Predictive models of implicit and explicit attitudes
Predictive models of implicit and
explicit attitudes
Perugini, M. (2005). Predictive models of implicit and explicit attitudes. The British Journal of Social Psychology, 44(1), 29-45. Retrieved from http://search.proquest.com/docview/219197582?accountid=17242
Perugini, M. (2005). Predictive models of implicit and explicit attitudes. British Journal of Social Psychology, 44(1), 29-45.
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Abstrak
Explicit attitudes
have long been assumed to be central factors influencing behaviour. A recent
stream of studies has shown that implicit attitudes, typically measured with
the Implicit Association Test (IAT), can also predict a significant range of
behaviours. This contribution is focused on testing different predictive models
of implicit and explicit attitudes. In particular, three main models can be
derived from the literature: (a) additive (the two types of attitudes explain
different portion of variance in the criterion), (b) double dissociation
(implicit attitudes predict spontaneous whereas explicit attitudes predict
deliberative behaviour), and (c) multiplicative (implicit and explicit
attitudes interact in influencing behaviour). This paper reports two studies
testing these models. The first study (N = 48) is about smoking behaviour,
whereas the second study (N = 109) is about preferences for snacks versus
fruit. In the first study, the multiplicative model is supported, whereas the
double dissociation model is supported in the second study. The results are
discussed in light of the importance of focusing on different patterns of
prediction when investigating the directive influence of implicit and explicit
attitudes on behaviours.
Explicit attitudes
have long been assumed to be central factors influencing behaviour. A recent
stream of studies has shown that implicit attitudes, typically measured with
the Implicit Association Test (IAT), can also predict a significant range of
behaviours. This contribution is focused on testing different predictive models
of implicit and explicit attitudes. In particular, three main models can be
derived from the literature: (a) additive (the two types of attitudes explain
different portion of variance in the criterion), (b) double dissociation
(implicit attitudes predict spontaneous whereas explicit attitudes predict
deliberative behaviour), and (c) multiplicative (implicit and explicit
attitudes interact in influencing behaviour). This paper reports two studies
testing these models. The first study (N = 48) is about smoking behaviour,
whereas the second study (N = 109) is about preferences for snacks versus
fruit. In the first study, the multiplicative model is supported, whereas the
double dissociation model is supported in the second study. The results are
discussed in light of the importance of focusing on different patterns of
prediction when investigating the directive influence of implicit and explicit
attitudes on behaviours.
The automatic, effortless,
and implicit aspects of human information processing are currently at the
centre of attention in social psychology and in attitude research, in
particular. Several recent studies have shown that implicit attitudes can be
activated automatically and guide behaviour directly outside of conscious
awareness (Bargh, Chen, & Burrows, 1996; Chen & Bargh, 1999; Dovidio,
Kawakami, Johnson, Johnson, & Howard, 1997; Fazio & Dunton, 1997;
Greenwald & Banaji, 1995). A number of paradigms to measure implicit attitudes
have been developed in recent years, such as the affective priming (Fazio,
Sanbonmatsu, Powell, & Kardes, 1986), the Go/no go task (CiNAT, Nosek &
Banaji, 2001), the Extrinsic affective Simon task (EAST, De Houwer, 2003), and
the masked affective priming (Frings & Wentura, 2003). Unfortunately, the
reliability of these measurement methods is either unknown (EAST, masked
affective priming), or is very low based on the handful of studies where it has
been tested (e.g. affective priming, α = .26, Banse, 2001; GNAT, split-half
reliability = .20, Nosek & Banaji, 2001). The most reliable procedure to
measure implicit attitudes has been the Implicit Association Test (IAT;
Greenwald, McGhee, & Schwartz, 1998). Several studies have shown good IAT
internal consistency values (usually α = .80), and reasonable test-retest
values (usually r = .60). The IAT is also the most widely used procedure, with
the greatest evidence of construct and predictive validity.
Briefly, the IAT is a
computerized method for indirectly measuring the strength of the association
between a target concept and a valence attribute via a double-categorization
task. It relies on the assumption that, if a target concept and an attribute
dimension are highly associated (congruent), the task will be easier, and,
therefore, quicker when they share the same response key than when they require
a different response key. The IAT needs one target category (e.g. flowers), one
contrast category (e.g. insects), one target attribute (e.g. positive), and one
contrast attribute (e.g. negative), each represented by a series of stimuli. In
the critical combined task, stimuli from all four classes are presented in
random sequence, and participants are asked to assign them correctly to one of
the two combined category-attribute pairs (e.g. left key for flowers [pleasant]
and right key for insects [unpleasant]). This combined task is successively
switched such that the pair category-attribute is different (e.g. left key for
insects [pleasant] and right key for flowers [unpleasant]). An IAT score is
computed as a function of the difference of the mean response times between the
two versions of the combined task. Thus, for instance, respondents will
generally be quicker to associate flowers with pleasant, compared to flowers
with unpleasant (or, conversely, will be slower to associate insects with
pleasant, compared to insect with unpleasant), therefore, revealing a positive
implicit attitude towards flowers relative to insects (for more details about
the procedure, see Greenwald et al., 1998; Greenwald & Nosek, 2001). Since
the original paper by Greenwald and colleagues, there has been a profusion of
studies on implicit attitudes using the IAT on a wide range of topics such as
prejudice (Dasgupta, McGhee, Greenwald, & Banaji,2000), self-esteem (Greenwald & Farnham, 2000), cognitive balance (Greenwald et al., 2002),
smoking (Swanson, Rudman, & Greenwald, 2001) consumers' choice of drinking
products (Maison, Greenwald, & Bruin, 2001), alcohol (Wiers, van Woerden,
Smulders, & de Jong, 2002), high-fat food (Roefs & Janssen, 2002),
homesexuality (Banse, Seise, & Zerbes, 2001), and condom use (Marsh,
Johnson, & Scott-Sheldon, 2001). In general, there is accumulated empirical
evidence that the IAT can predict specific behaviours, although in some studies
it failed to do so (e.g. Karpinski & Hilton, 2001).
Different accounts
have been put forward as far as the cognitive processes underlying the
functioning of the IAT are concerned. Although the IAT is clearly related to
associative knowledge structures (Greenwald et al., 2002), it appears unlikely
that they alone make up the processes underlying the IAT. Alternative models of
the IAT functioning have been articulated in terms of a random walk process
(Brendl, Markmann, & Messner, 2001), a figure-ground asymmetry (Rothermund
& Wentura, 2001), a task-switching account (Mierke & Klauer, 2001), and
a stimulus-response compatibility (De Houwer, 2001). Each of these models has
supporting evidence, and it appears premature at this stage to draw conclusions
about which of them offers the most adequate explanation of the cognitive
processes underlying the IAT.
On the other hand,
there is a long-standing tradition within attitude research of approaches that
focus on the explicit, deliberative, and volitional aspects of decision making.
In general, in these models, explicit attitudes are one of the determinants of
behaviour and intentions are assumed to mediate the impact of attitudes and of
the other predictors on behaviour. For instance, Ajzen's theory of planned
behaviour (Ajzen, 1991, 2001) assumes that, alongside attitudes, subjective
norms (i.e. the perceived social pressure to perform a given behaviour) and
perceived behavioural control (i.e. the perceived ease or difficulty of
performing the given behaviour) are influencing one's intention, which, in
turn, is the proximal cause of behaviour. Additionally, the model of
goal-directed behaviour (Perugini & Bagozzi, 2001; Perugini & Conner, 2000) assumes that
anticipated emotions and past behaviour influence desire, which, in turn,
influences intention and mediates the influence of previous constructs (i.e.
attitudes, subjective norms, perceived behavioural control, anticipated
emotions) on intention. Recent reviews (Armitage & Conner, 2001; Perugini & Bagozzi, 2004) support these models (explaining between
39% and 68% of the variance in intentions and between 27% and 30% of the
variance in behaviour). Thus, models of decision making within the deliberative
approach have shown robust predictive power for a range of behaviours.
Theoretical and predictive models of implicit
and explicit attitudes
The two traditions of
implicit and explicit attitudes have developed largely in isolation, and few
attempts have been made to develop comprehensive frameworks. Based on the
existing literature and empirical evidence, we can distinguish between three
main theoretical frameworks that are loosely associated with three alternative
predictive models.1
One of the most recent
and influential theoretical frameworks is the proposal by Wilson, Lindsey, and
Schooler (2000) of a model of dual
attitudes, defined as different evaluations, one implicit and one explicit, of
the same attitude object. In fact, Wilson and colleagues explicitly allow for
the coexistence in memory of independent implicit and explicit attitudes toward
the same attitude object. They distinguish between four main cases (repression,
independent systems, motivated overriding, and automatic overriding),
corresponding to the combination of awareness of the implicit attitude, once
activated, and the amount of motivation and cognitive effort needed for the
explicit attitude to override the implicit one. Given that implicit and
explicit attitudes can coexist in memory, one important question becomes how
they direct behaviours. Implicit attitudes are assumed to influence spontaneous
or implicit responses; that is, responses that are uncontrollable or with no
attempts to control them, whereas explicit attitudes are expected to influence
deliberative or explicit responses; that is, responses that are under conscious
control or are perceived as expressive of the relevant explicit attitude. This
theoretical framework would, therefore, predict a double-dissociation pattern,
which, indeed, has been confirmed in a few studies, although typically tested
in a weak form (e.g. Dovidio, Kawakami, & Gartner, 2002; Fazio, Jackson,
Dunton, & Williams, 1995; McConnell & Leibold, 2000; Spalding & Hardin, 1999).
The evidence for the
existence of two independent systems is, however, inconclusive. Usually, the
two systems are inferred, rather than directly tested (cf. Fazio & Olson,
2003). From this perspective, implicit and explicit attitudes can be best
understood as implicit or explicit measures of the same attitude. Their typically
low correlation (usually between 0.20 and 0.30) should be taken not as evidence
of the existence of two independent systems, but of the discriminant validity
between two different types of measures, one relying on self-report and on
explicit evaluations; the other relying on reaction times, which are assumed to
indicate the associative strength between target and evaluation in a task
without explicit evaluation. In this line of thinking, the question sometimes
becomes what is the 'real' attitude (cf. Fazio et al., 1995). If we follow this
assumption of a single system with a single attitude representation and two
different measures, the most direct predictive model is an additive pattern,
whereby both explicit and implicit attitudes can give a unique contribution to
the prediction of behaviours. Of course, the specific predictive power may
change from behaviour to behaviour, and in some cases, may be such that only
one of the attitudes has predictive power. However, the general case should be
that both measures of the same attitude provide a distinctive prediction of
behaviour.
A careful reading of
the theoretical framework proposed by Wilson et al. (2000) reveals a subtle bias. Practically all
theoretical definitions, conceptual examples, and evidence collected in support
of the theoretical framework are focused on cases where a negative implicit
attitude conflicts with a positive explicit attitude. For instance, the four
main cases previously described are all organized around the notion of
potential conflict between implicit and explicit systems. So far, little
theoretical work has explicitly focused on what happens when the two attitudes
are congruent and not conflicting. However, relevant elaborations can be found
in recent developments within the study of the self and in a recent model of
social behaviour. The concepts of defensive and secure self-esteem have been
defined in terms of combinations between implicit and explicit self-esteem.
Specifically, defensive self-esteem is denned as an incongruence between high
explicit self-esteem and low implicit self-esteem, whereas secure self-esteem
is defined as the congruence between high explicit and high implicit
self-esteem (Dosson, Brown, Zeigler-Hill, & Swann, 2003; Jordan, Spencer,
Zanna, Hoshino-Browne, & Correll, 2003). Participants with secure
self-esteem have been found to be less narcissistic, to show less in-group
bias, and to engage less in dissonance reduction compared to participants with
defensive self-esteem (Jordan et al., 2003). A more general theoretical
framework of social behaviour has been developed recently by Strack and Deutsch
(2004). The author's framework relies on the interaction between a reflective
system, characterized by propositional representations and explicit decision
making processes, and an impulsive system, conceived as a simple associative
network, whose processes are usually working automatically and without a
specific personal conscious awareness. Although behaviour is elicited through
different processes, there is a common executive pathway to overt behaviour. In
other words, the two systems use different operations, but they activate the
same behavioural schemata. A crucial corollary of this theoretical account is
that when both systems contribute synergistically to the activation of the same
behavioural schemata, behaviour is facilitated, the cognitive capacity required
to control the execution decreases, and behaviour may be accompanied by a
positive hedonic feeling of fluency (Winkielman & Cacioppo, 2001). Thus, a
considerable proportion of behaviour in human life falls somewhere in between
the two extreme forms of totally uncontrolled and totally controlled and
involves a mix of both automatic and controlled components, with the latter
more likely to act as a hierarchical self-regulatory system (Vancouver &
Scherbaum, 2000) or as an overriding
mechanism (Baumeister & Sommer, 1997). We can hypothesize, therefore, that
when implicit and explicit attitudes are congruent, their joint directive
function on behaviour is strongest. The corresponding predictive model would
call for an interactive pattern.
To sum up, it is
possible to articulate three predictive models that reflect the three different
theoretical frameworks about explicit and implicit attitudes and their relation
with behaviours. The three models correspond to the situation when implicit and
explicit attitudes provide unique predictive information about behaviour
(additive pattern), implicit attitude predicts spontaneous behaviour and
explicit attitudes predict deliberative behaviour and not vice versa (double
dissociation pattern), and implicit and explicit attitude interact
synergistically to predict behaviour (interactive pattern).
Aim of this contribution
The main aim of this
contribution is to test these three predictive models in two studies.
Particular attention will be paid to the interactive pattern because it is the
most novel and least tested predictive account of the effects of implicit and
explicit attitudes on behaviour. Two studies on two different health related
domains, smoking behaviour and eating snacks versus fruit, will be presented.
More specifically, the first study will compare an additive and an interactive
pattern, and it will use a know-group design by comparing smokers with
non-smokers. The second study will compare all three patterns simultaneously
about their prediction of both a spontaneous and a deliberative behaviour
concerning the relative preference of snacks over fruit.
STUDY 1
The first study
concerns smoking behaviour. The role of implicit attitudes in predicting
smoking behaviour has been investigated by Swanson et al. (2001) in three
experiments. The results of the experiments showed mixed evidence for the
predictive validity of the IAT. In the first two experiments, the IAT effect
was not significantly different for smokers compared to non-smokers. In the
third experiment, the difference was significant, with smokers showing
relatively more positive implicit attitudes than non-smokers. However, both
groups had a clear negative implicit evaluation of smoking, as indicated by the
average reaction times. On the other hand, explicit attitudes were clearly and
consistently more positive for smokers than for non-smokers, although, again,
negative for both groups in absolute values. The authors played down the
inconsistent pattern of results for implicit attitudes, and preferred to
explain their findings in terms of cognitive dissonance between implicit and
explicit attitudes due to smoking being a stigmatized behaviour. The presence
of additive or multiplicative effects of implicit and explicit attitude was not
tested.
Method
Participants
The sample consisted
of 50 participants recruited on campus, 37 female and 13 male, with an average
age of 22.7 (SD = 4.1). Two participants were discarded for different reasons,
leaving a total of 48, of whom 25 were smokers and 23 non-smokers. One
participant was discarded because of the excessive number of very short
latencies (more than 25% of the trials below 400 ms), and one because of the
excessive number of very long latencies (more than 25% of the trials above
3,000ms).
Materials and procedure
The experimental task
was closely modelled after Swanson et al. (2001; Study 1). It consisted of a
questionnaire and a computerized task (IAT). The questionnaire contained
questions concerning both smoking and exercise, and were identical except that
they were phrased for smoking and exercise, respectively. The items were chosen
to measure explicit attitudes with 11 bipolar scales (bad - good, harmful - harmless,
foolish - wise, unpleasant - pleasant, boring - exciting, not enjoyable -
enjoyable, sexy - not sexy, healthy - unhealthy, sociable - unsociable,
glamorous - ugly, calming - stressful) on a 7-step answer scale ranging from -
3 to +3. The pairs of adjectives reflected those originally used by Swanson et
al. (2001).
The computerized
categorization task is the Implicit Association Test (IAT), and it is described
in detail in several articles (e.g. Greenwald et al., 1998; Greenwald &
Nosek, 2001). The task was programmed using Psyscope 1.2.5 for Macintosh. The
target concept was smoking and its contrast was exercise, whereas the attribute
categories were pleasant and unpleasant. The choice of exercise as a contrast
category mirrored one of the contrast categories used by Swanson et al. (2001)
and it is justified by their finding that the IAT results did not differ as a
function of using a different contrast category (i.e. sweets). Participants
were required to assign stimuli as fast as possible to their appropriate
categories by pressing one of two response keys. Each task followed the
standard 5-step IAT sequence (cf. Greenwald et al., 1998). Steps 1, 2 and 4 are
practice phases, whereas the critical steps are the third and the fifth. In the
third step, participants assigned stimuli to the four different categories
combined in pairs. For instance, participants were required to press the left
key in response to stimuli belonging to either the smoking or the pleasant
category, and the right key in response to stimuli belonging to the exercise or
the unpleasant category. In the fifth step, the task was the same but with the
reversed response for the target stimuli; namely, left for smoking and
unpleasant, and right for exercise and pleasant. For each category, six stimuli
were used (see Appendix). All practice blocks consisted of 20 trials and each
critical block consisted of 41 trials.2
Participants were
individually contacted on campus and invited to participate in an experimental
session. They were paid £2 plus the possibility of winning a lottery with a £20
prize. Each participant was seated in a cubicle at a table with a desktop computer
and was debriefed at the end of the experiment. The LAT task was completed
before the questionnaire to minimize potential carry-over effects (cf. Egloff
& Schmukle, 2002).
Results
Trials with reaction
times below 300 ms or above 3,000 ms were recoded to 300 ms and 3,000 ms,
respectively. The first trial of each block was also removed due to typically
longer reaction latency. The participants made an average of 7.5% errors. Only
correct responses were considered for the calculation of the IAT score (cf. Mierke
& KJauer, 2001).3 The IAT score was calculated by taking the difference in
reaction times between Phase 3 and 5 and, thus, reflected the implicit positive
evaluation of smoking relative to exercise. The reliability of the IAT score
was good (α = .80).4 For purposes of analyses, data were log-transformed to
meet normality assumptions. The explicit attitude score was calculated by
subtracting the aggregate score for exercise from the one for smoking (ranging
from -6 to +6), thus indicating a positive evaluation for smoking relative to
exercise so that its interpretation is the same as for the IAT. The reliability
of this composite score was very good (α = .92).
Both implicit and
explicit attitudes revealed a similar pattern of results. Smokers had significantly
more positive implicit and explicit attitudes towards smoking than nonsmokers,
F(1, 47) =8.17, p = .006, and F(1, 47) = 31.77, p < .001, respectively. The
means are reported in Table 1. Note that all values are negative, indicating
that for both smokers and non-smokers alike, smoking is evaluated negatively
relative to exercise. However, non-smokers tended to evaluate smoking much more
negatively (-3.57 vs. -1.44) and were much quicker to associate smoking with
negative words (-214 ms vs. -89 ms) compared to smokers. These results are
reflected in significant correlations of 0.64 and 0.48 between being a smoker
and explicit and implicit attitude, respectively. These values correspond to
effect sizes (Cohen's d) of 1.67 and of 1.09, respectively, which would be
classed as large according to standard conventions (Cohen, 1988). The implicit
and explicit attitudes towards smoking were moderately correlated (r = .48).
A hierarchical logistic regression was performed to investigate
both the unique contributions of implicit and explicit attitudes (additive
pattern) and the presence of a multiplicative effect (interactive pattern). At
the first step, both attitudes were entered as predictors of being a smoker.
The model explained 54.8% of variance, but explicit attitudes were a
significant predictor (B = 2.02, SE = 0.63, p = .001), whereas implicit
attitudes were not significant (B = 0.45, SE = 0.44, p = .31). The multiplicative
term was entered at the second step and it improved the overall prediction
(Nagelkerke R^sup 2^ = 0.60, R^sup 2^ change = 5.2%), although showing only a
trend towards significance χ^sup 2^^sub 1^ = 3.22, p = .073). This trend
towards significance was reflected in the multiplicative term (B = 1.03, SE =
0.56, p = .064). To inspect the meaning of this interaction in further detail,
the predicted probabilities of being a smoker as derived from the logistic
model were plotted for a range of standardized values of the IAT for three
values corresponding to positive (z = 1), neutral (z = 0), and negative (z =
-1) explicit attitudes (Jaccard, 2001; see Fig. 1).
The presence of the interactive effect can be interpreted in
this way. For neutral explicit attitude towards smoking, the likelihood of
being a smoker increases with an increasing positive implicit attitude.
However, for negative explicit attitudes, the likelihood tends to decrease,
even with increasingly positive implicit attitudes. However, for positive
explicit attitudes, the likelihood increases very sharply with increasingly
positive implicit attitudes so to reach quickly a value of almost 100%. This
shows that the explicit attitude towards smoking moderates sharply the impact
of the corresponding implicit attitudes.
To summarize, the
results show that the additive pattern is not supported, given that when both
attitude measures are entered simultaneously as predictors in the same
equation, only one (the explicit attitude measure) predicts significantly
whether someone is a smoker or a non-smoker. The interactive pattern is
empirically supported and suggests that the prediction of being or not a smoker
is more effective when implicit and explicit attitudes are in the same
direction. This appears to be especially true for the likelihood of being a
smoker, given that even small increases in the implicit attitude score when
joint with a positive explicit attitude have a sharp effect in terms of
predicted probability.
STUDY 2
The results of the
first study confirmed the importance of the interactive effect between implicit
and explicit attitudes, although it did not provide supporting evidence for an
additive effect. In the second study, a double dissociation pattern will be
tested in a different behavioural domain; namely, preferences towards snacks
versus fruit. One study is particularly relevant in this respect. In their
second study, Karpinski and Hilton (2001) examined the predictive power of
implicit and explicit attitudes with respect to candy bars versus apples. After
the measurement session of the experiment was over, participants were presented
with a Snickers candy bar and a Red Delicious apple and asked to choose one of
them. This choice represented their behavioural criterion. The main results
were as follows. Firstly, Karpinski and Hilton found that implicit and explicit
attitudes did not correlate significantly between each other. Secondly, both
the IAT and the explicit attitude measures showed a preference for apples over
candy bars. Thirdly, the IAT failed to predict the behavioural criterion,
whereas the explicit attitude did predict it significantly.
The present study
expands on Karpinski and Hilton's (2001) second study in a number of ways.
Firstly, the examined attitudes are towards the more general categories of
snacks and fruit rather than candy bars and apples. Secondly, while retaining
their behavioural criterion (though modified to allow for a choice between
different types of snacks and fruit), an additional self-reported behavioural
measure of regular consumption of fruit and snacks was also obtained. The first
behavioural criterion can be classed as concerning mostly a spontaneous
behaviour, whereas the second behavioural criterion can be defined as mostly
deliberative. Finally, the presence of two behaviours thus differentiated will
allow a test of the double dissociation pattern, as well as a test of additive
and interactive effects. These issues were not addressed by Karpinski and
Hilton.
Method
Participants
The sample consisted
of 113 participants recruited on campus, 62 female and 51 male, with an average
age of 25.1 (SD = 6.8). Four participants were discarded for different reasons,
leaving a total of 109. One participant was discarded because the computer
failed to save the reaction latency data, one because of the excessive errors
(above 30% of the trials), one because of the excessive number of very short
latencies (25% of the trials below 400 ms), and one because of both (above 20%
of the trials with errors, and above 20% of trials below 400 ms).
Materials and procedure
The experimental task
mirrored the one described in the first study and consisted of a questionnaire
and a computerized task. The questionnaire contained questions concerning
attitudes towards both eating snacks and eating fruit. They were assessed with
six bipolar scales (bad-good, unpleasant-pleasant, negative-positive, not
enjoyable-enjoyable, unhealthy-healthy, unattractive - attractive) on a 7-step
answer scale ranging from -3 to + 3. Behaviour was measured in two ways. Firstly,
self-reported behaviour (SRB) was measured with three items. The first referred
to self-perception (e.g. To what extent would you describe yourself as a person
who regularly eats snacks [fruit]?' with a 7-step answer scale from not at all
to very much). The second referred to the average weekly consumption of a
series of types of snacks and fruit. Snacks included chocolate bars, plain
biscuits, chocolate biscuits, confectionery, cakes/pastries, bars, and other
sweet snacks. Fruit included apple, banana, pear, kiwi, grapes, berries, and
other fruit. Participants were asked to estimate how many in each category they
were eating during an average week. The score was obtained by adding up these
answers. The third item asked for the frequency of eating snacks [fruit] during
an average day. Secondly, behavioural choice (BC) was measured at the end of
the experiment (cf. Karpinski & Hilton, 2001). After the experiment was
finished, participants were informed that in addition to the standard fee and
the lottery ticket, they could choose a free snack or fruit to take with them.
They were presented two bowls containing a selection of fruit and snacks and
asked to choose one of them.
The computerized
categorization task was the IAT. The target concept was snacks and its contrast
was fruit, whereas the attribute categories were pleasant and unpleasant. For
each category, six stimuli were used (see Appendix). All practice blocks
consisted of 20 trials and each critical block consisted of 41 trials. In this
second study the order of Step 3 and Step 5 was counterbalanced. Furthermore,
the presentation order (IAT first vs. questionnaire first) was also
counterbalanced. Participants were individually contacted on campus and invited
to participate in an experimental session. They were paid £2 plus the
possibility to win a lottery with a £20 prize. Each participant was seated in a
cubicle at a table with a desktop computer. At the end of the experiment, they
were asked to exit the cubicle, pointed towards two bowls on a nearby table
containing snacks and fruit, asked to choose a free snack or fruit, and
debriefed afterwards.
Results
The first trial of
each block was removed due to a typically longer reaction latency. The IAT
score was calculated using the new algorithm developed by Greenwald, Nosek, and
Banaji (2003), specifically the algorithm D^sub 6^ (deletion of latencies below
400 ms and above 10,000 ms, errors replaced with the mean of the correct
responses plus 600 ms).5 The participants made on average 5.8% of errors. The
reliability of the IAT score was good (α = .86). The explicit attitude score
was obtained by subtracting the sum of the scores for snacks from those for
fruit, such that it expresses a relative preference for snacks over fruit, and
showed a good reliability (α = .80). The self-reported behaviour (SRB) index
was obtained by adding up the difference in z scores of the three items for
snacks minus those for fruit. The index was reliable (α = .82).
The results show that
there was a generalized preference for fruit over snacks. In fact, the mean raw
IAT score (M = -38 ms, SD = 206), as well as the explicit attitude score (M =
-2.26, SD = 1.10), express a preference for fruit over snacks. The preference
is confirmed also for the behavioural choice (53.2% of participants choose a
fruit).
The implicit and
explicit attitude measures were correlated with the two behavioural measures
(see Table 2).
The IAT had a
significant relation with the spontaneous behaviour (behavioural choice),
whereas the explicit attitude was significantly related with the deliberative
behaviour (self-reported behaviour), whereas the cross-relations were not
statistically significant. In terms of effect sizes, the IAT had values
corresponding to d = 0.45 and d = 0.32 for behavioural choice and self-reported
behaviour, respectively, whereas explicit attitudes had d = 0.33 and d = 0.82,
respectively. These effect sizes would be classed as medium to large. Implicit
and explicit attitudes were not significantly correlated with each other (r = .09).
To investigate the relation between attitudes and behaviours and to test the
three predictive models, a structural equation approach was adopted. There are
manifold advantages in using this approach over a traditional regression
approach: (a) it yields an overall test of goodness of fit, (b) it takes into
account measurement error, (c) it allows formal tests of specific hypotheses,
(d) it allows for simultaneous testing of the double dissociation and the
additive patterns.6 Unfortunately, the interactive pattern could not be tested
using the full structural equation model approach suggested by Joreskog and
Yang (1996), due to the relatively small sample size in respect to the
algebraic complexity, and the high number, of parameters in the equations involved
(i.e. the asymptotic covariance matrix was not positive definite). A simpler
two stage least squares (TSLS) approach7 was used for the continuous variable
self-reported behaviour, as recommended by Jöreskog, Sörbom, du Toit, and du
Toit (2000), and a logistic
regression model was used for the dichotomous variable behavioural choice.
The first structural equation model testing for the double
dissociation pattern is reported in Fig. 2. The fit was excellent (χ^sup 2^^sub
7^ = 5.10, p = .65, CFI = 1.00). The parameters clearly support the double
dissociation pattern, with implicit attitudes predicting significantly
spontaneous (behavioural choice; γ = .24), but not deliberative behaviour
(self-reported behaviour; γ = .14). However, explicit attitudes showed the
opposite pattern (γ = .17 and γ = .44 for spontaneous and deliberative
behaviour, respectively). To test formally for an additive effect, a modified
model without the additive crossed paths (i.e. implicit attitudes on
deliberative behaviour and explicit attitudes on spontaneous behaviour) was
run. This model is a more restricted model given that two parameters are fixed
to zero. The two models are nested and, therefore, it is possible to perform a
formal test of the need for the additive effects. If the more restricted model
will not be significantly different from the less restricted model, one can
conclude that it is statistically superfluous to consider the additive effects.
This is indeed what the result suggests (χ^sup 2^^sub a(2)^ = 5.70, p = .058).
This conclusion is reinforced by noticing that (a) the two additive parameters
are not statistically significant in the less restricted model, and (b) the
overall fit of the more restricted model is already excellent (χ^sup 2^^sub 9^
= 5.70, p = .29, CFI = 0.99), therefore, suggesting that any less restricted
model is at high risk of over fitting (Anderson & Gerbing, 1988).
The interactive
pattern was tested separately for the two dependent variables. The two stage
least squares model showed a significant effect for explicit attitudes (γ =
.57, SE = 0.15, t = 3-91) and non-significant effects for both implicit
attitudes (γ = .24, SE = 0.16, t = 1.56) and, crucially, for the interactive
term (γ = .20, SE = 0.16, t = 1.23). The logistic regression indicates a
significant effect for implicit attitudes (B = 0.42, SE = 0.20, p = .039) and
non-significant effects for both explicit attitudes (B = 0.39, SE = 0.23, p =
.085) and, crucially, for the interactive term (B = -0.27, SE = 0.22, p =
.224). These results, while indirectly confirming the double dissociation
pattern, do not support the interactive pattern. Both interactive terms do not
reach the significance level, although they show a slight tendency towards it.
The results of the two
studies underscore the importance of assessing both implicit and explicit
attitudes and of testing different predictive models. The most relevant issues
raised by the results will be discussed next.
Predictive validity of implicit attitudes
The efficacy of
implicit attitudes to predict relevant behaviour has been confirmed in the two
studies. Implicit attitudes, as emerging from the associated IATs, have shown
significant correlations with being a smoker and with snack choice during the
experimental session. In the first study, the best predictor of being a smoker
has been the explicit attitude towards smoking. When considered simultaneously
with the implicit attitude measure, only the former has emerged as a
significant predictor. However, the influence of implicit attitudes has emerged
also in the interactive term. Modelling explicit attitudes as the moderator, it
has been shown that implicit attitudes seem particularly relevant when
associated with positive explicit attitudes, so that small positive increases
in the implicit attitude towards smoking sharply change the predicted
probability of being a smoker. On the other hand, when explicit attitudes are
negative, they predict being a non-smoker even with increasing positive
implicit attitudes. Finally, when the explicit attitudes are neutral, implicit
attitudes predict linearly the probability of being a smoker. The second study
has shown that implicit attitudes predict more spontaneous behaviour, such as a
rapid choice about whether to take a free snack or piece of fruit on the spot.
It is interesting to note that this result contrasts with what has been found
by Karpinski and Hilton (2001). It is likely that the difference can be
explained by details in the selection of the stimuli as well as in the
operationalization of the behaviours. Firstly, four of the five stimuli used by
Karpinski and Hilton in their IAT (red, Macintosh, pie, and cider) were related
to apples, but not necessarily revealing about implicit preferences towards
apples as a fruit. Therefore, the resulting IAT score might be less predictive
of actually choosing an apple. Secondly, Karpinski and Hilton focused on apples
(a single fruit) versus snacks (a bundle of different products) and their
behavioural choice was the preference of a specific Red Delicious apple versus
a specific Snickers candy bar. In this study, the focus was on fruit and
snacks, both defined as a bundle of different products, and the behavioural
choice has been between a selection of fruit and a selection of snacks, so that
it was likely to include whatever specific fruit or snack each participant
preferred.
Predictive models of implicit and explicit
attitudes
One of the most
important issues emerging from this contribution is the necessity to test for
alternative predictive models when studying the directive function of implicit
and explicit attitudes. Among the several possible validity criteria, the
correlation between implicit and explicit attitudes is the weakest one for at
least two reasons. Firstly, it is an inherently ambiguous piece of information.
For instance, a low correlation can be taken as evidence of dissociation
between the two types of attitudes, as independence between different types of
measures, but also as lack of convergent validity between them. Equally, higher
correlations can be interpreted in both ways. Therefore, although the
correlation between implicit and explicit attitudes is useful on a descriptive
level, it is much less useful as far as predictive validity is concerned. In
this respect, the key information should be sought in the capability of both
implicit and explicit attitudes to predict relevant behavioural criteria. The
question thus becomes what kind of behaviours can be predicted, under which
conditions, and in which way. It has been argued that is worth examining at
least three key predictive models that are loosely related to three different
theoretical frameworks: additive, double dissociation, and interactive
patterns. In the first study, the interactive model has been supported more
than the additive model, although the double dissociation pattern could not be
properly tested given the presence of a single criterion. The second study,
where all models have been tested, has provided clear support for the double
dissociation pattern. Of course, this does not necessarily discredit the
additive model. It is highly possible that there will be behaviours and
situations where the specific results might change, and the additive model
might provide a superior explanation of the results. More research and
accumulated empirical evidence will be needed before any given model can be
either discounted or considered as superior, including experimental
manipulations of key parameters expected to influence the outcome. Indeed, it
is likely that the accumulated empirical evidence will result in a clearer
articulation of conditions and behaviours that can be explained preferentially
by any of these models. In other words, the key information to be sought
concerns the ideal and boundary conditions for the validity of each model
rather than a 'survival of the fittest' competition. The main message of this
contribution is that, whenever possible, all predictive models are compared for
their ability to predict the outcomes of specific studies, so that this crucial
information is gained over time.
The interplay between implicit and explicit
attitudes
Among the three
predictive models, the most novel and perhaps interesting appears to be the
interactive pattern. The key message is that implicit and explicit attitudes
can interact in influencing behaviour. This is probably the first time that
this hypothesis has been tested within the attitude field. The interactive
hypothesis is compatible with both a dual and a single system account of
attitudes, and it is directly connected with the theoretical framework proposed
by Strack and Deutsch (2004). In the first study, the hypothesis has been
supported, whereas in the second study it has not. Yet, it represents a
fundamental perspective that needs to be taken into account when examining the
interplay between implicit and explicit attitudes. There has been often a bias
in the literature towards providing evidence of dissociation between implicit
and explicit attitudes. This bias can be seen in models within the tradition of
dual theories, such as Fazio's (1990) motivation and opportunity as determinant
of behaviour (MODE) and Wilson, Lindsey, and Schooler (2000) model of dual attitudes. Albeit in different
ways, both models share an either/or perspective, and focus on when and how
explicit or implicit attitudes are more likely to direct behaviour. Neither
model focuses on the possibility that implicit and explicit attitudes can
jointly direct behaviour, nor on attempts to incorporate the specific
mechanisms in a more comprehensive network of theoretical constructs known to
influence behaviour alongside attitudes. The theoretical framework proposed by
Strack and Deutsch (2004) appears an important contribution that might correct
this bias and highlight the crucial notion that implicit and explicit attitudes
can, and often do, work synergistically in influencing behaviour. Little is
known about when this is more likely to happen, and for what kind of
behaviours. Several carefully planned studies will be needed to advance the
understanding of this important issue.
Limitations and conclusions
Some limitations of
this contribution should be acknowledged. First, it would be desirable to
extend these findings in domains other than health related behaviours, to which
both studies of this contribution pertain. Second, it would be desirable to
manipulate experimentally key parameters such that specific causal mechanisms
could be tested. For instance, one can expect that experimental conditions
where the central executive capabilities are reduced (e.g. dual attention
tasks, cognitive load paradigms) when executing behaviour should favour the
predictive power of implicit attitudes. Third, methods other than the IAT
should also be used to measure implicit attitudes, otherwise the risk is that
method and construct will become too closely overlapping. The IAT has a series
of limitations, such as, for instance, the necessity to define both a target
and a contrast category. Often, choosing a contrast category is neither easy
nor uncontroversial. Therefore, it is important to use also alternative
methods. There are some promising alternative paradigms (EAST, masked affective
priming) that could and should be used to complement or even supplement the
LAT, if warranted by empirical evidence.
Despite these
limitations, we believe that the results are clear enough to provide an
interesting picture of the predictive validity of implicit and explicit
attitudes. Models of explicit attitude functioning have been very important in
improving the understanding and prediction of a wide range of relevant
behaviours. More recently, models of implicit attitudes have added to this
understanding by clarifying the importance of automatic processes directing
behaviours. An important challenge for the future will be to develop and test
more comprehensive models of human decision making, incorporating findings from
both fields in a unified theoretical account. The framework proposed by Strack
and Deutsch (2004) seems an important step forward in this direction, although
several issues still need clarification. Among these, is the systematic
examination of alternative predictive models that articulate the influence of
implicit and explicit attitudes along theoretical lines.
Acknowledgements
I wish to thank Rick
O'Gorman for help in collecting the data and Mark Conner, Sheina Orhell and three anonymous reviewers for useful
comments on a previous draft of this manuscript. This research has been partly
funded by ESRC RES-000-23-0104.
Footnote
1 The correspondence
between theoretical frameworks and predictive models is only partial, because
all three frameworks are flexible enough to accommodate the three predictive models.
Therefore, even though from each framework is possible to articulate a
corresponding predictive model, the empirical evidence should not be taken
directly as evidence of the superiority of a specific framework, but it should
be seen in light of the specific conditions and the accumulated evidence that
favour any given theoretical model.
2 Note that the order
of Step 3 and Step 5 was not counterbalanced, as often done with the standard
IAT. This procedure of a fixed presentation order for all participants should
lead to higher validity coefficients (cf. Egloff & Schmukle, 2002).
However, the usual counterbalancing convention was followed in the second
study.
3 Unfortunately, only
correct responses were saved and therefore the new algorithm developed by
Creenwald, Nosek, and Banaji (2003) could not be used in this study. This
problem was corrected for the second study in which the new algorithm has been
used.
4 Different methods
can be adopted to calculate the reliability, meant as internal consistency, of
an IAT score. In this study, given that only correct responses were available,
the two key steps of 40 stimuli each were divided into four blocks each and an
IAT effect was calculated for each block. The four blocks were then used as
items to calculate Cronbach's alpha. In the second study, where all responses
were available, Cronbach's alpha was calculated by using all 40 items in the
two critical steps, each calculated as an IAT effect.
5 Differently from the
algorithm D^sub 6^, only the critical trials (40 stimuli) for each key step (3
and 5) were used. The specific instructions that were adopted did emphasize the
distinction between practice and critical trials. Therefore, it was deemed
appropriate to use trials for which participants were explicitly asked to
perform at their best as opposed to practice the task at hand.
6 It should be noted
that one DV is dichotomous, therefore, strictly speaking, it would be
statistically inappropriate to use a full SEM. However, given that the
distribution of the DV (behavioural choice) is very balanced, the distortion in
the parameters and standard errors is likely to be very small and basically
irrelevant for the main results, as can be seen by comparing the results of the
LISREL model with the other results (raw correlations and simpler predictive
models). Overall, the advantages of using a SEM approach clearly outweigh this
caveat.
7 The TSLS model takes
into account measurement error in the variables, but it does not provide
indicators about the goodness of the fit.
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