Implicit and explicit cognitions in physical activity Using the Single Category Implicit Association Test (SC-IAT)

Transcription

1 Master thesis Health and Society Implicit and explicit cognitions in physical activity Using the Single Category Implicit Association Test (SC-IAT) Sabina Super November 2012 Supervisor HSO: dr. Kirsten Verkooijen HSO-80333, Wageningen University

2 Preface During my two master studies of Applied Communication Science and Health & Society, I got interested in the unconscious processes in our mind. How come that, despite our best intentions, we often fail to perform healthy behaviour? Even if we are aware of the importance to eat healthy, we have made plans to do so, we have the knowledge of what is healthy food, and we feel confident that we can perform the required behaviour. we often find ourselves at the end of the day thinking Hmmm, something went wrong here, yet again. We did take that snack and left our fruits, which we took to work that day, to rot in our bag or drawer. Is it because we are not able to think straight anymore when we actually have to make our choices? Some years ago, I came across an article of which I lost the author s name. The point of the article, however, never left my mind. In our daily lives we take, on average, a 1000 food choices per day of which 95% is unconscious. So despite that you have made the best plans and hold the best intentions to do something, in face of these choices you may actually end up doing something completely different. This is not to say that we do not have any control over what we do. However, what is really going on in our mind is still unknown, and it is on this area that I hope to contribute with my thesis. The overall conclusion after writing my thesis is that both conscious and unconscious processes play an important role in our decision-making, but how and, most importantly, when they influence our behaviour is still a large question mark deserving much more attention. I would like to thank Marije Onnink for contributing to the pre-test of my experiment. I enjoyed working with you on the Single Category Implicit Association Test. In addition, I would like to say thanks to my sister Ingrid, who has been very patient with me, always listing to the unclear and overtheoretical explanations of my research. Finally, I appreciated working with my supervisor Kirsten Verkooijen. Your help with retrieving the IAT-scores and the advice you gave me on my writings is much appreciated. Wageningen, November 2012 Sabina Super 2

3 Summary Objectives: This study investigated the relationship between implicit and explicit measures of attitude and self-identity for physical activity. In addition, three models linking implicit and explicit measures with behaviour were tested: the additive model, the multiplicative model, and the double-dissociation model. Method: An experiment was conducted among 93 university students (16% male and 84% female). Participants were randomly assigned to do either an attitude Single Category Implicit Association Test (SC-IAT) or a self-identity SC-IAT. This test was followed by a questionnaire measuring various explicit motivational factors such as habit strength, self-efficacy, attitude, self-identity, and intentions. Also a measure for intentional physical activity was included in the questionnaire. The self-report measures were followed by the remaining SC-IAT. A measure of unintentional physical activity was obtained by letting respondents hand in their questionnaire on the second floor and noting whether they took the stairs or the elevator. After one week, intentional physical activity was again assessed via a questionnaire. Results: Implicit and explicit self-identity did not correlate, and also implicit attitude and explicit instrumental attitude did not correlate. Implicit attitude and explicit affective attitude did correlate significantly. Implicit attitude was also significantly positively correlated with previous intentional physical activity, whereas self-identity was not. A hierarchical regression analysis revealed that implicit self-identity predicted physical activity after controlling for explicit motivational factors. The same did not hold true for implicit attitude. The additive model was confirmed for attitude, but none of the models seem to fit for self-identity. The unintentional measure of physical activity (e.g., taking the stairs or the elevator) did not correlate with any of the implicit and explicit measures and, additionally, none of the models could be confirmed for unintentional physical activity. Conclusions: Both implicit and explicit cognitions are important to consider when trying to understand and predict physical activity. Future research should investigate further how and when both components influence behaviour across different content domains and research contexts. Keywords: implicit cognition; Implicit Association Test; self-identity; attitude; physical activity. 3

4 Table of Contents 1. Introduction Theoretical framework Current research on explicit cognitions for physical activity Current research on implicit cognitions for physical activity The Implicit Association Test The role of the other (SC-IAT) Theory behind implicit and explicit social cognitions Moderators of the implicit-explicit relationship How implicit and explicit cognitions predict behaviour This study Method Participants and procedure Materials and measures Single Category Implicit Association Test Questionnaire Questionnaire Data preparation The results Descriptive statistics Zero-order Pearson correlations Predicting physical activity Testing the models The additive model The double-dissociation model The multiplicative model Discussion General discussion Practical and theoretical implications Limitations and future directions

5 6. Conclusion References Appendix 1: Item translation and references questionnaire Appendix II: Questionnaire sport behaviour T Appendix III: Questionnaire sport behaviour T Appendix IV: Translation object and attribute dimensions SC-IAT Appendix V: Informed consent Appendix VI: Debriefing

6 1. Introduction Physical activity is a lifestyle behaviour that is associated with many health benefits ranging from short-term improved mood states to long-term improved physical and mental well-being (Penedo & Dahn, 2005; Warburton, Nicol, & Bredin, 2006). Physical activity contributes not only to the primary and secondary prevention of chronic diseases such as cardiovascular diseases and diabetes, but is also associated with a reduced risk of premature death (Warburton et al., 2006). However, the amount of people who engage in sufficient physical activity is limited, the WHO estimated that the proportion of adults who were inactive in 2008 was about 30% (World Health Organization, 2008). In the Netherlands the proportion of adults who are physically inactive is estimated to be 50% (Wendel-Vos, 2008). A large strand of research has sought to identify the determinants of physical (in)activity. A part of this research has focused on environmental determinants (e.g., social support, accessibility of recreational facilities, costs for physical activity etc.) using, for example, the ANGELO-framework to identify environmental constraints that hinder the adoption or maintenance of an exercise regime (e.g., Wendel-Vos, Droomers, Kremers, Brug, & Van Lenthe, 2007). Another important focus of research on physical (in)activity has been the individual. Theories such as the Theory of Planned Behaviour have sought to identify individual factors that predict intentions and behaviours. However, the predictive power of intentions is often limited, where meta-analyses show that intentions account for 20% to 40% of the explained variance of physical activity behaviour (Amireault, Godin, Vohl, & Pérusse, 2008; Bruijn de & Putte van den, 2012). One of the problems which has been identified, that could account for this intention-behaviour gap, is the idea that self-reports may be vulnerable to biases such as self-presentation and selfignorance (Banting, Dimmock, & Lay, 2009; Schnabel, Asendorpf, & Greenwald, 2008). Respondents may give social desirable answers for example to scales measuring their attitude or identity. These answers, subsequently, may not correspond with the performed behaviour, leading to a lower predictive value of the measured constructs. Moreover, participants may not be aware of their attitude towards specific topics, also called introspective limits (Greenwald, Poehlman, Uhlmann, & Banaji, 2009; Schnabel et al., 2008). According to Hyde et al. (2010), focusing on explicit measures that are accessible through introspection may overlook the automatic behavioural responses that can also influence behaviour. In a response to the limitations of self-reports for measuring socialpsychological constructs, implicit measurement tools were developed. The automatic evaluative preferences, or implicit attitudes, include those that people are unwilling or unable to report (Fazio & Olson, 2003). The most well-reported and often used implicit measure of cognitions is the Implicit Association Test, developed by Greenwald, McGhee and Schwartz (1998). Although the Implicit Association Test has been used many times for different study areas, there has been limited research 6

7 using this measure in the area of physical activity. This study tries to fill this gap and focuses on implicit and explicit measures of cognitions in the physical activity domain, and the predictive validity of these measures for physical activity. The Implicit Association Test (hereafter abbreviated IAT) measures the implicit cognitions by measuring the underlying automatic evaluations (Greenwald et al., 1998). For many implicit measurement methods the reliability is unknown or very low, but for the Implicit Association Test the reliability and validity has been good (Greenwald et al., 2009). The procedure assesses the strength of an association between a target or an object and an attribute (i.e., evaluative) dimension by calculating the latency with which participants can push response keys when different objects and evaluations are combined. The assumption is that participants take more time when objects and evaluations are combined on one response key that are not associated in their mind (for a further explanation of the IAT, see paragraph 2.3). The IAT is an often-used procedure to measure implicit cognitions in combination with explicit measures and behaviours in areas such as alcohol consumption (Gray, LaPlante, Bannon, Ambady, & Shaffer, 2011; Wiers, Van Woerden, Smulders, & De Jong, 2002), consumer attitudes (Maison, Greenwald, & Bruin, 2004), food and obesity (Roefs & Jansen, 2002), and, to a limited extent, physical activity and exercise behaviour (Banting et al., 2009; Calitri, Lowe, Eves, & Bennett, 2009; Conroy, Hyde, Doerksen, & Ribeiro, 2010). The correlations between implicit and explicit measures of cognitions vary across social fields but are often weakly correlated or even conflicting (Nosek, 2005). Additionally, research findings on the predictive value of the IAT for behaviour vary between different study domains. Nonetheless, Perugini (2005) argues that in general, there is accumulated empirical evidence that the IAT can predict specific behaviours, although in some studies it failed to do so (p. 30). A study conducted on food preferences is such a study in which implicit attitudes did not predict behaviour (Roefs & Jansen, 2002). Obese people reported a stronger negative implicit attitude towards high fat foods than normal-weight people. Interestingly, their implicit attitudes towards fat foods corresponded with their explicit preferences (e.g., a negative explicit attitude towards high-fat foods) but not with the performed behaviour. Similarly, in a study on alcohol consumption, heavy drinkers had a strong negative implicit association with alcohol, which contrasted with their positive explicit judgments and behaviour (Wiers et al., 2002). Many uncertainties exist about the relation between implicit and explicit measures of cognitions and the correlation of these measures with behaviour. Fazio and Olson (2003) conclude that more research is needed on the prediction of behaviour from IAT-scores. Especially in the area of physical activity, only a limited number of studies have been conducted using the IAT. These studies have been mainly cross-sectional, focusing on relations between implicit attitudes and previous physical activity and have not addressed the predictive validity of implicit cognitions for physical activity (Conroy et al., 2010). The purpose of this research is to contribute to the existing knowledge by 7

8 investigating further the link between implicit and explicit measures and the predictive validity of both measures for behaviour in the area of physical activity. More specifically, two types of cognitions will be addressed in this study: attitude and self-identity. Both attitude and self-identity are important individual determinants for physical activity (Jackson, Smith, & Conner, 2003) and may be vulnerable to response bias when they are explicitly measured. Whereas attitudes have often been put central in research on implicit and explicit cognitions, up to my knowledge, self-identity has deserved considerable less attention (see for one example of alcohol self-identity: Gray et al., 2011). However, as self-identity is a very important individual determinant for physical activity (Bruijn de & Putte van den, 2012; Hamilton & White, 2008; Jackson et al., 2003) and may be very vulnerable to selfpresentation bias, it can be interesting to include this variable in research on implicit and explicit cognitions. The central research question of this thesis is then: How are implicit and explicit measures of, prospectively, attitude and self-identity towards physical activity correlated and how do they predict physical activity? The results of this research are valuable for scientific development, that is the investigation into the correlations between implicit and explicit measures of cognitions and the predictive value of implicit and explicit measures in the area of physical activity. The results may also be valuable in health promotion settings to develop new strategies to overcome physical inactivity. The research results may point to a new direction for health professionals to encourage the adoption of an active lifestyle or may point to necessary improvements of already existing interventions. In the remaining part of this thesis, the theoretical framework underlying this research is introduced (Chapter 2). A detailed description of the experiment performed to test the research hypotheses is given in chapter 3. The results are discussed in chapter 4, followed by the discussion (chapter 5) en the conclusion (chapter 6). 8

9 2. Theoretical framework In this chapter the theoretical framework underlying the research is discussed. The chapter starts with an introduction to the concepts of attitude and self-identity, which are important explicit determinants in physical activity research. In the second paragraph, current research on implicit attitude and self-identity is discussed for physical activity. After introducing the most up-to-date knowledge on implicit and explicit cognitions for physical activity, a detailed description of the IAT and the related SC-IAT is given. In the fifth paragraph, an elaboration on the nature of implicitness is provided and different models that link implicit and explicit cognitions with behaviour are introduced. Additionally, recent research investigating the variability in correlations between implicit and explicit cognitions is discussed. At the end of this chapter, three hypotheses are introduced that guided the research Current research on explicit cognitions for physical activity Research into the determinants of physical activity flourished after the development of the Theory of Planned Behaviour (Azjen, 1991). A booming strand of research has tried to identify the most important determinants of physical activity with the use of this theory. Attitude has been one such determinant which has often been found an important predictor of physical activity (see for example: Jackson et al., 2003). Health promotion strategies that aim to encourage more active lifestyles often focus on more positive attitudes towards sports and being active, increased selfefficacy to engage in sports, and a social environment that encourages people to be active. The Theory of Planned Behaviour has been a good analytical tool for health professionals who wish to promote physical activity by changing individual determinants. Also relevant for research on explicit cognitions has been the notion of self-identity. Recent research suggests that adding ideas about the self to predictive models (such as the Theory of Planned Behaviour) improves the predictive validity (e.g., Bruijn de & Putte van den, 2012). The core idea is that the extent to which a person perceives a specific behaviour as a defining characteristic of his or her self-identity has an immense influence on subsequent behaviour (change). Self-identity involves personal ideas about who you are and behaviours that are not congruent with these ideas evoke negative feelings of dissonance. Moreover, people try to behave in ways that enhance their self-identity and so people who identify themselves as exercisers engage more in exercising behaviour, and also more vigorous exercising, than people who identify themselves less as an exerciser (Bruijn de & Putte van den, 2012; Strachan, Brawley, Woodgate, & Tse, 2005). In a study that included all the constructs of the Theory of Planned Behaviour and self-identity, it was found that self-identity was the second strongest predictor of physical activity behaviour after perceived behavioural control (Bruijn de & Putte van den, 2012). 9

10 A related domain of research involving the self and physical activity has focused on the concept of self-schema. Self-schemas are defined as cognitive generalizations about the self, derived from past experience, that organize and guide the processing of self-related information (Markus, 1977:63). They are the domain specific ideas about your identity, which are relatively stable. In a specific domain (e.g., physical activity) an individual may be schematic, aschematic or nonschematic (Banting et al., 2009; Kendzierski, 1988). 1. A schematic person is sensitive to information that is congruent with his/her self-schema. A schematic person is efficient in processing and evaluating information in a specific domain. In the domain of physical activity, a schematic person would find exercise related concepts to be descriptive of himself and would also value these descriptions more. 2. An aschematic person would not describe himself in a specific way and would also not attach value to these descriptions. In the domain of exercise, an aschematic person would not have an exercise-related schema. 3. A nonschematic person would describe himself not to be in a specific way and would rate these descriptions as being very important to his self-image. In the domain of exercise, a nonschematic person would not describe himself as an exerciser and attaches great value to that. Banting et al. (2009) have demonstrated that exerciser schematics show a higher level of physical activity than non-exerciser schematics and aschematics. Strachan et al. (2005) demonstrated that people who identify themselves as exercisers do not only engage more frequently in physical activity but also reported greater confidence in their skills and hold higher intentions to engage in frequent exercising. Recently, Berry (unpublished) has sought to discriminate between the two concepts of selfidentity and self-schema and concludes that there is evidence to regard the two constructs as theoretically different. Interestingly, exerciser schematics scored significantly higher on exercise identity than did either aschematics or nonschematics (Berry, unpublished). However, both aschematics and nonschematics scored close to or above mid-point on the exercise identity scale. As she points out: this finding suggests that individuals who truly see themselves as exercisers may not be identified until well-above the mid-point on the identity scale (Berry, unpublished:16 ). This finding may point to the self-presentation bias often found in self-report measures of self-identity. Following the results of this research, it can be argued that the self-report of self-schema may be less affected by this bias than self-reports of self-identity. For this reason, it will be interesting to investigate further the nature of implicit and explicit measures of self-identity and the predictive validity of both types of measures for physical activity behaviour. In the remaining part of this thesis the focus will be on the two cognitions of attitude and self-identity. 10

11 2.2. Current research on implicit cognitions for physical activity As demonstrated in the previous paragraph, explicit cognitions have received much attention in research on physical (in)activity. However, the predictive value of intentions on behaviour is limited. An important reason for this is that self-reports are limited in their ability to assess the real attitude or self-identity of participants. Who would dare to argue that sporting is not healthy or not good (i.e., report a negative attitude in a questionnaire)? In light of these limitations, implicit measures of cognitions received considerable attention. For attitudes typically, implicit and explicit measures are weakly correlated, although this may vary across research domains (Fazio & Olson, 2003; Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005; Nosek, 2005). In the domain of physical activity, only limited research is conducted on implicit and explicit measures of attitude, but those studies that have been conducted show a weak correlation in line with the observation of Fazio and Olson (e.g., Conroy et al., 2010; Hyde et al., 2010). Implicit attitudes were found to correlate positively with level of physical activity and an attentional bias towards exercise cues (Calitri et al., 2009). A study conducted by Conroy et al. (2010) suggests that implicit attitudes have unique predictive validity beyond explicit measures for unintentional physical activity. Up to my knowledge, studies investigating implicit and explicit self-identity in relation to physical activity have not been conducted. In the related field of self-schema, Banting et al. (2009) found that implicit and explicit components of exerciser self-schema were related but distinct constructs. A hierarchical regression analysis revealed that high levels of both implicit and explicit self-schema improved absolute levels of exercise. Research on implicit and explicit self-identity has been conducted in the health domain of alcohol consumption by Gray et al. (2011). It was shown that implicit alcohol self-identity was a predictor of future drinking practices, even after controlling for baseline alcohol consumption (Gray et al., 2011). The current study aims to contribute to the limited knowledge of implicit and explicit correlations and their predictive validity in the area of physical activity. The following two paragraphs will discuss the Implicit Association Test and the related Single Category Implicit Association Test The Implicit Association Test Implicit cognitions are often measured using the IAT. The IAT is designed to see how certain things are combined in people s mind, e.g., their associations with flowers, insects, Black people, White people etc. In an IAT the object of interest (also called a target) is combined with two attribute dimensions (i.e., evaluative dimensions), e.g., positive and negative. During a test, words appear on the screen that either belong to one of the objects (e.g., White people vs. Black people) or that belong to one of the attribute dimensions (e.g., positive vs. negative). Respondents have to push a left or a right response-key to sort the words appearing on the screen, which belong to the categories positioned on the top-left and top-right of the screen. The procedure starts with the introduction of the target, for example the well-known prejudice-task (e.g., Greenwald et al., 1998) would introduce the names of Black and White people. If a Black person s name would appear on the 11

12 screen, the participant should push the left response key and if a White person s name would appear on the screen, a right response key should be pushed. The second step of the procedure will introduce the attribute dimension (i.e., pleasant vs. unpleasant ) in the same way as the introduction of the target. In the third step, also called the initial combined task, a target and an attribute dimension are combined on one response key. So participants have to push the left response key if either a Black person s name or a positive attribute dimension (e.g., nice) appears on the screen, and the right response key if a White person s name or a negative dimension (e.g., awful) appears on the screen. In the fourth step, the target switches in response key (so Black would go to right and White would go to left). In the fifth and final step, respondents combine the new object responses with the old attribute dimension (left: Black + negative; right: White + positive). See Figure 1for an overview of the procedure. Figure 1. Illustration of the Implicit Association Test (Greenwald et al., 1998:1465). For each step of the procedure the task instructions and sample stimuli are given. If a target is associated with a specific attribute dimension, respondents should find it considerable easier to push the response-keys when they are combined in one key, than when they are not combined (Greenwald et al., 1998). The difference between the response times for both combination tasks, also called response latency, is a measure of this association. The following figure (Figure 2) shows the latency data of the prejudice-iat as conducted by Greenwald et al. (1998:1474). The difference between the two response times of the combined tasks signifies that the White participants in this test have an implicit association between White people s names and positive words and an implicit association between Black people s names and negative words. 12

13 Figure 2. The latency data for the prejudice-iat conducted by Greenwald et al. (1998) for White subjects. For each trail the latency times are given. The participants performed two tests in which the combined tasks were switched. Initially, the IAT-effect was defined as the difference in mean latency between these two conditions [initial combined task & reversed combined task] (Greenwald et al., 1998:1468). The scoring procedure, using the conventional algorithm, had the following features (Greenwald, Nosek, & Banaji, 2003:197): 1. dropping the test trail blocks for the IAT s two classification tasks; 2. recoding latencies outside of lower (300 ms) and upper (3000 ms) boundaries to those boundary values; 3. log-transforming latencies before averaging them; 4. including error-trail latencies in the analyzed data; 5. not using data from respondents for whom average latencies or error rates appear to be unusually high for the sample being investigated. In 2003, an improved scoring algorithm was developed which differed from the conventional algorithm in three ways (Greenwald et al.:213): 1. the use of practice-block data; 2. the use of error penalties; 3. the use of individual-respondent standard deviations to provide the measure s scale unit. This new algorithm outperforms the conventional scoring algorithm in many areas (Greenwald et al., 2003). This so-called D-score algorithm, for example, increases the correlation between the IAT and explicit measures, and increases the reliability of the IAT (Greenwald et al., 2003). 13

14 2.4. The role of the other (SC-IAT) The role of the other in the IAT has received quite some criticism. The main argument is that the IAT measures cognitions (for example attitudes) in comparison to some other category. Taking the prejudice-iat, the finding that white people associate white people with positive words and black people with negative words may be due to the fact that white people are compared to black people. As Karpinski (2004) noted, the IAT s dependence on relative associations of concepts with attributes is a central problem. Additionally, it is pointed out that the IAT measures only one dimension of information (Karpinski & Steinman, 2006). As the IAT-scores compare associations, a high score on the prejudice-iat could indicate (p. 16): 1. the presence of many positive White associations; 2. the presence of many negative Black associations; 3. the lack of negative White associations; 4. the lack of positive Black associations. Thirdly, in some instances the other-category may be difficult to define as well, for example for physical activity. The natural opposite of physical activity is unclear (e.g., inactivity, relaxation?) and, additionally, physical activity corresponds with a scale ranging from being active to being inactive and does not have a dual character (Calitri et al., 2009; Conroy et al., 2010). In a response to the difficulties with the other-category a Single Category Implicit Association Test was developed (hereafter abbreviated SC-IAT). In the SC-IAT only one target dimension is combined with two attribute dimensions (Karpinski & Steinman, 2006). When we want to use the SC-IAT to measure implicit attitudes towards sporting, in the first round of the procedure the target dimension (i.e., physical activity) is combined with positive words on one response key, and negative words are categorized on another response key. In the second round of the procedure the target dimension is combined with negative words on one key, and positive words are categorized on another response key (see Table 1). The SC-IAT has been found to predict unique behavioural intentions beyond those measured by the IAT and has been used in the physical activity domain several times (e.g., Conroy et al., 2010; Hyde et al., 2010). Although several SC-IAT s showed good internal consistencies and significant implicit-explicit correlations (sometimes even higher than for IATs), some have also failed to show good test properties (Schnabel et al., 2008). According to these authors, a possible cause for the fluctuation in SC-IAT reliability may be the fact that SC-IAT tasks can be facilitated by concentrating on the single category (Schnabel et al., 2008:213). More research is needed to see whether SC-IATs are a reliable measure for implicit cognitions. 14

15 Table 1. Comparison between the IAT and the SC-IAT (adapted from: Karpinski & Steinman, 2006). IAT SC-IAT Block Trails Function Left-key response Right-key response Block Trails Function Left-key response 1 30 practice positive negative 1c 24 practice positive + physical activity 2 30 practice physical activity physical inactivity 3a 30 practice positive + physical activity 4a 30 test positive + physical activity negative + physical inactivity negative + physical inactivity 5 30 practice physical inactivity physical activity 2c 72 test positive + physical activity Rightkey response negative negative 3d 24 practice positive negative + physical activity 4d 72 test positive negative + physical activity 6b 30 practice positive + physical inactivity negative + physical activity 7b 30 test positive + physical inactivity negative + physical activity Note. Blocks with a common subscript are experienced as one continuous block Theory behind implicit and explicit social cognitions Fazio and Olson (2003), in their review of implicit measurement methods, conclude that research on the IAT and the SC-IAT is quite atheoretical and mainly methodological and empirically driven. However, this does not mean that there is no theory underlying implicit and explicit measures of cognitions. Several researchers have tried to identify moderators that seem to influence the implicitexplicit relationship and have tried to explain the variability in correlations between the two types of measures (see paragraph ). Additionally, different models have been developed that try to explain the relation between implicit and explicit measures of cognitions and their link with behaviour (see paragraph ) Moderators of the implicit-explicit relationship In the first fifteen years of implicit social cognition research, the focus has been much on the development of measurement methods and their validity. However, recently there has been a shift towards The Age of Mechanisms (Nosek, Hawkins, & Frazier, 2011:152). Central in this strand of research is the question how (and more specifically, when) implicit and explicit social cognitions relate to each other. In a review of the implicit-explicit relationship, Hofmann et al. (2005) showed that, based on 126 studies, the mean effect size was.24, suggesting that implicit and explicit measures 15

16 are generally related, although often weakly. Also in the area of physical activity, the correlations between implicit and explicit measures are generally modest (e.g.,conroy et al., 2010). However, implicit-explicit correlations show a large variability. For example in a study conducted by Nosek (2005), in 57 research domains, the implicit-explicit correlations differed from -.05 to.70. As Nosek (2005) points out: the fact that the strength of the implicit-explicit relationship varies across social objects suggests that one or more factors moderate the relationship (p. 566). In the meta-analysis conducted by Hofmann et al. (2005), approximately half of the variability across correlations was attributable to moderator variables. Even though research in this area is still limited, a framework (see Figure 3) identifying 5 categories of moderators has been introduced (Hofmann, Gschwendner, Nosek, & Schmitt, 2005). Additional information integration - Adjustment - Method-specific variance + Reliability - Spontaneity + Delibaration + Cognitive dissonance + Representational strength + Dimensionality + Distinctiveness + Awareness explicit (propositional) representations Translation Explicit assessment explicit indicator Implicit-Explicit consistency Design factors ± Sampling bias ± Implicit-explicit order ± Measurement correspondence implicit (associative) representations implicit indicator Implicit assessment ± Situational malleability - Method-specific variance + Reliability Figure 3. Framework of moderators influencing the implicit-explicit relationship. Plus and minus signs indicate the direction of the hypothesized moderator effect on the correlation (taken from: Hofmann, Gschwendner et al., 2005). 16

17 The first category of moderators is called additional information integration which covers the difference in deliberation between implicit and explicit answers. These factors are based on the observation that greater spontaneity of explicit answers heightens the implicit-explicit correlation (Hofmann, Gawronski et al., 2005; Hofmann, Gschwendner et al., 2005). When a person deliberates more about a certain topic, additional information may be sought that can alter the explicit answer, whereas the implicit answer remains the same. The implicit-explicit correlation will then be lower. The second category of moderators is called explicit assessment and covers what has typically been called the problem of social desirable answers. Although evidence for differences in explicit-implicit correlations across topics varying in social desirability is weak, specific motivation to control explicit reactions does seem to have an influence on implicit-explicit correlations (Hofmann, Gschwendner et al., 2005). This means that for specific topics individuals may control their answers in explicit self-reports, whereas they are unable to control their implicit answers. This will then lower the implicit-explicit correspondence. However, it should be remarked that according to some researchers adjustment processes may not only occur in explicit responses but also in implicit responses, leading to implicit-explicit consistency (Moskowitz, Gollwitzer, Wasel, & Schaal, 1999). Implicit assessment is the third category of moderators and deals with factors that interfere with the measurement of implicit social cognitions, such as the possibility of faking and the variability of cognitive skills (Hofmann, Gschwendner et al., 2005). These factors may influence implicit responses and therefore the implicit-explicit correlation. The fourth category of moderators deals with the design factors of the measurement methods. Important issues are for example the principle of correspondence (i.e., the implicit-explicit relationship is much stronger when the measures are comparable in content and specificity) and the order of implicit and explicit measurement (Hofmann, Gschwendner et al., 2005). In the moderator framework by Hofmann et al. (2005), implicit and explicit cognitions are portrayed as two distinct components of our mind (see for further elaboration on this model of dual attitudes, paragraph 2.5.2). Apart from the moderators that influence the measurement of both implicit and explicit cognitions and the moderators related to the design of the study, there are also moderators that influence the actual implicit-explicit correlation. Translating an understanding of correlations between implicit and explicit measures into representations requires consideration of both the factors that affect the correlation among the representations themselves and the factors that affect the indicators measurement properties (Hofmann, Gschwendner et al., 2005:342). The first consideration (i.e., the correlation between implicit and explicit representations) is covered in the fifth category of moderators called translation. Whereas the abovementioned moderator categories deal with influences on the measurement level of cognitions (except for additional information integration ), this fifth moderator category is concerned with influences on actual cognition level. In the literature 17

18 generally four moderators within this translation process are identified that may predict when the implicit and explicit cognition align and, therefore, produce a higher correlation. 1. The first moderator variable has already been discussed: awareness (also called selfpresentation concerns, Hofmann, Gschwendner et al., 2005; Nosek, 2005). It is hypothesized that when self-presentation concerns are high, explicit responses may be altered (i.e., the conscious alteration of self-reports) but implicit responses may not. So for example, if a physical inactive person would do the IAT, it is likely that the implicit attitude towards exercising will be negative. At the same time, the explicit attitude may be more positive, if that person is aware of the importance of being physically active and experiences the social pressure to appear physically active (i.e., the self-presentation concern of being a responsible and healthy person). This may change the explicit measure of attitude in the opposite direction of the implicit one, reducing implicit-explicit correlation. 2. The second possible moderator identified is the evaluative strength (Hofmann, Gschwendner et al., 2005; Nosek, 2005). It is hypothesized that implicit and explicit evaluations are more consistent for strong evaluations than for weak ones. The idea is that strong evaluations are personally important, more stable, unambivalent, frequently thought about and, therefore, should elicit stronger implicit-explicit correspondence. So when a person holds a strong positive evaluation towards exercising, this evaluation is possibly very important to him and he has often thought about it. Additionally, he is very likely to engage in frequent exercising and the behaviour may have become (partially) automatic. When the evaluation of exercise behaviour is then assessed using a self-report, he may rate himself as an exerciser (as he exercises frequently) and, additionally, the implicit measure may point in the same direction because the behaviour may have become automatic. Hence, a strong correlation between the implicit and explicit measure may appear. 3. The third possible moderator identified is dimensionality, also called complementarity (Greenwald et al., 2009; Hofmann, Gschwendner et al., 2005; Nosek, 2005). It is argued that cognitions that show more bipolar than unipolar characteristics elicit greater implicitexplicit correspondence. Bipolar cognitions consist of two endpoints, whereas unipolar cognitions refer to a continuum. Evaluations that fit the bipolar structure may be more easily and more quickly made. As Nosek (2005) points out: A bipolar structure may have cognitive benefits that simplify or organize attitudinal information for more efficient and consistent processing [across contexts] (p. 567). In contrast to this bipolar structure, cognitions that fit the unipolar structure may reflect a more complex and less stable situation. As a consequence, explicit cognitions may be more influenced by the situational context and, thus, may not correspond with implicit cognitions. Attitudes towards physical activity are a typical example of unipolar cognitions as physical activity is a continuum from being 18

19 completely lazy to being a professional athlete. It is, therefore, likely that the implicitexplicit consistency is lower for physical activity cognitions than for bipolar cognitions. 4. Distinctiveness is the fourth and final possible moderator identified (Hofmann, Gschwendner et al., 2005; Nosek, 2005). Distinctiveness is defined as the magnitude of the discrepancy between one s own evaluations and the perceived norm (Nosek, 2005:567). A higher discrepancy means that people perceive their evaluations as different from people in general, and this discrepancy can be translated into a feeling of uniqueness. It is hypothesized that cognitions that are more distinctive may elicit self-reports that are more in line with implicit measures because 1.) people are more aware of this cognition in contrast to what other people think about the topic (i.e., it is a unique feature of your selfidentity), and 2.) people may be more able to accurately fill in self-reports because it is easier to assess a scale in relative standing to other people. The study conducted by Nosek (2005) demonstrated that each of the abovementioned variables moderated the implicit-explicit relationship across different content domains. In some domains explicit measures seem more predictive of behaviour, whereas in other situations implicit measures seem to have more predictive power. These observations confirm that neither implicit nor explicit measures have an advantage in being the truer measure of one s thoughts and feelings; both are valid assessments of unique aspects of social cognition (Nosek et al., 2011:155). Also Greenwald et al. (2009) support the idea that both implicit and explicit measures should be included in studies trying to predict behaviour. The next paragraph will discuss models that link implicit and explicit cognitions with behaviour How implicit and explicit cognitions predict behaviour When a distinction was made between implicit and explicit measures of cognitions, the question arose how they relate to behaviour. Three different conceptualizations of these relations were formulated: the additive model, the double dissociation model, and the multiplicative model (Perugini, 2005). When developing the IAT, Greenwald, McGhee and Schwartz (1998) defined implicit attitudes as: actions or judgments that are under the control of automatically activated evaluation, without the performers awareness of that causation (p. 1464). According to Nosek (2005), the distinction between implicit and explicit cognitions, for example attitudes, does not rest on the fact whether we are aware of these cognitions. In the case of attitudes, you cannot make a distinction between implicit and explicit attitudes based on the fact whether we are aware of the attitudes that we measure. That is, measuring attitudes using the IAT does not mean that the outcome represents those attitudes that the participants are unaware of. So even though an individual has more difficulty associating one object with one attribute dimension, this does not mean that the individual is unaware of this association. Additionally, as is pointed out by Fazio and Olson (2003), the implicit-explicit distinction 19

20 implies preexisting dual attitudes in memory. This means that if we use a self-report, we measure the explicit variant of the attitude, and when we use the IAT we measure the implicit variant. However, both the IAT and the self-report may have the same origin in our mind, meaning that the measure in itself is implicit or explicit, not the measured cognitions (Fazio & Olson, 2003). Following this observations made by Fazio and Olson (2003), on the meaning of implicitness (i.e., the measure is implicit or explicit, not the cognition), an additive model can be formulated. In this model there is a single mind with a single attitude towards a given object which can be measured with two different methods (either implicit or explicit). Both implicit and explicit attitudes have the same origin and can give a unique contribution to the prediction of behaviour (Perugini, 2005). This does not mean that both measurements predict 50% of the behaviour; this may depend on the context and the behaviour of interest. In some cases only one of the measurement methods may predict behaviour. A very influential conceptualization of implicit and explicit cognitions has been a model of dual attitudes (Wilson, Lindsey, & Schooler, 2000). In this model two different evaluations, that is implicit and explicit evaluations, of the same attitude object co-exist in memory. Implicit attitudes are assumed to influence spontaneous behaviour, whereas explicit attitudes are assumed to influence deliberative behaviour. The attitude people endorse at any point in time depends on whether they have the cognitive capacity to retrieve the explicit attitude and whether the explicit attitude overrides the implicit one (Wilson et al., 2000:102). The authors distinguish between four types of dual attitudes which differ in the capacity that is needed to override the implicit attitude and the awareness of the existence of the implicit attitude: repression, independent systems, motivated overriding, and automatic overriding (Wilson et al., 2000: ). The evidence for the existence of two independent evaluative systems is limited. Usually the low correlations between implicit and explicit measures have been taken as evidence for this double-dissociation model. However, the typical low correlations between implicit and explicit cognitions do not necessarily provide evidence for the double-dissociation model, but can point at the distinct values of the two ways of measuring cognitions (Perugini, 2005). In the third model, the interactive model, explicit and implicit cognitions interact synergistically to predict behaviour. This model starts from the observation that a large part of our behaviour lies somewhere on the continuum between completely controlled and completely uncontrolled. Depending on the situation, the behaviour may be influenced more by implicit or more by explicit cognitions (Perugini, 2005). This model 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 without a specific personal conscious awareness (Perugini, 2005:32). This model is based on the observation that stronger implicit-explicit relations seem to lead to stronger predictions of behaviour for both measures. This seems to suggest that implicit and explicit cognitions can mutually reinforce each 20

21 other when they point into the same direction or may disrupt each other s influence on behaviour when they point in different directions. Which of these models conceptualize the relation between implicit and explicit cognitions best, is still a question mark. Perugini (2005) has performed two studies to test these models and concludes that in the first study on smoking behaviour, the multiplicative model is supported, and in the second study on preferences for snacks versus fruit, the double-dissociation model is supported. In a more recent study, Conner et al. (2007) have found support for the double-dissociation model in fruit vs. snack consumption. In two different studies they found that the power of an implicit measure of attitude to predict behaviour was moderated by the habitualness of the behaviour (i.e., implicit attitude was better able to predict behaviour when it was more spontaneous). self-report Cognitions IAT Behaviour 1. The additive model Implicit cognitions Explicit cognitions IAT self-report Spontaneous behaviour Deliberative behaviour 2. The double-dissocation model Implicit cognitions Explicit cognitions self-report IAT Behaviour 3. The multiplicative model Figure 4. Graphical representations of the three models linking implicit/explicit cognitions to behaviour This study The purpose of this study is to see how implicit and explicit attitude and self-identity correlate and how they both predict physical activity. In this study three hypotheses will be tested. The first hypothesis addresses the implicit-explicit relationship. The outcomes of studies investigating implicitexplicit correlations have shown large variability (e.g., Nosek, 2005), and also in the area of physical activity a limited amount of studies have shown mixed results concerning implicit-explicit correspondence. Two studies have shown that implicit and explicit measures were independent constructs, with non-significant implicit-explicit correlations (Calitri et al., 2009; Conroy et al., 2010; Hyde et al., 2010). On the contrary, Banting et al. (2009) have found a weak but significant positive correlation between implicit and explicit measures of self-schema for physical activity. As the results for implicit-explicit correlations are mixed across various study domains, and as the implicit-explicit relationship has deserved limited attention in the area of physical activity, the following hypotheses were formulated to contribute to the existing body of knowledge: 21