Meaning and Intuition: Supplementary Materials 1
Meaning in Life and Intuition: Supplementary Materials
Samantha J. Heintzelman & Laura A. King
Study 1
PA as a mediator. Despite the weakness of our single item measure of MIL, we
conducted analysis to determine whether PA mediated the relationship between FI and MIL.
This mediational analysis (Hayes, 2009; Preacher & Hayes, 2005) showed that the indirect effect
of FI on MIL through PA was significant, z = 7.01, p < .001; 95% confidence interval (CI)
bootstrapped with 3000 re-samplings for the indirect effect = [.08, .15]. Still, the direct path
from FI to MIL remained significant, B = 0.16 (.04), p < .001, controlling for PA, suggesting the
relationship between FI and MIL was significantly, but only partially mediated by PA.
PA as a moderator. In addition to its potential role as a mediator, PA might also serve
as a moderator of the relationship between intuition and MIL. Hicks et al. (2010) examined
feelings of meaning for specific targets (e.g., ambiguous quotations, works of art, negative life
events, and linguistic triads) as a function of FI and PA. In those studies, main effects of
individual differences in reliance on intuitive processing on meaning ratings did not emerge.
Rather, FI interacted with PA to predict outcomes: PA led to enhanced meaning in quotations
and life events, and to superior discrimination between coherent and incoherent linguistic triads
(a measure of meaning accuracy, Hicks et al., 2010), as a function of FI. These results were
interpreted as indicating that PA was necessary to “clear the mental landscape of rational
interference” (p. 976), allowing individual differences in FI to influence outcomes.
MIL is, however, different from the circumscribed outcomes used in this research. When
judging whether a particular quotation, for instance, is meaningful, or whether a triad is coherent
or incoherent, there is a sense that a right answer exists, that the stimulus is either meaningful or
Meaning and Intuition: Supplementary Materials 2
not. For the question of life’s meaningfulness, however, the “right” answer is generally hoped to
be “yes” (Hicks, Schlegel, & King, 2010). In this sense, when judging MIL, PA would seem
unnecessary to quell rational override. Indeed, Humphrey (2006) has argued that the feeling of
significance is a key (perhaps the key) function of private mental states. Thus, we did not expect
PA to serve as a moderator of the relationship between FI and MIL (no such moderation was
found by King & Hicks, 2009). Still, we tested this potential moderation. PA and FI did not
interact (β = -.00 p = .98) to predict MIL.
Religion as a moderator. Religious identification and FI did not interact to predict MIL
(β = .05, p = .13).
Study 2
Intrinsic religiosity as a moderator. We also tested intrinsic religiosity as a moderator
of the relationship between FI and MIL. Intrinsic religiosity did not interact with FI to predict
MIL (β = .02, p = .60).
Study 3
All mediational analyses. The mediational analyses with all Study 3 variables are
shown in Supplementary Table 1. The second and third columns of the table show the
standardized beta weights for regression equations, first entering each of the predictors alone,
and then when paired with FI. As can be seen in the third column, FI remained significantly
associated with MIL when entered with each of the covariates. The final columns of the table
show the results of mediational analyses testing FI and each of the covariates as mediators of
each other in the prediction of MIL.
PA as a moderator. We tested PA as a potential moderator in the relationship between
FI and MIL. In this case, significant main effects for PA, β = .33, and FI, β = .25 (both p’s <
Meaning and Intuition: Supplementary Materials 3
.001), were qualified by a significant (though small) interaction, β = .07, p = .014.
Supplementary Figure 1 shows the regression lines generated by this equation for those +/- 1 SD
from the mean on PA and FI. As can be seen, the results show two main effects: largely
parallel, positively sloped lines representing high vs. low FI. Importantly, FI significantly
predicted MIL for those one SD above the mean on PA (n = 183), β = .41, p < .001; as well as for
those one SD below the mean on PA (n = 138), β = .27, p = .001, though the magnitude was
significantly higher for those high in PA, z = 1.82, p = .034.
NC and FI interaction. Next we tested whether NC, might interact with FI to predict
MIL. MIL might be expected to be low for those low on FI and high in the tendency to reflect.
In a hierarchical regression equation, the main effects contributed significantly, for FI, β = .29, p
< .001; for NC, β = .07, p = .03; but there was no interaction, β = .02, p =.52.
Study 4
Raw data. We wanted to discern whether the lines generated for the curvilinear
interactions for order by MIL predicting the CRT variables were indeed accurate reflections the
data. To do so, we first plotted the raw data across levels of MIL for each .5 standard deviation
step in the data (representing 6 levels). Results are shown in Supplementary Figure 2. As can be
seen, for the CRT measures, the raw data do follow the pattern suggested by the generated lines
reported in the manuscript, particularly for those low in MIL who completed the MIL measure
first. The raw data, however, do not appear to support a positive uptick in reflection after
moderate levels of MIL.
ANOVAs. Means for the groups for the low vs. not low MIL X order interaction for the
three CRT measures are shown in Supplementary Figure 3.
Meaning and Intuition: Supplementary Materials 4
Mediational Analyses. Mediational analyses showed that the relationship between FI
and MIL was partially mediated by PA, z = 5.11 (p < .001), but the direct path from FI to MIL
remained significant B = 0.15 (0.05), p = .006. Mediational analyses showed that agreeableness
significantly (z = 4.34, p < .001), 95% CI = [0.05 to 0.14] but only partially mediated the
relationship between FI and MIL. Controlling for agreeableness, the direct path from FI to MIL
remained significant, B = 0.19 (.06), p < .001.
PA as a moderator. In this sample, PA and FI did not interact to predict MIL, β = .05, p
= .20.
CRT and FI interactions. We also examined whether FI and CRT interacted to predict
MIL. In this analysis we used the dummy codes for CRT scores and the interactions of these
with FI were used to test the relationships of FI and CRT scores to MIL. Initial analyses
included order as a factor but order did not contribute to the equation. As such, the following
represent results using the entire sample, collapsed across orders. A main effect of the dummy
code for scoring 3 on the CRT, β = -.11 p = .008, was qualified by two interactions, FI X scoring
3, β = .12, p = 03 and FI X scoring 2, β = .11, p = 02. Graphing these interactions separately
showed a pattern that was essentially identical to that using the CRT as a continuous measure.
Specifically, predicting MIL from the CRT (β = -.10, p = .012) and FI (β = .16, p < .001) showed
that these main effects (ΔR2 = .05, p <.001), were qualified by a significant interaction (ΔR2 =
.011, p =.009), β = .11, p = .009. Supplementary Figure 4 shows the generated regression lines.
Those high in FI reported high levels of MIL regardless of their CRT scores. However, for
individuals 1 SD below the mean on FI, CRT performance was negatively correlated with MIL
r(106) = -.28, p = .004. This interaction remained significant (β = .08, p = .036), controlling for
religiosity (β = .30, p < .001).
Meaning and Intuition: Supplementary Materials 5
Analyses using dummy codes for heuristic answers produced essentially the same results
in the opposite direction, (e.g., β’s for the interactions between FI and the dummy codes for 2
and 3 heuristic answers = .16, p = .02 and .19, p = .005 respectively). These analyses suggest
that individual differences in processing styles may relate to MIL in the way we have proposed:
Those who are reliant on intuitive processing report high levels of MIL, but those who report
themselves as unlikely to rely intuitive impressions and who are in fact capable of overriding
intuitive impulses, report the lowest levels of MIL.
Interestingly, when NC, the dummy codes for CRT performance, and their interactions
were entered into a regression equation predicting MIL, only main effects of NC, β = .14, p =
.018, and the dummy code for scoring 3 on the CRT, β= -.20, p < .001, predicted MIL. No
interactions were found (all p’s > .18). When CRT was treated as a continuous measure, CRT
performance, β = -.18, p < .001, and NC, β = .20, p < .001, predicted MIL, with no interaction, β
= .04, p < .27.
CRT variables across orders.
CRT variables did not differ across orders: For correct answers, M(SD)’s CRT first =
1.25 (1.20), MIL first = 1.21 (1.24), t(610) = 0.32, p = .75, d = 0.03, JZS Prior Bayes factor =
14.82; for heuristic answers, CRT first = 1.47(1.46), MIL first = 1.49(1.18), t(612) = 0.25, p =
.81, d = 0.01, JZS Prior Bayes factor = 15.11; and for time spent on the CRT, CRT first = 22.53
(44.90), MIL first = 23.53 (26.23), t(610) = 0.44, p = .66, d = 0.04, JZS Prior Bayes factor =
14.16.
Did MIL predict processing independent of affect?
The main results suggest that information processing does not affect MIL. Instead,
levels of MIL predict subsequent information processing with low MIL being associated with
Meaning and Intuition: Supplementary Materials 6
reflection. Mood (PA and NA) are subjective states that have been shown to influence styles of
information processing (e.g., Clore & Palmer, 2009; Schwarz, 2012; Schwarz & Clore, 1988).
As such, it is of interest to see if MIL and/or MIL2 predict cognitive processing controlling for
mood. Table 3 of the manuscript shows the results of regression equations predicting the three
CRT measures from MIL and MIL2 as well as (centered) PA and NA, for those who completed
MIL prior to the CRT. As can be seen, controlling for PA and NA, the contribution of MIL2
remained significant for all CRT measures. In additional equations, we entered quadratic effects
for PA and NA, to insure that extremely low levels of PA or extremely high levels of NA did not
account for the patterns found for MIL2. In none of the equations did these variables contribute
significantly (all p’s > .10), and all of the effects for MIL2 remained significant (β’s = .17, -.17
and .14 for correct answers, heuristic answer and time spent on the task, respectively, p’s <
.023).
These patterns were only observed in those who completed the MIL assessment first.
Among those who completed the CRT first, both PA (β = -.19) and NA (β = -.18) independently
predicted fewer correct answers, p’s = .002 and more heuristic answers, β’s = .15 and .16 for PA
and NA respectively, p’s < .02. No other significant results were found for this group.
Did processing style moderate the effects of order on MIL?
Next we examined whether levels of reflection predicted differences in MIL. For these
analyses, MIL was the criterion and CRT scores were converted to three dummy codes (for
scoring 3, 2, or 1 respectively, with the 0 group representing the baseline). The main effects and
interactions of these with order were entered into a hierarchical regression equation predicting
MIL. Only a significant main effect of the dummy code for scoring 3 contributed significantly, β
= -.20, p = .002. None of the dummy codes interacted with order, β’s < .02, p’s > .79.
Meaning and Intuition: Supplementary Materials 7
A similar analysis was completed examining dummy codes for heuristic answers to the
CRT predicting MIL as a function of order. Again, no interactions with order were found.
Rather, main effects for the dummy codes for 2 heuristic answers, β = .16, p = .02; and 3
heuristic answers, β = .19, p = .005 emerged. For the interaction terms, all p’s > .34. These
analyses suggest that MIL is associated with heuristic processing but do not support the idea that
heuristic processing enhances subsequent MIL.
Regressing MIL on the time spent on the CRT and its interaction with order showed
only a marginally significant main effect of time, β = -.09, p = .072, and no interaction, β = .05 p
= .27. In order to examine whether time spent on the CRT was associated with MIL in a
curvilinear fashion (that potentially interacted with order), MIL was regressed hierarchically on
time, order, the time X order interaction, as well as time2, and time2 X order. None of the
predictors significantly contributed to the prediction of MIL. For time2, β = -.14, p = .19, for the
time2 X order interaction, β = -.05, p = .49. Overall, then, no evidence emerged for the possibility
that processing styles as evinced on the CRT predict subsequent MIL.
Study 5
As in Study 4, we wanted to determine whether generated regression lines represented
accurate reflections of the data. A plot of the raw data shown in Supplementary Figure 5
suggests the data are appropriately represented in our analyses. Means for the MIL (lowest to
highest quartile) X mindset induction (intuitive vs. reflective) are shown in the Supplementary
Figure 6.
Did FI moderate the effects of condition on MIL?
The lack of effects for mindset induction on MIL does not preclude the possibility that FI
might moderate the effects of condition on MIL. In order to examine whether FI interacted with
Meaning and Intuition: Supplementary Materials 8
condition (or order) to predict MIL, MIL was regressed on main effects of condition, order, and
FI, as well as all two and three-way interactions. Results showed only a main effect of FI, β =
.22, ΔR2 = .05, p = .003. No other step contributed a significant change in R2 (p’s > .22) and no
other predictors approached significance, p’s >.20. We also examined whether FI2 might qualify
this linear association in interaction with condition. For the main effect of FI, β= 21, p =.001, for
FI2, β = .10, p = .10, and for the FI2 X mindset interaction, β=.01, p = .87.
Meaning and Intuition: Supplementary Materials 9
References
Hayes, A.F. (2009). Beyond Baron and Kenny: Statistical mediation in the new millennium.
Communication Monographs, 76, 408-420.
Hicks, J. A., Cicero, D. C., Trent, J., Burton, C. M., & King, L A. (2010). Positive affect,
intuition, and the feeling of meaning. Journal of Personality and Social Psychology, 98,
967-979.
Hicks, J. A., Schlegel, R. J., & King, L. A. (2010). Social threats, happiness, and the dynamics of
meaning in life judgments. Personality and Social Psychology Bulletin, 36, 1305-1317.
Humphrey, N. (2006). Seeing Red: A Study in Consciousness. Cambridge, MA: Harvard
University Press.
King, L. A., & Hicks, J. A. (2009). Positive affect, intuition, and referential thinking. Personality
and Individual Differences, 46, 719-724.
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects
in simple mediation models. Behavior Research Methods, Instruments and Computers,
36, 717–731.
Meaning and Intuition: Supplementary Materials 10
Supplementary Table 1. Regression Analyses Predicting Meaning in Life, Study 3
Regression Results
Mediational Results
Indirect
Effect
Predictor
βx→MIL
Faith in Intuition (FI)
.31
Positive Affect
.37
FI
Need for Cognition
.23
FI
Religiosity
.27
FI
Self-Esteem
.36
FI
Need Satisfaction
FI
.29
βx→MIL.FI
z
95% CI
BMILX.FI
.33
5.76
[.05, .10]
0.27(.02)
.24
5.22
[.02, .06]
.19
3.84
[.01, .05]
.28
4.30
[.03, .09]
.24
2.78
[.01, .04]
.28
2.82
[.01, .06]
.30
6.75
[.06, .12]
.22
5.77
[.04, .10]
.21
5.71
[.04, .11]
.24
6.30
[.06, .13]
BMILFI.X
0.26(.03)
0.26(.04)
0.30(.03)
0.29(.03)
0.30(.03)
0.35(.04)
0.24(.03)
0.25(.04)
0.25(.03)
Note. N = 1026. The second and third columns show the standardized beta weights predicting
MIL for each predictor alone and when entered with FI simultaneously. All are significant, p <
.001. The fourth column contains the tests for mediation (z). Z values in the same row as a
variable test that variable as the mediator, all are significant, p<.005. The fifth column provides
the confidence intervals for the indirect effect of the mediator, using bootstrapping with 3000 resamplings. The sixth column presents the unstandardized regression weights predicting MIL
from each predictor controlling for FI. All are significant, p < .001. The final column presents
the same for FI, controlling for each predictor. All are significant, p < .001. Meaning in Life was
measured using the MLQ presence of meaning subscale.
Meaning and Intuition: Supplementary Materials 11
Supplementary Figure 1. Positive Affect and Faith in Intuition predicting Meaning in Life,
Study 3.
5
4.8
Meaning in Life
4.6
4.4
4.2
4
Low FI
3.8
High FI
3.6
3.4
3.2
3
Low
High
Positive Affect
Meaning and Intuition: Supplementary Materials 12
Supplementary Figure 2. Meaning in Life Predicting CRT Outcomes as a Function of Order,
Raw Data, Study 4
CRT Performance
3
2.5
2
1.5
MIL First
1
CRT First
0.5
0
Low
High
Heuristic Responses
Meaning in Life
3
2.5
2
1.5
1
0.5
0
MIL First
CRT First
Low
High
Time to Complete CRT
Meaning in Life
65
55
45
35
MIL First
25
CRT First
15
Low
High
Meaning in Life
cases
150
100
50
0
Meaning and Intuition: Supplementary Materials 13
CRT Scores
Supplementary Figure 3. CRT Measures as a Function of MIL Levels and Order, Study 4
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
MIL First
CRT First
Low
Not Low
Heuristic Answers
Meaning in Life
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
MIL First
CRT First
Low
Not Low
Meaning in Life
35
Time on CRT
30
25
20
MIL First
15
CRT First
10
5
0
Low
Not Low
Meaning in Life
Meaning and Intuition: Supplementary Materials 14
Supplementary Figure 4. MIL as a Function of FI X CRT, Study 4
4.8
4.6
Meaning in Life
4.4
4.2
4
3.8
Low FI
3.6
High FI
3.4
3.2
3
Low
High
Cognitive Reflection Task
Meaning and Intuition: Supplementary Materials 15
6
5.8
5.6
5.4
5.2
5
4.8
4.6
4.4
4.2
4
Reflective
Intuitive
Low
High
Meaning in Life
250
200
cases
Faith in Intuition
Supplementary Figure 5. FI as a function of MIL and Mindset Manipulation Raw Data,
Study 5
150
100
50
0
Low
High
Meaning in Life
Meaning and Intuition: Supplementary Materials 16
Supplementary Figure 6. Faith in Intuition as a Function of MIL and Mindset, Study 5
6
5.8
Faith in Intuition
5.6
5.4
5.2
5
Reflective
4.8
Intuitive
4.6
4.4
4.2
4
1st
2nd
3rd
Meaning in Life Quartile
4th