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Supplemental Materials
No Impact of Affective Person Knowledge on Visual Awareness: Evidence From Binocular
Rivalry and Continuous Flash Suppression
by T. Stein et al., 2017, Emotion
http://dx.doi.org/10.1037/emo0000305
Supplemental Results
Binocular rivalry task: Results for the whole sample
Results for the whole sample of 69 observers who participated in the BR task were
similar to the results for the subset of 60 participants reported in the main manuscript. The
inclusion of participants with >80% trials with no data in this auxiliary analysis resulted in an
overall shorter mean duration of any percept (faces, houses, or mixed) of 3.19 s (SD = 2.39).
Mean dominance durations were 1.96 s (SD = 1.20) for faces and 1.37 s (SD = 0.76) for houses.
The mean duration of mixed percepts was 1.64 s (SD = 1.01).
Mean dominance durations were 2.04 s (SD = 1.30) for negative faces, 2.00 s (SD = 1.29)
for neutral faces, 2.04 s (SD = 1.34) for positive faces, and 1.77 s (SD = 1.11) for novel faces. A
repeated-measures ANOVA revealed a significant main effect of face condition, F(3, 204) =
6.08, p = .001, ηp2 = .08. As with the analysis reported in the main manuscript, planned followup t-tests showed that this was due to novel faces being perceptually dominant for significantly
shorter times than negative, neutral, and positive faces, all t(68) > 3.17, all p < .002, all d > 0.38.
There were no significant differences between the other conditions, all t(68) < 0.71, all p > .484,
all d < 0.09.
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Again, there were no significant effects of face condition on the first percept, F(3, 204) =
1.55, p = .203, ηp2 = .02, or on alternation rates, F(3, 204) = 1.97, p = .119, ηp2 = .03.
Binocular rivalry task: Mixed percepts and face suppression durations
The percentage of mixed percepts in our study was lower than that in the study by
Anderson et al. (2011), possibly reflecting an improved mirror stereoscope setup or a more
liberal response criterion for reporting faces and houses in our study. To better match the mixed
percept results in the sample studied by Anderson et al., we analyzed the BR data after excluding
those 20 participants who had the lowest proportions of mixed percepts. For the remaining 40
participants, the proportion of mixed percepts was 37.8% (SD = 9.7%), very similar to Anderson
et al.’s Study 1 (38.4% mixed percepts). The results were similar to the analysis of the whole
sample: A repeated-measures ANOVA revealed a significant main effect of face condition, F(3,
117) = 2.86, p = .040, ηp2 = .07. Planned follow-up t-tests showed that this reflected shorter
dominance for novel faces compared to negative, neutral, and positive faces, all t(39) > 2.18, all
p < .036, all d > 0.34. There were no significant differences between the other conditions, all
t(39) < 0.31, all p > .551, all d < 0.10.
We also tested whether affective knowledge influenced the duration of mixed percepts. A
repeated-measures ANOVAs on mixed percept durations from the whole sample did not reveal a
significant effect of face condition, F(3, 177) = 0.65, p = .612, ηp2 = .01.
Binocular rivalry task: More data on potential order effects
Results presented in the main manuscript showed that the order of the BR/b-CFS task did
not interact with the face valence manipulation. In addition, we analyzed dominance durations
separately for those participants who did the BR task first vs. those who did the b-CFS task first.
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For those 29 observers who did the BR task first, mean dominance durations were 1.94 s
(SD = 1.05) for negative faces, 2.10 s (SD = 1.28) for neutral faces, 2.01 s (SD = 1.13) for
positive faces, and 1.77 s (SD = 0.85) for novel faces. Numerically shorter dominance durations
for novel faces were reflected in a marginally significant main effect of condition, F(3, 84) =
2.34, p = .079, ηp2 = .08.
For those 31 observers who did the b-CFS task first, mean dominance durations were
2.68 s (SD = 1.12) for negative faces, 2.56 s (SD = 1.02) for neutral faces, 2.52 s (SD = 1.12) for
positive faces, and 2.25 s (SD = 1.00) for novel faces. A repeated-measures ANOVA revealed a
significant main effect of condition, F(3, 90) = 5.02, p = .003, ηp2 = .14. Follow-up t-tests
showed that this was due to novel faces being perceptually dominant for shorter times than
negative, neutral, and positive faces, all t(30) > 2.18, all p < 0.038, all d > 0.39. There were no
significant differences between the other conditions, all t(30) < 1.59, all p > .122, all d < 0.29.
Together, these results confirm that experimental order did not affect the influence of face
condition on dominance durations. Also for those participants who did the BR task first there
was no significant difference between dominance durations for faces previously paired with
negative, neutral, and positive information.
Binocular rivalry task: Potential learning effects
We also examined the influence of the learning rounds required to reach the criterion of
60% correct on dominance durations. An additional ANOVA with the within-subject factor face
condition (negative, neutral, positive, novel) and the between-subjects factor learning rounds
(one, more than one) revealed only a significant main effect of condition F(3, 174) = 5.15, p =
.002, ηp2 = .08, but no main effect of learning rounds, F(1, 58) = 0.01, p = .914, ηp2 < .01, and no
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significant interaction, F(3, 174) = 0.37, p = .776, ηp2 < .01. Thus, the number of learning rounds
did not affect the influence of face condition on dominance durations.
Next, we tested for a relationship between accuracy in the face-learning test and
dominance durations. To test for an overall relationship between face learning and BR
dominance durations, we correlated face categorization accuracy with the difference in mean
dominance durations between learned (negative, neutral, positive) faces and novel faces. There
was no significant correlation, r(58) = .023, p = .861. To test for a relationship between face
learning and longer dominance durations for negative faces, we correlated face categorization
accuracy with the difference in mean dominance durations between negative and non-negative
(neutral, positive) learned faces. The correlation was not significant, r(58) = .128, p = .330.
We also tested for a relationship between accuracy in the face-learning test and
dominance durations only in those BR-task participants who finished face learning after one
round (N = 38). There was no significant correlation between face categorization accuracy and
the difference in mean dominance durations between learned (negative, neutral, positive) faces
and novel faces, r(36) = .118, p = .481. Similarly, the correlation between face categorization
accuracy with the difference in mean dominance durations between negative and non-negative
(neutral, positive) learned faces was not significant, r(36) = .119, p = .477.
Finally, we repeated the main analysis of mean dominance durations only for those faces
that were accurately categorized by participants in their last round of the face-learning test.
Results were similar to the main analysis including all faces: A significant main effect of face
condition, F(3, 177) = 4.31, p = .006, ηp2 = .07, reflected the fact that novel faces were
perceptually dominant for significantly shorter times than negative, neutral, and positive faces,
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all t(59) > 2.33, all p < .024, all d > 0.30. There were no significant differences between the other
conditions, all t(59) < 1.13, all p > .265, all d < 0.15.
Breaking CFS task: Potential learning effects
We repeated the analysis of the potential influence of the learning rounds required to
reach the criterion of 60% correct for the suppression times from the b-CFS task. An additional
ANOVA with the within-subject factor face condition (negative, neutral, positive, novel) and the
between-subjects factor learning rounds (one, more than one) revealed only a significant main
effect of condition F(3, 186) = 4.81, p = .003, ηp2 = .07, but no main effect of learning rounds,
F(1, 62) = 3.14, p = .082, ηp2 = .05, and no significant interaction, F(3, 186) = 1.78, p = .153, ηp2
= .03. Thus, the number of learning rounds did not affect the influence of face condition on
suppression times under b-CFS.
Next, we tested for a relationship between accuracy in the face-learning test and b-CFS
suppression times. To test for an overall relationship between face learning and suppression
times, we correlated face categorization accuracy with the difference in mean suppression times
between novel faces and learned (negative, neutral, positive) faces. There was no significant
correlation, r(62) = .141, p = .266. To test for a relationship between face learning and faster
breakthrough of negative faces, we correlated face categorization accuracy with the difference in
mean suppression times between non-negative (neutral, positive) learned and negative faces. The
correlation was not significant, r(62) = .050, p = .694.
We also tested for a relationship between accuracy in the face-learning test and b-CFS
suppression times in those b-CFS-task participants who finished face learning after one round (N
= 40). For this subset of participants the correlation between face categorization accuracy with
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the difference in mean suppression times between novel faces and learned (negative, neutral,
positive) faces was significant, r(38) = .407, p = .009 (Spearman’s rho, rs = .379, p = .016), and
this was also the case when using log-transformed suppression times, r(38) = .023, p = .023
(Spearman’s rho, rs = .328, p = .039). Thus, the advantage of learned vs. novel faces in breaking
CFS tended to be larger in those participants who were better in the face-learning test. This
suggests that face learning influenced access to awareness under CFS, such that better learning
was associated with a greater advantage of learned vs. novel faces in overcoming CFS (median
split according to face categorization accuracy: better learners, M = 88.1% correct, SD = 4.6, bCFS advantage for learned faces, M = 0.42 s, SD = 0.36; poorer learners, M = 70.9% correct, SD
= 5.2, b-CFS advantage for learned faces, M = −0.06 s, SD = 0.48). However, as these findings
are the result of exploratory analyses they need to be interpreted with caution.
To test for a relationship between face learning and faster breakthrough of negative faces
in those b-CFS-task participants who finished face learning after one round, we again correlated
face categorization accuracy with the difference in mean suppression times between nonnegative ( neutral, positive) learned and negative faces. There was no significant correlation,
r(38) = .016, p = .921.
Finally, we repeated the main analysis of suppression times only for those faces that were
accurately categorized by participants in their last round of the face-learning test. Results were
similar to the main analysis including all faces: A trend for a significant main effect of face
condition, F(3, 177) = 2.23, p = .086, ηp2 = .03, reflected the fact that novel faces were associated
with longer suppression times than negative and neutral faces, both t(63) > 2.11, both p < .039,
both d > 0.26. There was no significant difference between novel and positive faces, t(63) = 1.67,
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p = .100, d = 0.21. Most importantly, there were again no significant differences between the
affective learning conditions, all t(63) < 0.62, all p > .541, all d < 0.08.
Relationship between the binocular rivalry and the breaking CFS task
To measure to what extent BR dominance durations and b-CFS suppression times
captured the same underlying perceptual processes, we conducted individual difference analyses,
which tested whether there was any relationship between effects in the two tasks in those
participants who completed both (N = 59). To test for a relationship between the effects of face
learning in the two tasks, we correlated the difference in mean dominance durations between
learned faces (averaged across negative, neutral, positive) and novel faces with the difference in
mean suppression times between learned faces and novel faces. The correlation was not
significant, r(57) = .072, p = .588. To test for a relationship between the effects of negative
information in the two tasks, we correlated the difference in mean dominance durations between
negative faces and non-negative learned faces (averaged across neutral and positive) with the
difference in mean suppression times between negative faces and non-negative learned faces.
This correlation was not significant either, r(57) = −.158, p = .232. Thus, in our sample there was
no evidence for effects being consistent within individuals across tasks. This may suggest that
BR dominance durations and b-CFS suppression times tap into distinct underlying perceptual
processes. Note, however, that the absence of correlations could also simply reflect noisy
measurements.
Consistency of the advantage of learned over novel faces across exemplars
We found that learned face exemplars dominated awareness in BR longer and reached
awareness more quickly in b-CFS than novel faces. As detailed in the main text, this effect is
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difficult to interpret because we used the same set of learned and novel faces for all participants.
The effect could therefore, in principle, be due to the physical differences between the groups of
faces. We therefore tested whether this main effect of learning on BR dominance and b-CFS
suppression times was consistent across individual faces or due to only a few exemplars. The
rationale underlying these additional analyses is that physical differences may have caused
longer dominance and shorter suppression times for some of the novel face exemplars, but are
unlikely to systematically differ between novel and learned faces (the different groups of faces
were selected randomly).
Figure S1 visualizes results for different face exemplars, indicating some consistency
across exemplars. As can be seen from Figure S1a, mean dominance durations for seven out of
the ten novel face exemplars fell below the overall mean for the learned faces. To test whether
the data for individual faces, averaged across participants, differed significantly between the two
groups of faces, we conducted a permutation test. We shuffled the labels (learned, novel)
assigned to the faces and counted the number of permutations with a difference greater than the
observed difference. Dividing this count by the number of permutations (10,000) yielded p =
.023, indicating that the difference in mean dominance durations between novel and learned
faces was reliable. Figure S1b shows that mean suppression times for nine of the ten novel faces
were longer than for the overall mean suppression time for learned faces. Here, the permutation
test on raw mean RTs yielded p = .048, and the permutation test on log-transformed RTs p =
.042. These results provide some evidence that the learning effects on visual awareness were not
due to a few exemplars, but rather consistent across faces.
To simultaneously account for variability in dominance durations and suppression times
between individual faces and between participants, we performed linear mixed effects analyses
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using the lme4 package (Bates, Maechler, & Bolker, 2012) for R (R Core Team). To test for the
main effect of learning a reduced (i.e. null) model containing random intercepts for both
participants and for individual face exemplars was compared against a model containing the
additional fixed effect of learning (learned, novel), using likelihood ratio tests to find the model
that best fitted the data.
Binocular rivalry task. The comparison of the reduced model with the model containing
the additional fixed factor of learning was significant, χ2(1) = 4.05, p = .044, indicating that the
effect of learning was consistent across individual face exemplars.
Breaking CFS task. The comparison of the reduced model with the model containing the
additional fixed factor of learning only approached significance, both for raw mean RTs, χ2(1) =
3.46, p = .063, as well as for log-transformed RTs, χ2(1) = 3.78, p = .052. Thus, from these
analyses we cannot unequivocally conclude that the learning effect was robust across individual
face exemplars. It is possible that some (unknown) physical differences between novel and
learned face exemplars caused differences in suppression times.
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Figure S1. Results for all 40 individual face exemplars. (A) Mean dominance durations from the
BR task for all face exemplars, averaged across affective learning conditions for learned faces.
Learned face exemplars are shown in gray, novel face exemplars are shown in pink; the faces are
ordered by mean dominance duration to highlight the fact that seven of the ten novel face
exemplars had shorter dominance durations than the average dominance duration across all
learned faces. (B) Mean suppression times from the b-CFS task for all face exemplars, averaged
across affective learning conditions for learned faces. Learned face exemplars are shown in gray,
novel face exemplars are shown in pink; the faces are ordered by mean suppression time to
highlight the fact that nine of the ten novel face exemplars had longer suppression times than the
average suppression time across all learned faces. Error bars represent SEMs. Dotted lines
represent the overall means for novel and learned faces, respectively.
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