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Accident Analysis and Prevention 45 (2012) 522– 528

Contents lists available at SciVerse ScienceDirect

Accident Analysis and Prevention

jo ur n al hom ep a ge: www.elsev ier .com/ locate /aap

he effects of non-evaluative feedback on drivers’ self-evaluation anderformance

bru Dogana,∗, Linda Stega, Patricia Delhommeb, Talib Rothengatter1

Faculty of Behavioral and Social Sciences, University of Groningen, The NetherlandsIFSTTAR, Laboratory of Driver Psychology, France

r t i c l e i n f o

rticle history:eceived 25 May 2011eceived in revised form 2 August 2011ccepted 4 September 2011

eywords:on-evaluative feedbackelf-evaluationelf-regulation

a b s t r a c t

Drivers’ tend to overestimate their competences, which may result in risk taking behavior. Providingdrivers with feedback has been suggested as one of the solutions to overcome drivers’ inaccurate self-evaluations. In practice, many tests and driving simulators provide drivers with non-evaluative feedback,which conveys information on the level of performance but not on what caused the performance. Isthis type of feedback indeed effective in reducing self-enhancement biases? The current study aimedto investigate the effect of non-evaluative performance feedback on drivers’ self-evaluations using acomputerized hazard perception test. A between-subjects design was used with one group receivingfeedback on performance in the hazard perception test while the other group not receiving any feedback.

azard perceptionelf-enhancement bias

The results indicated that drivers had a robust self-enhancement bias in their self-evaluations regardlessof the presence of performance feedback and that they systematically estimated their performance tobe higher than they actually achieved in the test. Furthermore, they devalued the credibility of the testinstead of adjusting their self-evaluations in order to cope with the negative feelings following the failurefeedback. We discuss the theoretical and practical implications of these counterproductive effects ofnon-evaluative feedback.

. Introduction

We are motivated to see ourselves in a positive way inrder to feel good about ourselves and to maintain a high self-steem (Steele, 1988). This applies to drivers as well. Driversery often believe that they drive better than other drivers orhat they are more competent than they actually are, showing aelf-enhancement bias in their self-evaluations (see Sundström,008). Generally, drivers consider themselves to be more skill-ul than other drivers (Svenson, 1981; DeJoy, 1989; Delhomme,991; Gregersen, 1996; McKenna et al., 1991; Groeger and Grande,996), indicating that at least some of them overestimate theirkills. Different motivational explanations have been offered forhe mechanisms underlying the self-enhancement bias in drivers’kill evaluations. McKenna et al. (1991) suggested that drivers

nflate their own abilities instead of deflating those of otherrivers. Walton (1999), on the other hand, found that truck driversowngraded other drivers’ abilities rather than inflating their

∗ Corresponding author at: Faculty of Behavioral and Social Sciences, Departmentf Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen,he Netherlands. Tel.: +31 50 3634722; fax: +31 50 3636304.

E-mail address: e.b.dogan@rug.nl (E. Dogan).1 Talib Rothengatter passed away during the course of this work.

001-4575/$ – see front matter © 2011 Elsevier Ltd. All rights reserved.oi:10.1016/j.aap.2011.09.004

© 2011 Elsevier Ltd. All rights reserved.

own abilities. Whichever motivational mechanism explains self-enhancement biases, such biases seem to be persistent for drivingskills. In fact, this self-enhancement bias has been found to be evenstronger when measured implicitly (Harré and Sibley, 2007), sug-gesting that drivers’ beliefs about the superiority of their drivingcompetence are deeply rooted. Paradoxically, people also believedthat they are less susceptible to judgmental biases than oth-ers (Pronin et al., 2004), which makes these biases even morerobust.

The overestimation of skills and competence is associated withperceiving less risks, either by perceiving one’s self as a less riskydriver (Svenson, 1981) or by perceiving one’s own crash risk aslower (DeJoy, 1989; Deery, 1999; Harré and Sibley, 2007). Driversgenerally take regulatory actions when they perceive that theircompetence falls short to meet the demands of the situation (Fuller,2008). When drivers overestimate their competence, they mayexpect their performance to be better than it really is. Conse-quently, when drivers overestimate their skills and underestimatethe risks involved, they may be more likely to take risks on theroad, for instance, by driving faster. This leaves shorter time mar-gins to detect hazardous situations in time, which in turn may

hinder one’s ability to respond timely to dangers as to avoid nega-tive consequences. It is therefore of great importance that drivershave accurate estimations of their competence and abilities (seeRothengatter, 2002).

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Kruger and Dunning (1999) suggested that inaccurate self-valuations of competence, either overestimation or underesti-ation, is due to lack of metacognition about one’s skills and

ompetence. They suggest four possible feedback-related reasonsor inaccurate self-evaluations: lack of feedback, attributing failureeedback to some other causes than lack of skills, not understandinghy failure occurred, and not receiving self-corrective informa-

ion. The driving task is subject to all the aforementioned deficiencyactors because of the lack of systematic feedback in and the for-iving nature of the traffic environment for errors. Drivers may notevelop a realistic representation of their abilities and competenceecause not every error or violation made while driving results indverse consequences such as accidents, near accidents, or penal-ies, which implies that drivers do not receive explicit feedback onheir performance. This is particularly problematic for learner andovice drivers because they are more in need of feedback in ordero comprehend the effects of their behaviors on other road users,he road environment, what mistakes they do, and how to avoiduch mistakes. Feedback from an instructor or from the environ-ent may enable drivers to develop a sense of possible situations

hat they may encounter in the traffic environment and their abili-ies or lack thereof to deal with different traffic situations (Groeger,000; Kuiken and Twisk, 2001; Hatakka et al., 2002).

Kuiken and Twisk (2001) emphasized the importance of feed-ack for a safe calibration (i.e. self-regulation) of skills and drivingask demands. In line with the self-regulation theory, they pro-ose that adequate self-assessment of skills is crucial for a safealibration of driving skills. They propose that provision of com-rehensive feedback, by providing information on the way theask was performed and how it could be improved, is needed tonhance safe regulation of driving behavior because it enablesriver to safely match their capabilities with the task demands. Aafe match between the capabilities and task demands reflects onriver’s goal setting at various stages of the driving task from routehoice to the actions taken behind the wheel (cf. Rothengatter,002). Similarly, Hatakka et al. (2002) suggest that self-evaluationf one’s driving skills should be integrated in the driver trainingn order to develop learner drivers’ metacognitive skills for spe-ific tasks of driving such as vehicle control or hazard perception,nd that this can be realized by providing drivers with feedbackn their performance. Such training is expected to promote learnerrivers’ self-regulatory behaviors in different road situations andask demands (Kuiken and Twisk, 2001; Hatakka et al., 2002).n more and more European countries structured feedback thatocuses on higher order safety skills and self-assessment of themre integrated in the driver training as part of the driver licens-ng systems (Twisk and Stacey, 2007), with promising effects inhe short term. Research revealed that after this training, learn-rs assessments of their skills were positively correlated with theirrainers’ assessments of the same skills, suggesting that learnersssessed their skills accurately (Boccara et al., 2011; Mynttinent al., 2009a,b). The long-term effects of this training have not beentudied yet, i.e. it is not clear whether accurate self-assessmentsbserved during the training are retained after the training andhether the training indeed results in less risk taking behavior and

ccidents.In the meantime, non-evaluative feedback is increasingly

dopted in traffic for training purposes as well. This type of feed-ack is less comprehensive since typically, information is providedn actual performance levels only. Examples are the increased usef simulators and computer-based tests such as hazard percep-ion tests, which provide non-evaluative feedback on one’s driving

kills. In essence, people taking these tests learn their absolutecores on a test or their scores relative to other test-takers on par-icular skills, but do not receive any information on why their scoreas low or high or on how scores may be improved. Despite being

Prevention 45 (2012) 522– 528 523

frequently used in driver training we do know little about the effec-tiveness of non-evaluative performance feedback as given in theseinstruments.

Research on air traffic control indicates that non-evaluativefeedback on performance may be effective in promoting accurateself-evaluations (e.g. Mitchell et al., 1994). Mitchell et al. (1994)used a computerized test to simulate an air traffic controller’s task,which is a complex rule-driven task requiring participants to learnvarious rules about safe and efficient landing conditions. Partic-ipants received two sorts of non-evaluative feedback: a runningfeedback score on their performance after each landing and anoverall performance score. Mitchell and colleagues found a strongpositive correlation between the expected and actual performancescores of participants, suggesting that participants had an accurateview of their performance. Also, the relationship became strongerat the later trials, suggesting that the feedback enabled partici-pants to further improve their self-evaluations in subsequent trials.Participants used two different strategies of self-regulation, thatis, the non-evaluative feedback led to an adjustment of eithertheir actual performance or their expected performance score.This suggests that the non-evaluative feedback resulted in a moreaccurate self-evaluation of performance, which improved the self-regulation of participants’ expected performance throughout theskill acquisition. Could such feedback on performance be beneficialin overcoming the self-enhancement biases for certain driving skillsrelated to drivers’ hazard perception as well? Or is non-evaluativefeedback not effective or even counterproductive because, forinstance, such feedback does not provide any information on howpeople can improve their performance? In the current research, wewill address this question via a hazard perception test.

Hazard perception is a higher-order safety skill which is usedto anticipate the road environment and behavior of other roadusers (Horswill and McKenna, 2004). Specifically, hazard percep-tion skills involve estimating what threats are present in theenvironment, as well as knowing what to do in order to avoid andhandle those threats. Thus, hazard perception skills cover detectionand anticipation of threats as well as one’s assessment of abili-ties to handle those threats (Grayson et al., 2003). While hazardperception skills improve as drivers gain experience, hazard per-ception does not become automated, but rather becomes a lesseffortful process with practice (McKenna and Farrand, 1999 as citedin Horswill and McKenna, 2004). Therefore, drivers need to payattention to information from constantly evolving situations andfrequently take action in order to handle dangers safely and intime. As we have mentioned earlier, drivers’ self-regulatory behav-iors to avoid hazards may be influenced by overestimation of theircompetence. This is particularly the case among novice driversbecause their higher order safety skills (such as hazard perceptionskills) to handle relatively complex traffic situations have probablynot sufficiently developed yet (OECD-ECMT, 2006). Accurate self-evaluations in a hazard perception task are particularly importantbecause computerized hazard perception tests are integrated aspart of licensing system in several countries including the UnitedKingdom and the Netherlands. What happens when drivers receivenon-evaluative feedback telling them that they are in fact not asgood as they think they are, and learn that they are overestimatingtheir competence and performance?

The perceived discrepancy between what drivers actually cando and what they believe they can do is assumed to triggerself-regulatory behaviors (cf. Carver and Scheier, 1998; Fuller,2008). Specifically, feedback may elicit self-regulation by enablinga comparison between the expected and actual situation, and con-

sequently making people aware of any discrepancy or balancebetween the expected and actual situation (Cervone and Wood,1995; Carver and Scheier, 1998). An adaptive response to deal witha discrepancy would be to adjust the effort put in the task and try

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24 E. Dogan et al. / Accident Analys

o do better or to adjust the expectations about one’s performanceevel, which would result in a closer match between one’s expec-ations and reality. A non-adaptive response to deal with such aiscrepancy, on the other hand, would imply not making thesedjustments in effort or expectations. This could result in negli-ence of the feedback or detachment from the task goals such asisengaging from the effort and quitting the task after a few trials,r devaluation of the task if detachment is not possible (cf. Carvernd Scheier, 1998; Kuiken and Twisk, 2001). Thus, there may beccasions where non-evaluative feedback does not promote adap-ive self-regulation and thus does not reduce the self-enhancementiases. In the current study, we investigated the effect of non-valuative performance feedback on self-evaluations and actualerformance, and whether non-evaluative feedback reduces theelf-enhancement bias among young, novice drivers.

We first examined the accuracy of driver’s self-evaluations ofheir hazard perception skills via a computerized test. The per-ormance criterion was the total score obtained at the end of theazard perception test. Additionally, we examined the effect ofon-evaluative feedback on these self-evaluations and on actualerformance in subsequent trials of the test. In line with previoustudies, we expected the majority of the participants to overes-imate their expected performance in the hazard perception testnitially in the first trial before they received feedback (hypothesis). This implies that we expect that the majority of the partici-ants would receive negative feedback on their performance, and

earn that they perform worse than expected. Based on the stud-es discussed above, we propose two competing hypotheses for theffects of this negative feedback on self regulation in later trials.irst, the negative feedback can elicit adaptive responses, in whicharticipants adjust their self-evaluations or their performance2 inhe subsequent trials in accordance with the negative feedback inhe former trials. This implies that negative feedback results in aower estimated test score in the subsequent trials compared to thestimated test score in the first trial, and a closer fit between esti-ated and actual performance in the subsequent trials (hypotheses

). Alternatively, negative feedback may result in non-adaptiveegulatory responses and not change their performance or theirstimations of their performance. If participants do not adjust theirerformance estimations or their actual performance, the discrep-ncy between the estimated and actual performance will remain.n line with the self-regulation process (Carver and Scheier, 1998;uiken and Twisk, 2001), we expect this unresolved discrepancyetween expected and actual performance to reflect on partici-ants’ performance evaluations and task evaluations (hypothesis). Specifically, we expect that the feedback results in a more neg-tive evaluation of one’s performance in previous trials, becausehe feedback makes participants aware of the discrepancy betweenheir expected and actual performance. Also, we expect that theegative feedback will elicit negative feelings. More importantly,e expect that participants will try to restore a positive self image

nd feel good about themselves by devaluating the test.

. Method

.1. Participants

We tested our hypotheses in an experimental study. The partic-pants were 36 students (11 male, 25 female) from the Universityf Groningen who held a valid drivers’ license for at least one year

2 Although theoretically it is plausible to expect an adjustment in performance,e think this it is not likely that participants improve their performance because theon-evaluative feedback does not convey information about what they did wrongr what they should do differently to increase their performance.

Prevention 45 (2012) 522– 528

(M = 2.5 years; SD = 1.17) and had driven an average of 6505 kmsince licensure (SD = 4820). The mean age was 21 years (M = 21.22,SD = 1.44); age ranged from 19 years to 24 years. Participants wererecruited via the student participant pool of the University ofGroningen and received course credit in return for their partici-pation.

2.2. Hazard perception test

To test our hypotheses, we developed a hazard perception testcomprising natural traffic scenes that were recorded around thecity of Groningen, the Netherlands. The recordings were taken fromthe drivers’ point of view during daylight in bright and dry weatherconditions. A team of experts watched the recordings to mark thehazardous events following the definition and criteria set out byGrayson and Sexton (2002), who argued that a good hazard per-ception measure should capture respondent’s scanning skills andanticipation of the developing situation rather than detecting only aquick reaction to the situation. Based on this, clips that would detectdrivers’ anticipation of developing dangers were kept in, while theones that would create difference only with respect to reaction timeto suddenly occurring hazards were left out. The selected clips werethen tested in a pilot study. At this stage, we applied two otherselection criteria proposed by Grayson and Sexton (2002), namelya mean score of the hazard event between 2 and 3 for participants(the scoring system is explained below), and significantly differenthazard perception scores between experienced and inexperiencedparticipants. Then, all these clips were re-evaluated by the sameteam of experts. Five of the clips were omitted at this stage becausethe hazards in those clips did not differentiate between the expe-rienced and inexperienced groups or they were not detected. Theremaining 36 clips were used for the test, after the start and endscenes of the hazard situations were adjusted based on the reactiontimes obtained in the pilot test.

The shortest clip was 22 s while the longest one was 69 s. Thenumber of hazards in each clip ranged from one to three. Exam-ples of the hazards included were a car emerging from the right,pedestrians getting out of parked cars, a lane reduction in a con-struction area, and a cyclist crossing the road. We developed threedifferent versions of the hazard perception test to be used in threetrials, all of which had 12 comparable hazard situations, and tookapproximately 10 min to complete.

The clips were shown to participants on a 19′′ computer screenwith a 3-s interval between the clips. Participants used an externalbutton to respond to the hazard situations. They were instructed topress the button as soon as they thought they should modify theirbehavior to avoid a potential danger. Participants were informedthat their response would not be valid should they press the buttonmore than five times for each hazard situation.

The hazard perception test enabled us to provide participants’with feedback in terms of a non-evaluative test scores. To do so,responses were scored following the method developed by Graysonand Sexton (2002), that is, the starting and ending frames of thehazard situations were marked and the time range was dividedinto 5 equal intervals. As different hazards had different durationsand require different response times, the time interval was idiosyn-cratic for every item. The closer the response to the starting frame ofthe hazard situation, the higher the points gained for each hazard (5points for the fastest response and 1 point for the slowest response).We recorded the time frame in which participants responded. Themaximum possible score for each version of the hazard perception

test was 60 meaning that the participant detected each hazard andreacted to all hazards in a very short time, while the minimum scorewas 0 meaning that the participant failed to detect any hazard intime.

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action between group and trial did not have a significant effecton the performance estimations, F (2, 68) = .31, p = .737, or on theactual performance, F (1, 68) = .40, p = .670. For both the experimen-tal and control group, neither estimated nor actual performance

3 We computed the difference score between the estimated and actual perfor-

E. Dogan et al. / Accident Analys

.3. Measures

.3.1. Self-evaluations for the expected performance in theazard perception test

We measured self-evaluations of the expected performance inhe hazard perception test based on Bandura’s (1997) self efficacycale, because the structure of the scale enabled us to measurexpected performance for different levels, i.e. scores, of the task.articipants were presented a table with possible scores on theazard perception test, ranging from 20 to 60 with 5-point inter-als (9 levels in total). They indicated whether or not they couldeach any of the given test scores. The number of level that par-icipants reported that they could execute was summed and useds an indicator of the estimated performance score. For instance, ifhe participant reported he or she could reach the test scores men-ioned in the first four levels, the estimated performance would be5 in the hazard perception test. Scores on self-evaluations for thestimated performance could range from 20 (i.e. I can detect fewazards in time) to 60 (i.e. I can detect all the hazards present inhe video clips in time).

.3.2. Experience of correspondence between estimated andctual performance

After completing each trial, we measured how participantsxperienced their test performance. Specifically, we asked par-icipants how successful they felt about their performance in theazard perception test, and how satisfied and frustrated they wereith their performance, respectively. Participants in the experi-ental group answered these questions after receiving their true

erformance score, whereas participants in the control group didot receive this feedback. Additionally, participants’ rated howffortful it was for them to perform at the level they did. Next,he experimental group received a final question asking whetherhey thought their test score reflected their true performance. Thisuestion was not presented to the control group as they did noteceive feedback on their test score. Participants answered theseour questions on a 7-point scale ranging from 1 – not at all to

– very much.

.4. Procedure and design

The experiment consisted of three trials of the computerizedazard perception test. This enabled us to examine the effects of theon-evaluative feedback on self-evaluations over time. In each trial,articipants completed a different version of the hazard perceptionest. The three versions of the hazard perception test were counter-alanced. Before starting the experiment, participants were givenhe instructions on the hazard perception test identical to thoserayson and Sexton (2002). Thus, a hazard was defined as “some-

hing that a driver should keep an eye on because it could lead to anccident situation” (Grayson and Sexton, 2002, p. 6). Participants’ere informed that they should press the external button as soon

s they recognize a hazard developing. We also mentioned thatigher scores on this test would mean safer reactions to a hazardhile lower scores would mean unsafer reactions. Afterwards, par-

icipants received a brief training on the hazard perception test withhree sample clips. During the training we explained how the testorked and how their responses were scored. They were allowed to

epeat the training if they needed to. During the training, all partic-pants received non-evaluative feedback on their scores followingach clip and then received a total test score once all the clips inhe relevant test had been shown. This was done to inform par-

icipants about their performance. After the training, participantslled in a questionnaire. The questionnaire included a scale for theirelf-evaluations of their performance in the hazard perception testn the coming trial as well as a few demographics questions.

Prevention 45 (2012) 522– 528 525

Participants were then randomly assigned to the experimentalor control group. Participants in the experimental group receivedperformance feedback on their score on the hazard perception tasksimilar to the feedback they received during the training, whileparticipants in the control group did no longer receive feedback.Running feedback was presented after each video clip that informedparticipants about the score obtained for the particular clip. Atthe end of the hazard perception test, overall performance feed-back was provided reflecting the total score obtained at the end ofthe test. The feedback (i.e. experimental group, E) and control (i.e.C) groups were similar in license duration, t (34) = −.52, p = .610,(ME = 2.47, SDE = 1.23; MC = 2.68, SDC = 1.13) and total mileage, t(34) = 1.60, p = .117, (ME = 7700, SDE = 5095; MC = 5170, SDC = 4246).We also checked whether the two groups were similar in their per-formance estimations at the beginning of the study. As expected,the experimental and control groups did not differ in terms of theirperformance estimations in the first trial, F (1, 34) = 2.68, p = .111.

Next, participants performed the first hazard perception testand either did or did not receive performance feedback depend-ing on the group in which they were. After completing the firsttest (for the experimental group after learning their test score),all participants filled in a short scale measuring their experienceof the contingency or the discrepancy between their performanceand estimated performance in the previous trial. Next, they filledin the self-evaluation scale for their estimated performance in thesubsequent trial. The same procedure was repeated after the sec-ond and third trials, except that the self-evaluation scale was notadministered after the third trial because there were no more trials.

3. Results

In order to test our first hypothesis on the accuracy of the per-formance estimations on the hazard perception test, we comparedthe estimated and actual test scores for each trial via paired samplet-test analysis.3 As expected, performance estimations for the firsttrial and the actual test scores on this trial significantly differed:participants in both groups overestimated their hazard perceptiontest score (t (18) = −7.88, p < .001) for the experimental group andt (18) = −6.53, p < .001 for the control group, see Fig. 1.

Our second and third hypotheses predicted competing regu-latory responses for the discrepancy between the estimated andactual performance. The second hypothesis predicted an adaptiveregulatory response, in which the experimental group would adjusttheir performance expectations in the subsequent trials on the basisof the feedback they received. We expected the majority of theparticipants in the experimental group to receive negative feed-back. Indeed, no participant in the experimental group receivedpositive feedback, i.e. no participant performed better than theirestimated performance. On the contrary, all participants receivednegative feedback, i.e. they performed worse than they expected.Fig. 1 shows that participants in both the experimental and controlgroups maintained their overestimated performance estimationsin all three trials. Indeed, a mixed model Anova with group as abetween factor and trial as a within factor showed that the inter-

mance. The difference score was used as the dependent variable in a mixed ANOVAwith group as the between measure and trial as the within measure. This analysisrevealed no significant main and interaction effects indicating that the discrep-ancy between the estimated and actual performance was not different betweenthe experimental and control groups or across trials.

526 E. Dogan et al. / Accident Analysis and Prevention 45 (2012) 522– 528

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hanged over time. Thus, contrary to the second hypothesis, non-valuative feedback did not result in adaptive self-regulatoryesponses.

So, we found that the participants showed non-adaptive regula-ory responses. How does this reflect on the way they experiencedheir performance? How do they cope with the discrepancyetween their estimated and actual performance? The thirdypothesis was about the way participants felt about their per-

ormance in the previous trials in case they did not adjust theirstimated or actual performance. Since the control group did noteceive the performance feedback, we expected the experimen-al group, who was made aware of a discrepancy between theirxpected and actual performance, to be less satisfied and more frus-rated about their performance and to feel less successful than theontrol group. We conducted independent sample t-test analysiso compare the satisfaction, frustration, and the perceived successf the performance level in the previous trials for the experimen-al and control group. After the first trial, the participants in thexperimental group were significantly less satisfied (t (34) = −3.94,

< .001) and marginally more frustrated (t (34) = 1.80, p = .080) withheir performance compared to the control group participants whoad not received feedback on their performance on the hazard per-eption test in the previous trials. Similar results were observed forhe responses following the second and the third trial (see Fig. 2).n addition, the control group participants considered themselveso be more successful in each preceding trial than the experimentalroup participants did after the first trial, t (34) = −4.58, p < .001. Anndependent sample t-test revealed that perceived effort did notiffer between the two groups in any trial, t (34) = −.69, p > .10 in

he first trial, t (34) = .56, p > .10 in the second trial, and t (34) = .44,

> .10 in the third trial. So the test was perceived as equally effort-ul by both groups, and differences in experienced effort cannotxplain our results.

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imations before the trial and actual performance for three trials.

In line with our third hypothesis, the maladaptive self-regulatory responses resulted in devaluing the hazard perceptiontest. In all three trials, participants in the experimental groupbelieved that their test scores did not reflect their actual perfor-mance well (M1 = 2.37, SD1 = 1.11; M2 = 2.21, SD2 = 0.98; M3 = 2.42,SD3 = 1.07). So, in line with our hypothesis, the experimental groupdowngraded the credibility of the test, presumably to handle thediscrepancy between their estimated and actual performance.

4. Discussion

Drivers’ self-serving evaluations of their driving competenceand abilities may negatively affect their risk taking behavior androad safety. Self-enhancement biases have been found for variousdriving skills and appear to be robust. We first tested whether theself-enhancement bias was also apparent in hazard perception. Inline with our expectation, we found a strong self-enhancementbias: participants systematically overestimated their hazard per-ception skills and thought they would perform better than theyactually did.

Lack of feedback may be one of the reasons why people do nothave accurate beliefs about their skills and competence. In the cur-rent study we investigated whether inaccurate self-evaluations canbe overcome by giving participants’ non-evaluative performancefeedback. More specifically, we tested whether non-evaluativefeedback would result in adjustment of the self-evaluation of one’sperformance. We did not expect the non-evaluative feedback toresult in changes in one’s performance, as the feedback did not indi-cate why participants’ performance was lower than they expected

(so participants’ did not learn how to improve their performance),and indeed, actual performance did not change over time. Interest-ingly, we did not find any effects of the non-evaluative feedbackon one’s self-evaluations in subsequent trials. On the contrary, our

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esults indicate a profound self-enhancement effect in terms of per-ormance estimations among both the feedback (experimental) ando-feedback (control) groups. Furthermore, although the test waserceived as similarly effortful by both groups, the experimentalroup who learned about the discrepancy between their expectednd actual performance experienced a higher level of frustrationnd feeling of failure, and a lower level of satisfaction with theirerformance than the control group who did not explicitly learnbout this discrepancy. Thus, the experimental group experiencedegative feelings about their performance, probably as a result ofhe discrepancy between their estimated and actual performance.onetheless, they did not respond to this discrepancy in an adap-

ive way by adjusting their estimated performance scores or actualerformance. Rather, they downgraded the credibility of the haz-rd perception test by indicating that they believed that their testcores did not reflect their true performance, supporting our thirdypothesis.

Why did the experimental group participants, who were explic-tly informed about their scores on the hazard perception test andherefore understood that they failed to perform at the level theyxpected, keep providing higher performance estimations thanhey actually achieved? Why did they change their performancessessments on the basis of the feedback on their test score butather disqualified the test?

One explanation is related to the nature of hazard perceptionkills. Hazard perception requires integration of several drivingkills, which makes it rather ambiguous to assess one’s performancen hazard perception skills. Indeed, when a self-evaluation dimen-ion is ambiguous, it may result in self-enhancement biases pere (Dunning et al., 1989; Ackerman et al., 2002). A recent studyemonstrated that somewhat experienced young drivers showedelf-enhancement biases for their skills and risk assessments evenhen they were told that their driving skills would be assessed by

bjective measures, namely a driving simulator (White et al., 2011).urthermore, White et al. (2011) argued that computerized testsay not represent the true difficulty of the task involved in various

riving situations. Therefore, it is plausible to expect that partici-ants’ underestimated the difficulty of the computerized task andverestimated their performance to some extent, particularly inhe initial trial. We expected this initial self-enhancement effect toiminish in the subsequent trials for the experimental group. How-ver, the robustness of the self-enhancement bias observed in theurrent study in all three trials study calls for further explanation.

The number of hazards detected in all trials, although partici-ants did not know this, was rather high (around 9 out of 12 in eachrial), especially considering the experience level of our sample. So,ower-than-expected scores must be due to reaction time latency.hus, either the importance of reaction time latency was not clearo the participants, despite the information provided beforehandnd the training with the initial clips, or participants could notccurately estimate the timing of their reactions to hazardousituations. In fact, Chapman and Underwood (1998) found thatovice drivers had a longer fixation time for hazardous situationshan experienced drivers. Furthermore, inexperienced drivers hadess accurate estimations of time-to-collision (Cavallo and Laurent,988). So, it is not surprising that our participants, who are rel-tively inexperienced drivers, had a higher reaction time latency,espite detecting a rather high number of hazards.

Another explanation to address the robust self-enhancementias may be that the non-evaluative feedback on the test per-ormance was not effective in changing drivers’ self-evaluationsnd actual performance because it did not convey information on

hy participants’ scores were lower than they expected. Thus,on-evaluative feedback was not specific to elicit an adaptive self-egulation (Bandura, 1997). Not knowing why their expectationsere not accurate may not be sufficiently motivating for drivers’ to

Prevention 45 (2012) 522– 528 527

change the self-assessments of their competences, but it apparentlydid induce negative feelings (such as higher frustration and dissat-isfaction) among the experimental group that participants couldnot ignore. Thus, affective self-regulatory process was activated(Carver and Scheier, 1998). When faced with negative feedbackwithout knowing the exact cause of it, the feedback group deval-ued the task by degrading its credibility instead of adjusting theirestimated performance. This may be because they could not disen-gage from the task due to their commitment to complete the task,despite being frustrated and dissatisfied with their performance(Carver and Scheier, 1998; Kuiken and Twisk, 2001). They mayhave dealt with these unpleasant feelings due to the disconfirmingfeedback by downgrading the credibility of the test. Furthermore,non-evaluative feedback of this kind might have created differ-ences between the experimental and control group in how theyfelt about their performance by triggering two mechanisms: cre-ating a threat to participants’ self-view and giving a competitivenature to the hazard perception test. These points will be furtherelaborated below.

The feedback made participants’ aware of the discrepancybetween their expected and actual performance, which may havethreatened their self-perception. We expected this to be the caseif participants would not adjust their self-evaluations or perfor-mance, that is, we expected that negative feedback would yieldnegative feelings and external attributions. Apparently, partici-pants coped with these negative feelings by downplaying thecredibility of the task (cf. Carver and Scheier, 1998; Kuiken andTwisk, 2001). Hepper et al. (2010) argued that defensiveness istriggered by a threat to one’s self-concept and such defensivenessis notable by attributing failure to the situation or the task ratherthan to one’s ability. Research showing the strength of implicit self-enhancement bias among drivers indicates that driving abilitiesare core for drivers’ self-concept (Harré and Sibley, 2007). Indeed,research indicates that threatening information is especially pro-cessed defensively if the information is related to issues that areimportant for individuals (Pietersma and Dijkstra, 2011). Therefore,non-evaluative feedback may have prompted a self-threat amongthe feedback group.

Considering the fact that our sample consisted of universitystudents, who are presumably very much used to performanceevaluations via grades, the feedback we used might have added acompetitive nature to an ordinary risk perception task. Delhommeand Meyer (1998) showed that failure in a speed regulation taskresulted in a worse speed regulation among novice drivers espe-cially when the task was presented as a competitive one than acooperative task. People exhibit a self-serving bias by making exter-nal attributions to temporary and specific sources for their failuresin order to protect their ego and self-image. However, discountingown responsibility for negative outcomes weakens the motivationand ability to take necessary regulatory actions to change thoseoutcomes (see Mezulis et al., 2004). Therefore, the type of feedbackand how it should be given are crucial especially for inexperienceddrivers, who are more likely to be influenced by external motiva-tional factors (Delhomme and Meyer, 1998). Using computerizedtests or simulators for training driving skills may backfire if traineesdo not learn the consequences of their behaviors, and do not learnwhy their performance was high or low, and how to improve theirperformance.

In the current study, we were mainly interested in the effectof non-evaluative feedback on self-enhancement biases. In doingso, we focused on participants’ estimated performance rather thantheir performance goals. It is likely that actual performance is not

only related to one’s estimated performance, but also to one’s goals.We did not measure participants’ goals related to their performancein the hazard perception task; and consequently, we do not knowwhether participants’ motivation to not adjust their estimated or

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ctual performance was influenced by their goals. Furthermore,orrespondence between specificity of goals and feedback, and thesefulness of feedback for self-regulation are interrelated (Cervonend Wood, 1995). Future research could incorporate goal settingnd self-evaluations in order to disentangle their effect on self-egulation. This could improve our understanding of the way affectnfluences regulatory responses to feedback and how drivers cope

ith negative feedback.In conclusion, our results suggest that non-evaluative perfor-

ance feedback may not be suitable for improving self-evaluationsnd performance that are measuring (complex) skills becauset can alter the way the task is perceived. Most importantly,on-evaluative feedback may not change self-evaluations orerformance but rather result in devaluation of the task. Non-valuative feedback was effective in regulating self-evaluations andxpected scores in an air traffic controller task (Mitchell et al.,994), but our results suggest it is not effective for a driving task.his might be due to the differences between driving tasks andir traffic controller tasks in terms of the amount of informationvailable to the operators and the level of automation assisting theperator. Lack of feedback contributes to self-enhancement biases;onetheless, the mere presence of feedback is not sufficient forvercoming such biases for driving competence. The content ofhe feedback seems to be more crucial than the presence of feed-ack (Kruger and Dunning, 1999). We think that providing driversith detailed information on what caused their failure or success

nd what they should do in order to improve their performance iseeded for feedback to be effective in reducing self-enhancementiases.

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