ergonomics cognitive control by brain-injured car drivers

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Cognitive control by brain-injured car drivers: an exploratory studyCamilo Charrona; Jean-Michel Hocb; Isabelle Milleville-Pennelb

a University of Rennes 2, CRPCC, Campus Villejean, Place du Recteur Henri Le Moal, CS 24307,Rennes, France b CNRS, IRCCyN, Research Institute in Communications and Cybernetics, Nantes44321, France

Online publication date: 24 November 2010

To cite this Article Charron, Camilo , Hoc, Jean-Michel and Milleville-Pennel, Isabelle(2010) 'Cognitive control by brain-injured car drivers: an exploratory study', Ergonomics, 53: 12, 1434 — 1445To link to this Article: DOI: 10.1080/00140139.2010.532880URL: http://dx.doi.org/10.1080/00140139.2010.532880

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Cognitive control by brain-injured car drivers: an exploratory study

Camilo Charrona*, Jean-Michel Hocb and Isabelle Milleville-Pennelb

aUniversity of Rennes 2, CRPCC, Campus Villejean, Place du Recteur Henri Le Moal, CS 24307, Rennes 35043, France; bCNRS,IRCCyN, Research Institute in Communications and Cybernetics, Nantes 44321, France

(Received 29 March 2009; final version received 8 October 2010)

Cognitive control is a key tool for adaptation in dynamic situations. The aim of the study is to assess the relevance ofa theoretical framework for cognitive control in dynamic situations, in order to understand brain-injured (BI) cardrivers’ cognitive impairment. The framework supports a cognitive control multimodality based on the crossing oftwo orthogonal dimensions: symbolic/subsymbolic; anticipative/reactive control. BI car drivers’ behaviour wascompared with that of a control group (CTRL) during driving simulator scenarios. Eye movement analysis, amongother variables, revealed that BI car drivers made use of a more symbolic and reactive control than did CTRLdrivers. CTRL drivers showed a more stable cognitive compromise than BI drivers. The latter became less symbolicand more reactive in the case of difficult scenarios. In addition, BI drivers focused on the main task of trajectorymanagement, with fewer resources devoted to traffic interaction management.

Statement of Relevance: An explanation of differences between BI and CTRL drivers in terms of cognitive controlrequirements, attention and processing speed is put forward. From this, it is possible to derive some implications interms of driver assistance (e.g. lane keeping or a warning assistance device) and rehabilitation.

Keywords: anticipative and reactive behaviour; brain-injured car driver; cognitive control; symbolic and subsymbolicprocessing

Introduction

Brain injury often impacts on young drivers and canresult in an impediment to autonomy and mobility.After recovery, two questions are raised: how to evaluatedriving ability and whether rehabilitation is possible.The present paper focuses on the first question. Thepopulation of brain-injured (BI) drivers is heteroge-neous. Frontal brain damage frequently occurs, bringingwith it associated difficulties in terms of planning(Debelleix 2001). However, anatomical injuries canvary widely. Thus, the current research approach isfunctional. Some authors try to define driving simulatoror real driving tests in order to identify the functionaldeficits related to and within the context of driving (e.g.Lew et al. 2005). A first experiment was undertaken thatis in line with this approach and is particularly relevantfor identifying cognitive control differences between BIdrivers and a control group (CTRL).

Cognitive control is a key tool for adaptation indynamic situations. Hoc and Amalberti (2007) havedeveloped a model of cognitive control in dynamicsituations that is based mainly on studies of industrialprocess control and transportation. This model wasapplied to the study of BI drivers who had recoveredfrom their brain injury and had been driving again for

several years. As a matter of fact, adaptation tounexpected situations is a key feature of driving skills.Although attention is frequently addressed within thiscontext (Parasuraman and Nestor 1991), it is notsufficient to account for adaptation mechanisms. Thepresent results will be related to the attentionframework, before some issues in terms of car-drivingassistance are discussed.

Theoretical framework

Hoc and Amalberti (2007) defined cognitive control asa process of bringing into play the cognitiverepresentations and operations required foradaptation, both in the correct order and with theappropriate intensity. In order to account for cognitivecontrol dynamics, they considered two complementaryaspects: the notion of satisficing performance(borrowed from Simon 1969) and the cognitivecompromise, which is determined by the former.

Satisficing performance is the level of performancethat is considered by the individual to be acceptablewithin a certain context (e.g. motivation, socialacceptability, invested resources). Satisficingperformance may have several criteria, with different

*Corresponding author. Email: [email protected]

Ergonomics

Vol. 53, No. 12, December 2010, 1434–1445

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levels of priority. Before the beginning of an activity,the level of performance considered acceptable is thebaseline satisficing performance. During the activityitself, the different levels of priority may change inorder to cope with the needs of the tasks or with thedifficulties encountered.

In order to enable the individual to achieve asatisficing performance at an acceptable cost in termsof invested resources, cognitive control is distributedbetween diverse modalities. Resources include energy,which is limited in quantity, as well as cognitiveprocesses, ranging from high level to routine skills, andattentional processes.

Cognitive control modalities are defined within aplane generated by two orthogonal dimensions (bot-tom of Figure 1). The first dimension contrastssymbolic control with subsymbolic control. Symboliccontrol applies to information interpretation, whereassubsymbolic control is directly based on the superficial(e.g. perceptive) features of information. Symboliccontrol is more costly than subsymbolic control. Thesecond dimension contrasts anticipative control withreactive control. The former applies more to internalinformation (mental models) and the latter more toexternal information (situational cues). However,within this context, control reactiveness has nothingto do with processing speed. Rather, it concernsthe need to rely on external information. Someparallelism is possible between several control mod-alities (e.g. symbolic control supervising subsymboliccontrol or reactive control correcting anticipativecontrol errors).

The distribution of control within this planeconstitutes the cognitive compromise. The functionof the cognitive compromise is twofold: (a) to avoidexhaustion, notably guaranteeing a relatively stableperformance for the entire task period; (b) to permitthe investment of resources in useful parallel activities,for instance, to process a secondary subtask within the

main task. At any one time, an individual tries to reachan appropriate cognitive compromise.

The dynamics of cognitive control for adaptationare determined by meta-knowledge that enables theindividual to evaluate anticipated performance andnecessary resources. If anticipated performance is lowerthan satisficing performance and/or anticipatedresources need a larger investment, there is a feeling thatsituation mastery is low. In this case, the individual canreduce the satisficing performance level or increase theinvested resources. The cognitive compromise can thenbe adjusted. For example, if the individual experiences ahigh rate of action slips (bad performance), there is afeeling of low situation mastery. The individual can thendecide to invest in more resources, exerting greatersymbolic control over the activity.

Satisficing performance may be measured bysubjective assessment, such as through a questionnaire.Moreover, it can be deduced from the performanceobtained when the individual estimates that he or shehas mastered the situation.

The main methodological difficulties relate toattempts to identify the distribution of control amongits diverse modalities using non-invasive indicators. Theimportance of visual information in car driving made itpossible to base identification on several parameters ofeye movements. However, other kinds of variables wererecorded, such as speed, speed variability, and so on.

Symbolic control needs deeper processing, as it isbased on interpretation. This is consistent with theliterature (Rayner 1998), which considers thatsymbolic control can be identified by a longer meanfixation duration than subsymbolic control.

Anticipative control can be identified by fixationdistance, as is shown by an abundance of data onvisual activity during car driving. Healthy andexperienced drivers mostly have far fixations. Any nearfixations are of quite a short duration and occur instraight lines (Underwood et al. 2003). In bends,

Figure 1. Cognitive control dynamics.

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drivers spend a large amount of time fixating an areaaround the tangent point, inside the curve. This is away of anticipating the road curvature and theappropriate steering wheel angle (Land and Lee 1994,Land and Horwood 1995). However, other distantpoints on the road can play the role of the tangentpoint in anticipation (Mars 2008). Thus, the fixationdistance and position can be an indicator ofanticipation when driving.

The ability to process several subtasks within a maintask is assessed by the time spent looking at the mirror,whichis related to time-sharing between trajectorycontrol and traffic interaction management, and bythe number of gazes devoted to cues of a possibleunpredicted event (such as a pedestrian who could movefrom the road verge to suddenly cross the road).

Objective

This experiment investigated cognitive control andadaptation differences between BI and healthy drivers.This was carried out within the context of arbitrationbetween conflicting performance criteria in the drivingsituation; namely, speed and safety. A speed or safetyinstruction is supposed to directly act on the driver’sdefinition of satisficing performance. Events occurringduring the experimental scenarios are assumed toprovoke conflict between performance criteria andpossible changes in the cognitive compromise. Forexample, a fast-moving vehicle approaching from therear may cause a driver to speed up, despite there beinga safety instruction aimed at encouraging the adoptionof a low speed. In order to overcome such conflict, thedriver has to invest more resources. The cognitivecompromise may or may not be robust enough toallow the driver to do so. In the second case, the drivermay simplify the definition of satisficing performanceby relaxing some constraints or changing the cognitivecompromise.

Method

Participants

The BI group comprised seven male drivers who hadagreed to participate in the experiment. They had allrecovered from a brain injury caused by a trauma andwere aged between 35 and 50 years (mean age 43years). They had recorded a score equal to or below 8on the Glasgow Coma Scale and had been in a comafor at least 48 h.

All participants (BI and CTRL) were individuallyevaluated with the following neuropsychological tests:the subtests Alertness, Go-Nogo, Divided Attentionand Visual Scanning of the Test for Attentional

Performance (TAP; Zimmermann and Fimm 2007);the subtest Digit Span of the Wechsler AdultIntelligence Scale III (WAIS III; Wechsler 2000); theStroop Colour Word Test (Spreen and Strauss 1998);the subtest Zoo Map of Behavioural Assessment ofDysexecutive (BADS; Wilson et al. 1998); TrailMaking Test (Tombaugh 2004); the D2 test(Brickenkamp 1998).

On the whole, neuropsychological tests showed thatthey all had difficulty (that is to say, results below 1 SDor more with regard to the mean) with storage andmanipulation in working memory (WAIS-III: DigitSpan). More than half of the BI group experiencedproblems in target detection (TAP: Visual Scanning),planning (BADS: Zoo Map), anticipation (BADS: ZooMap) and speed of information processing (D2). Withthe exception of one participant, they did not haveparticular difficulty in alertness (TAP), cognitiveinhibition and selective attention (Stroop). Each of theBI participants had gained their driving licence at least 2years before the brain injury occurred. All of them hadrecovered and were driving again. On average, they eachhad 11 years of driving experience.

CTRL comprised six male participants with noimpairment. The neuropsychological tests showed thatthey had a normal profile. They were recruited fromwithin IRCCyN and were aged between 35 and 50years (mean age 44 years). On average, they hadobtained a driving licence 23.6 years ago.

All the participants had normal or corrected-to-normal vision and driving experience of more than30,000 km.

Apparatus

The driving simulator software, Sim2 (developed bythe Modelisations SImulations et Simulateurs (MSIS)team at the Institut National de Recherchesur lesTransports et leur Securite (INRETS)) was used,coupled with a FAROS fixed-base driving simulator(FAROS: ECA Group, Lannion, France). It wasequipped with an automatic gearbox, a steering wheelfitted with force feedback, brake, accelerator andclutch pedals and a speedometer. The visual scene wasprojected onto a screen (3.02 m height 6 2.28 mwidth, which corresponds to a visual angle of 808height and 668 width). A 3.5-km main road, forming acircuit, was simulated with traffic and with about 10bends of various directions.

An eye-tracker, IviewX (SensoMotoric InstrumentsGmbH, Berlin, Germany), was used to investigatevisual exploration. This eye-tracker consists of abarely invasive, lightweight head-mounted camerathat captures images of the subject’s eye and field ofview.

1436 C. Charron et al.


Procedure and experimental design

Following a familiarisation stage, participants had tocomplete six laps, with a rest period of 5 min betweeneach. Two laps were base laps. These comprised simplescenarios: one without a car in the lane occupied by theparticipant and one where a slow car was present to actas an incentive for the driver to slow down or toovertake. Each participant then had to perform fourexperimental laps, generated by crossing two binaryand independent variables. For each lap, a pedestrianwas present on the road verge. The independentvariables are as follows.

Type of instruction. Half of the experimental laps wereperformed with a safety instruction (SAI: ‘imaginethere is a child with you in the car and you must bevery careful on the road’); the other half wereperformed with a speed instruction (SPI: ‘imagineyou have a very important appointment, for example,a job interview, and you are late’).

Type of scenario. With each type of instruction, therewere two typesof scenario. Inoneof them, only the slow-moving car interacted with participants, acting as anincentive to slow down; thus, defining a safety scenario(SAS). In the other scenario, when participants ap-proached the slow-moving car, a fast-moving car caughtthem up. This car was visible in the mirror. It followedthem for the remainder of the lap and was an incentivefor them to speed up; thus, defining a speed scenario(SPS). Two distinct but similar scenarios of each type (1,2) were presented in order to avoid familiarisation.

The order of presentation of the four experimentalconditions was balanced over participants. Before thefirst lap and after each lap, a questionnaire was givento the participants in order to collect their performanceand situation mastery assessments (see below).

Data recording

Within the restricted scope of this pilot study, only themain results will be presented.Three types of data wererecorded: subjective assessment; driver–car behaviourvariables; eye fixation parameters.

Subjective assessments

(a) Baseline satisficing performance. Before drivingthe simulator, each participant was invited tochoose the most important and then the leastimportant driving performance assessmentcriteria: speed; regulation compliance; safety.

(b) Situation mastery assessment. After each lap,the participant was invited to answer on a 4-point scale from ‘very low’ to ‘very high’.

(c) Memory of pedestrian. After each experimentallap, two photographs were shown to

participants in a counterbalanced order. Theseshowed part of the road and were taken fromthe driver’s point of view. One photographshowed a curve where no pedestrian waspresent, neither during the simulation nor onthe photograph. The other photograph showedanother curve where a pedestrian was presentduring the simulation, but did not appear onthe photograph. The participant was invited tosay whether the curve scenery was the same asthat which had appeared during the simulation.In the case of a negative reply, the participantwas asked to say what was different. Memoryof the pedestrian was analysed in relation to thenumber of gazes to the pedestrian and to themirror, in order to evaluate time-sharing skillswithin the driving task.

(d) Perceived performance assessment. After eachexperimental lap and after the situation masteryassessment, each participant was invited to saywhich performance criteria had been mostsatisfied and which had been least satisfied (seebaseline satisficing performance given above).This measure gives the level of priority fordifferent criteria of the satisficing performance.

Driver–car behaviour

This paper will mainly consider speed and speedvariability adjusted by mean speed (as a co-variable:when mean speed increases, the difference betweenstraight line and bend speed also increases). Due tosome failures in data recording, the data of one CTRLand one BI participant were not taken into account forthe speed variability.

Speed will be used to identify the effect of theinstruction on the satisficing performance in the speeddimension (if the participant felt that he had masteredthe situation). Adjusted speed variability will beinterpreted in terms of reactiveness. The higher thevariability, the higher the reactiveness of cognitivecontrol. In order to exclude the possibility that thisvariable is a cue for a neural-based generalised increasein variability for the BI group, instead of a cue forcognitive control reactiveness, it is necessary to identifya difference between laps.

Performance in lane-keeping will be assessed by thedistance (measured in mm) between the centre of thecar and the centre of the lane.

Eye fixation parameters

Due to some failures in data recording, the data of twoBI participants were not taken into account for themeasures (a) and (b).

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(a) Mean duration of fixation. An individualfixation was defined as a stable position of theeye lasting for more than 80 ms. The longerthis duration, the more symbolic the cognitivecontrol is assumed to be. However, since thisvariable for the BI drivers could just besynonymous with their decreased processingspeed, the differences between the laps for thegroups will be taken into account. To bedefinitively interpreted as a symbolic indicatorfor the BI drivers, the variation of meanduration of fixation for the lap must bedifferent for the BI group and the CTRLgroup.

(b) Percentage of time spent in particular areas ofinterest with regard to the drive time of a lap.The areas of interest (six for straight lines andseven for curves) are presented in Figure 2. Anear area of fixation in straight lines is relatedto reactive processing. A far area of fixation instraight lines and at the tangent point in curvesis related to anticipative processing. Finally,the mirror area is related to time-sharing

between trajectory control and trafficinteraction management.

(c) Number of gazes devoted to the pedestrian onthe road verge. This is related to considerationof other road users (time-sharing), as in thecase of mirror fixation.

Analysis

As usual, for numerical variables and comparisonswith 1 degree of freedom, in order to conclude whethera population effect (d) is non-null on the basis of asample effect (d), a student’s t-test of significance wascalculated. The t-test was associated with an observedtwo-tailed threshold (p). However, in order to drawconclusions in terms of population effect sizes and togo beyond a conclusion in sole terms of non-nulleffects, a variant of Bayesian statistical inference(fiducial inference: Rouanet 1996, Rouanet andLecoutre 1983, Lecoutre and Poitevineau 2005), whichconsiders test power, was used. On the basis of amaximal a priori uncertainty, the technique enables theuser to emit a probabilistic judgement on thepopulation effect size. For example, if the sample effect(d) can be considered as large, then a conclusion suchas: ‘there is a high probability (guarantee g) that thepopulation effect is larger than a notable value’ is tried(P(d)4a ¼ g; shortly d4a). Conversely, if theobserved effect is negligible, the expected conclusion isthat ‘there is a high probability that the absolutepopulation effect is lower than a negligible value’,(P(jdj)5e ¼ g). All fiducial conclusions below will begiven with the guarantee g ¼ 0.90. When no relevantconclusion could be reached, at least with thisguarantee, it has been noted as ‘no gen.’, meaning thatno generalisation in terms of population effect sizecould be reached. When the comparison has more than1 degree of freedom, the effect indicator selected is l,the quadratic mean.

The variables from the subjective assessment wereanalysed with the binomial law. The exact binomialtest with a two-tailed threshold was used to discoverwhen the answers differed from random events. Thehypothesised probability of success chosen was 1/r(where r is the number of possible responses, that is, 3or 4) and the number of trials was given by the size ofthe considered group (BI or CTRL). On the otherhand, the group differences were tested with theSchwarz Bayesian Index (BIC; Schwarz 1978). Thisindex permits the selection of the best model fromamongst those tested (the null hypothesis vs. the groupdifference model). The model retained is the one thatobtains the smallest BIC; that is, the model thatpresents the best compromise between likelihood andeconomy (minimum number of parameters).Figure 2. Areas of interest: (a) straight lines; (b) bends.



Main hypotheses

Instruction effects

Instruction type is assumed to directly determine thesatisficing performance. The effect of instruction onactual performance is expected to be massive.

Scenario effects

The effect of scenario on performance is not direct. It ismediated by whether or not conflict is generatedbetween what actually happens and the satisficingperformance and whether or not there is an arbitrationfacility. However, the scenario type could have aglobal, if slight, effect on performance.

Combination of type of instruction and type of scenario

Table 1 sums up expectations when combining the twoindependent variables.

The interaction between instruction and scenario isrelated to a possible conflict between the reality andsatisficing performance. Conflict resolution can befacilitated by a particular property of the scenarioitself. Such a property can modify the satisficingperformance in accordance with the instruction, witha possible change in cognitive compromise. This is thecase for the speed scenario, whatever the instruction.The most difficult scenario to manage is the combina-tion of speed instruction and safety scenario. Finally,there is no conflict when combining the safety scenarioand the safety instruction.

Group and interaction effects

In line with existing literature, BI drivers are expectedto adopt more reactive control, in relation to theirdifficulty to anticipate and plan (e.g. Lundqvist andRonnberg 2001, Lew et al. 2005, Ponsford et al. 2008,Stinchcombe et al. 2008). As far as symbolic control is

concerned, the question remains open. Possibledifferences between the two groups in terms of theeffects of instruction and scenario could be related todifferences in cognitive control. The question ofadaptation skill is also open.

Results

Baseline satisficing performance and situation mastery

First, results that relate to the satisficing performanceand that have been derived from questionnaires andperformance will be presented. The instruction andscenario effects on the two dimensions of cognitivecontrol (symbolic/subsymbolic and anticipative/reactive) will then be examined. Finally, the outcomesof cognitive control on time-sharing will be presented.

Satisficing performance

Baseline satisficing performance and situation mastery

First, the BI group seemed to be more concerned withregulation compliance than with other criteria. On theother hand, no major criterion of baseline satisficingperformance was detected for CTRL. Indeed, in thebaseline satisficing performance questionnaire, theregulation compliance criterion was seen as the mostimportant for five out of the seven BI participants(binomial test: p � 0.05). Conversely, just two out ofthe six CTRL participants made the same choice.However, the answers given by CTRL did not appearto differ from random events (binomial test: p 4 0.99).

Second, all participants always evaluated thesituation mastery 42 on the 4-level scale.Furthermore, for the experimental scenarios, thesituation mastery was �3 for at least four of the sevenBI participants and for four of the six CTRLparticipants. With BIC, there was found to be nodifference between groups.

Most of the participants had the feeling that theyachieved a satisficing level of performance. Thus, it

Table 1. Conflict between type of instruction and type of scenario. Arbitration between performance criteria.

Speed scenario: slow car in front andfaster car to the rear Safety scenario: slow car only

Speed instruction Conflict between reality (slow car)and satisficing performance (speedinstruction) but the faster carfacilitates arbitration in favour ofspeed

Conflict between reality (slow car) and satisficingperformance (speed instruction) without any arbitrationfacilitation

Safety instruction Conflict between reality (fastercar) and satisficing performance(safety instruction) but the slow carfacilitates arbitration in favour of safety

No conflict

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may be possible, considering the mean speed analysis,to interpret group differences as an indicator ofsatisficing performance set by each group during thedriving activity, based on the speed criterion.

Instruction and scenario effects on satisficingperformance

With regard to perceived performance assessment,more than two-thirds of the participants stated thatthey had satisfied the criterion that was given in theinstruction; that is to say, speed for the speedinstruction and safety for the safety instruction. Forthe CTRL group, the ratios were exactly 4 out of 6 forthe SAI/SAS scenario (p 5 0.11) and 5 out of 6 for theothers (p 5 0.05). For the BI group, the consistencybetween the perceived performance and the instructionwas weaker. The ratio was 6 out of 7 (p 5 0.01), 3 outof 7 (p 5 0.69 ns), 4 out of 7 (p 5 0.23 ns) and 5 outof 7 (p 5 0.05) respectively for the SPI/SPS, SPI/SAS,SAI/SPS and SAI/SAS scenarios. Nevertheless, nogroup difference was found with BIC.

As expected, Figure 3 shows that the speedinstruction produced a higher speed than in the baselaps (d ¼ 2.06 m/s; t(11) ¼ 3.15; p 5 0.01; d 41.17m/s), whereas the safety instruction loweredthe speed (d ¼ 1.95 m/s; t(11) ¼ 3.47; p 5 0.01;d 4 1.18). Thus, the instruction had a clear effect onthe satisficing performance. The speed scenarios led toa higher speed than the safety ones (respectively 18.23m/s vs. 17.20 m/s; d ¼ 1.03 m/s; t(11) ¼ 2.86; p 50.02; d 4 0.54 m/s). Thus, the faster car to the rearplayed a major role in the arbitration of satisficingperformance than the slower car when they were usedtogether (speed scenario).

Moreover, the effect of the faster car to the rear isclear for the BI group. With the safety instruction, theBI group drove slightly faster in the speed scenario

than in the safety scenario (d ¼ 1.22 m/s; t(6) ¼ 2.13;p 5 0.08; d 4 0.39), whereas for CTRL this differenceis small (d ¼ 0.36 m/s) and non-significant(t(5) ¼ 0.49; p 4 0.65; no gen.). Thus, the BIparticipants were sensitive to the pressure of the fastercar to the rear, modifying their satisficing performancein favour of the speed criterion.

Both groups succeeded in keeping the car in thecentre of the lane (d ¼ 39 mm, t(10) ¼ 0.68; p 4 0.51NS; jdj5122 mm). Although the BI group alwaysdrove a little bit more to the right-hand side of the lanethan CTRL, the difference was negligible(d ¼ 139 mm, t(9) ¼ 1.20 p 4 0.26; NS,jdj5300 mm). No instruction or scenario effect onkeeping the car in its lane was found (from bothcomparisons with the training laps made for eachgroup: |dmaxj5149, tmax (9) ¼ 2.37; p ¼ 0.04; limmax:jdj5 314 mm).

Symbolic control

The mean fixation duration times obtained by eachgroup in straight lines are presented in Figure 4. Thelocation of control on the subsymbolic/symbolicdimension, measured by mean fixation duration, showssome contrasts in straight lines and small differences inbends.

Figure 4 shows that, in straight lines, cognitivecontrol appeared to be more symbolic for the BI groupsince this group had a longer mean fixation duration(d ¼ 98 ms; t(8) ¼ 2.10; p 5 0.07; d 4 32.81 ms).Could this result just be an indication that the BIgroup has a generalised decreased processing speed?Probably not. Indeed, the location of cognitive controlon the symbolic/subsymbolic dimension remainedquite stable for all laps for CTRL (l ¼ 18.64 ms;F(5,25) ¼ 0.23; p 4 0.95 ns; jlj561.08 ms). l is the

Figure 3. Mean speed with standard errors. CTRL ¼control group; BI ¼ brain-injured group; SP ¼ speed;SA ¼ safety; I ¼ instruction; S ¼ scenario.

Figure 4. Mean fixation duration time in straight lines withstandard errors. CTRL ¼ control group; BI ¼ brain-injuredgroup; SP ¼ speed; SA ¼ safety; I ¼ instruction;S ¼ scenario.



quadratic mean, which can be interpreted as thefluctuation of the mean duration from one lap toanother, whereas it varied for the BI group (l ¼ 73.94ms; F(5,15) ¼ 1.08; p 4 0.41 NS; l 4 64.47 ms).Moreover, compared with CTRL, the cognitivecontrol of the BI group was much more symbolic inthe first base lap (d ¼ 178.27 ms; t(8) ¼ 2.02;p 5 0.08; d 4 55.24 ms) and in the experimental lapcontaining the safety instruction and the safetyscenario (d ¼ 126.76 ms; t(8) ¼ 3.31; p 5 0.02;d 4 73.28 ms). In all other laps, the differenceremained small (d ¼ 71.16 ms; t(8) ¼ 1.32; p 4 0.23ns; no gen.). Thus, one can acknowledge that the BIgroup’s cognitive control was always more symbolicthan that of CTRL, particularly in the laps where thedriving task was less demanding.

The analysis showed that the group difference wasnegligible in bends (d ¼ 1.86 ms; t(8) ¼ 0.09; p 4 0.93NS; jdj538 ms) with no particular effect in each lap(from the six comparisons: dmax5 60 ms; tmax(8) 50.40; p 4 0.68 NS; limmax: jdj584 ms).

Reactive control

The anticipative/reactive dimension of cognitive con-trol was assessed in terms of speed variability adjustedby mean speed and by the percentage of time per lapspent looking at the anticipative area (far area instraight lines and at the tangent point in bends) and atthe reactive area (near area). Both of those measuresshowed that the BI group was more reactive thanCTRL. However, the percentage of time spent lookingin specific areas was more sensitive to groupdifferences.

Figure 5 shows that the mean speed variability wasslightly greater for the BI group (d ¼ 0.55 m/s; t(8) ¼1.48; p 4 0.18 NS; d 4 0.03 m/s). This difference was

mostly due to the laps where a safety instruction wasgiven (d ¼ 0.64 m/s; t(8) ¼ 1.58; p 4 0.16 NS;d 4 0.07 m/s and d ¼ 0.71m/s; t(8) ¼ 2.02; p 5 0.08;d 4 0.22 m/s respectively for the speed scenario andthe safety scenario). This result cannot be interpretedin terms of just a generalised increase in variability inperformance of the BI group; rather, it is in accordancewith a more reactive (less anticipative) cognitivecontrol for the BI group than for CTRL. This wasparticularly the case when the safety instruction wasintroduced, where the BI group did not reduce itsreactiveness to the same extent as CTRL.

The percentage of time spent in particular areas ofinterest for straight lines is presented in Figure 6.CTRL spent more time looking at the far area than theBI group (d ¼ 25.14%; t(8) ¼ 1.65; p 5 0.14;d 4 3.83%). The main group difference relates to thenear area; the BI group fixated more on this(d ¼ 27.42%; t(8) ¼ 2.98; p 5 0.02; d 4 14.55%). Inthe curves, CTRL looked slightly more at the tangentpoint (d ¼ 8%; t(9) ¼ 1.95; p 5 0.09; d 4 2.45%)than the BI group. These results show that BIparticipants devoted fewer fixations than CTRLparticipants to anticipative areas. Such behaviour canbe interpreted as being more reactive, which confirmsthe analysis of speed parameters.

Figure 7 presents the percentage of time per lapthat was spent looking in the near area in straight lines.It can be seen from this figure that the cognitivecontrol reactiveness of the BI group was not stableover the laps. In comparison with CTRL, it decreasedin the only non-conflicting experimental lap (safetyinstruction and safety scenario: d ¼ 4.54%;t(8) ¼ 0.63; p 4 0.55; d514.37%). This was the lapwith the safety instruction and the safety scenario. Itwas stronger in all others (d ¼ 32%; t(8) ¼ 3.02;p 5 0.02; d 4 17.19%). No other particularsignificant differences were found.

Figure 5. Speed variability adjusted by mean speed withstandard errors. CTRL ¼ control group; BI ¼ brain-injuredgroup; SP ¼ speed; SA ¼ safety; I ¼ instruction;S ¼ scenario.

Figure 6. Percentage of time per lap spent in particularareas of interest in straight lines. CTRL ¼ control group;BI ¼ brain-injured group.

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Time-sharing

In straight lines, the CTRL participants looked in themirror more than the BI participants (d ¼ 5.66%;t(8) ¼ 4.52; p 5 0.01; d 4 3.91%). This can beinterpreted as a weaker ability to switch to a secondarypart of the task.

As shown in Figure 8, the time spent looking in themirror was higher in the experimental laps than in thebase laps. However, the percentage changed from onelap to another more for CTRL (l ¼ 6.68; F(5,25) ¼5.91; p 5 0.001, l 4 5.60%) than for the BI group(l ¼ 1.80%; F(5,15) ¼ 1.56; p 4 0.24NS, l53.11%).In fact, the CTRL participants looked in the mirrormore during the speed scenario (d ¼ 7.86%;t(5) ¼ 4.70; p 5 0.005, d 4 5.40%) compared withthe BI participants (d ¼ 1.14%; t(3) ¼ 1.05, p 4 0.37;l53.12%). This result suggests that the BI group was

less able to process a secondary part of the task,especially when it was necessary to check on thebehaviour of the faster car to the rear.

Overall, the participants took at least one look at thepedestrian on the road verge. However, the CTRLparticipants made more than twice as many gazes (3.21)than the BI group (1.54). The difference is significantand notable (t(10) ¼ 2.25; p 5 0.05; d 4 0.65). Half ofthe CTRL participants remembered having detected thepedestrian on the road verge. However, none of the BIparticipants stated that they had detected thepedestrian. This difference is marginally significant(Fisher exact probability: p ¼ 0.07). Thus, the BIparticipants seemed to be less able than the CTRLparticipants in detecting an unpredictable but relevantevent, such as a pedestrian on the road verge. This resultis consistent with a difficulty to process informationacquired by looking in the mirror.

Summary of results

To sum up, five main results can be gathered from thisexperiment.

(1) BI participants favoured regulationcompliance, whereas CTRL participants didnot express any priority among theperformance criteria. This could possibly berelated to the BI participants’ feeling that thereis a risk of losing their driving licence bybreaking the rules. BI participants evaluatedtheir situation mastery as positively as CTRLparticipants. Thus, the participants consideredthat their adaptation to the instructions andscenarios was acceptable. They also consideredthat their satisficing performance was achieved.Overall, the actual performance can beinterpreted as the satisficing performance.Adaptation was successful, leading participantsto situation mastery.

(2) Instruction and scenario types produced theexpected effects on the satisficing performance.The participants found their perceivedperformance was consistent with instructions,even if BI participants were less affirmativethan CTRL participants. Moreover, all theparticipants modified their speed in accordancewith the instruction and the scenario types.Thus, participants succeeded in modifying theirsatisficing performance to adapt to thesefactors. Although the main BI participants’baseline satisficing performance criterion wasregulation compliance, they were able to givepriority to speed when needed. But, incomparison with CTRL participants, the

Figure 7. Percentage of time per lap spent looking in thenear area in straight lines with standard errors.CTRL ¼ control group; BI ¼ brain-injured group; SP ¼speed; SA ¼ safety; I ¼ instruction; S ¼ scenario.

Figure 8. Percentage of time per lap spent looking at themirror (straight lines).CTRL ¼ control group; BI ¼ brain-injured group; SP ¼ speed; SA ¼ safety; I ¼ instruction;S ¼ scenario.



satisficing performance of the BI participantsseemed to be more sensitive to the pressure ofthe faster car to the rear.

(3) BI participants adopted a more symboliccontrol, especially in easy situations (that is tosay, in non-conflictual scenarios). However, inorder to adapt to more difficult situations theyneeded to change their cognitive compromisetoward a less symbolic control. CTRL partici-pants were able to face every kind of situationwith the same cognitive compromise, with lesssymbolic control than BI participants. Thus, onthis basis, CTRL participants’ cognitive com-promise appeared to be the most robust.

(4) BI participants were more reactive than CTRLparticipants in several respects. They spentmore time looking in the near area. Theobservations show that this was not the casefor CTRL participants, although without gen-eralisation. BI participants adopted a morevariable speed. They were able to reduce theirreactiveness (near area fixations) in the lap inwhich there was no conflict. In other words, BIparticipants were less adaptable in terms ofreactiveness than in terms of symbolic proces-sing. Again, CTRL participants showed a morerobust compromise from the reactiveness pointof view.

(5) BI participants were less able to processsecondary subtasks within the driving task(looking in the mirror and at pedestrians).Multitasking is a typical feature of driving.Drivers must navigate, control the trajectory inthe medium term, handle various pieces ofequipment inside the cockpit, converse withpassengers, and so on. However, the trajectorycontrol task is composed of several subtasks.The main subtask is keeping the car in its lane,as far as possible. Many other trajectorycontrol subtasks are also implied; for example,those related to managing interaction withtraffic (pedestrians, traffic to the rear, etc.).

Discussion

BI participants showed a more symbolic and morereactive control. This result has a theoretical valuebecause it validates the model put forward by Hoc andAmalberti (2007), in which two orthogonal dimensionsare considered. Symbolic control is not necessarilyanticipative; it can also be reactive. The BI partici-pants’ difficulties in multitasking can be explained bytheir cognitive compromise. Symbolic control is costlyin terms of cognitive resources and slow. Reactive

control implies frequent information-gathering for thesame subtask. These two features can explain why BIparticipants were confronted with difficulties inmultitasking.

Another validation of the model is the dynamicfeature of cognitive control when adapting to varioussituations, especially conflicting ones, in terms ofsatisficing performance. BI participants were able tomodify their cognitive compromise in terms ofsymbolic/subsymbolic trade-off, as well as in terms ofreactiveness. CTRL participants kept their distributionof control along the symbolic/subsymbolic dimensionmore stable than BI participants. Overall, theparticipants were able to change their satisficingperformance in order to adapt to experimentalsituations. However, adaptation could be more costlyfor BI participants than for CTRL participants,because it also implies a change in cognitivecompromise.

Although attention was not directly addressed inthis study, albeit globally through psychologicaltesting, some discussion is needed. Following theapproach made by James (1890), attention isconsidered as a filtering resource to manage theinformation bombing that is experienced in order toprocess the most relevant part. There are three mainattentional processes (Parasuraman and Nestor 1991):selective attention (the main filtering process); dividedattention (the ability to filter distinct parts); sustainedattention (the duration of the activation/inhibitionprocess). From the present authors’ point of view,attention is not cognitive control but a resource forcognitive control.

The symbolic feature of BI participants’ cognitivecontrol could be related to selective attentiondifficulties. BI participants could have deliberatelyinvested more resources in the processing ofinformation from the main subtask, in order to avoidbeing distracted by signals coming from theirsurroundings and to counter difficulties in receivingrelevant information. However, another possibleexplanation for the fact that BI participants relied onsymbolic control more than the CTRL participantscould be that the former have suffered from a decreasein processing speed. This is incompatible with anover-reliance on routines, but is acceptable forsymbolic processing. The BI participants’ reactivenessis clearly related to frontal injuries, which arecorrelated with planning and anticipation deficits. TheBI participants’ difficulty to manage multitaskingcould be related to divided attention difficulties;however, it could also be a direct consequence of asymbolic and reactive control. A new researchprogramme has been established in order to findanswers to these questions. A better understanding of

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these mechanisms is needed if one wants to deriveimplications in terms of rehabilitation.

In the short term, some implications can be drawnin terms of driving assistance. Trajectory controlsupport devices are currently being developed (Hocet al. 2009b) for longitudinal (e.g. cruise control) aswell as lateral (e.g. lane keeping warning or assistancesystems) control. They are designed not only forcomfort but also for safety, and in such a way thatthey are also useful for normal drivers. Car manufac-turers are interested in equipping vehicles with drivingassistance devices that are relevant to all drivers, notjust those with a disability. Apart from rehabilitationtraining sessions, the type of assistance currently inuse, both under development and under study, couldbe of interest for BI drivers.

For whatever reason, if BI drivers need to exert asymbolic control of the trajectory, and consequentlyare confronted with multitasking difficulties, drivingassistance could improve the situation. For example, alane keeping assistance system can correct the trajec-tory automatically, saving processing time for thedriver, and allowing the driver to manage othersubtasks, such as interaction with the traffic. Anotherresearch programme should be devoted to the evalua-tion of ‘normal’ driving assistance for this type ofdriver. Today, elderly drivers are frequently used forthe evaluation of driving assistance devices; suchevaluations could be extended to include otherpopulations.

There could also be some commonality of func-tional impairment among various populations, includ-ing the elderly, BI people and Alzheimer and strokesufferers. In this case, driving assistance devices couldbe of interest for several populations. This is one of thereasons for comparing BI drivers with young driversand elderly drivers. The other reason is to gain anunderstanding of the role of meta-knowledge inadaptation. Young drivers are not skilful at driving,as a more symbolic control of the task is needed beforedeveloping routines. In addition, they do not have richmeta-knowledge and cannot avoid situations wherethey perform poorly as drivers. Elderly drivers couldalso be more symbolic, for similar reasons to BIdrivers, but with the addition of meta-knowledge. Itcould be relevant to identify to what extent BI drivershave meta-knowledge of their impairment and imple-ment-coping strategies. This point could be of im-portance in designing rehabilitation sessions.

Nevertheless, some caution must be taken whengeneralising the results shown in this paper. Thepresent pilot study has some limitations, such assample size and the inclusion of only middle-agedsubjects. Moreover, cognitive control was evaluatedonly in a rural road environment. In order to evaluate

cognitive control and re-adaptation, it is necessary tocarry out a more extensive experiment using moresubjects and scenarios that have a longitudinalperspective. Such a study is being carried out atIRRCyN.

Acknowledgements

Portions of these data were presented at the EuropeanConference on Cognitive Ergonomics (Helsinki, October2009) and appear in the proceedings of that conference (Hocet al. 2009a). This work took place within the French CNRSresearch network ‘Psycho Ergo’. This experiment was carriedout with the collaboration of ARTA and Johanna Pothier,who invited the BI drivers to participate in the study on thebasis of neuropsychological tests. We thank the participantsfor their contribution to the study. We are grateful to theINRETS MSIS team for their technical assistance. We thankSusan Watts for English revision.

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