Running head: GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 1
Reflecting on Twenty-Five Years of Research Using Implicit Measures: Recommendations
for their Future Use
Pieter Van Dessel, Jamie Cummins, Sean Hughes, Sarah Kasran, Femke Cathelyn, & Tal Moran
Ghent University, Belgium
Author note: Pieter Van Dessel and Jamie Cummins contributed equally. Correspondence
concerning this article should be addressed to Pieter Van Dessel or Jamie Cummins, Department
of Experimental-Clinical and Health Psychology, Ghent University, Henri Dunantlaan 2, 9000
Ghent, Belgium. E-mail: Pieter.VanDessel@UGent.be or Jamie.Cummins@UGent.be. This
manuscript is supported by the Scientific Research Foundation, Flanders under Grant
FWO19/PDS/041 and by Ghent University under grants BOF16/MET_V/002 and 01P05517. The
authors declare that they have no competing interests.
The authors would like to thank all our present and past collaborators for the many discussions that
shaped this paper, foremost Jan De Houwer for his continued support and critical insight, and Ian
Hussey for valuable comments and contributions on drafts of this manuscript.
This paper is not the copy of record and may not exactly replicate the final, authoritative version
of the article as published in Social Cognition special issue on "25 Years of Research Using Implicit
Measures". The final article will be available, upon publication, via its DOI.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 2
For more than twenty-five years implicit measures have shaped research, theorizing, and
intervention in psychological science. During this period, the development and deployment of
implicit measures have been predicated on a number of theoretical, methodological, and applied
assumptions. Yet these assumptions are frequently violated and rarely met. As a result, the merit
of research using implicit measures has increasingly been cast into doubt. In this paper, we argue
that future implicit measure
research could benefit from adherence to four guidelines based on a
functional approach wherein performance on implicit measures is described and analyzed as
behavior emitted under specific conditions and captured in a specific measurement context. We
unpack this approach and highlight recent work illustrating both its theoretical and practical value.
Keywords: implicit measures, behavior, automaticity, levels of analysis
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 3
Reflecting on Twenty-Five Years of Research Using Implicit Measures: Recommendations
for their Future Use
Implicit measures are widely used in psychological science (see Gawronski & Hahn, 2019).
Their popularity has been primarily based on three key assumptions. First, implicit measures are
assumed to provide unique insight into mental processes operating under conditions of automaticity
(Greenwald et al., 1998). For instance, the Implicit Association Test (IAT; Greenwald et al., 1998),
was originally introduced as a measure of ‘unconscious associations’ between mental concepts
(Greenwald & Banaji, 1995). Such mental associations were assumed to drive more unconscious
and spontaneous thoughts and behavior (Fazio & Olson, 2003; Smith & DeCoster, 2000). The
assumption that implicit measures provide a window into the ‘unconscious’ mind gave rise to a
second key assumption: that implicit measure performance predicts other behavior in a unique
manner, either independently, additively, or interactively with self-reports (Dovidio et al., 1997).
Finally, at the methodological level, the use of implicit measures was (and still is) predicated on a
third assumption: that these measures represent a reliable and valid indicator of the probed
construct of interest (e.g., Nosek et al., 2005).
It seems fair to say that implicit measures have sparked an incredible amount of empirical
work and led to some useful insights. For instance, implicit measures research has stimulated a
massive amount of theory-building, and these theories have been used to generate new research
hypotheses (e.g., Dovidio et al., 1997; Greenwald et al., 2003). There is also evidence to suggest a
practical value of implicit measures, in the sense that implicit measure responses sometimes predict
behavior (Friese et al., 2009). Yet, reflecting back on twenty-five years’ worth of work, it is also
clear that the initial fervor and enthusiasm has not been met. Indeed, together with the recent shift
in focus in psychological science towards openness and replicability, it has become clear that
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 4
research on implicit measures is not without its problems, and that the three aforementioned
assumptions are unlikely to be true in the ways previously assumed.
A Critical Analysis of Three Assumptions Underlying Implicit Measures Research
Assumption 1: Implicit measure performance is mediated by specific mental
processes. Evidence supporting the assumption that (a) implicit measure responses are mediated
by specific mental processes and (b) these processes are distinguishable from those mediating
responses on other (explicit) measures is weak. Researchers have typically treated responses on
most implicit measures as proxies for mental associations (or associative processes). Yet, claiming
that a behavior (e.g., an IAT score) is a proxy for a mental process (e.g., activation of a mental
association) builds on untested and questionable assumptions (e.g., that the sole determinant of the
behavior is the mental process: De Houwer, 2011). As one example, though IAT performance is
often equated with association activation, research repeatedly shows that associative explanations
of IAT performance fail to adequately account for empirical findings (see Brownstein, Madva, &
Gawronski, 2019; Corneille & Stahl, 2019; De Houwer, 2014) and that observed discrepancies
between IAT scores and explicit measure scores can be explained without reference to distinct
types of processes (Heycke et al., 2018; Payne et al., 2008). As we discuss later on, this problem
of (unverified) behavioral proxies can be solved by defining responses on implicit measures in
behavioral rather than mental terms.
In addition to these issues with treating behavior as a proxy for mental processes, there is
the long-standing problem that researchers use the same term (‘implicit’) in many different ways
(Corneille & Hütter, 2020). Some use the term ‘implicit’ to refer to conditions under which mental
processes are assumed to operate (i.e., the mental processes are implicit in the sense of ‘automatic’).
This is problematic, as this has led researchers to map behavior in implicit measures onto a whole
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 5
class of mental processes. Others use the term ‘implicit’ to refer to a class of procedures (i.e., the
procedures are implicit in the sense of ‘indirect’). Still others use ‘implicit’ in reference to the
outcome of an indirect procedure (i.e., the IAT effect is ‘implicit’). This heterogeneity has long
been an issue that has plagued the literature and one that leads to conceptual confusion and wrong-
headed debate, slows communication, and impedes scientific progress. Despite being repeatedly
highlighted (Brownstein et al., 2019), the issue remains. As we note below, this can also be solved
by defining performance on implicit measures in behavioral terms.
Assumption 2: Implicit measures have added value in predicting behavior. After
twenty-five years of work, it seems reasonable to say that implicit measures have generally proven
far worse at predicting behavior than was initially hoped (for a review see Oswald et al., 2013).
Although applied research provides some evidence for their predictive utility (e.g., in the prediction
of suicidality: Nock et al., 2010; see Tello et al., 2018, for a direct replication), when we step back
and consider the field as a whole, there are many more instances where implicit measures have
failed to provide any added utility in predicting behavior above and beyond simply asking people
what they think, feel, or do (e.g., Larsen et al., 2012; Lindgren et al., 2019).
In part, this may be explained by poor measurement of the to-be-predicted ‘behavior’ in
experimental work. For instance, measurement of the target behavior often involves verbal report
(e.g., in self-reported behavioral intention measures) and it is unsurprising that such reports more
strongly correspond to responses from other self-report measures (see Payne et al., 2008). Studies
that do look at real-life behavior often use poorly-validated measures of behavior that may have
little to do with the probed construct (e.g., seating distance from out-group members to validate
racial prejudice measures: Amodio & Devine, 2006; see Dang et al., 2020, for a similar
argumentation in the context of correspondence between self-report and behavioral measures).
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 6
Research that better deals with these measurement issues might show more evidence for predictive
validity of implicit measures (e.g., when IAT scores are related to medical records of suicide
attempts: Nock et al., 2010).
Furthermore, some recent studies have provided initial evidence that, in specific contexts,
performances on implicit measures might explain added variance in other types of behavior after
controlling for performances on explicit measures (Kurdi et al., 2019; see also recent research
looking at predictive validity of response scores at the aggregate level: Payne et al., 2017). Yet, it
remains clear that the field as a whole has fallen short of the broader claim that these measures
provide substantive predictive utility when used in tandem with (or instead of) self-report measures
(Meissner et al., 2019). Similarly, the promise that implicit measures would translate into
interventions that could be used to impact real-life behavior has not come to pass (e.g., Carter et
al., in press; Cristea et al., 2015; Forscher et al., 2019; Lai et al., 2014, 2016). This is not to say
that intervention research is not at times useful, but rather that much of this research was based on
a number of assumptions about these measures that do not concord with the actual trend of
evidence. After twenty-five years of work, we still do not know in which real-life contexts (if any)
implicit measures have any substantial added value (i.e., domains where they do not merely have
a statistically significant impact but a clinical, practical, or meaningful one).
Assumption 3: Implicit measure scores are valid and reliable. Methodological research
has long indicated that the psychometric properties of implicit measure scores are poor. First, there
are problems with construct validity. These problems are not surprising given that the construct
has often been tied to specific mental processes (e.g., the automatic activation of mental
associations). However, even when implicit measures are defined at the behavioral level (i.e., as
behavior that occurs ‘automatically’, irrespective of the mental processes at play) this leads to
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 7
issues (Cummins et al. 2019). For instance, automaticity is often considered a multi-dimensional
concept with conditions defined at the level of mental processes (e.g., unconsciousness,
unawareness, unintentionality; Moors & De Houwer, 2006). As a solution, automaticity conditions
can be defined at the behavioral level (e.g., implicit measure performance can be defined as
unintentional when instructions to try and modify performance do not lead to congruent changes
in performance) but even then, these conditions do not always relate to implicit measures in the
way that they were originally assumed (Cummins et al., 2019; Hahn & Gawronski, 2019). Because
of these conceptual issues, some authors have reversed or revised their earlier stance on this issue
(e.g., Greenwald & Banaji, 2017) which has further complicated testing this kind of validity.
There are also issues with other psychometric properties of implicit measures, such as
structural and external validity. In particular, research has often demonstrated poor test-retest
reliability and weak correlations among different implicit measures of the same construct (e.g.,
Bar-Anan & Nosek, 2014; Blanton & Jaccard, 2006). Scores on implicit measures also fail to meet
the measurement model they have been assumed to meet (i.e., that they load onto a single factor
distinct from that of an explicit measure; see Schimmack, 2019). Interestingly, these issues have
been noted for many years and guidelines have been provided to improve the statistical properties
of implicit measure scores (e.g., Nosek et al., 2007). While there is published work on (a) the
reliability of implicit measures, (b) calls to improve their reliability, and (c) suggestions on how to
do so, this body of work is ignored by the majority of studies that use implicit measures. To take a
concrete example, the procedural parameters of the IATs used in contemporary published studies
are most often identical to those proposed in the original Greenwald et al. (1998) paper with no
modifications. In short, implicit measures in general have failed to meet normative criteria at all
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 8
three levels of validity (i.e., construct, structural, and external validity), but their use has persisted
regardless of this.
Conclusion. Several decades of work has eroded our confidence in three key assumptions
underpinning research on implicit measures, and scholars have grown increasingly uncertain
whether those measures can (a) shed light on specific types of mental processes (Schimmack,
2019), (b) be used for predicting behavior in unique and substantial ways (Jost, 2019), and (c)
represent reliable and valid measures of the construct of interest (Mitchell & Tetlock, 2017). This
work has in turn led to growing doubt about the nature, role, and utility of implicit measures in
general (e.g., De Houwer, 2019; Jost, 2019; Payne et al., 2017). In what follows, we offer
recommendations designed to improve research on implicit measures in the years to come.
Four Guidelines for Future Research Using Implicit Measures
We are not the first to raise these issues with implicit measures: many others have offered
important perspectives which have too often been disregarded (Flake et al., 2017; Payne et al.,
2008; Rothermund & Wentura, 2004). As a result, we face a situation where we collectively make
use of measures that are riddled with issues and often continue “business as usual”. Clearly
something needs to change. With this in mind, we reiterate and expand these prior suggestions,
offering four concrete guidelines that researchers should adhere to when using implicit measures,
both now and in the future. We will first outline these guidelines and then illustrate their potential
theoretical and applied value.
Guideline 1: Define implicit measure responses as functional effects. In line with De
Houwer et al. (2013), we believe that scientific progress is facilitated when researchers separate
the phenomenon they want to explain (behavior) and the thing they use to explain that phenomenon
(mental constructs and environmental variables; see also De Houwer, 2019; Hughes et al., 2016).
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 9
Therefore, in the context of research with implicit measures, our first recommendation is that
researchers start by describing implicit measures in purely functional terms (i.e., as behavior
observed in the context of a specific procedure). Following this guideline requires that implicit
measures research begins with an analysis of (a) the contextual properties that characterize the
procedure (e.g., the stimuli) and (b) the behavior captured by that procedure. The behavior of
interest is “automatic behavior” (i.e., behavior captured under conditions of automaticity) and the
set of contextual properties are those that influence the emission or elicitation of automatic behavior
(see De Schryver et al., 2018; Gawronski & De Houwer, 2014). We will return to what we mean
by “automatic” in Guideline 2.
For now, let us illustrate our first guideline using research on ‘implicit racial bias’. Previous
work has long equated the phenomenon that needs to be explained (e.g., race IAT scores) with the
phenomenon that is used to explain (e.g., mental associations; Hughes et al., 2011). When IAT
scores are found to predict racial bias on other measures, researchers have assumed that it was
mental associations which predicted such performances. Problems arise when evidence emerges
questioning such an associative account, and because IAT scores and mental associations are
conflated, the validity of the race IAT effect is also drawn into question (e.g., Schimmack, 2019).
Adopting a functional perspective avoids this issue: by viewing IAT scores as behavior that is
emitted in the context of the IAT procedure we separate the phenomenon which needs to be
explained (IAT scores) from the phenomenon used to explain it (e.g., mental associations). This
has several advantages: (a) uncertainty regarding mental causes does not lead to uncertainty about
the observed racial IAT effect, (b) the behavioral effect can now be of interest regardless of its
assumed mediators, and (c) researchers can investigate relations between race IAT scores and other
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 10
(behavioral) phenomena while remaining agnostic to their assumed mental mediators.
defining implicit measure responses as behavioral effects will improve clarity and reduce bias,
improving implicit measures research.
Guideline 2: Specify what you mean by ‘automatic’. A second guideline for implicit
measures research is to perform a precise analysis of what exactly qualifies a behavior as being
“automatic” in the context of the procedure being used. Given the multiplicity of ways the term
‘implicit measures’ is used, and the long history of confusion it has left in its wake, we echo
Corneille and Hütter’s (2020) suggestion that the term be abandoned. We propose that researchers
instead (a) clearly specify which properties of the behavior being captured in a given task qualify
as “automatic” and (b) outline how the procedure serves to elicit the “automatic” behavior of
interest. We unpack each of these recommendations in turn.
2.1. Specify and test automaticity conditions. We view automaticity as an ‘orientating
term’, a word that serves to highlight that behavior can occur in ways that are uncontrolled,
unaware, efficient, or fast (see Moors & De Houwer, 2006). Importantly, however, each of these
automaticity conditions is ill-defined at the behavioral level and continuous rather than
dichotomous. In addition, recent work suggests that these conditions do not map onto one another
(Melnikoff & Bargh, 2018). It is not surprising then that implicit measure performance does not
relate to automaticity in the way it was often thought (with all measures having all automaticity
conditions; Cummins et al., 2019; Hahn et al., 2018) and it seems of little use to say that automatic
behavior in general is measured within a given procedure (such over-simplification is often tied to
Note that research designed to address questions about mental mediators of race IAT effects can also benefit from
the functional approach outlined here. Defining behavior on implicit measures as automatic behaviors emitted in a
specific context ensures theoretical freedom and debate at the mental level, allowing for those effects to be mediated
by any number of mental processes, not just associations (for more see Hughes et al., 2011; De Houwer et al., 2020).
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 11
very broad theoretical perspectives; Melnikoff & Bargh, 2018). Instead, researchers using
automatic behavior measures should always clarify (and possibly test) to what extent procedures
give rise to behavior under different automaticity conditions.
In this analysis, we recommend taking into account each of the three issues noted above.
First, behavioral definitions of the automaticity conditions of interest should be provided. For
instance, intentionality can be functionally defined as the extent to which performance in the
measure can be changed in a certain direction when instructed to do so (see De Houwer & Moors,
2007, for such an approach). Second, the continuous nature of automaticity conditions should be
taken into account. We propose that researchers describe automaticity conditions in relative terms
(e.g., in contrast to behavior in other measures that might be used; see De Schryver et al., 2018).
For instance, researchers could test the extent to which IAT performance more strongly adheres to
a condition than behavior in self-report evaluation measures. Third, it should be clarified which of
the conditions of automaticity have been tested. When only one automaticity condition is met, the
probed measure can be described accordingly (e.g., as a measure of fast evaluation).
For example, IAT effects have been considered to be more “unintentional” than responses
on self-reported liking scales on the basis that stimulus evaluations within the IAT are not task-
relevant (e.g., Banaji, 2001). When one uses the functional definition of intentionality noted above,
it can be examined whether IAT scores can be intentionally shifted compared to a baseline (e.g.,
the baseline of no shift in effects), and the extent to which this intentional shift occurs relative to a
self-report measure of the same construct. Some studies have provided evidence for the
intentionality of IAT scores in this sense (Fiedler & Bluemke, 2005; Stieger et al., 2011). Similarly,
the automaticity condition of awareness can be examined when defined in behavioral terms (e.g.,
whether people report awareness when probed). Here, research suggests that participants
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 12
sometimes show a similar level of awareness of their IAT effects as of their self-reported ratings
(Hahn & Gawronski, 2019).
Given that implicitness is the raison d’être for the use of implicit measures, it is both
surprising and worrying that we have failed to substantively investigate the validity of the claim
that these measures are in fact implicit. It therefore seems unwarranted to draw any strong
conclusions about automaticity in implicit measures at present. We recommend that researchers
refrain from saying that behavior in the IAT or any other task is or is not ‘automatic’ in general.
Instead, when using implicit measures, researchers can highlight for which automaticity conditions
there is behavioral evidence compared to a set value and in a given (procedural) context, or they
can perform their own measurement of these conditions in the context of their experimentation.
Notably, this approach is imperfect, given that there are currently no agreed-upon methods which
can be used to test for different automaticity conditions. However, this is likely a by-product of
failing to define automaticity conditions in functional terms. By describing automaticity as a
function of environmental (i.e., task) conditions, arriving at agreed-upon methods for testing these
conditions should be more easily facilitated.
2.2. Specify and test the directness of the measure. As previously mentioned, the term
‘implicit measures’ has been used to refer to measures that do not ‘directly’ ask about the behavior
of interest (i.e., there are several steps required to infer the targeted behavior from the responses
on the measure; see also Corneille & Hütter, 2020). We do not recommend this terminology, simply
because there is no task that directly probes a given construct (i.e., a construct is always inferred
on the basis of behavioral indicators, even in self-report scales) and it therefore makes little sense
to refer to indirect measures as a distinct class of measures. Instead, as noted above, we propose to
generally refer to implicit measures in terms of the automaticity conditions behavior is captured
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 13
under. When doing so, however, we recommend taking “indirectness” into account by clarifying
(and possibly testing) in what regard the phenomenon of interest is indirectly inferred from
behavior in the task.
For instance, when the IAT is used as a measure of automatic evaluation, researchers should
explain not only how the probed behavior is automatic (e.g., it is fast to the extent that it is emitted
more quickly than behavior in a self-report measure) but also how the probed measurement index
(e.g., the IAT score) relates to automatic evaluation. In an IAT, automatic evaluation is typically
inferred on the basis of differences in response times in categorization. Specifically, because the
procedure elicits categorization in the context of evaluative categorization of other stimuli with the
same response keys, (differences in) fast categorizations of stimuli are thought to reflect automatic
evaluation. Importantly, this inferential step is based on many assumptions and these assumptions
can depend on procedural aspects of the measure. For instance, in the context of differences in the
salience of stimuli used in the IAT, IAT scores may reflect salience asymmetries rather than
automatic evaluation (see Rothermund & Wentura, 2004). When using implicit measures,
researchers should clarify indirectness, testing or noting how the phenomenon of interest is inferred
from behavior in the task and how this relates to procedural aspects of the selected measure.
Summary. In sum, we recommend that researchers describe implicit measures in terms of
automatic behavior and conceptualize automaticity conditions from a functional perspective, as
well as clarify how the phenomenon of interest relates to the measure.
In principle, researchers could also omit any reference to automaticity (or implicitness and indirectness) and simply
refer to a measure by its name (e.g., the race IAT) without framing it as a measure of automatic behavior when they
are not interested in making any claims about the construct that is measured. For instance, one could use a race IAT
because it has been found to predict certain behavior. However, even in this case, the researcher inherits the theoretical
assumptions which underpin IAT use in that context in the first place, and so knowledge of the probed construct is
important to facilitate adequate explanation and interpretation of the findings.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 14
Guideline 3: Choose the implicit measure that has the characteristics that you require.
A meter stick might help us measure a person’s height, but not the size of a subatomic particle.
Relatedly, it should be clear that there is little benefit in adopting an ‘off-the-shelf’ or ‘one-size-
fits-all’ approach to implicit measures. Selection of implicit measures in the absence of
consideration of their automaticity conditions is likely to reduce applied (and theoretical) value. It
is clear that not all implicit measures have the same predictive utility (e.g., Spruyt et al., 2013,
2015) and the approach of using any implicit measure because it is ‘implicit’ in a generic sense
does not seem to generate the applied value that was initially promised from these measures
(Lindgren et al., 2019). Instead, we recommend carefully selecting implicit measures that most
closely fulfil or meet the specific conditions that characterize the phenomenon one is trying to
assess/predict. In other words, select implicit measures so that there is a ‘match’ between the
measure and the aims of the research. Researchers have already applied this logic to stimulus
identity; Irving and Smith (2019) found that donations to build a border wall between Mexico and
the USA were better-predicted by IATs which assessed evaluations of border walls (i.e., a good
match between stimuli and outcome) compared to IATs which assessed generic evaluations of
immigrants. This same matching logic can be applied to automaticity conditions: if researchers
wish to predict a behavior that is unintentional, then a measure that meets the automaticity
condition of unintentionality should be employed. If, for example, an addictive behavior of interest
appears to occur under one particular condition of automaticity based on prior investigations, then
subsequent work for predicting this behavior can be well-informed in terms of how to best optimize
this prediction (i.e., by focusing on the relevant automaticity condition).
How can this selection be done? Take again the example of a race IAT. Researchers
investigating automatic racial bias can select from a range of different procedures (e.g., an IAT,
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 15
AMP, PEP). When they do so, we recommend they consider which automaticity conditions
performances will be emitted under, and to determine if such conditions are consistent with the
broader aims of their research agenda. For instance, one might be interested in people’s immediate
racial evaluations (i.e., in ‘fast’ behavior). Although the IAT may be a tempting candidate
procedure, one first needs to ensure that adequate research exists showing that IAT performance
qualifies as ‘fast’ relative to other tasks. Such research might be available for some measures (albeit
in certain contexts; Gawronski & De Houwer, 2014), but for other measures (or for adapted
measures) researchers will need to test the conditions themselves. In sum, just as improving the
match between stimuli in implicit measures and to-be-predicted behavior can improve the utility
of implicit measures, so too can specifying and matching the automaticity conditions under which
behavior in the measure is captured and the conditions assumed to be present in the to-be-predicted
As an important note, adherence to this guideline might seem difficult given that there is
ample debate on almost every well-known implicit measure in terms of the automaticity conditions
that performances on that procedure do or do not exhibit (e.g., Bar-Anan & Nosek, 2014; Fiedler
et al., 2006). This is unsurprising given that there are few (if any) agreed-upon methods for testing
automaticity conditions (see Cummins et al., 2019). Essentially all methods of testing automaticity
conditions rely on bespoke manipulations, which are problematic because the manipulations
themselves have unknown measurement properties (see Chester & Lasko, 2019).
We hope that defining automaticity in terms of the properties of measurement procedures
can lead to agreed-upon ways of testing these conditions. Indeed, it seems imperative that
researchers focus on the development of normative methods for testing each automaticity condition
which can - in principle - be applied regardless of the specific implicit measure being investigated.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 16
One potential method to achieve this is by starting from validated nonautomatic measures,
modifying them, and then testing for differences between the original and modified measures. For
example, suppose a researcher wishes to develop a measure which captures body dissatisfaction
under the automaticity condition of fast. Researchers might typically opt for an IAT or some other
similar measure in this context. However, if valid nonautomatic measures of body dissatisfaction
already exist, it might be more useful to start with such a measure, and then manipulate the measure
in order to ensure that responding is fast (for example, by requiring responses within 1s). The
original and modified measures should be essentially identical and vary only in terms of speed of
responding. From here, the researcher can compare the original and manipulated measure to (i)
ensure that responding is faster, and (ii) determine whether capturing such fast responding provides
any incremental utility beyond the original measure (see Payne et al., 2008).
Guideline 4: Thoroughly examine and improve the psychometric properties of implicit
measures. We believe that research using implicit measures needs to give more priority to work
assessing and improving the psychometric properties of those measures. The assumption that
implicit measures are both reliable and valid is the foundation upon which all other research,
theory, and intervention proceeds. If that foundation is weak then so too are the pillars that stand
upon it. For example, the extent to which theory-oriented researchers can clarify the automaticity
conditions of probed behavior critically depends on progress that is made by researchers
investigating the psychometric properties of specific implicit measures. Likewise, applying
implicit measures requires adequate information be available about the specific psychometric
properties of different measures in order to select an optimal measure. We see two general issues
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 17
First, questions regarding measurement generally begin and end with a report of the most
common metrics of internal consistency and test-retest reliability (see Flake et al., 2017; Hussey &
Hughes, 2019). Little measurement work has assessed other key assumptions in implicit measure
research (e.g., that effects conform to specific assumed measurement models, etc.) despite an
abundance of freely available and large scale datasets (although see Schimmack, 2019, for a recent
exception). Second, when psychometric issues are highlighted, such as poor test-retest reliability,
these issues are likely to either be defended as desirable in some way (e.g., implicit attitudes being
seen as unstable rather than implicit measures being seen as having poor measurement properties;
see Gawronski, 2019) or to be ignored. For example, despite how well-known the IAT’s relatively
poor test-retest reliability is, to our knowledge, no research has identified ways to remediate this
(perhaps except for recommendations to aggregate responses at the group level: Payne et al., 2017).
Elsewhere, suggestions to alter the procedural properties of tasks are frequently not taken up
in the event that criticisms of an implicit measure are incorporated, the response of the field tends
towards the development of a new measurement procedure, rather than the refinement of the extant
measure (De Houwer et al., 2015; Müller & Rothermund, 2019). Such new procedures may
themselves be poorly or improperly validated, which would only serve to propagate the cycle of
implicit measures with undesirable measurement properties.
A much greater body of psychometric validation research is required in this regard. A reader
may ask: why has this work not been done yet? The answer likely resides in the incentives provided
to researchers to date. For example, studies are more likely to be published if measures are shown
For example, in their methodological review of the IAT, Nosek et al. (2007) demonstrate that increasing the number
of trials in Block 5 from 20 to 40 reduces the magnitude of the block-order effect between participants. However,
subsequent research frequently includes only 20 trials in Block 5 on the basis that this is what was included in the
original description of the IAT by Greenwald et al. (1998).
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 18
to perform well rather than poorly. As such, studies where measures perform poorly
psychometrically will be less likely to be made public. For the same reason, psychometric tests that
are more stringent tend to be less likely to be conducted: a good margin of literature might report
reliability statistics (which measures tend to perform relatively well in) but few if any studies will
report tests of measurement invariance or confirmatory factor analyses (which measures tend to
fare much more poorly with; see Hussey & Hughes, 2019). Of course, in principle there is little
stopping researchers from continuing to ignore or neglect psychometrics of implicit measures.
However, if researchers truly wish to improve their utility, then they would do well to pay more
heed to these basic psychometric barriers.
Advantages of Following these Guidelines
Theoretical value. Following our guidelines has theoretical value. Describing implicit
measure performance as behavior in a specific context can help those interested in developing new
or refining their existing theories. To illustrate, imagine that one wants to use an implicit measure
to test their novel theory of addiction. If one follows the guidelines mentioned above, then one
might start by systematically examining behavioral predictions (e.g., that specific addictive
behavior depends on automatic evaluation of specific addiction stimuli) using measures that probe
different types of (evaluative or addictive) behavior under different automaticity conditions (e.g.,
Thush et al., 2007). Once such behavioral evidence is obtained, it can facilitate further development
of this theory or other theories. For instance, the study results can help scrutinize the relation
between the probed behaviors and mental processes as well as the extent to which these mental
processes depend on specific automaticity conditions.
Separately, following the outlined approach to implicit measures research promotes
theoretical debate and avoids theoretical hegemony. Approaching implicit measure performance as
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 19
an ‘act-in-context’ frees one up to consider how well any one theory accommodates the data which
can allow not only dominant theories (e.g., that relate automatic evaluation to mental processes
such as the automatic activation of associations that are acquired via stimulus pairings; Gawronski
& Bodenhausen, 2006) but also other perspectives to flourish and thrive (Cone et al., 2017; Van
Dessel et al., 2017). For example, taking this approach has led to new theories that explain
responding on implicit (e.g., automatic evaluation) measures in terms of propositions or automatic
inferences (De Houwer, 2014; Van Dessel, Hughes et al., 2019).
Another value of our approach can be found in the domain of dissociations between implicit
and explicit measures (Gawronski & Brannon, 2019). The idea that inherently stable mental
associations underlie implicit evaluations is often directly mapped onto empirical dissociations
between implicit and explicit evaluation (e.g., in research on impression formation: Okten, 2018;
racial prejudice: James, 2018; addiction: Wiers et al., 2017). Yet the approach we outline here
suggests that evidence for dissociations should not to be interpreted as strong or direct evidence
for distinct types of mental constructs or cognitive processes (Van Dessel, Gawronski et al., 2019).
Instead, dissociations should be investigated at the functional level to allow adequate conclusions
about the environmental conditions that produce these dissociations, such as the operating
conditions of the measures (see also Gawronski, 2019). First, observed dissociations are often due
to issues of fit between implicit and explicit measures (e.g., when the procedures are structurally –
or quantitatively – dissimilar). This issue has already been raised by others (e.g., Payne et al., 2008)
but is infrequently taken into account when designing research (although see Cummins & De
Houwer, 2019; Moran & Bar-Anan, 2020; Van Dessel et al., 2020). Alternatively, dissociations
can also reflect differences in the automaticity conditions under which behavior is emitted or
elicited within a given procedure as well as differences in psychometric properties of the measures.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 20
To examine this, studies are needed that probe effects of manipulations on multiple measures that
differ only in terms of their automaticity conditions, thus providing behavioral evidence which can
be used to test different theoretical explanations (e.g., associative vs. inferential processes) in a
Consider the finding that the recency and diagnosticity of information play a key role in
producing implicit-explicit dissociations. Specifically, diagnostic counter-attitudinal information
can sometimes lead to changes in explicit but not implicit evaluation (e.g., Gregg et al., 2006;
Rydell et al., 2007). Importantly, however, these studies typically use measures with very different
procedures to establish these dissociations (e.g., self-reported liking scales and IAT procedures).
In contrast, a recent study manipulated information diagnosticity and primacy and investigated
distinct effects on evaluative responding in two AMPs that differed only in their instructions.
Specifically, they instructed participants to either evaluate a target person prime or a Chinese
ideograph that followed the prime and were therefore assumed to differ only in terms of
intentionality, and not in terms of structural fit or any of the other automaticity conditions (Van
Dessel et al., 2020; see also Payne et al., 2008). Whereas information diagnosticity had strong
effects on scores on both measures and influenced the former AMP scores more strongly,
information primacy did not influence scores on either of these measures, hinting at the importance
of only the diagnosticity manipulation in relation to the intentionality of evaluation. This provided
information unbiased by specific explanatory mental theories which could be used to update certain
assumptions of dual-process and inferential theories of evaluation, facilitating theoretical precision
and improving the value of these theories (see Moran & Bar-Anan, 2020, for an example of this
approach in the context of testing differential effects of pairings versus relational information).
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 21
Applied value. Adopting our four guidelines can also facilitate progress in applied
research. The question of whether implicit measures have something to offer in the prediction of
real-life behavior can be addressed systematically, in research that directly compares the ‘match’
between behavior that is measured under different automaticity conditions and real-life behavior.
This research can be closely tailored to those specific conditions of implicit measures that are of
interest in the relevant context (e.g., probing unintentional responding). Adhering to our guidelines
means that, as well as examining and improving the measurement properties of implicit measures,
we should also be strategic about the contexts in which we attempt to find utility for those measures.
First, it might have little use to build measures for behavior that applied fields are not immediately
asking for simply because such measures are not likely to be of any use. Second, researchers should
also be more selective and explicit about choosing domains and contexts in which there may be
reason that an implicit measure has (added) utility. For example, rather than a broad appeal to their
supposed ability to tap unconscious or unaware processes, applied researchers should attempt to
define whether their behavioral phenomenon of interest is evident under a given condition of
automaticity and, if so, then select an implicit measure that demonstrably captures behavior under
that same automaticity condition(s). We recommend that researchers give greater thought to the fit
or congruence between the behavior they are attempting to predict and understand and what they
are capable of capturing within an implicit measure (see also Irving & Smith, 2019). Doing so
could help specify which implicit measures are most likely to have utility in which contexts and
help highlight those contexts in which implicit measures are less likely to be useful.
To illustrate, consider work on lie detection. In this applied domain there is a specific need
to measure behavior that people have good reasons not to be truthful about, and by implication, for
measures that can capture behavior under conditions that reduce intentionality. Rather than asking
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 22
people to confirm that they recognize a stimulus, they might be asked to reject recognizing that
stimulus by pressing a certain button. The time it takes them to do so (relative to their responses to
other stimuli which they actually do not recognize) might then be used as an index of recognition.
This general approach has utility for detecting previous (illegal) behavior and is regularly utilized
by police forces in Japan (Matsuda et al., 2012; Verschuere et al., 2011). Here researchers have
specified the automaticity condition of interest (unintentionality), and have utilized a measurement
procedure which focuses on capturing unintentional responding, demonstrating the importance of
considering automaticity overlap between to-be-predicted behaviors and the measurement
procedure to be used.
Applied implicit measures research might particularly benefit from adhering to two specific
recommendations. First, one should use those measures that have appropriate psychometric
properties for the question of interest. For instance, when lasting individual differences in racial
bias are of interest, it might be of little use to sample racial prejudice IAT scores which have low
test-retest reliability at the individual level (Lai et al., 2014, 2016; Payne et al., 2017). Second, one
should stay as close as possible to the behavior that one wants to predict. That is, the stimuli used
within the procedure should relate as closely as possible to the behavior to be predicted, such as in
the previously mentioned study where trying to predict border wall donations was better achieved
by using an IAT that examined border wall evaluations rather than immigrant evaluations (Irving
& Smith, 2019).
As a more detailed example, consider driving under the influence of alcohol (DUIA).
People might not always deliberately report on their DUIA behaviour given the negative
consequences of doing so. Hence there is an urgent need for a way to measure whether people have
driven under the influence of alcohol that is less intentional than the measures that are currently
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 23
used. In a recent study, we sought to develop a measure of unintentional evaluation of statements
that people had driven drunk (Cathelyn et al., 2019). We based our research on theoretical models
about beliefs underlying evaluation, but we defined the measure in behavioral terms (Guideline 1).
Next, we looked at research providing evidence for measures that evoke behavior under this
automaticity condition (defined as behavior being more difficult to change when instructed to do
so compared to other measures) and selected a measure with this desired condition in the current
context (Guideline 2.1, 3). We therefore decided to develop a variant of the autobiographical IAT
(Sartori et al., 2008) that required participants to categorize sentences related to drunk driving
behavior as well as other, unrelated autobiographical statements of a known truth value (e.g., “I am
doing a computer task”). We assured good correspondence between the probed behavior and the
measure as the aIAT’s drunk-driving sentences were related directly to having committed such an
act in the past (e.g., the sentence “I have driven while drunk”) (Guideline 3). After initial testing
of psychometric properties such as reliability (Guideline 4), further relevant properties of the aIAT
were optimized for the context within which it was to be employed. For example, this aIAT was
optimized to have good predictive utility for this specific context by calibrating score thresholds to
have a low false positive rate. Though more research is needed to establish the utility of this specific
measure, this example provides an initial illustration of the potential utility which keeping the noted
guidelines in mind may have in developing new and optimizing existing implicit measures for
The use of implicit measures has brought about many opportunities in several fields of
research, but the measures have not always lived up to their original claims or promise. After 25
years, there is still a lack of clarity as to what they measure, their mediating mental mechanisms,
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 24
their applied predictive utility, and their psychometric properties. While some may view this
uncertainty positively (i.e., as justification for further debate and work on implicit measures), this
uncertainty also begs the question: how much more time and resources should we collectively
spend creating and defending these measures?
Of course, the aforementioned is beyond the scope of the manuscript. We recognize that
implicit measures research will continue, and if it will continue it is better that it be done well. We
have here outlined four guidelines to aid future implicit measures research in moving beyond the
issues which have held the field stationary for many years. Some readers might argue that they are
already aware of these guidelines. But awareness clearly does not equate to action: we have not
collectively adhered to these guidelines despite knowing about them. Thus, in addition to providing
these guidelines, we have tried to provide concrete ways to follow them. As such, our first
recommendation is to approach performance on implicit measures as an ‘act-in-context’ (i.e., as
behavior emitted within a measurement context and under certain environmental conditions;
Guideline 1). We suggest that the use of mental-level theories should only be introduced after the
measures are well-described at the functional level and even then, one should always avoid defining
the measure in terms of mental processes. We also recommend that researchers better specify and
test the key features (e.g., automaticity, indirectness) that behavior in a given implicit measure is
assumed to have and provide a label that better fits these features than the label of ‘implicit
measures’ (Guideline 2). Researchers should also test the match between behavior in the implicit
measure and the behavior of interest when selecting measures for their research (Guideline 3). Our
final recommendation is that researchers focus more acutely on assessing and improving the
psychometric properties of the measurement procedures that they are using (Guideline 4).
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 25
Taking these guidelines into account has numerous benefits for theory and practice and
avoids certain pitfalls. It avoids theoretical hegemony and helps to advance theory, predictions, and
possibilities. It might also inform researchers about the contexts in which implicit measures are
more likely to provide utility and help improve existing implicit measures. Of course, it may not
be possible for every researcher to fully adhere to all the proposed guidelines. However, taking
these guidelines into account when doing implicit measures research might already help the field
progress and avoid continuing to succumb to the issues which have been present in the last twenty-
five years of work.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 26
Amodio, D. M., & Devine, P. G. (2006). Stereotyping and evaluation in implicit race bias: evidence
for independent constructs and unique effects on behavior. Journal of personality and social
psychology, 91, 652.
Banaji, M. R. (2001). Implicit attitudes can be measured. In H. L. Roediger, J. S. Nairne, I. Neath,
& A. Surprenant (Eds.), The nature of remembering: Essays in honor of Robert G. Crowder
(pp. 117–150). Washington, DC: American Psychological Association.
Bar-Anan, Y., & Nosek, B. A. (2014). A comparative investigation of seven indirect attitude
measures. Behavior Research Methods, 46(3), 668-688.
Blanton, H., & Jaccard, J. (2006). Arbitrary metrics in psychology. American Psychologist, 61, 27-
Brownstein, M., Madva, A., & Gawronski, B. (2019). What do implicit measures measure? Wiley
Interdisciplinary Reviews: Cognitive Science, 10(5):e1501.
Carter, E., Onyeador, I., & Lewis, N. A., Jr. (in press). Developing and Delivering Effective Anti-
Bias Training: Challenges and Recommendations. Behavioral Science and Policy.
Cathelyn, F., Van Dessel, P., Cummins, J., & De Houwer, J. (2019). Driving Under the Influence
of Alcohol. Retrieved from osf.io/b7wur
Chester, D., & Lasko, E. (2019). Construct Validation of Experimental Manipulations in Social
Psychology: Current Practices and Recommendations for the Future.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 27
Cone, J., Mann, T. C., & Ferguson, M. J. (2017). Changing our implicit minds: How, when, and
why implicit evaluations can be rapidly revised. In Advances in Experimental Social
Psychology (Vol. 56, pp. 131–199). Academic Press.
Corneille, O., & Hütter, M. (2020). Implicit? What Do You Mean? A Comprehensive Review of
the Delusive Implicitness Construct in Attitude Research. Personality and Social
Psychology Review. 1088868320911325. https://doi.org/10.1177/1088868320911325
Corneille, O., & Stahl, C. (2019). Associative Attitude Learning: A Closer Look at Evidence and
How It Relates to Attitude Models. Personality and Social Psychology Review, 23(2), 161–
Cristea, I. A., Kok, R. N., & Cuijpers, P. (2015). Efficacy of cognitive bias modification
interventions in anxiety and depression: Meta-analysis. British Journal of Psychiatry, 206, 7-
Cummins, J., & Houwer, J. D. (2019). An inkblot for beliefs: The Truth Misattribution Procedure.
PLOS ONE, 14(6), e0218661.
Cummins, J., Hussey, I., & Hughes, S. (2019). The AMPeror’s New Clothes: Performance on the
Affect Misattribution Procedure is Mainly Driven by Awareness of Influence of the Primes.
Dang, J., King, K. M., & Inzlicht, M. (2020). Why are Self-Report and Behavioral Measures
Weakly Correlated? Trends in Cognitive Science, 24(4), 267-269. 10.1016/j.tics.2020.01.007
De Houwer, J. (2011). Why the cognitive approach in psychology would profit from a functional
approach and vice versa. Perspectives on Psychological Science, 6, 202–209.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 28
De Houwer, J. (2014). A Propositional Model of Implicit Evaluation. Social and Personality
Psychology Compass, 8, 342–353.
De Houwer, J. (2019). Implicit Bias Is Behavior: A Functional-Cognitive Perspective on Implicit
Bias. Perspectives on Psychological Science, 14, 835-840.
De Houwer, J., Gawronski, B., & Barnes-Holmes, D. (2013). A functional-cognitive framework
for attitude research. European Review of Social Psychology, 24(1), 252–287.
De Houwer, J., Heider, N., Spruyt, A., Roets, A., & Hughes, S. (2015). The relational responding
task: toward a new implicit measure of beliefs. Frontiers in Psychology, 6: 319.
De Houwer, J., Van Dessel, P., & Moran, T. (2020). Attitudes Beyond Associations: On the Role
of Propositional Representations in Stimulus Evaluation. Advances in Experimental Social
Psychology, 61, 127-83.
De Schryver, M., Hughes, S., De Houwer, J., & Rosseel, Y. (2018). On the Reliability of Implicit
Measures: Current Practices and Novel Recommendations.
Dovidio, J. F., Kawakami, K., Johnson, C., Johnson, B., & Howard, A. (1997). On the nature of
prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology,
Fazio, R. H., & Olson, M.A. (2003). Implicit measures in social cognition research: Their meaning
and uses. Annual Review of Psychology, 54, 297–327.
Fiedler, K., & Bluemke, M. (2005). Faking the IAT: Aided and unaided response control on the
implicit association tests. Basic and Applied Social Psychology, 27, 307–316.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 29
Fiedler, K., Messner, C., & Bluemke, M. (2006). Unresolved problems with the “I”, the “A”, and
the “T”: A logical and psychometric critique of the Implicit Association Test (IAT). European
Review of Social Psychology, 17, 74–147.
Flake, J. K., Pek, J., & Hehman, E. (2017). Construct Validation in Social and Personality
Research: Current Practice and Recommendations. Social Psychological and Personality
Science, 8(4), 370–378.
Forscher, P. S., Lai, C. K., Axt, J. R., Ebersole, C. R., Herman, M., Devine, P. G., & Nosek, B. A.
(2019). A meta-analysis of procedures to change implicit measures. Journal of Personality
and Social Psychology, 117(3), 522-559.
Friese, M., Hofmann, W., & Schmitt, M. (2009). When and why do implicit measures predict
behaviour? Empirical evidence for the moderating role of opportunity, motivation, and
process reliance. European Review of Social Psychology, 19, 285-338.
Gawronski, B. (2019). Six lessons for a cogent science of implicit bias and its
criticism. Perspectives on Psychological Science, 14, 574-595.
Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in
evaluation: An integrative review of implicit and explicit attitude change. Psychological
Bulletin, 132, 692-731.
Gawronski, B., & Brannon, S. M. (2019). Attitudes and the implicit-explicit dualism. In D.
Albarracín & B. T. Johnson (Eds.), The handbook of attitudes. Volume 1: Basic
principles (2nd edition, pp. 158-196). New York, NY: Routledge.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 30
Gawronski, B., & De Houwer, J. (2014). Implicit measures in social and personality psychology. In
H. T. Reis, & C. M. Judd (Eds.), Handbook of research methods in social and personality
psychology (2nd edition, pp. 283–310). New York: Cambridge University Press.
Gawronski, B., & Hahn, A. (2019). Implicit measures: Procedures, use, and interpretation. In H.
Blanton, J. M. LaCroix, & G. D. Webster (Eds.), Measurement in social psychology (pp.
29-55). New York, NY: Taylor & Francis.
Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and
stereotypes. Psychological Review, 102(1), 4–27.
Greenwald, A. G., & Banaji, M. R. (2017). The implicit revolution: Reconceiving the relation
between conscious and unconscious. American Psychologist, 72, 861–871.
Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences
in implicit cognition: The Implicit Association Test. Journal of Personality and Social
Psychology, 74, 1464-1480.
Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit
Association Test: I. An improved scoring algorithm. Journal of Personality and Social
Psychology, 85, 197-216.
Gregg, A. P., Seibt, B., & Banaji, M. R. (2006). Easier done than undone: Asymmetry in the
malleability of implicit preferences. Journal of Personality and Social Psychology, 90, 1-
20. doi: 10.1037/0022-35220.127.116.11
Hahn, A., & Gawronski, B. (2019). Facing one's implicit biases: From awareness to
acknowledgement. Journal of Personality and Social Psychology, 116, 769–794.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 31
Heycke, T., Gehrmann, S. M., Haaf, J., & Stahl, C. (2018). Of two minds or one? A registered
replication of Rydell et al. (2006). Emotion and Cognition, 32, 1-20.
Hughes, S., Barnes-Holmes, D., & De Houwer, J. (2011). The dominance of associative theorising
in implicit attitude research: Propositional and behavioral alternatives. The Psychological
Record, 61, 465–498.
Hughes, S., De Houwer, J., & Perugini, M. (2016). Expanding the boundaries of evaluative learning
research: How intersecting regularities shape our likes and dislikes. Journal of
Experimental Psychology: General, 145(6), 731-754.
Hussey, I., & Hughes, S. (2020). Hidden Invalidity Among 15 Commonly Used Measures in
Social and Personality Psychology. Advances in Methods and Practices in Psychological
Science, 3(2), 166–184.
Irving, L. H., & Smith, C. T. (2019). Measure what you are trying to predict: Applying the
correspondence principle to the Implicit Association Test. Journal of Experimental Social
James, L. (2018), The stability of implicit racial bias in police officers, Police Quarterly, 21, 30-
Jost, J. T. (2019). The IAT is dead, long live the IAT: Context-sensitive measures of implicit
attitudes are indispensable to social and political psychology. Current Directions in
Psychological Science, 28, 10-19.
Kurdi, B., Seitchik, A. E., Axt, J. R., Carroll, T. J., Karapetyan, A., Kaushik, N., Tomezsko, D.,
Greenwald, A. G., & Banaji, M. R. (2019). Relationship between the Implicit Association
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 32
Test and intergroup behavior: A meta-analysis. American Psychologist, 74(5), 569–586.
Lai, C. K., Marini, M., Lehr, S. A., Cerruti, C., Shin, J. E. L., Joy-Gaba, J. A., et al. (2014).
Reducing implicit racial preferences: I. A comparative investigation of 17 interventions.
Journal of Experimental Psychology: General, 143, 1765-1785.
Lai, C. K., Skinner, A. L., Cooley, E., Murrar, S., Brauer, M., Devos, T., et al. (2016). Reducing
implicit racial preferences: II. Intervention effectiveness across time. Journal of
Experimental Psychology: General, 145, 1001–1016.
Larsen, H., Engels, R., Wiers, R., Granic, I., & Spijkerman, R. (2012). Implicit and explicit alcohol
cognitions and observed alcohol consumption: three studies in (semi)naturalistic drinking
settings. Addiction, 107(8), 1420-1428.
Lindgren, K. P., Baldwin, S. A., Ramirez, J. J., Olin, C. C., Peterson, K. P., Wiers, R. W., ... &
Neighbors, C. (2019). Self-control, implicit alcohol associations, and the (lack of)
prediction of consumption in an alcohol taste test with college student heavy episodic
drinkers. PloS one, 14, e0209940.
Matsuda, I., Nittono, H., & Allen, J. J. B. (2012). The current and future status of the concealed
information test for field use. Frontiers in Psychology, 3, 532.
Meissner, F., Grigutsch, L.A., Koranyi, N. Müller, F., & Rothermund, K. (2019). Predicting
behavior with implicit measures: Disillusioning findings, reasonable explanations, and
sophisticated solutions. Frontiers in Psychology.
Melnikoff, D. E., & Bargh, J. A. (2018). The Mythical Number Two. Trends in Cognitive Sciences,
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 33
Mitchell, G., & Tetlock, P. E. (2017). Popularity as a poor proxy for utility: The case of implicit
prejudice. In S. Lilienfeld & I. Waldman (Eds.), Psychological science under scrutiny:
Recent challenges and proposed solutions (pp. 164–195). West Sussex, England: Wiley.
Moors, A., & De Houwer, J. (2006). Automaticity: A theoretical and conceptual analysis.
Psychological Bulletin, 132(2), 297–326.
De Houwer, J., & Moors, A. (2007). How to define and examine the implicitness of implicit
measures. In B. Wittenbrink & N. Schwarz, Implicit measures of attitudes: Procedures
and controversies (pp. 179–194). Guilford Press.
Moran, T., & Bar-Anan, Y. (2020). The effect of co-occurrence and relational information on
speeded evaluation. Cognition and Emotion, 34(1), 144-155.
Müller, F., & Rothermund, K. (2019). The Propositional Evaluation Paradigm (PEP): Indirect
Assessment of Personal Beliefs and Attitudes. Frontiers in Psychology.
Nock, M. K., Park, J. M., Finn, C. T., Deliberto, T. L., Dour, H. J., & Banaji, M. R. (2010).
Measuring the “suicidal mind:” Implicit cognition predicts suicidal behavior. Psychological
Science, 21, 511-517.
Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and using the Implicit
Association Test: II. Method variables and construct validity. Personality and Social
Psychology Bulletin, 31, 166-180.
Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7:
A methodological and conceptual review. In JA. Bargh (Ed.), Automatic processes in social
thinking and behavior (pp. 265–292). New York, NY: Psychology Press.
Okten, I. O. (2018). Studying First Impressions: What to Consider? APS Observer, 31.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 34
Oswald, F. L., Mitchell, G., Blanton, H., Jaccard, J., & Tetlock, P. E. (2013). Predicting ethnic and
racial discrimination: A meta‐analysis of IAT criterion studies. Journal of Personality and
Social Psychology, 105, 171–192.
Payne, B. K., Burkley, M. A., & Stokes, M. B. (2008). Why Do Implicit and Explicit Attitude Tests
Diverge? The Role of Structural Fit. Journal of Personality and Social Psychology, 94, 16–
Payne, B. K., Vuletich, H. A., & Lundberg, K. B. (2017). The Bias of Crowds: How Implicit Bias
Bridges Personal and Systemic Prejudice. Psychological Inquiry, 28, 233-248.
Rothermund, K., & Wentura, D. (2004). Underlying processes in the Implicit Association Test
(IAT): Dissociating salience from associations. Journal of Experimental Psychology:
General, 133, 139–165.
Rydell, R., McConnell, A. R., Strain, L. M., Claypool, H. M., & Hugenberg, K. (2007). Implicit
and explicit attitudes respond differently to increasing amounts of counterattitudinal
information. European Journal of Social Psychology, 37, 867–878.
Sartori, G., Agosta, S., Zogmaister, C., Ferrara, S. D., & Castiello, U. (2008). How to accurately
detect autobiographical events. Psychological Science, 19(8), 772–780.
Schimmack, U. (2019). The Implicit Association Test: A Method in Search of a Construct.
Perspectives on Psychological Science.
Smith, E. R., & DeCoster, J. (2000). Dual-process models in social and cognitive psychology:
Conceptual integration and links to underlying memory systems. Personality and Social
Psychology Review, 4, 108–131.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 35
Spruyt, A., De Houwer, J., Tibboel, H., Verschuere, B., Crombez, G., Verbanck, P., Hanak, C.,
Brevers, D., & Noël, X. (2013). On the predictive validity of automatically activated
approach/avoidance tendencies in abstaining alcohol-dependent patients. Drug and Alcohol
Dependence, 127, 81-86.
Spruyt, A., Lemaigre, V., Salhi, B., Van Gucht, D., Tibboel, H., Van Bockstaele, B., De Houwer,
J., Van Meerbeeck, J., & Nackaerts, K. (2015). Implicit attitudes towards smoking predict
long-term relapse in abstinent smokers. Psychopharmacology, 232, 2551-2561.
Stieger, S., Goritz, A. S., Hergovich, A. & Voracek, M. (2011). Intentional faking of the single
category Implicit Association Test and the Implicit Association Test. Psychological
Reports, 109, 219-230
Tello, N., Harika-Germaneau, G., Serra, W., Jaafari, N., & Chatard, A. (2018). Forecasting a Fatal
Decision: Direct Replication of the Predictive Validity of the Suicide-Implicit Association
Thush, C., Wiers, R. W., Ames, S. L., Grenard, J. L., Sussman, S., & Stacy, A. W. (2007). Apples
and oranges? Comparing indirect measures of alcohol-related cognition predicting alcohol
use in at-risk adolescents. Psychology of Addictive Behaviors, 21(4), 587-591.
Van Dessel, P., Cone, J., Gast, A., & De Houwer, J. (2020). The Impact of Valenced Verbal
Information on Implicit and Explicit Evaluation: The Role of Information Diagnosticity,
Primacy, and Memory Cueing. Cognition & Emotion., 34, 74-85.
Van Dessel, P., Gawronski, B., & De Houwer, J. (2019). Does explaining social behavior require
multiple memory systems. Trends in cognitive sciences, 23(5), 368-369.
GUIDELINES FOR FUTURE RESEARCH WITH IMPLICIT MEASURES 36
Van Dessel, P., Hughes, S., & De Houwer, J. (2019). How Do Actions Influence Attitudes? An
Inferential Account of the Impact of Action Performance on Stimulus Evaluation.
Personality and Social Psychology Review, 23, 267-284.
Van Dessel, P., Gawronski, B., Smith, C. T., & De Houwer, J. (2017). Mechanisms underlying
approach-avoidance instruction effects on implicit evaluation: Results of a preregistered
adversarial collaboration. Journal of Experimental Social Psychology, 69, 23-32.
Verschuere, B., Ben-Shakhar, G., & Meijer, E. (2011). Memory detection: Theory and application
of the concealed information test. Cambridge University Press.
Wiers, C. E., Gladwin, T. E., Ludwig, V. U., Gröpper, S., Stuke, H., et al. (2017) Comparing three
cognitive biases for alcohol cues in alcohol dependence. Alcohol, 52, 242-248.