People may be unaware of important underlying beliefs
and attitudes that affect their behavior. Even when they are aware of these
beliefs and attitudes, they may be reluctant to report them veridically. This
is especially true in the domain of intergroup perception, where people may
face strong social sanctions for expressing negative attitudes about social
groups. These so-called “willing and able” problems are significant
impediments to studying stereotyping and prejudice.
In recent years, an increasingly popular response to these problems has been
the use of implicit measures of stereotyping and prejudice (for a review,
see Fazio & Olson, 2003). These measures aim to circumvent the “willing
and able” obstacles by measuring attitudes and beliefs without participants’
awareness that they are being measured. Many proponents of these measures
further argue that, even if made aware of the nature of the task, people are
unable to control their responses. Thus, these measures are seen as reflecting
the unintended, stimulus-driven, automatic activation of information in memory,
whose expression largely cannot be altered or inhibited (e.g., Devine, 1989;
Fazio, Jackson, Dunton, & Williams, 1995; Greenwald, McGhee, & Schwartz,
1998). When these measures are taken in combination with explicit measures
(e.g., questionnaires), researchers aim to compare and contrast automatic
and controlled facets of stereotyping and prejudice.
Implicit Measures are not Process-Pure
However, there are two significant drawbacks to this approach. First, it confounds
the processes of interest (automatic vs. controlled) with the particular measurement
tasks. Because the tasks may differ in a number of ways beyond the extent
to which they tap automatic versus controlled processes, there is a danger
of misinterpreting dissociations in task performance. For example, many observed
dissociations between implicit and explicit memory tasks may be reinterpreted
as dissociations between tasks that tap perceptual versus conceptual processes
(e.g., Roediger, 1990).
A second drawback is that no task is process pure. Undoubtedly, implicit measures
of stereotyping and prejudice are less susceptible to the influence of intention
and controlled processes than are explicit measures. Nevertheless, any behavioral
task that requires an observable response (e.g., a button press) likely involves
an ongoing interplay between simultaneously occurring automatic and controlled
processes. As such, the behavioral response, in and of itself, is incapable
of specifying the nature of the underlying processes that produced the response.
Consider the Stroop Task, for example (Stroop, 1935). A fully literate adult
and a young child who knows colors but does not know how to read may make
an equally small number of errors on the task. However, very different processes
are at work for the adult and the child. On incompatible trials (e.g., the
word “Blue” written in red ink), the adult must overcome a habit
to read the word in order to name the color of the ink correctly. In contrast,
the child has no habit to overcome; s/he simply responds to the color of the
ink.
The same principle applies to implicit measures of stereotyping and prejudice,
many of which have a Stroop-like structure of compatible (e.g., Black faces/negative
words; White faces/positive words) and incompatible (e.g., Black faces/positive
words; White faces/negative words) trials. The performance of two people who
appear to have equally strong implicit biases may reflect very different underlying
processes. Whereas one person may have strong implicit associations that are
successfully overcome, the other may have weaker associations that are not
overcome so well. Thus, behavioral outcomes on implicit measures of stereotyping
and prejudice may not reflect differences in underlying attitudes, per se.
Separating Multiple Automatic and Controlled Components of Implicit
Measures: The Quad Model
In our research (Conrey, Sherman, Gawronski, Hugenberg, & Groom, in press),
we have attempted to separate the automatic and controlled components of responses
within individual implicit measures of stereotyping and prejudice. In taking
this approach, we avoid the task/process confound that is problematic for
many investigations of automatic and controlled processes. This approach also
allows us to examine the simultaneous operation and interaction of multiple
processes in implicit task performance.
We base our analysis on the Process Dissociation Procedure (PD) pioneered
by Jacoby and his colleagues (e.g., Jacoby, 1991) to separate different processing
components within a single task. However, our research extends the basic PD
model in important ways. Whereas basic PD analyses produce a single estimate
of automatic and controlled processing within a given task, we believe it
is critical to distinguish between two distinct automatic processes and two
distinct controlled processes. To assess each of these processes, we have
proposed the Quadruple Process Model of implicit task performance (Conrey
et al., in press).
The Quad Model (see Figure 1) is a multinomial model (see Batchelder &
Riefer, 1999) designed to disentangle four qualitatively distinct processes
that contribute to performance on implicit measures that rely on the logic
of response compatibility (as illustrated above with Stroop Task). The four
processes are: The automatic activation of an association (Association Activation,
AC), the ability to determine a correct response (Discriminability, D), the
success at overcoming automatically activated associations (Overcoming Bias,
OB), and the influence of a general response bias that might guide responses
in the absence of other available guides to response (Guessing, G). Whereas
AC and G are automatic processes (though G need not be), D and OB are controlled
processes.
As an example of how the four process operate, consider an evaluative priming
task using pictures of Black and White faces as primes and positive and negative
words as targets (e.g., Fazio et al., 1995). In such a task, the presentation
of a Black face may automatically activate a negative evaluation (AC) that
influences responses to a subsequently presented stimulus word. Depending
on the trial type, this automatic tendency may be compatible or incompatible
with the correct response determined through discrimination (D) of the target
word. If the target word is negative, then the response tendency produced
by the automatic evaluation and the response determined via discrimination
are compatible. In this case, there is no conflict, and there is no need to
overcome bias (OB) in order to produce the correct response. However, if the
two response tendencies are incongruent (a Black prime followed by a positive
target word), whether the automatic association or accurate discrimination
drives the response is determined by whether the participant succeeds in overcoming
his or her associations. If no association is activated and the correct response
cannot be determined, participants must guess (G).
Though I have used an evaluative priming example, the logic is exactly the
same with any implicit measure that compares compatible and incompatible trials.
Indeed, to date, our results have come primarily from two different tasks,
the Implicit Association Test (IAT: Greenwald et al., 1998) and the Weapons
Identification Task (e.g., Payne, 2001).
Results
Analyses using the Quad Model are based on error rates occurring on different
types of trials. The processing tree presented in Figure
1 illustrates how the model predicts correct and incorrect responses on
compatible and incompatible trials as a function of the operations of the
four different processes. For example, there are three different ways to arrive
at an incorrect response on incompatible trials. Each of these three combinations
of processes represents a set of conditional probabilities by which the incorrect
response is produced. These sets of conditional probabilities are used to
generate model predictions that are compared to actual results to test for
model fit, and are used to generate parameter estimates for each of the four
processes (for details, see Conrey et al., in press).
At the most basic level, our data demonstrate that performance on both the
IAT and the WIT is a function of all four of the proposed processes. If any
process is removed from the model, the model fails. Other data showed that
forcing participants to respond quickly on an IAT significantly reduced Discrimination
and Overcoming Bias, but did not affect Activation and Guessing. This supports
our view that D and OB are controlled processes, whereas AC and G are relatively
automatic. In another study, we used the parameter estimates of the four processes
to predict biases in response latencies on an IAT. The data showed that response
time bias was positively correlated with estimates of the AC parameter, supporting
the status of AC as a measure of automatic attitudes. In contrast, response
time bias was negatively correlated with the OB parameter, confirming that
success at overcoming automatic biases results in smaller estimates of implicit
prejudice.
In another application of the model, we re-analyzed data collected by Lambert,
Payne, Jacoby, Shaffer, Chasteen, and Khan (2003). In their study, they showed
that an anticipated public context ironically increased the extent of implicit
stereotyping. Based on a standard PD analysis, they concluded that this effect
was due to diminished control in the public versus private context rather
than to an increase in stereotype activation in the public context (which
could be predicted by drive-based models of social facilitation; e.g., Zajonc,
1965). However, re-analysis with the Quad Model showed a very different result.
When automatic and controlled processes were decomposed into four separate
components, the results showed that, although Discrimination was diminished
in the public condition, Overcoming Bias was enhanced in that condition. Thus,
one type of controlled process was inhibited by an audience, and another was
enhanced by the audience. Moreover, our analysis showed that the Activation
parameter did increase in the public condition. In Lambert et al.’s
analysis, this effect had been obscured by the simultaneous increase in Overcoming
Bias, which was not measured. Together, these results show that an anticipated
audience increases bias on an implicit measure because it inhibits people’s
ability to discriminate the correct response on the task, and because it increases
activation of the dominant stereotypic response.
Conclusion
There are two main conclusions from our research. First, in research on automatic
and controlled processes it is useful to move beyond task dissociation paradigms
and use process dissociation procedures, instead. Second, it is important
to move beyond the simple distinction between automatic and controlled processing,
and begin to address important qualitative differences among automatic and
controlled processes. As an example, our re-analysis of Lambert et al. (2003)
showed that two different controlled processes were affected in opposite ways
by the same manipulation. In each of our studies, by assessing all four of
the processes in the Quad Model we were able to provide a more comprehensive,
nuanced, and accurate description of implicit task performance. To date, the
Quad Model has been applied only in the domain of stereotyping and prejudice.
However, it should apply more generally to any domain in which automatic impulses
are either compatible or incompatible with controlled attempts to overcome
those impulses, including research on phobias, addictions, aggression, persuasion,
and more. We hope the model will prove to be a useful tool for researchers
in many areas of psychology.
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Figure 1. The Quadruple Process Model (Quad-Model). Each
path represents a likelihood. Parameters with lines leading to them are conditional
upon all preceding parameters. The table on the right side of the figure depicts
correct (+) and incorrect (-) responses as a function of process pattern and
trial type.