Unconscious Attitudes toward L2 Speakers

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Breaking Theory: New Directions
in Applied Linguistics
Proceedings of the 48th Annual Meeting
of the
British Association for Applied Linguistics
3-5 September 2015
Aston University, Birmingham
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Unconscious Attitudes toward L2 Speakers
Ali H. Al-Hoorie
87
Unconscious Attitudes toward L2
Speakers
Ali H. Al-Hoorie
Nottingham University, UK
The English Language Institute, Jubail Industrial
College, Saudi Arabia
hoorie_a@jic.edu.sa
The most common instrument used in attitude and motivation studies is the
questionnaire. The researcher asks a group of respondents some questions,
and the answers to these questions form the empirical basis for theory
development (a similar logic applies to qualitative interviews). The
underlying assumption of this approach is that the respondent is able to
recognize and articulate the determinants of their behavior. This view is in
line with Gordon Allport’s (1953, p. 114) view that “simple, conscious report
is the whole truth. It can be taken at its face value.”
However, motivational psychologists have been increasingly moving away
from this view. It is now taken for granted by most researchers that there are
more influences than those reportable in questionnaires, and more than meets
the eye. If this is the case, then researchers need tools that can tap into these
unconscious (or implicit) influences. One of the most commonly used tools
is the Implicit Association Test.
What is the Implicit Association Test?
The Implicit Association Test (IAT, Greenwald, McGhee, & Schwartz, 1998)
is a computerized reaction-time measure. Participants classify a series of
stimuli either to the right or left of the screen depending on their semantic
meaning. As an illustration, imagine that a study was designed to measure
attitudes toward English and Arabic. In this case, words like Pleasant and
Unpleasant are paired with each of English and Arabic. In the first condition,
Arabic may be paired with Pleasant and English with Unpleasant (see Figure
1). In the second condition, the pairing is reversed so that Arabic is now
paired with Unpleasant while English with Pleasant. The score is calculated
by comparing the reaction time in each condition. Most Arabic speakers find
it easier to perform the task when Arabic is paired with Pleasant, whereas
most English people find the opposite easier.
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Breaking Theory: New Directions in Applied Linguistics
Aston University, Birmingham
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Figure 1: A trial of the IAT. The correct answer here would be the left pair
because the stimulus Honest belongs to Pleasant.
Just as its name suggests, the IAT measures an ‘association’ between two
words. It is ‘implicit’ because most people are unaware of these associations
and usually find their own results surprising. Whether these associations also
qualify to be considered an attitude has been a subject of debate. However,
an increasing number of empirical studies are showing that implicit
associations indeed predict meaningful outcomes and behaviors that are
consistent with the claim that these associations are attitudes (e.g., see
Rudman, 2008, for a review).
More specifically, the most interesting findings come from tests of known-
groups validity and predictive validity. As for known-groups validity,
research shows that the IAT is capable of correctly distinguishing among
members of different groups in accordance with our a priori knowledge of
them, such as reliably determining the participant’s gender, nationality, and
even affiliation to a group artificially created in the laboratory (for a review,
see Lane, Banaji, Nosek, & Greenwald, 2007). As for predictive validity, a
range of criterion variables—including behavioral, judgmental, and
physiological measures—have been successfully predicted by the IAT in
various areas including consumer references, political preferences,
personality traits, sexual orientations, and close relationships.

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In one meta-analysis, the predictive validity of the IAT averaged r = .274,
and the IAT and self-report measures displayed incremental validity, in that
each predicted criterion variance over and above that predicted by the other
measure alone (Greenwald et al., 2009). In another meta-analysis (Oswald et
al., 2013), the average predictive validity was lower (r = .15) in the area of
racial discrimination. However, the latter meta-analysis was criticized for
including correlations that were not theoretically expected, which suppressed
the resulting correlation (Greenwald, Nosek, & Banaji, 2015).
As an illustration of this type of research, consider a study by Glaser and
Knowles (2008). The researchers examined the relationship between the
implicit stereotype associating Blacks with guns and the tendency to shoot
Blacks faster than Whites in a computer simulation game (known as the
‘Shooter Bias’). The participants viewed pictures of Blacks or Whites
holding either a weapon or a benign object. Although the task was to shoot
individuals holding a weapon regardless of their skin color, participants with
a stereotype associating Blacks with guns (especially those with low
motivation to control their prejudice) misidentified Blacks as holding guns
more frequently.
Implicit attitudes in second language learning
In the remainder of this chapter, I will give a brief summary of two initial
studies I conducted using the IAT in the area of second language (L2)
learning. In the first study, Arab learners of English (N = 365) who were
studying in the UK performed the IAT and completed a questionnaire. The
results showed that learners with explicit–implicit congruence (i.e., with
favorable attitudes toward English both explicitly and implicitly) were
significantly more open to the L2 group than were learners with explicit–
implicit incongruence (i.e., favorable at the explicit level only). These results
showed that the IAT scores behave in a theoretically meaningful way.
The first study did not include a measure of actual L2 achievement because
the participants came from various universities and language institutes in the
UK. Therefore, no standardized measure of their achievement was available.
However, the implicit test would become much more useful if it can also
predict who would (not) be successful in the L2. The second study therefore
included a measure of L2 achievement in order to test the hypothesis that
implicit attitudes are also associated with L2 achievement.
The second study involved learners (N = 311) studying English at one
institution in Saudi Arabia. The participants performed the Single-Target

Breaking Theory: New Directions in Applied Linguistics
Aston University, Birmingham
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Implicit Association Test (ST-IAT, Wigboldus, Holland, & van
Knippenberg, 2005), which is a variation of the IAT that can use one
category only (e.g., English without having to compare it with Arabic). In
addition to successfully replicating the first study, the results of the second
study showed that learners who scored higher in the implicit test did achieve
higher in the L2 course.
Conclusion
Going back to Gordon Allport’s above-mentioned claim that simple self-
report is ‘the whole truth’, the results of these two studies demonstrate that
self-report may not constitute the whole truth after all. This is because
expressing positive attitudes toward at the explicit level does not always
reflect positive attitudes at the implicit level as well. Researchers should
therefore examine implicit attitudes in order to find out whether they are in
harmony or in conflict with explicit attitudes, and this cannot be done by
self-report measures alone.
When it comes to L2 learning, the results presented above show that an
implicit dimension exists. They also demonstrate that explicit–implicit
harmony and conflict are related to both attitudes and achievement in L2
learning. These findings are encouraging, as they open up a whole new line
of inquiry with ample research opportunities for L2 motivation researchers.
References
Gordon W. Allport (1953). The trend in motivational theory. In American Journal of
Orthopsychiatry 23:1, pp107-119.
Jack Glaser & Eric D. Knowles (2008). Implicit motivation to control prejudice. In
Journal of Experimental Social Psychology 44, pp164-172.
Anthony G. Greenwald, Debbie E. McGhee & Jordan L. K. Schwartz (1998).
Measuring individual differences in implicit cognition: The Implicit Association
Test. In Journal of Personality and Social Psychology 74:6, pp1464-1480.
Anthony G. Greenwald, Brian A. Nosek & Mahzarin R. Banaji (2015). Statistically
small effects of the Implicit Association Test can have societally large effects. In
Journal of Personality and Social Psychology 108:4, pp553-561.
Anthony G. Greenwald, T. Andrew Poehlman, Eric Luis Uhlmann & Mahzarin R
Banaji (2009). Understanding and using the Implicit Association Test: III. Meta-
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97:1, pp17–41.
Kristin A. Lane, Mahzarin R. Banaji, Brian A. Nosek & Anthony G. Greenwald
(2007). Understanding and using the Implicit Association Test: IV. What we
know (so far) about the method. In Bernd Wittenbrink & Norbert Schwarz (eds),
Implicit Measures of Attitudes. Guilford Press, New York, pp59–102.

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Frederick L. Oswald, Gregory Mitchell, Hart Blanton, James Jaccard & Philip E.
Tetlock (2013). Predicting ethnic and racial discrimination: A meta-analysis of
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Daniël H. J. Wigboldus., Rob W. Holland & Ad van Knippenberg (2005). Single
Target Implicit Associations. Unpublished manuscript.

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Aston University, Birmingham
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