Judgment and Decision Making, Vol. 14, No. 6, November 2019, pp. 711-720
Variations on anchoring: Sequential anchoring revisited
Štěpán Bahník*
Petr Houdek#
Lucie Vrbová#
Jiří Hájek#
|
The anchoring effect, the assimilation of judgment toward a previously
considered value, has been shown using various experimental paradigms. We
used several variations of the sequential anchoring paradigm, in which a
numeric estimate influences a subsequent numeric estimate on the same
scale, to investigate how anchoring is influenced by multiple anchors, a
comparison question, and by a newly introduced debiasing procedure. We
replicated the anchoring effect using the sequential anchoring paradigm
and showed that, when two anchors of opposite directions are presented,
the second seems to influence a subsequent judgment somewhat more. A
comparison of a target with another object before the numerical estimate
was not sufficient to elicit anchoring, but it might have increased the
sequential anchoring effect. The debiasing procedure, based on providing
reference points on the numerical scale, prevented the sequential
anchoring effect. The results are in accord with the scale distortion
theory of anchoring, but other theories may also account for the observed
findings with additional adjustments.
Keywords: anchoring, judgment, scale distortion, selective accessibility, debiasing
1 Introduction
The anchoring effect refers to the assimilation of judgment toward a
previously considered value — the anchor (Bahník, Englich & Strack,
2017; Tversky & Kahneman, 1974). Anchoring has been usually studied using
the so-called standard anchoring paradigm. In this paradigm, people are
first asked to compare a target value with an anchor and then make an
absolute judgment about the target value. For example, in a comparison
question, people may be asked whether the average annual temperature in
New York City is lower or higher than 102 °F. Then, they estimate the
average annual temperature in New York City. The absolute judgment is
assimilated toward the anchor value, and people thus provide higher
estimates if they compared the temperature with 102 °F than if they had
compared the temperature with a lower value. Anchoring has been shown to
be robust (Klein et al., 2014) as well as relevant to various applied
domains (e.g., Englich, Mussweiler & Strack, 2006; Galinsky &
Mussweiler, 2001). Even though it has been studied for more than 40 years,
its underlying mechanism is still debated (e.g., Bahník & Strack, 2016;
Frederick & Mochon, 2012; Harris & Speekenbrink, 2016; Lewis, Gaertig,
& Simmons, 2019; Mochon & Frederick, 2013). In particular, the recently
introduced scale distortion theory of anchoring (Frederick & Mochon,
2012; Mochon & Frederick, 2013) is supposed to represent an alternative
to the previously favored explanation of the anchoring effect—the
selective accessibility model (Strack, Bahník & Mussweiler, 2016; Strack
& Mussweiler, 1997).
According to the selective accessibility model, anchoring is a result of
increased accessibility of information consistent with the anchor, which
results from positive hypothesis testing. This means that in the standard
anchoring paradigm people test, in a biased way, the hypothesis that the
correct target value is equal to the anchor value when they answer the
comparison question. In the example of the judgment of the average annual
temperature in New York City, they would answer the comparison question by
recalling information that is compatible with the two values (the average
annual temperature in New York City and 102 °F) being equal (Klayman & Ha,
1987). Even when the recalled information is not consistent with the
equivalence of the two values, it is subsequently more easily accessible in
mind. Such information is therefore more likely to be used when answering
the absolute judgment question, and thus bias the judgment in the direction
of the anchor.
The scale distortion theory argues that the anchor value influences the
perception of the scale on which the judgment is made (Frederick &
Mochon, 2012; Mochon & Frederick, 2013). The comparison question
introducing a high anchor of 102 °F would thus influence the subsequent
judgment of the average annual temperature in New York City through the
subjective change of perception of the Fahrenheit scale. The same moderate
temperature would seem relatively lower when the high anchor value was
previously considered. Consequently, the perceived correct value of the
average annual temperature in New York City would be mapped to a higher
value on the scale. Among other evidence, Frederick and Mochon support
their theory using a sequential anchoring paradigm, in which participants
make two subsequent absolute judgments. The first judgment (e.g., of
wolf’s weight) serves as an anchor and influences the second judgment
(e.g., of giraffe’s weight), but only if they are on the same scale.
The two theories of anchoring give different predictions in various cases
(see Bahník et al., 2017, for a summary of evidence for and against both
theories). For example, the scale distortion theory predicts that the
comparison question is not sufficient to elicit anchoring when the target
is compared with an object rather than a numerical value because scale
distortion requires consideration of a numerical value on the scale. On the
other hand, such comparison question might influence the absolute judgment
according to the selective accessibility model because people should
activate information that is compatible with the target value being the
same as the anchor value, even if it is not explicitly provided. Mochon
and Frederick (2013, Study 1) tested the two opposing predictions and found
that the comparison question without an explicit anchor value does not
elicit the anchoring effect. However, Mochon and Frederick tested the
effect using a single item in an experiment with limited statistical
power. Chapman and Johnson (1994) also tested the effect of a comparison
question and found a marginally significant effect of the comparison
question, which could be consistent with the existence of the effect as
well as with a null effect. Therefore, a replication of the finding could
help further strengthen the objection against selective accessibility model
or alleviate it if the anchoring effect was found even without the presence
of an explicit anchor value. We thus use a larger sample of scales and a
higher number of participants to replicate the apparent null effect
obtained by Mochon and Frederick.
While the comparison of the target with another object may not elicit the
anchoring effect, it could still increase the anchoring effect in the
sequential anchoring paradigm. Consistent with this hypothesis, Mochon and
Frederick (2013, Study 4) showed that a sequential anchor does not
influence the target judgment if the two absolute judgments refer to
dissimilar targets, but the anchoring effect reappears when a comparison
question is introduced between them. Presumably, people are more likely to
automatically compare similar targets and the comparison has to be
externally elicited if the targets are dissimilar. Similarly, Harris and
Speekenbrink (2016) showed that the comparison question may induce
anchoring in the sequential anchoring paradigm even in the case that the
anchor related to a different dimension (e.g., weight instead of height as
the target judgment), in which case no effect of the anchor is otherwise
observed. It is thus also possible that the comparison question may
generally increase the anchoring effect because it makes people focus on
similarities between the two targets as the selective accessibility model
argues. The comparison question may thus increase the anchoring effect not
only for dissimilar targets as Mochon and Frederick showed, but for
targets belonging to the same category as well. We test this possibility
in the present study.
Apart from the effect of a single sequential anchor, Mochon and Frederick
(2013, Study 2) also showed that using two low anchors results in a
stronger anchoring effect than just using one of the anchors. We follow up
on this result by using both high and low sequential anchors before the
target judgment. We manipulate the order of the two anchors to find out
which of the two anchors influences the target judgment more. The
inclusion of the second anchor could also be considered as a way of
debiasing the sequential anchoring effect, similar to the strategy of
considering the opposite, which was shown to reduce the anchoring effect
in the standard anchoring paradigm (Mussweiler, Strack & Pfeiffer,
2000).
Given that the scale distortion theory argues that the anchoring effect is
a result of inappropriate mapping of a judgment to a response scale, we
developed a debiasing technique specifically targeting the assumed
mechanism. The technique aimed to provide participants with reference
points which could be used in mapping the value of the target judgment to
the correct value on the scale, thus reducing the possibility of
subjective distortion of the scale. To make participants think about the
reference points, we asked them to estimate two values on the scale before
introducing a sequential anchor. Notably, this manipulation should prevent
the distortion of the scale and it should not therefore influence the
estimate of the anchor value, only its subsequent effect on the absolute
judgment of the target value.
In sum, we test several hypotheses related to the scale distortion theory
and sequential anchoring paradigm. First, we examine whether a comparison
of two objects on a scale is sufficient to influence a subsequent judgment
of one of the objects on that dimension. Second, we test whether a
comparison question increases the anchoring effect in a sequential
anchoring paradigm. Third, we examine whether the first or the second
sequential anchor influences the target judgment more. Fourth, we test a
technique that may debias anchoring in the sequential anchoring paradigm.
Finally, while previous anchoring research used mostly single items, we
employ a larger set of items, which enables us to treat them properly as a
random effect, and thus check the robustness and generalizability of some
previously observed effects (Bahník & Vranka, 2017; Judd, Westfall &
Kenny, 2012).
2 Methods
2.1 Participants
We recruited 424 Czech-speaking participants for a set of studies conducted
on a computer in a laboratory. Participants were compensated with 250 CZK
(~12 USD) for their participation in the whole set of studies, which
took about two hours to complete. The study was conducted in groups of up
to 17 participants and participants were invited for sessions some time in
advance, so the sample size differed slightly from 400, which we planned
to collect.1 The set of studies included an instructional manipulation check
(Oppenheimer, Meyvis & Davidenko, 2009) and three items in various
scales that instructed participants to pick a specific response. According
to a pre-registered exclusion criterion, we excluded from analysis 20
participants who failed to correctly answer the instructional manipulation
check or at least two of the control items in the scales. We also excluded
data from 16 additional participants from a single session where
participants answered each question three times due to a software bug. Out
of the remaining 388 participants, 70% were women. In terms of
employment, 71% were students, 22% employed, and the remaining 7% had
some other employment status. The median age of the participants was 23.5
years (IQR = 5).
| Table 1: An overview of experimental conditions with an example.
The column on the right shows an example of the materials used for the
weight scale (kg). See Appendix for the list of all the scales. |
| Condition | Example of materials |
| Control | What is the weight of a donkey in kilograms? |
| Low sequential anchor | What is the weight of a fox in kilograms? |
| | What is the weight of a donkey in kilograms? |
| High sequential anchor | What is the weight of an elephant in kilograms? |
| | What is the weight of a donkey in kilograms? |
| Low sequential anchor with comparison | What is the weight of a fox in
kilograms? |
| | Does a fox weigh more or less than a donkey? |
| | What is the weight of a donkey in kilograms? |
| High sequential anchor with comparison | What is the weight of an elephant
in kilograms? |
| | Does an elephant weigh more or less than a donkey? |
| | What is the weight of a donkey in kilograms? |
| Low comparison anchor | Does a fox weigh more or less than a donkey? |
| | What is the weight of a donkey in kilograms? |
| High comparison anchor | Does an elephant weigh more or less than a
donkey? |
| | What is the weight of a donkey in kilograms? |
| Low-high sequential anchor | What is the weight of a fox in kilograms? |
| | What is the weight of an elephant in kilograms? |
| | What is the weight of a donkey in kilograms? |
| High-low sequential anchor | What is the weight of an elephant in
kilograms? |
| | What is the weight of a fox in kilograms? |
| | What is the weight of a donkey in kilograms? |
| Low sequential anchor with debiasing | Estimate how many kilograms does a
fox and elephant weigh and imagine these estimates on a numerical scale. |
| | What is the weight of a fox in kilograms? |
| | What is the weight of a donkey in kilograms? |
| High sequential anchor with debiasing | Estimate how many kilograms does an
elephant and fox weigh and imagine these estimates on a numerical scale. |
| | What is the weight of an elephant in kilograms? |
| | What is the weight of a donkey in kilograms? |
2.2 Procedure and materials
Participants were presented with 22 trials which always ended with an
absolute judgment question asking for a numerical judgment. This
target judgment was used as the dependent variable in analysis.
Before the target judgment, participants were given various questions
depending on the condition in a given trial. All questions within a trial
related to the same scale (kg, km/h, °C, …; see Appendix for the full
list). Each of the 22 trials used a different scale, the order of which was
randomly determined for each participant. Only after each question was
answered was a subsequent question displayed on the same screen.
Participants were thus able to reply only to a single question at any given
time. After making the target judgment, participants confirmed their
response by pressing a button, which led them to the next trial on a
different screen.
In total, there were 11 conditions and each participant received 2 trials
for each condition (see Table 1 for an overview of all conditions).
Assignment of the conditions to the scales was randomized for each
participant. In the control condition, participants were given
only the question asking for the target judgment (e.g., “What is the
weight of a donkey in kilograms?”). In the high and low
sequential anchor conditions, participants answered before the target
judgment the same question about an object for which the correct answer
was higher or lower than the correct value of the target (e.g., “What is
the weight of an elephant in kilograms?” or “What is the weight of a fox
in kilograms?”).2 In the
high and low sequential anchor with comparison
conditions, participants were additionally asked to compare the target to
the high or low anchor between providing judgments about the anchor and
the target (e.g., “Does an elephant weigh more or less than a donkey?” or
“Does a fox weigh more or less than a donkey?”). The high and
low comparison anchor conditions consisted only of the comparison
question preceding the target judgment. The high-low and
low-high sequential anchor conditions asked absolute judgment
questions relating to both anchors before asking for the target judgment
and they differed only in the order of presentation of the two anchors.
Finally, high and low sequential anchor with debiasing
conditions were the same as the sequential anchor conditions but included
an additional instruction to mentally map two reference points on a scale
before presenting the anchor. The participants were asked to estimate
values of both anchors and imagine them on a numerical scale (e.g.,
“Estimate how many kilograms does an elephant and fox weigh and imagine
these estimates on a numerical scale.” or “Estimate how many kilograms
does a fox and elephant weigh and imagine these estimates on a numerical
scale.”).
3 Results
3.1 Answers to the comparison
question
In most of the scales, participants answered the comparison question
correctly in more than half of the cases. For one scale (precipitation in
mm per month), the majority of participants answered the comparison
question incorrectly for both low and high anchor, and this scale was
therefore excluded from analysis.3 For all other scales, the answers were correct in the
majority of cases (56%-100% for high anchors and 88%-100% for low
anchors, with averages of 89% and 96%). The comparison question was
presented to participants in two conditions — comparison anchor and
sequential anchor with comparison. It was therefore possible to test
whether the initial absolute judgment of the anchor value influenced the
answer to the comparison question. The results of a mixed-effect logistic
regression with the correctness of the answer to the comparison question as
the dependent variable and direction of the anchor (low X high) and
condition (comparison anchor X sequential anchor with comparison) as
predictors showed that there was no systematic effect of condition
(z = −1.57, p = .12, OR = 0.64, 95% CI = [0.37,
1.11]). However, the interaction between condition and direction of the
anchor showed that the effect of condition differed for high and low
anchors (z = −3.24, p = .001, ratio of OR = 0.22, 95%
CI = [0.09, 0.55]). While there was no effect for low anchors (z =
0.82, p = .41, OR = 1.49, 95% CI = [0.57, 3.89])
participants were less likely to answer the comparison question correctly
for high anchors when it was preceded by an estimate of the anchor value
(z = −3.47, p < .001, OR = 0.31, 95%
CI = [0.16, 0.60]).
| Table 2: Comparison of the anchoring conditions with the control
condition. |
| Condition | Estimate | CI | p |
| Low sequential anchor | −0.100 | −0.230 to 0.030 | .147 |
| High sequential anchor | 0.150 | 0.037 to 0.264 | .016 |
| Low sequential anchor with comparison | −0.177 | −0.314 to –0.040 | .019 |
| High sequential anchor with comparison | 0.200 | 0.074 to 0.326 | .005 |
| Low comparison anchor | −0.058 | −0.154 to 0.037 | .241 |
| High comparison anchor | 0.014 | −0.095 to 0.123 | .804 |
| Low-high sequential anchor | 0.117 | −0.006 to 0.240 | .075 |
| High-low sequential anchor | 0.021 | −0.101 to 0.143 | .742 |
| Low sequential anchor with debiasing | −0.038 | −0.160 to 0.083 | .544 |
| High sequential anchor with debiasing | 0.011 | −0.106 to 0.128 | .854 |
3.2 Differences between the control and anchoring conditions
Next, we performed pre-registered analyses using a mixed-effect linear
regression. To allow inclusion of all the scales in one model, the target
judgment was transformed to z-scores using the distribution of the target
judgment in the control condition for each scale. That is, we computed in
which percentile would a given value of the target judgment be if it was
an answer in the control condition. We then used this percentile to
compute the z-score. The first step served to alleviate the effect of
outliers and to make the values comparable between scales. The second step
made distributions of values close to normal and it reduced the impact of
large differences in percentiles resulting from small differences around
average values, where the estimates are more condensed. The z-scores were
then used as the dependent variable in a mixed-effect model, in which
dummy variables for conditions served as predictors (apart from the
control condition) and which did not include an intercept. The estimated
coefficient for each condition therefore shows the difference of the
condition from the control condition. We included random intercepts for
participants and scales as well as random slopes for scales. The results
of the analysis are displayed in Table 2. It is possible to see that
sequential anchors led to the anchoring effect, but the comparison
question itself did not elicit the anchoring effect.
| Figure 1: Results of analyses comparing absolute judgments of the
target value. The points and error bars represent estimates of the
effects and their 95% confidence intervals for comparison with the
control condition, which are also reported in Table 2. At the upper part
of the graph are results of all comparisons of absolute judgments of the
target value reported in the text. The bottom line of results shows
p-values for comparisons of pairs of conditions and the remaining results
pertain to 2×2 interaction effects. A comparison of sequential anchors and
two-anchors conditions can be performed in two ways, both of which are
reported in the top line of results. The first analysis tests the effect
of the first anchor and the second tests the effect of the second anchor. |
3.3 Sequential anchoring
For all subsequent analyses, we conducted similar mixed-effect regressions
including only selected conditions to test specific hypotheses. All models
included a full random structure for scales and random intercepts for
participants. For models including four conditions (i.e., testing
differences of effects of high and low anchors between two conditions), we
report the interaction effect. All the results pertaining to the absolute
judgment question of the target value are displayed in Figure 1.
First, the high and low sequential anchor conditions differed from each
other, replicating the sequential anchoring effect (t(20.3) =
3.44, p = .003, b = 0.247, 95% CI = [0.106, 0.389]).
Next, we assessed the effect of the two anchors in the high-low and
low-high sequential anchor conditions. The answer to the second sequential
anchor was influenced by the first anchor. That is, participants gave
higher absolute judgments for the low sequential anchor when it was
preceded by the high anchor (t(1481.0) = 3.33, p
< .001, b = 0.173, 95% CI = [0.071, 0.274]), and to the
high sequential anchor when it was not preceded by the low
anchor (t(19.4) = 1.74, p = .10, b = 0.118,
95% CI = [−0.015, 0.251]), even though only the former effect was
significant. While the effect was not significant, the low-high condition
led to somewhat higher target judgments than the high-low condition
(t(17.9) = 1.78, p = .09, b = 0.098, 95% CI =
[−0.010, 0.206]), suggesting that, if there is any difference, the second
anchor might influence the target judgment more. Correspondingly,
presentation of the second anchor decreased the anchoring effect of the
preceding, opposite anchor (t(23.4) = −3.75, p = .001,
b = −0.348, 95% CI = [−0.529, −0.166]), but we did not find
evidence for the influence of the first anchor on the anchoring effect of
the subsequent, opposite anchor (t(19.2) = −1.62, p =
.12, b = −0.153, 95% CI = [−0.338, 0.032]).
3.4 Debiasing
To test the effect of the debiasing procedure, we compared the sequential
anchor with debiasing conditions with sequential anchor conditions. The
interaction between condition and direction of an anchor showed that
debiasing reduced the sequential anchoring effect (t(45.5) =
−2.57, p = .01, b = −0.202, 95% CI = [−0.355,
–0.048]). When low and high anchors were analyzed separately, anchoring
effect was weaker for both the low sequential anchor condition with
debiasing (t(18.7) = −1.06, p = .30, b =
−0.062, 95% CI = [−0.177, 0.053]), and for the high sequential anchor
with debiasing (t(351.2) = 2.68, p = .008, b =
0.141, 95% CI = [0.038, 0.244]) in comparison to the sequential anchor
conditions even though only the latter effect was significant. Consistent
with the lack of the anchoring effect after debiasing, the two debiasing
conditions also did not differ significantly from each other
(t(19.5) = −0.63, p = .54, b = −0.044, 95%
CI = [−0.179, 0.092]).
The debiasing condition can be compared to the sequential anchor condition
with two anchors since both conditions introduce both anchors, although in
a different way, before the target judgment. We found no significant
difference between the low-high sequential anchor condition and high
sequential anchor condition with debiasing (t(18.0) = 1.70,
p = .11, b = 0.108, 95% CI = [−0.017, 0.232]), as well
as no significant difference between the high-low sequential anchor
condition and low sequential anchor condition with debiasing
(t(19.2) = 0.94, p = .36, b = 0.058, 95% CI =
[−0.063, 0.179]). When the four conditions were analyzed together, we found
no difference between the differences of the debiasing conditions and
two-anchors conditions (t(19.7) = −0.47, p = .65,
b = −0.047, 95% CI = [−0.246, 0.151]), suggesting that the
effects of the debiasing manipulation and opposite anchor on subsequent
sequential anchoring effect did not differ. However, unlike the two-anchor
conditions, the debiasing conditions did not influence estimated values of
the anchors. That is, the estimate of high anchors did not differ between
the high sequential anchor with debiasing and high sequential anchor
conditions (t(19.2) = 0.01, p = 1, b = 0.000,
95% CI = [−0.110, 0.110]). Similarly, the estimates of low anchors did not
differ between the low sequential anchor with debiasing and low sequential
anchor conditions (t(1115.6) = −0.68, p = .50,
b = −0.034, 95% CI = [−0.134, 0.065]). On the other hand, the
estimates of high anchors for the low-high sequential anchor condition
were lower than for the high sequential anchor with debiasing condition
(t(18.1) = −1.93, p = .07, b = −0.121, 95% CI =
[−0.243, 0.002]), and the estimates of low anchors for the high-low
sequential anchor condition were higher than for low sequential anchor
with debiasing condition (t(21.1) = 2.42, p = .02,
b = 0.136, 95% CI = [0.026, 0.247]). These results suggest that
the debiasing manipulation worked through moderation of the effect of the
anchor on the absolute judgment of the target value rather than mediation
through its effect on the anchor.
3.5 Effect of the comparison
question
The two sequential anchor with comparison conditions clearly differed
between each other (t(17.9) = 7.12, p < .001,
b = 0.376, 95% CI = [0.273, 0.480]). To find out whether the
comparison question increases the anchoring effect in the sequential
anchoring paradigm, we compared the effect of high and low anchors between
the sequential anchor with comparison conditions and the sequential anchor
conditions. Sequential anchors with the comparison question led to a
somewhat stronger anchoring effect than sequential anchor conditions
without the comparison question (t(106.5) = 1.71, p =
.09, b = 0.128, 95% CI = [−0.019, 0.274]), but the effect was not
significant. Separate analyses of high and low anchors showed that the
inclusion of a comparison question led to a stronger anchoring effect both
for the high anchor (t(219.1) = 1.02, p = .31,
b = 0.053, 95% CI = [−0.049, 0.156]), and for the low anchor
(t(17.8) = −1.40, p = .18, b = −0.078, 95% CI =
[−0.187, 0.031]); however, neither effect was significant.
The comparison question was not sufficient to produce the effect by itself
— the two comparison anchor conditions did not significantly differ
(t(566.5) = −1.42, p = .16, b = −0.070, 95% CI
= [−0.167, 0.027]). While the effect of the comparison question in
sequential anchoring was not supported, the anchor itself clearly played a
role, as seen by the difference of effects of high and low anchors between
the comparison anchor condition and the sequential anchor with comparison
condition (t(75.9) = 4.13, p < .001,
b = 0.304, 95% CI = [0.160, 0.449]). Separate analyses for low
and high anchors showed that the sequential anchor with comparison
condition led to stronger anchoring effect both for low (t(17.2)
= −2.15, p = .05, b = −0.128, 95% CI = [−0.245,
−0.011]), and high anchors (t(141.2) = 3.61, p
< .001, b = 0.182, 95% CI = [0.083, 0.281]).
4 Discussion
Using a set of scales, we replicated the sequential anchoring effect
demonstrated by Mochon and Frederick (2013). We thus corroborated their
findings and showed that they generalize to different stimuli as well.
When two sequential anchors were used, the second anchor seemed to
influence the target judgment somewhat more. The effect was, however, not
significant and should be replicated. While the second anchor clearly
influenced the target judgment, we did not find strong evidence for the
effect of the first anchor. The data therefore did not fully corroborate
the finding of Mochon and Frederick that two low anchors
influence the target judgment more than one.
Similarly as Mochon and Frederick (2013), we did not find an effect of the
comparison question alone when the target was compared with an object
rather than with a numeric value as in the standard anchoring paradigm.
Mochon and Frederick argued that the effect should have been observed even
in this case according to the selective accessibility model. The selective
accessibility model claims that the comparison question makes people
activate information that is compatible with the anchor value being the
true value of the target. When an anchor is an object, the mechanism
should be presumably the same and the present finding would therefore pose
a problem for the selective accessibility model. Yet, it is still possible
that an object is compared differently to another object than to a numeric
value. In the case of two objects, the comparison is symmetrical and the
underlying mechanism could be therefore different than in the case of a
comparison with a numeric value, where activation of information about an
object might be much more natural — the numeric value is precise and might
not need activation of additional information to be assessed.
Nevertheless, the present study further corroborates the importance of a
specific numeric value in the anchoring effect (but see also Oppenheimer,
LeBoeuf & Brewer, 2008).
While Mochon and Frederick (2013) showed that a comparison question might
make anchoring reappear for an anchor dissimilar from a target, they did
not test the influence of the comparison question on the effect of an
anchor similar to the target. Even though we found an effect consistent
with the possibility that the comparison question increases the anchoring
effect, the effect was not significant. Therefore, as Harris and
Speekenbrink (2016, Experiment 1), we did not find evidence that inclusion
of a comparison question increases the anchoring effect even when the
anchoring judgment and target judgment are made on the same scale.
Nevertheless, given the equivocal results, a future study should further
test the possibility that the comparison question might increase the
anchoring effect generally. It is possible that the comparison question
makes people test the hypothesis that the target value is equal to the
anchor value, as argued by the selective accessibility model. The initial
absolute judgment of the anchor value would make the comparison question
asymmetrical because then the anchor value would have been already
estimated when the comparison was made, which could explain the difference
in the effect of the comparison question with a preceding estimate of the
anchor value and without it. It is also possible that the explanation used
by Mochon and Frederick for the effect of the comparison question may
apply even to an anchor that is similar to a target. Spontaneous
comparison of the anchor to the target may not always occur even when they
belong to the same category. The comparison question may therefore make
this comparison more likely or its effect stronger and thus increase the
effect of the anchor. These possible processes would be compatible with
the effect of the comparison question which was somewhat indicated by
previous studies as well as the present results.
Mussweiler et al. (2000) used the selective accessibility model to design a
strategy for debiasing the anchoring effect. By asking people to consider
arguments against an anchor value, they were able to reduce the anchoring
effect in the standard anchoring paradigm. Here, we tested a procedure
aimed at debiasing the anchoring effect in the sequential anchoring
paradigm. Given that according to the scale distortion theory, people are
influenced by an anchor because the anchor distorts the numerical scale on
which the judgment is made, we asked participants to first imagine two
numeric values on opposite ends of the scale to reduce scale distortion.
We did not observe any effect of anchors after the manipulation. The
debiasing manipulation included judgment about both low and high anchors,
so the lack of the anchoring effect could have been caused by opposing
anchoring effects of the two anchors, which was observed in the two
conditions presenting the two anchors without the additional instructions
used in the debiasing condition. However, the lack of an effect of
debiasing instructions on the estimate of the anchor suggests that the
debiasing manipulation worked through a different mechanism than
presentation of an opposite anchor, which influenced the estimate. Yet, it
is not clear whether the imagination of a numerical scale or consideration
of two values on opposite ends of the scale debiased the anchoring effect.
Even though the precise process which led to the effect of the debiasing
instructions is a topic for future studies, the instructions could be used
for debiasing the anchoring effect in the sequential anchoring paradigm
and situations where the same underlying process operates.
While the present study focused on selective accessibility and scale
distortion, there are other explanations of the anchoring effect as well.
In fact, it is likely that the anchoring effect can occur through various
mechanisms (Bahník et al., 2017; Turner & Schley, 2016). However, these
explanations do not easily account for the effects observed in the
sequential anchoring paradigm. According to one alternative view, an
anchor can serve as a source of information and is used in subsequent
judgment because it is seen as informative. Given that the anchor value is
not provided externally in the sequential anchoring paradigm (unlike in
the standard anchoring paradigm), it does not provide any new information
to the participant. If the mere mention of a specific object was
informative, we would also expect an effect in the comparison anchor
conditions in our study, where we found none. A numeric priming account of
anchoring (Wilson, Houston, Etling & Brekke, 1996; Wong & Kwong, 2000)
could account for most of the results observed in the present study.
However, it is not clear why the debiasing manipulation would prevent the
anchoring effect if it was a result of numeric priming. If the effect of
an anchor was debiased because participants were primed in the opposite
direction by an estimate of the object on the opposite side of the scale,
this estimate would have influenced the estimate of the anchor as well.
Furthermore, numeric priming cannot explain other findings in studies
using the sequential anchoring paradigm; for example, that the anchoring
effect is observed only when the anchor and target judgments are made on
the same scale and that the comparison question between the two judgments
might influence the anchoring effect (Frederick & Mochon, 2012; Harris &
Speekenbrink, 2016). Finally, anchoring could be a result of insufficient
adjustment of judgment from the anchor value (Epley & Gilovich, 2001;
Simmons, LeBoeuf & Nelson, 2010). However, it is not clear why two
sequential anchors of the same direction, as used by Mochon and Frederick
(2013), should have a stronger effect on the target judgment than one,
when people can adjust only from one of the anchors. The
anchoring-and-adjustment account also does not readily explain the effect
of the debiasing manipulation in our study. These alternative accounts
therefore do not present a parsimonious explanation for the anchoring
effect in the sequential anchoring paradigm.
In this study, we replicated and extended findings of Frederick and Mochon
(2012; Mochon & Frederick, 2013). As in their studies, we found the basic
sequential anchoring effect. When two anchors were presented, the second
seemed to influence the target judgment somewhat more even though it was
itself affected by the first anchor. We replicated the finding that a
comparison of a target with a different object is not sufficient to elicit
the anchoring effect. A comparison introduced between two estimates on the
same scale also did not reliably increase the anchoring effect of the
first estimate. Finally, we developed a debiasing procedure, which was
shown to prevent the anchoring effect in the sequential anchoring
paradigm. Future studies could further elucidate the process underlying
the effect of the debiasing procedure. The findings are potentially
compatible with both the selective accessibility model and scale
distortion theory; however, the selective accessibility model would need
some revisions to account for all the observed effects. Namely, the
selective accessibility model does not currently explain why a comparison
of an object with a target does not elicit the anchoring effect. It is
possible that an anchor in the form of a numerical value is required for
positive hypothesis testing, which is argued to cause the anchoring effect
by the selective accessibility model. The efficacy of the debiasing
manipulation is also not easy to explain from the view of the selective
accessibility model. Even though the scale distortion theory is most
compatible with the findings of the present study, it is possible that
selective accessibility or other processes used to explain the anchoring
effect operate together or that they operate separately in different
people or situations.
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6 Appendix
A list of scales used in the present study in a format “scale
(unit): low anchor, target, high anchor”:
age (years):
Jiří Ovčáček, Michal Horáček, Karel Schwarzenberg
altitude (metres above sea level):
Amsterdam (Netherlands), Berlin
(Germany), Lhasa (Tibet)
annual deaths in the Czech Republic (people/year):
drowning, car
accident, heart failure
area (ha):
soccer field, Prague zoo, Ostrava
area (km2):
Austria, France, Canada
average annual temperature (°C):
Oslo, Rome, Dubai
average precipitation (mm per month):
Prague in March, Prague in
September, Prague in July
calories (kcal):
carrot, strudel piece, normal package of butter
decibels (db):
whispering, normal speech, shouting
distance (km):
Prague - Munich, Prague - Brussel, Prague - Athens
duration of a flight (h.):
Prague - London, Prague - Tel Aviv,
Prague - New York
fuel consumption (l/100km):
average car, fully loaded truck,
Boeing 737
GDP (billions of CZK):
Somalia, Canada, Germany
length (cm):
table tennis paddle, tennis racquet, hockey stick
movement speed (km/h):
turtle, pig, tiger
population ():
Belgium, Poland, Brasil
speed (rpm):
gramophone record, car wheels on a highway, PC hard
drive
unemployment (%):
Germany, Italy, Greece
voltage (V):
AA battery, electric chair, railway lines
volume (m3):
average car,
subway carriage, airship
weight (kg):
fox, donkey, elephant
year of birth (year of common era):
Otakar II. of Bohemia, George
of Poděbrady, Maria Theresa
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