Laila M. Martinussen Technical University of Denmark Implicit attitudes towards risky and safe driving in a Danish sample

Transcription

1 Laila M. Martinussen Technical University of Denmark Implicit attitudes towards risky and safe driving in a Danish sample 1

2 INTRODUCTION Since the work of Greenwald and Banaji (1995), the literature on attitudes distinguishes between implicit or automatic attitudes, and explicit or deliberate attitudes. Explicit attitudes are conscious evaluative judgments that have their roots in propositional reasoning (Gawronski and Bodenhausen, 2006). Implicit attitudes are attitudes that reflect introspectively unidentified (or inaccurately identified) traces of past experience (Greenwald and Banaji, 1995, p. 5). Measures of implicit cognition reveal associative information that people are either unwilling to share or that they are not conscious of, and therefore not able to share (Nosek, Greenwald, and Banaji, 2007). These associative evaluations can be described as automatic affective reactions resulting from a particular association to that stimulus. Implicit cognition have been tested in a broad range of disciplines including including social and cognitive psychology (Fazio and Olson, 2003), clinical psychology (Teachman et al., 2001), developmental psychology (Baron and Banaji, 2006; Dunham et al., 2004; Phelps et al., 2000), market research (Maison et al., 2001, health psychology (Teachman et al., 2003), and recently also in traffic psychology (Harre and Sibley, 2007; Hatfield et al., 2008; Sibley and Harre, 2009a; Sibley and Harre, 2009b). Implicit cognition has been shown to predict behavior particularly well if the behavior is associated with social desirability concerns and/or if a decision must be made spontaneously. Driving behavior is characterized by frequent decisions made under time pressure that potentially can be costly and/or dangerous both for the driver and other road users. Further, in our society, driving in a safe manner is socially desirable as safety is largely promoted. Thus, safe driving is expected, not only by car users, but by all road users. Because drivers have to make frequent decisions under time pressure and self-reports of the intention to drive safely (or not) can be socially sensitive, the motivation behind the present study was to measure implicit attitudes towards safe and risky driving. Measuring implicit driving-related cognition Research applying implicit cognitive methods in traffic psychology is quite limited. To the authors knowledge there are only four studies so far (Harre and Sibley, 2007; Hatfield et al., 2008; Sibley and Harre, 2009a; Sibley and Harre, 2009b). Thus, there are no standard methods available to test implicit attitudes towards safe and risky driving. The present study applies the method called the Go/No-go Association Task (GNAT; Nosek and Banaji, 2001) to the traffic psychology settings for the first time (to the authors knowledge). The GNAT is similar to its closest relative, the implicit association test (IAT), in that it assesses strengths of associations between concepts in simple computer-administered categorizationtasks (Nosek and Banaji, 2001). In an IAT, the participants are typically asked to distinguish between two contrasting pairs of target concepts like for example men versus women or speeding versus keeping the speed limit, and contrasting pairs of attributes or evaluations, for example good versus bad, or pleasant versus unpleasant. In critical trial blocks, the participants distinguish between four concepts at the same time. For example, they may be asked to press one response key when a word is shown that represents speeding or bad, but to press a different key when the word represents keeping the speed limit or good. The instructions are later changed such that in subsequent trial blocks, speeding and good 2

3 share a response key, whereas keeping the speed limit and bad share the other key. Differences in the task performance between critical blocks are interpreted as indicators of the relative preference of a person, that is, her/his implicit attitude. The Implicit Association Test uses contrasting target concepts, or pairs of attitude objects. However, having a positive attitude towards one attitude object does not necessarily imply that one has a negative attitude towards the opposite attitude object. Further, there might not always be a natural opposite concept to the concept under investigation. For example, having a positive attitude towards talking on the mobile phone while driving, does not automatically mean to have a negative attitude towards not talking on the phone when driving. Thus, it would be useful to measure attitudes towards single attitude objects. This is what the GNAT has to offer Different from the IAT, the GNAT does not need contrasting pairs of attitude objects in order to test the attitudes, as with the GNAT one can assess attitudes towards concepts without a clear opposite attitude object. Similar to the IAT, the theory behind the GNAT is that it is easier for people, i.e., goes faster, to associate concepts that are more strongly associated in the mind than concepts that are not (for further reading about IAT and the GNAT see Nosek and Banaji, 2001; Nosek et al., 2007). Measuring driving behavior Driving behavior can be both costly, time consuming and unethical to measure directly. Therefore, researchers often measure driving behavior through self-report measures. Two of the most influential and most frequently applied instruments to measure driving behavior and driving skills are respectively, the Driver Behavior Questionnaire (DBQ, Reason et al., 1990) and the Driving Skill Inventory (DSI, Lajunen and Summala, 1995). The DBQ measures aberrant driving behavior by asking drivers how frequent drivers perform intentional violations, and unintentional errors and lapses while driving (for further reading see Martinussen et al., 2013; Reason et al., 1990). The DSI measures driving skills by asking how good drivers consider themselves to be in perceptual-motor skills (technical driving skills) and safety skills (abiding rules and considering other road users) (for further reading see Lajunen and Summala, 1995). Both the DBQ and the DSI have been shown to predict selfreported accidents (de Winter and Dodou, 2010). The present research The aims of the current study are (1) to develop a GNAT which can be used to assess implicit attitudes towards risky and safe driving; (2) to explore the relationship between implicit attitudes towards risky and safe driving, and self-reported driving behavior and driving skills as measured with two established instruments: the DBQ, and the DSI. METHOD Design of the GNAT The current GNAT consisted of two target categories, namely pictures of risky and safe driving situations, and two attribute dimensions, namely good and bad words. The implicit attitude is assessed by measuring the strength of association between the target situation 3

4 (risky versus safe) and the evaluative dimension (good versus bad). The GNAT works by presenting the stimuli for a short time on the computer screen, one stimulus at a time. The participants are asked to press a response button ( go ) if the stimulus on the screen belongs to either a given target situation (for example risky driving) or a given evaluation (for example good). If the stimulus does not belong to either of these categories, then the participants are asked to do nothing ( no-go ). The participants are given a short deadline (less than one second) for their decision, after which the computer proceeds automatically. Measures of task performance under the various pairings (e.g., risky driving + good) are computed from the proportions of correct and wrong responses. The difference in task performance between situation/evaluation pairings (e.g., risky driving + good vs. risky driving + bad) reflects the association between that kind of situation and its implicit evaluation by the participant. This association is taken to be a measure of the participants automatic or implicit attitude. The proportion of correct responses in the GNAT is a trade-off between response speed and response accuracy: Increasing the speed of one s responses increases the potential for errors, and the other way around. To compensate for potential differences in the participants response strategies (fast versus error-free), signal-detection theory s d measure of sensitivity was used as a measure of task performance. Further, two different response strategies were induced for each participant in a controlled way, namely by repeating all parts of the GNAT with two response deadlines: 750 milliseconds (ms) and 600 ms. From a traffic-safety point of view, two performance patterns in the GNAT indicate desirable attitudes: performance should be greater when the go response is required for (a) pictures of risky situations and negative (rather than positive) words, and (b) pictures of safe situations and positive (rather than negative) words. Selection of GNAT materials The stimuli used as the target categories were pictures of respectively risky and safe driving situations, and the attributes were respectively good and bad words (see Figure 1 and Table 1). The reason for using pictures as stimulus was that the link between attitude and behavior is stronger if the measure used is similar to the actual behavior (Ajzen and Fishbein, 1977). The pictures used as stimuli in the current GNAT study were borrowed from the Transport Educational Center (TUC, Fyn) which uses these pictures in the Danish driver schools (totally 108 pictures). The pictures included many different safe and risky traffic situations seen from the driver perspective. The reason the pictures was seen from the driver perspective was that the participants should feel that he or she was driving, thus in the control over the situation. The good and bad words were selected by the researchers to be both familiar to, and unambiguously classifiable, by the potential participants (totally 68 words). An online pre-test with a convenience sample of 80 drivers was performed in order to identify which of the driving situations on the pictures was considered most dangerous and least dangerous, and which of the attribute words were considered most positive and most negative. The situations on the pictures was ranged on a 5-point Likert scale from not dangerous to very dangerous (0 = not dangerous, 4 very dangerous), and the words from very positive to very negative (2 = very positive, 1=positive, 0=neither positive nor negative, -1=negative, -2 = very negative). The 12 pictures rated as most dangerous and the 12 pictures rated as least dangerous, and the 12 words rated as most positive and 12 words rated as most negative were used in the main study. 4

5 Figure 1. Pictures used as stimulus in the GNAT. Left = risky situation. Right = safe situation. Table 1. Words used as stimulus in the GNAT. Negative words Catastrophe (Katastrofe) Evil (Ondskab) Hatred (Had) Terrible (Forfærdeligt) Nasty (Ækel) Tragic (Tragisk) Brutal (Brutal) Evil (Onde) Sickening (Kvalmende) Nauseous (Væmmelig) Painful (Smertefulde) Anxiety (Angst) Note. Danish translation in brackets. Positive words Laugh (Grine) Smile (Smile) Sweet (Sød) Joy (Glæde) Pleasure (Fornøjelse) Lovely (Dejlig) Friendly (Venlig) Beautiful (Flotte) Happy (Glad) Comfortable (Behageligt) Cosy (Hyggeligt) Cheerful (Munter) Self-report measures of driving behavior and skill Two measures of driving behavior were included in the study, namely the DBQ and the DSI (see Table 2). The DBQ measures aberrant driving behavior by asking drivers how frequently they perform intentional violations as well as unintentional errors and lapses while driving a six-point Likert scale (0 = never, 5 = nearly all the time) across different driver behaviors (see Table 2; for a detailed description see Reason et al., 1990). The DSI measures driving skills by asking drivers how good they consider themselves to be in perceptual-motor skills (technical driving skills) and safety skills (abiding rules and considering other road users) on a five-point scale (0 = well below average, 4 = well 5

6 above average) across different driving situations (see Table 2; for a detailed description see Lajunen and Summala, 1995). Table 2. Examples of DBQ and DSI items. DSI items Fluent driving (management of your car in heavy traffic) Conforming to the traffic rules Performance in a critical situation Driving carefully Perceiving hazards in traffic Paying attention to other road users DBQ items Unknowingly speeding Turn right on to vehicle s path Drive as fast on dipped lights Try to pass without using mirror Overtake on the inside Fail to see pedestrian waiting Participants In the main study, drivers with a type B driver license (Danish license for personal car) were randomly selected from the Danish Driving License Register. The participants were contacted by mail and asked to participate in the experiment online. Out of 600 contacted drivers, 77 letters came in return because the person either had moved out of the country or passed away. Of the remaining 523 persons, 114 participated in the experiment leading to a total response rate of 22 %. The participants had all finished the DBQ and the DSI on a previous occasion. Statistical analysis From the GNAT data, signal detection sensitivity scores (d ) were computed (see Nosek and Banaji, 2001 for further information). This measure reveals how good the participants can discriminate or distinguish between the foreground categories (for example risk and good) from the noise or the background stimulus (for example safe and bad). Then implicit attitude scores were computed for safe and risky driving by subtracting the sensitivity scores in counter-normative blocks from the sensitivity scores in normative blocks ( risky driving is bad minus risky driving is good, and safe driving is good minus safe driving is bad blocks). Greater values on the implicit attitude scores thus indicate more normative (or socially desirable) implicit attitudes. Then, Pearsons correlations between the attitude scores for safe and risky driving were computed. Finally, Pearsons correlations between the attitude scores of safe and risky driving on the one hand, and the scores in the DBQ and the DSI on the other were calculated. RESULTS Inter-correlations between implicit-attitude scores As can be seen in Table 3, across the two response deadlines, implicit attitudes towards the same attitude object (safe driving vs. risky driving) correlated positively and significantly; that was observed both for implicit attitudes towards safe driving (r =.39, p <.01) and for 6

7 implicit attitudes towards risky driving (r =.46, p <.01). This finding shows that implicit attitudes towards each driving style can be measured reliably, with repeatable results. In contrast, within each response deadline, the correlation between implicit attitudes towards different attitude objects (safe driving and risky driving) was smaller and non-significant; that was observed both for the 750ms deadline (r =.27, p >.05) and for the 600ms deadline (r =.25, p >.05). This findings show that implicit attitudes towards the two driving styles are empirically separable constructs, rather than redundant with each other. Table 3. Correlations between implicit attitudes towards risky and safe driving. NoRisk600 Risk750 Risk600 NoRisk ** NoRisk Risk ** Note. Cell entries are Pearson correlation coefficients. ** p <.01, + p <.10. N = because not all participants finished all trials. Implicit attitudes and self-reported driving behavior To gain insight into the relation between implicit attitudes and self-reported driving style, we first collapsed the GNAT scores across response deadlines. Then, we correlated the resulting two implicit attitude scores (towards risky driving and towards safe driving) with the participants DSI and DBQ scores. This was done separately for female and male participants. Table 4 shows the relations between implicit attitudes and self-reported driving style, separately for women and men. For the women, none of the correlation coefficients was close to statistical significance. Thus, our female participants implicit attitudes were not related to their self-reported driving style. For men, in contrast, two significant correlations were observed. First, the DBQ scores and implicit attitudes towards risky driving correlated significantly such that a greater number of own traffic violations and errors was associated with more risk-aversive implicit attitudes towards risky driving, r =.45, p <.05. Second, the DSI scores and implicit attitudes towards safe driving correlated significantly such that lesser/lower self-reported driving abilities and skills were associated with more positive implicit attitudes towards safe driving, r = -.63, p <.01. Table 4. Correlations between implicit attitudes and self-reported driving style. Gender DSI DBQ Male Implicit attitude risk * Implicit attitude no risk -.63 **.08 Female Implicit attitude risk Implicit attitude no risk Note. Cell entries are Pearson correlation coefficients. ** p <.01, * p <.05. N male = 23, N female = 30. DISCUSSION The current study is, to the authors knowledge, the first GNAT study performed in order to test implicit attitudes towards risky and safe driving. The present GNAT results show great promise as a measure to reveal implicit attitude towards both risky and safe driving. 7

8 Psychometric properties of the GNAT Two response deadlines (600ms, 750ms) were used to measure implicit attitudes towards each of two attitude objects (safe driving, risky driving). The inter-correlations between the resulting four GNAT scores show that the instrument reveals similar implicit attitudes towards the same attitude object, independent of the particular response deadline used. These results speak to the reliability of the research instrument. Within each response deadline, implicit attitudes towards different (though related) attitude objects were found to correlate moderately, with the expected positive sign. The observation that the attitude scores correlated positively may be interpreted as first evidence for the GNAT s convergent validity: when measuring attitudes towards related objects, the instrument reveals related attitudes. At the same time, the positive correlation of implicit attitudes towards safe and risky driving was only of moderate magnitude and just marginally significant. This may be interpreted as first evidence for the GNAT s discriminant validity: when measuring related attitudes, the GNAT is sensitive enough to capture differences in the two attitudes. The DSI, the DBQ, implicit attitudes, and gender The results showed that self-reported judgments of own driving skills correlate with an implicit pro-safety attitude, though not with an implicit anti-risk attitude. Conversely, selfreported violations and errors correlated with an implicit anti-risk attitude, though not with an implicit pro-safety attitude. This pattern was observed in male, but not in female participants. Neither the double-dissociation pattern nor the gender difference had been predicted. Are these chance findings, or do they make sense? To cover the gender difference first, some prior evidence for gender differences in the effects of implicit cognition in relation to driving can be found in the literature. Harré and Sibley (2007) found a stronger effect of the implicit driver self-image in men than in women. It has been shown that men report greater gender-stereotypical macho driving attitudes than women (Harré et al., 1996), and that such attitudes actually are linked to greater driving aggression (Krahé and Fenske, 2002). From such findings it appears that when it comes to driving, men tend to rely on their intuition, using stereotypical roles and gender prototypes such as "machos" as the basis for their driving behavior. Implicit attitudes are one of several psychological mechanisms that create "intuitions," and may here fore be more important determinants of men s (rather than women s) driving behavior. The present data are thus in line with prior findings as well as current theorizing in the field. To cover the double-dissociation pattern next, evidence from the literature suggests that drivers who think highly of their own driving skills also perceive a lesser risk than others of becoming involved in an accident (DeJoy, 1989; Harré, Foster, and O Neill, 2005; Harré and Sibley, 2007). Conversely, drivers who think lowly of their own driving skills and abilities should then perceive a greater risk and value driving safety more than others. This relation may explain the negative correlation between self-reported driving abilities skills and implicit safety attitudes: drivers who estimate their own skills to be rather low, have a greater implicit desire for safety in driving. The second correlation in the doubledissociation pattern indicates that drivers with more self-reported traffic violations have greater risk-aversive implicit attitudes. Although counter-intuitive at first glance, that correlation may be explained as the result of a learning process: to the degree that the own past violations had unpleasant consequences, these drivers may have learned their lesson -- which is not to like safety, but to dislike risk. 8

9 As a theoretical model that covers both of the observed correlations, we tentatively suggest that pro-safety attitudes and anti-risk attitudes may have different experiential sources: Pro-safety attitudes may essentially come from social comparison processes that lead to low self-ratings of own skill, and thereafter to the insight that "in traffic, I better play it safe." One could call this source of experience "soft variables" or "psychological evidence." Anti-risk attitudes, in contrast, may come from external variables such as one s own past, rule-violating behavior that had unpleasant consequences, teaching the driver that "taking risk is bad." One could call this source of experience "hard variables" or "real-world evidence." We readily admit that our study does not demonstrate all steps in the model. At the same time, however, the study illustrates the heuristic, theory-building value of measuring implicit cognition with instruments such as the GNAT. Practical applications The results can give valuable practical value as it potentially can be applied in driver testing both for transport companies employing new personnel and for the state driving license administration. The results should, however, be replicated with a larger sample as the limitations of the current study is the small sample size. Moreover, the link between explicit attitudes, implicit attitudes and actual behavior should be explored. Self-report measures have not always shown to be predictive of actual behavior (af Wåhlberg and de Winter, 2012), thus, the implicit attitudes of drivers towards risky and safe driving might give valuable information that can explain the relationship between self-reported behavior and actual behavior to a greater extent, and also to help predict driving behavior better. CONCLUSION Pending replication in future research, the apparent dissociation between implicit attitudes towards safe versus risky driving that we observed may contribute to a greater theoretical understanding of the causes of unsafe and risky driving behavior. A practical advantage of measuring implicit attitudes is a lesser susceptibility to social desirability biases, compared to self-report methods. It is proposed that research on driving behavior may benefit from routinely including measures of implicit cognition. REFERENCES Af Wåhlberg, A., & de Winter, J.C.F. (2012). Commentaries and responses to The Driver Behavior Questionnaire as a predictor of accidents: A meta-analysis. Journal of Safety Research, 43, Ajzen, I., and Fishbein, M. (1977). Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychological Bulletin, 84, Baron, A. S., and Banaji, M., R. (2006). The Development of Implicit Attitudes : Evidence of Race Evaluations From Ages 6 and 10 and Adulthood. Psychological Science, 17, DeJoy, D. M. (1989). The optimism bias and traffic accident risk perception. Accident Analysis and Prevention, 21, Dunham, Y., Baron, A. S., and Banaji, M. R. (2004). From American city to Japanese Village: A cross-cultural investigation of implicit race attitudes. Child Development, 77 (5), Fazio, R. H., and Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and use. Annual Review of Psychology, 54,

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