An empirical comparison of different implicit measures to predict consumer choice
An empirical comparison of different implicit measures to predict consumer choice
Oliver Genschow 0
Alessio Avenanti, University of Bologna,
0 Social Cognition Center Cologne, University of Cologne , Cologne, Germany , 2 Department of Experimental Psychology, Ghent University , Ghent , Belgium , 3 Department of Psychology, University of Basel , Basel , Switzerland
While past research has found that implicit measures are good predictors of affectively driven, but not cognitively driven, behavior it has not yet been tested which implicit measures best predict behavior. By implementing a consumer context, in the present experiment, we assessed two explicit measures (i.e. self-reported habit and tastiness) and three implicit measures (i.e. manikin task, affective priming, ID-EAST) in order to test the predictive validity of affectively versus cognitively driven choices. The results indicate that irrespective of whether participants chose affectively or cognitively, both explicit measures, but not the implicit measures, predicted consumer choice very strongly. Likewise, when comparing the predictive validity among all measures, the explicit measures were the best predictors of consumer choice. Theoretical implications and limitations of the study are discussed.
Funding: This work was supported by a grant from
the Swiss National Science Foundation (grant
number PZ00P1_168007). The funder had no role
in study design, data collection and analysis,
decision to publish, or preparation of the
The main goal of psychological research is to predict individuals' future behavior. In order to
predict a person's future, researchers have focused on the assessment of explicit as well as
implicit attitudes (for overviews, see [
]). Explicit attitudes refer to the construct social
psychologists commonly try to assess by means of questionnaires or interviews. For implicit
attitudes different tasks and definitions have been proposed. For the present purpose, implicit
attitudes can be defined as evaluative responses toward an object, which, in contrast to explicit
attitudes, are not necessarily subject to introspection ([
] for a discussion of the term implicit
]). To measure such implicit attitudes, researchers within the last decades have developed
different tasks. Thereby, three classes of measures can be differentiated: affective priming
tasks, implicit associations tests and approach-avoidance tasks.
Affective priming. One of the first and most often used implicit measures is the affective
priming task [
]. Within this task, participants categorize target stimuli as being positive or
negative. Each target stimulus is preceded by a prime. The core idea underlying an affective
priming task is that targets sharing the valence of the prime lead to faster categorizations than
targets that do not share the valence of a prime. For instance, in order to measure a person's
attitude toward chocolate and fruit, one can present on each trial the picture of a chocolate or
a fruit as prime stimulus followed by a positive or a negative target stimulus that participants
categorize as being positive or negative in valence. If chocolate facilitates responding to
positive relative to negative target stimuli, this effect would indicate a positive attitude toward
Implicit association tests. During a typical Implicit Association Test (IAT; [
participants see stimuli that belong to one of four categories (e.g., positive, negative, chocolate, fruit)
and are asked to categorize each stimulus by pressing one of two keys. Two of the four
categories (e.g., positive and chocolate) are assigned to the first key, and the two other categories
(e.g., negative and fruit) are assigned to the second key. The core idea underlying the IAT is
that responses should be speeded up when the two categories of a response are strongly
associated with each other. For instance, if a person perceives chocolate very positive, she or he
should be faster in responding to chocolate stimuli when the same key has to be pressed for
positive stimuli, as compared to negative stimuli.
A task similar to the IAT is the Identification Extrinsic Affective Simon Task (ID-EAST;
]). Like the IAT, the ID-EAST relies on the principle that it is easier to give a response that is
associated with positive valence to positive as compared to negative items and vice versa for
responses associated with negative valence. For instance, in order to measure the associations
of the concepts chocolate and fruit, stimuli of both categories are presented intermixed with
positive and negative stimuli. All stimuli are presented in different colors (e.g., green and
blue). Participants are instructed to evaluate all stimuli by pressing one key for positive stimuli
and the other key for negative stimuli, irrespective of the color of the stimuli. The function of
these trials is to link the responses with positive or negative valence. However, for chocolate
and fruit stimuli, participants are asked to respond not on the basis of valence but on the basis
of the color. In the case of a positive attitude toward chocolate, individuals are expected to
respond faster to chocolate stimuli when the same response key is used to identify positive
stimuli, as when it is used to identify negative stimuli. Whereas previous versions of the ID-EAST
(i.e. the standard EAST) failed to provide a reliable (split-half reliability) or valid (correlations
with explicit ratings) measure of attitudes (for an overview, see [
]), the ID-EAST has been
found to provide scores that were fairly reliable and valid (e.g., [
]). Moreover, the
ID-EAST has been found to perform at a level close to that of the IAT while overcoming some
of the limitations of the IAT (e.g., the ID-EAST provides a measure of single attitudes; see
Approach-avoidance measures. During the last decade, different approach-avoidance
measures have been put forward (e.g., [13,14±18]). The common ground of all these
approachavoidance tasks is the hedonic principle that people approach pleasure and avoid pain (for a
review, see [
]). Based on this principle it is assumed that positive items elicit a behavioral
approach tendency and negative items elicit an avoidance tendency in individuals. Taking
advantage of this principle, Chen and Bargh [
] let participants respond to positive and
negative stimuli by pushing or pulling a lever. The results showed that participants were faster at
responding to negative stimuli when pushing rather than pulling the lever, but were faster at
responding to positive stimuli when pulling rather than pushing the lever. Brendl et al. [
introduced a slightly different technique. In their studies, they presented participants' name
next to either positive or negative stimuli. Participants then moved the presented stimuli with
a joystick as quickly as possible either toward or away from their name. The results showed
that participants moved positive stimuli faster toward their name than away from their name
and vice versa for negative stimuli. Despite using a joystick to measure approach and
avoidance tendencies, other researchers measured finger movements toward or away from a
stimulus and found similar results (e.g., [
2 / 13
While the above-reviewed literature indicates that movements toward oneself represent
approach tendencies and movements away from oneself represent avoidance tendencies, other
research suggests that it is not the movement per se, but rather the context that gives any
movement its meaning (e.g., [18,21,22±24]). In line with this idea, in the manikin task [
participants press a neutral key on a computer keyboard to let a manikin run toward or away
from a stimulus on a screen. In line with other approach-avoidance measures, the common
finding is that participants respond faster to positive stimuli when their response makes the
manikin move toward the stimuli than away from the stimuli. Conversely, reaction times are
faster to negative stimuli when the response makes the manikin walk away from the stimuli as
compared to walk toward the stimuli (see also [
]). When comparing joystick tasks with
the manikin task, Krieglmeyer and Deutsch  found that the manikin task is more sensitive
to valence and more strongly related to self-report scales than the joystick task.
Context factors and predictive validity
Millar and Tesser [
] assumed that behavior is either driven cognitively or affectively and
showed that attitudes measured under an affective focus correlate more strongly with
affectively driven behavior than with cognitively driven behavior and vice versa for attitudes
measured under a cognitive focus (cf. also ). Thereby, it has been found that explicit measures
are of particular importance for the prediction of deliberate, controlled and cognitively driven
]. Conversely, implicit measures are better predictors of impulsive and
affectively driven behavior [17,32±37]. For instance, Scarabis, Florack and Gosejohann ([
see also [
]) assessed the IAT as implicit measure and then let participants chose between a
chocolate bar and a fruit while either being in an affective or cognitive focus. The results show
that the IAT was a better predictor when participants chose in an affective, as compared to a
cognitive focus. When implementing the same focus manipulation and choice options,
Genschow and colleagues [
] found the same results for an approach-avoidance measure.
In sum, it is well known that implicit measures better predict affective driven than cognitive
driven behavior. But which measure actually predicts behavior the best? Bar-Anan and Nosek
] compared seven different implicit measures in terms of psychometric qualities of seven
indirect attitude measures across three attitude domains (race, politics, and self-esteem).
Specifically, they assessed the Implicit Association Test (IAT; [
]), the Brief IAT (BIAT; [
Single-Target IAT (ST-IAT; [
]), the Go±No-Go association task [
], the Affective
Misattribution Procedure (AMP; [
]), and the Sorting Paired Features task (SPF; [
]), as well as
Evaluative Priming [
] and found overall good psychometric quality. Thereby, the IAT showed
highest and the Evaluative Priming lowest quality. Despite this comprehensive comparison of
implicit measures, to the best of our knowledge, no study actually compared the predictive
validity of different implicit measures against each other. Moreover, while the predictive
validity of the IAT is well studied (e.g., [
]), less is known about the predictive
validity of other implicit measures. By implementing a consumer setup, the aim of the present
study was, thus, to compare different implicit measures with explicit measures and to test their
predictive validity of affectively and cognitively driven choices of chocolate versus fruit. As
implicit measures we chose tasks that have been reported to be valid and reliable as well as can
be used with pictures as stimuli. That is, we chose a standard affective priming task that has
been used previously by Degner and Wentura . For an IAT-like task we chose the
ID-EAST, because it has been found to perform at a level close to that of the IAT while
overcoming some of the IAT's limitations [
]. For the approach-avoidance task we applied the
manikin task, because it is more sensitive to valence and more strongly related to self-report
scales than the joystick tasks [
]. Finally, in order to cross-validate the predictive validity of
3 / 13
the implicit measures we assessed also two explicit ratingsÐnamely participants' self-reported
habit and taste of chocolate and fruit.
The study was conducted in accordance with the ethical standards of the 1964 Declaration of
Helsinki and approved by the rules of the Institutional Review Board from the Faculty of
Psychology and Educational Science of Ghent University. All participants provided informed
consent at the beginning of the experiment and were informed that participation was voluntary
and that all answers were processed and stored anonymously.
Data availability statement
The data file of the study is available from the Open Science Framework database. The URL
necessary to access our data is: https://osf.io/wnp9k/
We recruited participants via the participant pool of the Department of Experimental
Psychology at Ghent University. We excluded two participants before analysis. One person did not
understand the instructions of the tasks due to language problems and one person accidently
did the focus manipulation and the choice scenario before the assessment of the measures. The
final sample of the study consisted of 91 participants (73 female, 17 male, 1 not reported) with
an age ranging from 18 to 48 (M = 22.80; SD = 5.00). Participants received 10 € in return for
their participation and were allowed to take home a snack (i.e. Mars, Snickers, banana or
apple) after the study was finished.
Upon arrival, participants were seated in separate cubicles and signed an informed consent.
We then assessed two explicit measuresÐthat is, tastiness and habit of chocolate and fruit
consumption. In order to minimize the influence of carry-over effects of the explicit measures on
choice we assessed the explicit measures always first. Afterwards, we assessed three different
implicit measures: the manikin task, the affective priming task and the ID-EAST. The order of
the implicit measures was counterbalanced across participants. To complete all three implicit
measures, participants needed approximately 40 minutes. After the assessment of all measures
we induced in half of the participants an affective focus and in the other half of participants a
cognitive focus. Participants then completed two different choices. First, they hypothetically
chose on paper five food options (i.e. Mars, Snickers, banana or apple). Second, they actually
chose one of the chocolate bars or fruits that they were allowed to take home for consumption.
Finally, participants were fully debriefed and dismissed.
Materials and apparatus
For the implicit measures, we took eight fruit pictures that included four different pictures of
bananas and four different pictures of apples. Chocolate pictures included eight pictures of
two different chocolate brands: four pictures of ªMarsº and four pictures of ªSnickersº. For the
affective priming task and the ID-EAST we selected from the International Affective Picture
System [IAPS; 49] in addition to the food pictures, pictures that differed in valence, but were
equal in arousal. That is, we selected eight negative pictures (i.e. 6200, 9325, 6563, 3185, 2770,
4 / 13
2900.1, 9341, 9584) and eight positive pictures (i.e. 2216, 2209, 8186, 5825, 4610, 5210, 2070,
8162). All pictures had a size of 1024 x 768 pixels.
We conducted the experiment on Asus Eee PC 1215N laptops with Windows 7 as the
system software. Instructions and tasks were presented on external 17-inch Dell monitors.
Participants viewed the screen from a distance of approximately 45 cm and provided responses on a
ªQUERTYº keyboard. The implicit tasks were programmed using Tscope5 software [
Self reported tastiness rating. Participants were asked to indicate on 7-point scales
ranging from 1 (not at all) to 7 (very much) how several adjectives applied to Mars, Snickers,
banana, and apple. Half of the items were positive (i.e., tasty, pleasant, delicious), whereas
the other half of the items was negative (i.e., unsavory, disgusting, unpleasant). To build a
relative measure for the self-reported tastiness rating, we first subtracted separately for
chocolate and fruit the negative items from the positive items. We then computed the difference
between these two scales such that high values indicate a positive taste of chocolate, compared
Self reported habit. On 7-point scales ranging from 1 (very rarely) to 7 (very often)
participants indicated how often they eat each of the different food options (i.e., Mars, Snickers,
banana, apple). To compute a relative habit score, we subtracted the mean of the fruit scales
from the mean of the chocolate scales such that high values indicate higher habit of chocolate,
compared to fruit.
Manikin task. We used the same food pictures as in all other implicit tasks. The manikin
was a simple drawing of a person. Participants were instructed to imagine being the manikin
and to move with the manikin by pressing the up and down keys of the keyboard. Following
the procedure used by De Houwer et al. [
], a trial started with the manikin appearing in
either the upper or the lower half of the screen. After 750 ms, a picture of a food option was
presented in the center of the screen. Then, participants had to press the appropriate key three
times in order to move the manikin up or down the screen. Depending on the initial position
of the figure and the movement direction, the figure stopped either at the edge of the screen or
close to the picture. The screen turned black 100 ms after the third key press. If participants
made an incorrect response, an error message appeared immediately after the first key press
for 500 ms. The time between the onset of the stimulus and the first key press served as the
Participants completed two experimental blocks. In one block, participants were instructed
to move the manikin as quickly and accurately as possible toward chocolate pictures and away
from fruit pictures. In the other block, participants had to move the manikin away from
chocolate pictures and toward fruit pictures. The order of the blocks was counterbalanced across
participants and each block contained 80 trials. Each block was preceded by 16 practice trials.
The manikin appeared equally often in the top half and the bottom half of the screen.
In line with the suggestion from Krieglmeyer et al. [
] we discarded trials with incorrect
responses (7%) and responses with latencies below 150 ms and above 1,500 ms (l.1% of the
correct responses) to prepare data for analysis. In addition, the scores of three participants were
discarded, because of a defective number of trials suggesting that the program crashed or that
the participants restarted the program themselves after a couple of trials. We log-transformed
all latencies. To compute a total manikin score, for each food category (i.e. chocolate and
fruit), we first subtracted mean approach scores from mean avoidance scores. Then, we
subtracted the fruit score from the chocolate score such as high values indicate a relative strong
approach (as compared to avoidance) tendency toward chocolate (as compared to fruit).
5 / 13
Affective priming. We adapted an affective priming task that was already used by Degner
and Wentura [
]. As primes, we used the same fruit and chocolate pictures that we used in
the other implicit tasks. The target pictures consisted of positive and negative valenced
pictures. The task included one training block containing 64 trials in which only the valenced
pictures were presented and a second training block of 32 trials in which the fruit pictures were
included as trials and one experimental block containing 160 trials. Every trial began with the
presentation of a fixation cross for 100 ms. Then, in all blocks except the first training block, a
prime stimulus (i.e., chocolate or fruit picture) was presented for 300 ms. After a blank screen
of 100 ms a positive or negative picture appeared. Participants' task was to quickly and
accurately categorize these target pictures according to their valence by means of a key press
(Q = negative vs. P = positive). Participants received for 500 ms instantaneous accuracy
feedback after each trial. Within the experimental block, each prime was presented five times in
each target condition. To prepare data for analyses, we first deleted all trials with no response
(1.1%) and erroneous trials (5.5%). In addition, the score of one participant, who responded
with the wrong keys, was deleted. Similarly as in the Manikin task, we deleted responses with
latencies below 150 ms and above 1,500 ms (0.2% of the correct responses) and then
log-transformed all latencies. To prepare data for analysis, we subtracted the difference between the
mean latency of fruit/negative trials and fruit/positive trials from the difference between the
mean latency of chocolate/negative trials and chocolate/positive trials. Thus, positive affective
priming scores indicate a relative preference for chocolate over fruit.
We used an adapted version of the picture-based ID-EAST that was introduced by Huijding
and De Jong [
]. Target stimuli in the ID-EAST were the same chocolate and fruit pictures
that we used within the other implicit measures. Positive and negative attribute stimuli were
the same stimuli that we used in the affective priming task. In contrast to the other implicit
tasks, we used two versions of food pictures. In one version the frame of the food pictures was
colored blue and in the other version the frame was colored green. Participants were told that
if the picture was not a food item, the meaning of the picture was important. That is, they were
instructed to press the positive key (Q) for positive pictures, and the negative key (P) for
negative pictures. Participants were also informed that some pictures would be food items. If one of
these food items appeared in a green frame, they had to press the positive key; if it was
presented in a blue frame, they had to press the negative key. Participants were instructed to
respond as fast as possible and as accurately as possible.
The experiment started with a target practice block of 32 trials in which each of the food
pictures was presented once in a green frame, and once in a blue frame. This block was
followed by an attribute practice block of 32 trials in which the attribute pictures were each
presented two times. Finally, participants were presented with four mixed experimental blocks of
32 trials each. In every experimental block, each of the 16 attribute pictures was presented
once. Each of the 8 food pictures was presented once in a blue frame and once in a green
frame. In all blocks, stimuli were presented in random order.
Each trial started with a fixation cross for 500 ms, followed by the stimulus, which stayed on
the screen for 2500 ms. If participants made an incorrect response, a error feedback appeared
on the screen for 500 ms. In order to prepare data for analysis we followed the
recommendations of De Houwer and colleagues [
]. That is, we first discharged trials of incorrect
responses (9.2%). Furthermore, reaction times shorter than 300 ms (0.1%) or longer than 3000
ms (0%) were recoded to 300 and 3000 ms, respectively. Then, we log-transformed all
latencies. To compute an EAST total score, we first calculated an EAST score for each food category
6 / 13
(i.e., chocolate and fruit) by subtracting positive responses from negative responses. Then, we
subtracted the fruit EAST score from the chocolate EAST score such that high values indicate
a relative preference for chocolate over fruit.
Induction of affective or cognitive focus and choice tasks. Before participants chose a
snack, they were randomly assigned to one of two focus conditions. The same focus
manipulation was already used by Scarabis et al. [
]. In the affective focus condition participants
imagined a situation in which they would really enjoy eating a bar of chocolate or fruit and were
asked to think about which of the two snacks would make their mouths water more.
Furthermore, they were asked to close their eyes and to take a moment to imagine the taste of
chocolate or fruit. Participants in the cognitive focus condition were also instructed to think about
their preference for one of the snacks, but in contrast to the affective focus condition, they
were asked to carefully analyze their reasons and to think of at least five arguments concerning
the snacks. The processing time for both conditions was limited to 3 minutes. After
participants completed the manipulation, we administered two different choices.
Hypothetical choice. First, we told participants to imagine that they could take home 5
food items. Participants then indicated on a paper questionnaire how many of each food
option (i.e. Mars, Snickers, apple, banana) they would choose. Participants were allowed to
chose 5 times the same food or to chose different food items. To prepare data for analysis we
subtracted the amount of chosen fruits from the amount of chosen chocolate bars. High values
indicate a relative preference for chocolate as compared to fruit.
Actual choice. Second, the experimenter gave participants a plate with a lid on it. On the
plate the same snacks that we presented in the implicit measures (i.e. Mars, Snickers, apple,
banana) were arranged. When the experimenter lifted the lid, participants were asked to grab
one of the snacks. After participants left the lab, the experimenter recorded their choice
(0 = fruit; 1 = chocolate).
Correlation among measures
To test how the different measures are related to each other, in a first analysis, we investigated
the correlation among all applied explicit and implicit measures. As can be seen in Table 1 the
two explicit measures (i.e. self-reported tastiness rating and habit) highly correlate with each
other, r = .65, p < .001. Also, the manikin task correlates with the EAST, r = .25, p = .019, and
marginally with self-reported habit, r = .20, p = .061. All other measures did not correlate with
Prediction of choice
To test whether the administered measures predict choice, we ran two regression analyses for
each measure. First, we applied multiple regression analyses in order to predict the
hypothetical choice on paper. In line with Aiken and West [
] we first z-standardized all continuous
variables. We entered the dummy-coded focus manipulation (0 = affective, 1 = cognitive),
each measure, and the interaction between the manipulation and each measure as predictors.
Second, we ran logistic regression analysis with the MODPROPE macro [
] in order to
predict actual choice. We entered the different measures (z-standardized) as predictor and the
dummy-coded focus manipulation (0 = affective, 1 = cognitive) as moderator into the
Self-reported tastiness rating. In a first analysis we investigated the predictive validity of
the self-reported tastiness rating on the hypothetical choice. The results of the regression
yielded a significant main effect of tastiness rating, β = .66, t = 7.94, p < .001, indicating that
the tastiness rating predicted choice across both focus manipulations. Neither the main effect
for the focus manipulation, nor the interaction between the focus manipulation and the
tastiness rating were significant, βs < .08, ts < .70, ps > .48.
In a second analysis, we ran logistic regression analysis with actual choice as dependent
variable, the tastiness rating as predictor and the manipulation as moderator. Again, the main
effect for the tastiness rating was significant, b = .86, SE = .40, p = .033, indicating that the
tastiness rating predicted choice across both focus manipulations. Neither the main effect for the
manipulation, nor the interaction between the focus manipulation nor the tastiness rating
were significant, ps > .60.
Self-reported habit. To test the predictive validity of the self-reported habit score, we ran
the same regression analyses. For the hypothetical choice the regression yielded a significant
main effect for the habit score (β = .57, t = 6.30, p < .001) indicating that the habit rating
predicted choice across both focus manipulations. The main effect for the manipulation and the
interaction between habit and manipulation was not significant, βs < .07, ts < .63, ps > .53.
When predicting actual choice, the logistic regression analysis yielded a marginally
significant main effect for the habit score, b = .59, SE = .31, p = .055. Neither the main effect of the
manipulation nor the interaction between habit and the manipulation was significant, ps >
Manikin task. When analyzing the manikin task, the results indicate that the manikin
task was neither a significant predictor of hypothetical choice (β = .16, t = 1.40, p = .164), nor
for actual choice (b = .46, SE = .33 p = .17). For both dependent variables, neither the focus
manipulation, nor the interaction between the manikin task and the focus manipulation were
significant, ps > .13.
Affective priming. The same analyses for the affective priming as predictor yielded no
significant results at all, ps > .16. The only trend that could be detected was a marginal
significant main effect for the affective priming task for the prediction of hypothetical choice, β = .18,
t = 1.70 p = .094.
ID-EAST. For the ID-EAST as predictor, the regressions did not yield any significant
effects nor trends, ps > .39.
Incremental predictive validity. To test which of the measures contribute to the
prediction of choice independently from each other, we, first, computed a step-wise multiple
regression analysis in order to predict hypothetical choice. As predictors, we entered the
selfreported tastiness rating, self-reported habit, the manikin score, the affective priming score,
the ID-EAST, the dummy-coded focus manipulation (0 = affective, 1 = cognitive), as well as all
interactions between the focus manipulation and the different measures. The regression
8 / 13
suggests a model with self-reported tastiness (β = .67, t = 8.54 p < .001) and the affective
priming score (β = .17, t = 2.20 p = .031) as predictors. Adding any of the other predictors does not
increase the predictive validity of the hypothetical choice.
For actual choice, we ran a logistic regression with the same predictors. The results indicate
that the best predictor is self-reported habit, b = .87, SE = .28 p = .002. Adding any other
predictor does not improve the prediction.
Reliability of implicit measures
In a further analysis, we tested the split-half reliability of the implicit measures. First, we
computed the manikin score, the affective priming score and the ID-EAST score separately for
even trials and for odd trials. Second, we computed the Spearman-Brown coefficient in order
to test the reliability of the implicit measures. The results yielded ρ = .90 for the manikin task
indicating high reliability. However, the Spearmen-Brown coefficient for the affective priming
task (ρ = -.11) and the ID-EAST (ρ = -.02) were negative indicating non-reliability.
Past research suggests that implicit measures are good predictors of behavior. Thereby, it has
been demonstrated that implicit measures are especially good predictors of impulsive and
affectively driven behavior [17,32±37]. However, to the best of our knowledge, no study
actually tested which implicit measure best predicts behavior. Therefore, in the present study we
applied three implicit measures (i.e., manikin task, affective priming, ID-EAST) and tested
their predictive validity of either affectively or cognitively driven consumption behavior. To
cross-validate the predictive validity of the implicit measures we assessed in addition two
explicit measures (i.e. self-reported habit and tastiness).
Correlational analyses among the implicit measures show that the manikin task correlates
with the ID-EAST, but the affective priming task does not correlate with any of the implicit
measures. This indicates that the manikin task and the ID-EAST share variance independent
from the affective priming task. This might be due to similarities and differences between the
tasks' characteristics. In the manikin task as well as in the ID-EAST, participants respond to
the target stimuli (i.e. chocolate and fruit) by the selection of two key-presses that are related to
valence. However, in the affective priming task, participants respond not at all to the target
category. Here, the target stimuli are the primes that precede either positive or negative stimuli
and participants respond to the positive and negative stimuli, but not to the target stimuli itself.
Thus, it might well be that this difference in the response accounts for the lack of correlation
among the measures.
When testing the association of the implicit measures with consumption behavior, we
found that the manikin task marginally correlates with self-reported habit. All other implicit
measures did not correlate with any self-reported measure. When testing the predictive
validity of all measures, the results indicate that the two explicit measures (i.e. self-reported tastiness
and habit), but not the implicit measures (i.e., manikin task, affective priming, ID-EAST),
predict consumer choice across both foci very well. Further regression analyses testing the
incremental validity of the different tasks indicated that the explicit measures were the best
predictors of choice. When adding the implicit measures into the regression equations only
the affective priming task could add some variance in explaining consumer choice. This overall
pattern suggest that explicit measures are better predictors of consumer choice than implicit
An interesting question is why the implicit measures showed rather low predictive validity.
First, a possible reason may lay in the reliability of the tasks. Psychometric theory has shown
9 / 13
that unreliable tasks are less likely to correlate with other tasks (cf., [
]). Both, the ID-EAST
and the affective priming task were unreliable and may, thus, suffer from lack in predictive
validity. In contrast, the manikin task showed high reliability, but did not show high predictive
validity for consumer choice. A plausible reason may lay in our chosen consumer products
(fruit vs. chocolate), which do not very much differ in valence. While past research has shown
that approach-avoidance tasks are sensitive for stimuli differing in valence [13±16,18,20], our
research indicates that such tasks might be less sensitive for stimuli that differ to a smaller
degree in valence. Second, another reason for the bad predictive validity might be lack of
power. In fact, it could well be that with more participants in our sample some correlations
would have become significant. However, even if this would be the case, it would indicate that
the predictive validity of the implicit measures is rather low and not as high as the predictive
validity of the explicit measures. Third, we have to mention possible demand effects that could
explain the good predictive validity of explicit measures in dispense of the implicit measures.
That is, during choice, participants may still have remembered their explicit ratings and then
went along with their previous indication. Unfortunately, we cannot rule out this alternative
explanation although we tried to reduce potential demand effects by assessing the explicit
measures as the first measures in the hope that the impact of the assessment of choice would be
minimized by such a procedure. However, we cannot rule out the potential influence it may
had. Fourth, it could be that the large number of trials across all the implicit measures induced
noise into the data, because participants started habituating to the stimuli. In order to deal
with this issue we recommend for future research between-subject designs in which only one
implicit measure per between-factor is applied.
Another limitation of our experiment concerns the focus manipulation. While other studies
found that by applying the same manipulation implicit measures better predicted choice in the
affective focus condition than in the cognitive focus condition [
], we were not able to
replicate such a finding. There might be a couple of reasons why this is the case. First, past
research used different tasks in order to predict behavior than we did. Genschow and
] found that another approach task, but not a similar affective priming task,
predicted consumer choice in the affective, but not in the cognitive focus condition. Scarabis et al.
] as well as Smith and Nosek [
] used an IAT and found increased predictive validity in
the affective, as compared to the cognitive condition. It might well be that our applied tasks are
not as much dependent on the mode of choice as the tasks that have been assessed by other
researchers. However, future research may test this interpretation in more detail by applying
other manipulations and by comparing other tasks with each other. Second, it might be that
due to the fact that participants saw the stimuli within all tasks many times, they started
thinking about the stimuli already before the manipulation, which could have diminished the
effectiveness of the affective focus manipulation. In addition, for both manipulations we gave
participants 3 minutes time. Potentially, 3 minutes could have been too long for the affective
focus manipulation, so participants started elaborating on the food stimuli, which in turn
might have diminished the affective manipulation even more strongly. Third, the affective
focus manipulation might have manipulated different forms of affect. That is, research has
shown that affect is related to fast and intuitive decisions (e.g., [
]), but also to emotions
(e.g., ). These two different forms of affect might not influence choices in the same
direction and might, thus, have, diminished our manipulation. Future research should consider
disentangling different forms of affect in order to establish a more precise manipulation. Fourth,
it might be that our manipulation actually did not work. That is, the manipulation failed in
inducing an affective and cognitive focus. Although we followed the procedure of other
research, we cannot rule out this alternative explanation. One may argue that a manipulation
check would have allowed gaining knowledge about the effectiveness of the manipulation.
10 / 13
However, to the best of our knowledge, such a manipulation check does not exist in the
literature and has, thus, never been previously applied.
Conceptualization: Oliver Genschow, Jelle Demanet, Lea Hersche, Marcel Brass.
Data curation: Oliver Genschow, Lea Hersche.
Formal analysis: Oliver Genschow, Jelle Demanet.
Funding acquisition: Oliver Genschow.
Investigation: Lea Hersche.
Methodology: Oliver Genschow, Lea Hersche.
Project administration: Oliver Genschow.
Resources: Oliver Genschow.
Software: Jelle Demanet.
Supervision: Oliver Genschow, Marcel Brass.
Writing ± original draft: Oliver Genschow, Jelle Demanet, Lea Hersche.
Writing ± review & editing: Oliver Genschow, Jelle Demanet.
11 / 13
12 / 13
1. Greenwald AG , Banaji MR ( 1995 ) Implicit social cognition: attitudes, self-esteem, and stereotypes . Psychological review 102: 4±27. PMID: 7878162
Wilson TD , Lindsey S , Schooler TY ( 2000 ) A model of dual attitudes . Psychological review 107: 101± 126. PMID: 10687404
3. De Houwer J ( 2006 ) What are implicit measures and why are we using them. The handbook of implicit cognition and addiction: 11 ± 28 .
4. Fazio RH , Jackson JR , Dunton BC , Williams CJ ( 1995 ) Variability in automatic activation as an unobtrusive measure of racial attitudes: a bona fide pipeline ? Journal of personality and social psychology 69 : 1013± 1027 . PMID: 8531054
5. Fazio RH , Sanbonmatsu DM , Powell MC , Kardes FR ( 1986 ) On the automatic activation of attitudes . Journal of Personality and Social Psychology 50 : 229 ± 238 . PMID: 3701576
6. Greenwald AG , McGhee DE , Schwartz JL ( 1998 ) Measuring individual differences in implicit cognition: the implicit association test . Journal of personality and social psychology 74 : 1464± 1480 . PMID: 9654756
7. De Houwer J , De Bruycker E ( 2007 ) The identification-EAST as a valid measure of implicit attitudes toward alcohol-related stimuli . Journal of Behavior Therapy and Experimental Psychiatry 38 : 133 ± 143 . https://doi.org/10.1016/j.jbtep. 2006 . 10 .004 PMID: 17109815
8. De Houwer J , Teige-Mocigemba S , Spruyt A , Moors A ( 2009 ) Implicit measures: A normative analysis and review . Psychological bulletin 135 : 347± 368 . https://doi.org/10.1037/a0014211 PMID: 19379018
9. De Houwer J , De Bruycker E ( 2007 ) The implicit association test outperforms the extrinsic affective Simon task as an implicit measure of inter-individual differences in attitudes . British Journal of Social Psychology 46 : 401 ± 421 . https://doi.org/10.1348/014466606X130346 PMID: 17565789
10. Schmukle SC , Egloff B ( 2006 ) Assessing anxiety with extrinsic Simon tasks . Experimental Psychology 53 : 149 ± 160 . https://doi.org/10.1027/ 1618 - 3169 . 53 .2.149 PMID: 16909940
11. Teige S , Schnabel K , Banse R , Asendorpf JB ( 2004 ) Assessment of multiple implicit self-concept dimensions using the Extrinsic Affective Simon Task (EAST) . European Journal of Personality 18 : 495 ± 520 .
12. De Houwer J ( 2003 ) The extrinsic affective Simon task . Experimental psychology 50 : 77± 85 . https:// doi.org/10.1026//1618- 3169 . 50 .2.77 PMID: 12693192
13. Bamford S , Ward R ( 2008 ) Predispositions to approach and avoid are contextually sensitive and goal dependent . Emotion 8 : 174 ± 183 . https://doi.org/10.1037/ 1528 - 3542 .8.2.174 PMID: 18410191
14. Brendl CM , Markman AB , Messner C ( 2005 ) Indirectly measuring evaluations of several attitude objects in relation to a neutral reference point . Journal of Experimental Social Psychology 41 : 346 ± 368 .
15. Chen M , Bargh JA ( 1999 ) Consequences of automatic evaluation: Immediate behavioral predispositions to approach or avoid the stimulus . Personality and Social Psychology Bulletin 25 : 215 ± 224 .
16. De Houwer J , Crombez G , Baeyens F , Hermans D ( 2001 ) On the generality of the affective Simon effect . Cognition & Emotion 15 : 189 ± 206 .
17. Genschow O , Florack A , Chib VS , Shimojo S , Scarabis M , WaÈnke M ( 2013 ) Reaching for the (product) stars: Measuring recognition and approach speed to get insights into consumer choice . Basic and Applied Social Psychology 35 : 298 ± 315 .
18. van Dantzig S , Pecher D , Zwaan RA ( 2008 ) Approach and avoidance as action effects . The Quarterly Journal of Experimental Psychology 61 : 1298 ± 1306 . https://doi.org/10.1080/17470210802027987 PMID: 19086189
19. Elliot AJ ( 2008 ) Handbook of approach and avoidance motivation . New York: Psychology Press.
20. Genschow O , Florack A , WaÈnke M ( 2014 ) Recognition and approach responses toward threatening objects . Social Psychology: 904 ± 907 .
21. Eder AB , Rothermund K ( 2008 ) When do motor behaviors (mis) match affective stimuli? An evaluative coding view of approach and avoidance reactions . Journal of Experimental Psychology: General 137 : 262 ± 281 .
22. Morange F , Bloch H ( 1996 ) Lateralization of the approach movement and the prehension movement in infants from 4 to 7 months . Early Development and Parenting 5 : 81 ± 92 .
23. Seibt B , Neumann R , Nussinson R , Strack F ( 2008 ) Movement direction or change in distance? Selfand object-related approach-avoidance motions . Journal of Experimental Social Psychology 44 : 713 ± 720 .
24. von Hofsten C , RoÈnnqvist L ( 1988 ) Preparation for grasping an object: A developmental study . Journal of Experimental Psychology: Human Perception and Performance 14 : 610 ± 621 . PMID: 2974872
25. Krieglmeyer R , Deutsch R ( 2010 ) Comparing measures of approach-avoidance behaviour: The manikin task vs. two versions of the joystick task . Cognition and Emotion 24 : 810 ± 828 .
26. Krieglmeyer R , Deutsch R , De Houwer J , De Raedt R ( 2010 ) Being moved valence activates approachavoidance behavior independently of evaluation and approach-avoidance intentions . Psychological Science 21 : 607 ± 613 . https://doi.org/10.1177/0956797610365131 PMID: 20424109
27. Millar MG , Tesser A ( 1986 ) Effects of affective and cognitive focus on the attitude±behavior relation . Journal of Personality and Social Psychology 51 : 270 ± 276 .
28. Millar MG , Tesser A ( 1992 ) The role of beliefs and feelings in guiding behavior: The mismatch model . The construction of social judgments: 277 ± 300 .
29. Zanna MP , Rempel JK ( 1988 ) Attitudes: A new look at an old concept . In: Bar-Tal D , Kruglanski AW , editors. The social psychology of knowledge . Cambridge, England: Cambridge University Press. pp. 315 ± 334 .
30. Asendorpf JB , Banse R , MuÈcke D ( 2002 ) Double dissociation between implicit and explicit personality self-concept: the case of shy behavior . Journal of personality and social psychology 83 : 380± 393 . PMID: 12150235
31. Dovidio JF , Kawakami K , Johnson C , Johnson B , Howard A ( 1997 ) On the nature of prejudice: Automatic and controlled processes . Journal of experimental social psychology 33 : 510± 540 .
32. Florack A , Friese M , Scarabis M ( 2010 ) Regulatory focus and reliance on implicit preferences in consumption contexts . Journal of Consumer Psychology 20 : 193 ± 204 .
33. Friese M , Hofmann W , WaÈnke M ( 2008 ) When impulses take over: Moderated predictive validity of explicit and implicit attitude measures in predicting food choice and consumption behaviour . British Journal of Social Psychology 47 : 397 ± 419 . https://doi.org/10.1348/014466607X241540 PMID: 17880753
34. Friese M , Hofmann W , WaÈnke M ( 2009 ) The impulsive consumer: Predicting consumer behavior with implicit reaction time measures . In: WaÈnke M, editor. Social psychology of consumer behavior . New York: Psychology Press. pp. 335 ± 364 .
35. Friese M , WaÈnke M , Plessner H ( 2006 ) Implicit consumer preferences and their influence on product choice . Psychology & Marketing 23 : 727 ± 740 .
36. Hofmann W , Gschwendner T , Nosek BA , Schmitt M ( 2005 ) What moderates implicitÐexplicit consistency? European Review of Social Psychology 16 : 335 ± 390 .
37. Scarabis M , Florack A , Gosejohann S ( 2006 ) When consumers follow their feelings: The impact of affective or cognitive focus on the basis of consumers' choice . Psychology & Marketing 23 : 1015 ± 1034 .
38. Smith CT , Nosek BA ( 2011 ) Affective focus increases the concordance between implicit and explicit attitudes . Social Psychology 42 : 300 ± 313 .
39. Bar-Anan Y , Nosek BA ( 2014 ) A comparative investigation of seven indirect attitude measures . Behavior research methods 46 : 668± 688 . https://doi.org/10.3758/s13428-013 -0410-6 PMID: 24234338
40. Sriram N , Greenwald AG ( 2009 ) The brief implicit association test . Experimental psychology 56 : 283± 294 . https://doi.org/10.1027/ 1618 - 3169 . 56 .4.283 PMID: 19439401
41. Karpinski A , Steinman RB ( 2006 ) The single category implicit association test as a measure of implicit social cognition . Journal of personality and social psychology 91 : 16. https://doi.org/10.1037/ 0022 - 3514 . 91 .1.16 PMID: 16834477
42. Nosek BA , Banaji MR ( 2001 ) The go/no-go association task . Social cognition 19 : 625± 666 .
43. Payne BK , Cheng CM , Govorun O , Stewart BD ( 2005 ) An inkblot for attitudes: affect misattribution as implicit measurement . Journal of personality and social psychology 89 : 277± 293 . https://doi.org/10. 1037/ 0022 - 3514 . 89 .3.277 PMID: 16248714
44. Bar-Anan Y , Nosek BA , Vianello M ( 2009 ) The sorting paired features task: A measure of association strengths . Experimental psychology 56 : 329± 343 . https://doi.org/10.1027/ 1618 - 3169 . 56 .5.329 PMID: 19447749
45. Brunel FF , Tietje BC , Greenwald AG ( 2004 ) Is the implicit association test a valid and valuable measure of implicit consumer social cognition ? Journal of Consumer Psychology 14 : 385 ± 404 .
46. Greenwald AG , Poehlman TA , Uhlmann EL , Banaji MR ( 2009 ) Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity . Journal of personality and social psychology 97 : 17± 41 . https://doi.org/10.1037/a0015575 PMID: 19586237
47. Maison D , Greenwald AG , Bruin RH ( 2004 ) Predictive validity of the Implicit Association Test in studies of brands, consumer attitudes, and behavior . Journal of consumer psychology 14 : 405± 415 .
48. Degner J , Wentura D ( 2010 ) Automatic prejudice in childhood and early adolescence . Journal of personality and social psychology 98 : 356± 374 . https://doi.org/10.1037/a0017993 PMID: 20175618
49. Lang PJ , Bradley MM , Cuthbert BN ( 2008 ) International affective picture system (IAPS): Affective ratings of pictures and instruction manual . Technical report A-8.
50. Stevens M , Lammertyn J , Verbruggen F , Vandierendonck A ( 2006 ) Tscope: AC library for programming cognitive experiments on the MS Windows platform . Behavior Research Methods 38 : 280 ± 286 . PMID: 16956104
51. Huijding J , De Jong P ( 2005 ) A Modified Extrinsic Affective Simon Task (EAST) to assess the affective value of pictorial stimuli: No influence of age and educational level . Psychologica Belgica 45 : 241 ± 255 .
52. Aiken LS , West SG ( 1996 ) Multiple Regression testing and interpreting interactions London: Sage Publications .
53. Hayes AF , Matthes J ( 2009 ) Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations . Behavior research methods 41 : 924± 936 . https://doi.org/ 10.3758/BRM.41.3.924 PMID: 19587209
54. Crocker L , Algina J ( 1986 ) Introduction to classical and modern test theory . Fort Worth , TX: Holt, Rinehart, & Winston .
55. Cronbach LJ ( 1990 ) Essentials of psychological testing ( 5th ed .). New York: Harper & Row.
56. Slovic P , Peters E , Finucane ML , MacGregor DG ( 2005 ) Affect, risk, and decision making . Health psychology 24 : 35 ± 40 .
57. Topolinski S , Strack F ( 2009 ) The analysis of intuition: Processing fluency and affect in judgements of semantic coherence . Cognition and Emotion 23 : 1465 ± 1503 .
58. Schulz R ( 1985 ) Emotion and affect . In: Birren JE , Schaie KW , editors. Handbook of the psychology of aging , 2nd ed. New York, NY: Van Nostrand Reinhold Co. pp. 931 pp.