the observation period, χ2(1, N = 8,748) = 202.61, p < .001. To determine whether partner life satisfaction predicted actor mortality, I used multilevel (dyadic) survival analysis. Specifically, because time was measured on a continuous scale (in months), I used the Cox proportional hazards model. Given the clustered timeto-event data (individuals were clustered within dyads), I used an extension of the Cox model that accounts for Stavrova correlated observations by implementing robust sandwich variance estimators. The analyses were conducted with the survival package (Therneau, 2015) in R. Note that using a frailty model with penalized likelihood estimation produced the same results (see Table S4 in the Supplemental Material). Time to event was measured in months, from the baseline measurement of life satisfaction until death or censoring. I additionally checked for robustness of the results by conducting analyses using participants’ age as a time scale. These analyses provided the same results and are reported in the Supplemental Material (Table S4). All continuous variables were standardized before analysis, so the coefficients can be interpreted in terms of standard deviations. The full estimation results are presented in Table S2 in the Supplemental Material. Model 1 showed that greater partner life satisfaction at baseline was associated with lower actor mortality risk. Specifically, a 1-standarddeviation-higher level of spousal life satisfaction was associated with a 13% lower risk of dying within the following 8 years (hazard ratio, or HR = 0.87, 95% confidence interval, or CI = [0.83, 0.91], p < .001). Figure 1 plots the cumulative hazard of death during the observation period, separately for individuals with a happy spouse (life satisfaction above the median) and individuals with an unhappy spouse (life satisfaction below the median). The figure shows that as time went by, the mortality risk of individuals with a happy spouse rose more slowly than the mortality risk of individuals with an unhappy spouse. To make sure that this effect was not just a result of confounding with participants’ own life satisfaction, I added actor life satisfaction at baseline in Model 2 (see Table S2 in the Supplemental Material). The results showed that both greater actor life satisfaction at baseline (HR = 0.86, 95% CI = [0.82, 0.91], p < .001) and greater partner life satisfaction at baseline(HR = 0.92, 95% CI = [0.87, 0.97], p = .001) were associated with lower mortality risk. Model 3 (see Table S2 in the Supplemental Material) showed that these effects were robust to controlling for major sociodemographic variables: actor gender, actor and partner age, actor and partner ethnicity, actor and partner education level, household income, baseline year, and couple type (same-sex vs. heterosexual 4). A 1-standard deviation-higher level of actor life satisfaction was associated with an 18% lower mortality risk (HR = 0.82, 95% CI = [0.78, 0.86], p < .001), and a 1-standard deviation-higher level of partner life satisfaction was associated with a 10% lower mortality risk (HR = 0.90, 95% CI = [0.85, 0.95], p < .001). In Model 4, I added actor and partner health indicators (self-rated health and morbidity) and partner mortality (whether the partner died during the observation Life Satisfaction and Mortality 801 Partner Life Satisfaction Below Median Partner Life Satisfaction Above Median 0.25 Cumulative Hazard 0.20 + 0.15 + 0.10 0.05 0.00 0 25 50 75 100 Survival Time (Months Since Baseline) Fig. 1. Cumulative hazard of death (including 95% confidence bands) during the observation period. Results are shown separately for individuals whose spouses reported life satisfaction below the median at baseline and those whose spouses reported life satisfaction above the median at baseline. period). The effect of actor life satisfaction on actor mortality was rendered nonsignificant (HR = 0.96, 95% CI = [0.90, 1.02], p = .155). In contrast, the effect of partner life satisfaction remained (HR = 0.92, 95% CI = [0.87, 0.97], p = .005). Similarity effect To explore whether the level of actor-partner similarity in life satisfaction was associated with actor mortality, I used a dyadic polynomial regression analysis, the state-of-the-art approach to testing similarity effects (Weidmann, Schönbrodt, Ledermann, & Grob, 2017). Actor mortality was regressed on actor and partner life satisfaction (xa and xp), their interaction term (x axp), and the quadratic terms (xa2 and xp2). The quadratic and interaction terms were not significant (ps > .57).
The only terms with significant effects were the linear
terms of actor life satisfaction (HR = 0.85, 95% CI = [0.79,
0.92], p < .001) and partner life satisfaction (HR = 0.93, 95% CI = [0.86, 0.996], p = .039). Hence, I concluded that the data do not provide evidence for a similarity effect. Overall, these results suggest that having a partner who is more satisfied with life is associated with lower mortality regardless of one’s own level of life satisfaction. Exploratory mediation analyses The variables available in the data set allowed me to explore two potential mediation processes. First, I hypothesized that individuals with a happier partner experience more partner support, and that greater perceived partner support is associated with lower mortality. However, an examination of the zero-order associations among partner life satisfaction, perceived partner support, and actor mortality revealed that this mediation path is unlikely: Although having a happier partner was indeed associated with greater perceived partner support (r = .27, 95% CI = [.25, .29], p < .001), perceived partner support was not related to actor mortality (HR = 0.99, 95% CI = [0.94, 1.04], p = .69). Second, I explored the role of partner and actor physical activity as sequential mediators. Specifically, on the basis of previous research (Kim et al., 2017), I hypothesized that greater partner life satisfaction is associated with increased partner physical activity, which in turn is associated with greater actor physical activity ( Jackson et al., 2015) and, consequently, lower actor mortality. A look at the zero-order associations showed that, indeed, partner life satisfaction was positively associated with partner physical activity (r = .17, 95% CI = [.15, .19], p < .001), partner and actor physical activity were positively related to each other (r = .24, 95% CI = [.22, .26], p < .001), and actor physical activity negatively predicted actor mortality (HR = 0.75, 95% CI = [0.71, 0.79], p < .001). Therefore, I proceeded to test for sequential mediation using multilevel structural equation modeling. The model (see Fig. S1 in the Supplemental Material included a set of multilevel (participants nested within couples) regression equations, in which partner life satisfaction predicted partner physical activity (path a, multilevel linear regression), partner physical activity predicted actor physical activity (path d, multilevel linear regression), and actor physical activity predicted actor mortality (path b, multilevel Cox regression). The indirect effect was computed by multiplying the a, d, and b paths, and its significance was tested using the delta method. The model included random intercepts for actor and partner physical activity and actor mortality and used clustered robust standard errors. The analyses were conducted with Stata/MP Version 14.2. The results showed that partner life satisfaction was positively associated with partner physical activity (b = 0.08, 95% CI = [0.07, 0.09], p < .001), which in turn was positively associated with actor physical activity (b = 0.23, 95% CI = [0.20, 0.26], p < .001), which was negatively associated with actor mortality (HR = 0.64, 95% CI = [0.61, 0.68], p < .001). The coefficient for the indirect effect was significant, b = −0.008, 95% CI = [−0.01, −0.006], p < .001, which provided support to the sequential mediation. The indirect effect was robust to adding the control variables as predictors of both the mediators and the dependent variable (see Table S5 in the Supplemental Material). Stavrova 802 Exploratory moderation analyses I explored whether the effect of partner life satisfaction on actor mortality depended on various actor and partner characteristics: gender, age, ethnicity, education, income, health indicators, physical activity, perceived partner support, and partner mortality. I ran 16 models testing the interactions between partner life satisfaction and these variables (by adding the respective interaction terms, one at a time, to Model 4; see Table S2 in the Supplemental Material). The only significant interaction was between partner life satisfaction and partner mortality (HR = 1.15, 95% CI = [1.02, 1.31], p = .027). Partner life satisfaction was negatively associated with actor mortality only when the partner remained alive through the end of the observation period (partner alive: HR = 0.90, 95% CI = [0.83. 0.96], p = .003; partner deceased: HR = 1.03, 95% CI = [0.93, 1.15], p = .553). Yet the exploratory nature of these analyses and the multiple testing do not allow strong conclusions to be drawn. Discussion Previous research has shown that individuals’ career success and relationship and life satisfaction are predicted by their spouses’ dispositional characteristics (Dyrenforth et al., 2010; Solomon & Jackson, 2014). The present research suggests that spouses’ reach might extend even further. A dyadic survival analysis using the data from 4,374 couples showed that having a spouse who was more satisfied with life was associated with reduced mortality. What explains this interpersonal effect of life satisfaction? Exploratory mediation analyses established partner and actor physical activity as sequential mediators. One partner’s life satisfaction was associated with his or her increased physical activity, which in turn was related to increased physical activity in the other partner, which predicted that partner’s mortality. Yet, given the correlational nature of these data, these results should be interpreted with caution. It is noteworthy that the effect of spousal life satisfaction was comparable in size to the effects of other well-established predictors of mortality, such as education and income (in the present study, HRs = 0.90 for partner life satisfaction, 0.93 for household income, and 0.91 for actor education). In fact, spousal life satisfaction predicted mortality as strongly as (and even more robustly than) an individual’s own life satisfaction and as strongly as basic personality traits, such as neuroticism and extraversion, predicted mortality in previous work ( Jokela et al., 2013). Although most existing research on predictors of mortality has focused nearly exclusively on individuals’ own characteristics, the present analyses revealed that the characteristics of a person who is close to an individual, such as a spouse, might be an equally important determinant of that individual’s mortality. Continuing this line of research, future studies might explore whether the interpersonal effect of life satisfaction on mortality is restricted to (marital) dyads or whether it extends to larger social networks. To conclude, happiness is a desirable trait in a romantic partner, and marriage to a happy person is more likely to last than is marriage to an unhappy person (Lucas, 2005). The present study showed that having a happier spouse is associated not only with a longer marriage but also with a longer life. Action Editor James K. McNulty served as action editor for this article. Author Contributions O. Stavrova is the sole author of this article and is responsible for its content. ORCID iD Olga Stavrova https://orcid.org/0000-0002-6079-4151 Acknowledgments I would like to thank Anthony M. Evans for his statistical advice and general support. Declaration of Conflicting Interests The author(s) declared that there were no conflicts of interest with respect to the authorship or the publication of this article. Supplemental Material Additional supporting information can be found at http:// journals.sagepub.com/doi/suppl/10.1177/0956797619835147 Open Practices All analysis code for this study has been made publicly available via the Open Science Framework and can be accessed at https://osf.io/geq9x/. The data are available through the Health and Retirement Study’s Web site (http://hrsonline.isr .umich.edu/). The design and analysis plans for this study were not preregistered. The complete Open Practices Disclosure for this article can be found at http://journals.sagepub.com/doi/ suppl/10.1177/0956797619835147. This article has received the badge for Open Materials. More information about the Open Practices badges can be found at http://www.psychological science.org/publications/badges. Notes 1. Additional analyses using only married couples produced identical results (see Table S4 in the Supplemental Material). Life Satisfaction and Mortality 2. These two groups of censored observations did not differ from each other on any variable included in the analyses, except for the number of chronic conditions: Participants who dropped out reported fewer chronic conditions (M = 1.87, SD = 1.40) than did participants who stayed in the panel (M = 2.07, SD = 1.46), t(7296) = 3.46, p = .001. 3. As a robustness check, I used standardization to normalize the data instead (i.e., I standardized the values within the two subsamples). The analyses using the standardized scale produced the same results as the analyses presented in the main text (see Table S3 in the Supplemental Material). 4. Being part of a same-sex couple positively predicted mortality (HR = 2.72, p = .018; there were 9 gay and 11 lesbian couples in the sample). The interaction between couple type (gay vs. lesbian) and actor gender was not significant (HR = 0.38, p = .260). References Bosma, H., Dike van de Mheen, H., Borsboom, G. J. J. M., & Mackenbach, J. P. (2001). 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