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Driving performance and specific attentional domains
Transportation Research Interdisciplinary Perspectives 3 (2019) 100077
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Driving performance and specific attentional domains ⁎
Magnus Liebherr a, , Stephanie Antons a, Stephan Schweig b, Niko Maas b, Dieter Schramm b, Matthias Brand a a b
University of Duisburg-Essen, Department of General Psychology: Cognition, Duisburg, Germany University of Duisburg-Essen, Department of Mechatronics, Duisburg, Germany
A R T I C L E
I N F O
Article history: Received 6 September 2019 Received in revised form 17 November 2019 Accepted 17 November 2019 Available online 28 November 2019 Keywords: Driving simulator Attention Age Inhibition Working memory
A B S T R A C T
Converging evidence from numerous previous studies highlights the relevance of attention in driving. However, these studies mostly conclude from respective situations or use complex tests that tap into further cognitive processes. Aiming a better understanding of specific attentional domains, we investigated the relation between visual selective attention, auditory selective attention, visual divided attention, switching attentional demands, switching between attributes, switching between rules, vigilance and driving performance in a driving simulator. Furthermore, we tested three-way interaction effects with respective attentional domains, inhibition and working memory. In the present study, 123 participants completed a driving scenario as well as commonly used measures of attention (SwAD-task, Oddball-task, MCST, TMT-B, D2), inhibition (Go/NoGo-task), and working memory (visual digit-span-task). Findings indicate no correlations between the tested attentional domains and driving performance. Furthermore, we found no interaction effects with the attentional domains and the two factors of inhibition and working memory on simulator driving performance. The present findings suggest no possibility to transfer findings from specific attentional domains, as well as the used measures for inhibition, and working memory to peoples' simulator driving performance. Along with previous findings we suggest using rather context-specific tasks than basic neuropsychological measures to quantify specific attentional domains, in order to predict peoples' driving performance.
1. Introduction Converging evidence from numerous previous studies highlights the relevance of attention for driving performance and accidental rate (e.g., Horberry et al., 2006a; Young and Regan, 2007). For example, Klauer et al. (2006) report an accidental rate based on inattention of 80%. Especially long glances inside the vehicle are frequently pointed out as related to crash involvements (Horrey and Wickens, 2007; Klauer et al., 2006; Wikman et al., 1998). The magnitude of this problem is likely to increase with increasing popularity of in-vehicle technologies (Lerner and Boyd, 2004; Strayer et al., 2003a, 2003b). In past, different attentional domains have been reported to be of outmost relevance for successfully driving. For example, selective attention is frequently discussed as important ability and its limitation is associated with an increased crash risk as well as a decreased driving performance (e.g., De Raedt and Ponjaert-Kristoffersen, 2000; Lundqvist et al., 2000; Richardson and Marottoli, 2003). In this context, previous studies report relations between visual selective attention (Baldock et al., 2007; Richardson and Marottoli, 2003) as well as auditory selective attention (Arthur Jr and Doverspike, 1992) and on-road driving performance as well as accidental rate. ⁎ Corresponding author. E-mail address: [email protected]. (M. Liebherr).
http://dx.doi.org/10.1016/j.trip.2019.100077 2590-1982/©2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Since talking on a phone while driving became increasingly popular, the attentional domain of divided attention is frequently discussed in driving studies (e.g., Becic et al., 2010; McCartt et al., 2006; Strayer and Drews, 2007; Strayer and Drews, 2004; Strayer et al., 2003a, 2003b; Strayer and Johnston, 2001). For example, Strayer and Johnston (2001) show that cell phone conversations but not simply holding a cell phone or listening to audiobooks interferes with detection of traffic lights. Additional eye-tracking data indicate a reduced attention to foveal information when talking on a cell phone (Strayer and Drews, 2007). In addition, studies show a relation between deficits in divided attention of older adults and poorer performance on a driving test (De Raedt and Ponjaert-Kristoffersen, 2000), increased crash involvement (Owsley et al., 1998), as well as a reduced recognition of road signs (Chaparro et al., 2005). Along with selective and divided attention, the ability of task switching or switching attention is frequently discussed to be relevant in driving. For example, findings demonstrate a relation between measures of switching attention and drivers' crash risk (Elander et al., 1993; Mihal and Barrett, 1976). Furthermore, it is argued that slower reaction times to targets in hand-held and hands-free (through earphones) conditions compared to hands-free (through loudspeakers) condition are based on a required shifting of attention between different spaces (Fagioli and Ferlazzo, 2006; Ferlazzo et al., 2008). Hunt et al. (1993) found that performance in a simple paper/pencil
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3. Methods
test of switching attention significantly correlates with driving performance in healthy elderly as well as people with mild senile dementia. A further attentional domain which seems to play an important role in driving is vigilant attention (Mackworth, 1957). Findings from driving studies indicate affected driving performance during periods of hypo-vigilance (Larue et al., 2011). Furthermore, participants show increased crash risk on monotony roads due to lapses of vigilance (Larue et al., 2011; Thiffault and Bergeron, 2003). Schmidt et al. (2007) demonstrate a linear degradation of reaction times, P300-amplitude and parietal alpha power during a three-hour monotonous daytime driving. In order to test attention in a driving setting, Ball et al. (1993) developed the Useful Field of View test (UFOV), which comprises three subtests of processing speed, divided attention, and selective attention (Ball et al., 1993; Ball and Rebok, 1994; Owsley, 1994; Owsley et al., 1991; Owsley et al., 1995). Ball et al. (1993) report a high sensitivity (89%) and specificity (81%) of the test for predicting older drivers' crash risk. More recently, Clay et al. (2005), demonstrate in their meta-analysis a large effect size (Cohen's d = 0.945) for the relationship between the UFOV test and negative driving outcomes in the elderly. To sum up, previous driving studies commonly highlight the relevance of attentional processes. However, these studies mostly conclude from the respective driving situations and the corresponding driving behavior on attentional processes. Furthermore, studies which used most commonly neuropsychological tests either focused on age and disease effects or not domain specific. Although the UFOV test addresses two of the respective domains of attention, it taps into further visual and cognitive processing.
3.1. Participants In the present study, 123 participants (age: min-max: 23–89, M = 57.39 years, SD = 15.61; 31 women) completed a driving scenario as well as commonly used measures of several subdomains of attention, inhibition, and working memory. Exclusion criteria were neurological or cardio-vascular diseases as well as impairments in the ability to see or hear, which was assessed/tested by a physician, prior to the study. Additionally, participants above 60 years were excluded as soon as they showed first signs of dementia (DemTect < 9) (Kalbe et al., 2004; Kessler et al., 2000). In order to participate in the study, the people had to drive actively, at least 3000 km/year. Participants signed written informed consent prior to the investigation. The study was approved by a local ethics committee. 3.2. Measures 3.2.1. Switching Attentional Demands Task (visual selective attention, visual divided attention, switching attentional demands) The Switching Attentional Demands Task (SwAD-task) constitutes a paradigm for measuring selective and divided attention, but more important the process of switching between these demands (Liebherr et al., 2019). Participants have to manually respond on different visually presented stimuli. Stimuli in all conditions include numbers (1–9) and shapes (triangle, rhombus, rectangle, circle, star, octagon), which are presented simultaneously. In four blocks of selective attention, participants were asked to respond either on a specific number (e.g., two) or shape (e.g., rhombus), and to ignore other stimuli. Under divided attention, participants had to respond to a target-number (e.g., seven) and a target-shape (e.g., star) with two different buttons. All other shapes and numbers should be ignored. The switching condition comprises four blocks of selective and four of divided attention which are presented alternately. Each block – whether single or switching demand – includes a total of 26 trials (presented for 250 ms), with five to eight target stimuli/block. Interstimulus intervals are randomized between 500 ms and 2300 ms, in which a fixation-cross is presented in the middle of the screen. Maximum response time is set to 1800 ms after each stimulus (see Fig. 2). Response time and error rate are measured.
2. The current study The present study aims to test the relation between domain specific attentional abilities and driving performance in a simulator. To investigate the respective attentional domains as specific as possible, we applied tests of visual selective attention, auditory selective attention, visual divided attention, switching attentional demands, switching between attributes, switching between rules, and vigilance. Since numerous behavioral as well as neurophysiological studies highlight a close relationship between attention and processes of inhibition as well as working memory (e.g., Awh et al., 2006; Awh and Jonides, 2001; Booth et al., 2003; Chun, 2011; Conway et al., 2001; Lijffijt et al., 2009), we tested three-way interaction effects with the respective attentional domains and the two factors of inhibition and working memory (see Fig. 1). We additionally considered the process of aging by focusing on a wide range of age.
3.2.2. Oddball Task (auditory selective attention) Previously, the oddball task has been used in an uncountable number of behavioral and neurophysiological studies in both patients and healthy participants (e.g., Fichtenholtz et al., 2004; Huettel and McCarthy, 2004; Squires et al., 1975). In the study at hand, we used a modified version of
Fig. 1. Three-way interactions are tested for respective attentional domains with inhibition and working memory. 2
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Fig. 2. Schematic overview of the SwAD-task – Differentiation between selective and divided attention depends on instructions. After stimulus presentation for 250 ms, participants have a maximum time of 1800 ms to respond. During this period the screen is black. After responding, the randomized ISI, represented by a white cross in the middle of the screen, starts until the next stimulus is presented.
an auditory oddball task to measure selective attention. After a short training trial (6 stimuli, 2 target/4 non-target), participants were asked to react on 20 target tones and to ignore 90 non-target tones. Stimuli were presented in the same length (200 ms) and volume as well as in a randomized order (interstimulus interval randomized between 500 ms and 1000 ms). Within a time-window of 2000 ms after stimulus presentation participants had to react to target tones as fast as possible by pressing the space bar. Simultaneously to the auditory oddball task, we presented a video of a car ride on a monitor. Participants were asked to simply watch the video without video-specific action. Error rate and reaction time were measured.
3.2.5. D2 (vigilance) The D2 test was used to measure vigilance (Brickenkamp, 1978). The test comprises 14 rows, each with 47 “p” and “d” characters with one to four dashes above and/or below each letter, on a single sheet of paper (Brickenkamp and Zillmer, 1998). Participants were asked to identify all “d” with two dashes, regardless of whether the dashes are above or below the “d”, or one is above, and one is below the “d”. The time for each row is limited to 20 s with no break in between. Test performance was quantified by counting the errors participants made (both the right ones who were not recognized and the wrong ones who were marked).
3.2.3. The Trail Making Test (switching between attributes) Switching between different attributes was assessed by the Trail Making Test Part B (TMT-B) (Reitan, 1958; Reitan and Wolfson, 1995). The test includes a sheet of paper with encircled numbers (1−13) and letters (A–L). Participants were asked to connect numbers and letters alternating in numerical, respectively alphabetical order, starting with one, and ending with 13. We measured the time, participants needed to complete the task. Once the participants made an error, the experimenter made them aware of it, what is reflected in the required time.
3.2.6. Visual digit span task backwards (working memory) Working memory capacity was measured by a visual digit span task (backwards) (Ramsay and Reynolds, 1995). The computerized task comprises sequences of digits, one digit presented after the other. The number of digits in a sequence increase after every second, from two to a maximum of nine digits. Participants were asked to repeat the presented sequence backwards by pressing the numbers on the keyboard, directly after its presentation. As soon as a sequence has been answered incorrectly twice or the nine digits are completed successfully, the task ends. The maximum number of digits was used to quantify working memory capacity (see also Hilbert et al., 2014).
3.2.4. Modified Card Sorting Test (switching between rules) The Modified Card Sorting Test (MCST) (Nelson, 1976) was used to measure switching between rules (e.g., Miyake et al., 2000). Within the test, participants need to categorize options, detect rules and maintain sets. The task comprises different decks of cards, each card with a certain number (1–4) of shapes (triangle, square, star, or circle) in a certain color (green, yellow, blue, or red). Participants need to sort each appearing card to one of the four target cards, although they do not know which rule to apply (sorting rules: by shape, color, or number of shapes). Participants were provided with feedback (visual and acoustic) in order to find the correct sorting rule. Once the participants have sorted six cards correctly, the rule changed. Performance was quantified by the number of perseverative errors.
3.2.7. Go/NoGo-task (inhibition) We measured inhibition with a recently developed auditory Go/NoGo task (Wegmann et al., 2017). During four blocks participants were faced with four different tones, one selected as go- and one as nogo-stimuli. Each block comprises five go-stimuli and five nogo-stimuli. The stimuli were presented in a random order with two go-stimuli not consecutive. We changed go- and nogo-stimuli randomly during the four blocks, but all stimuli had the same length (1150 ms) and volume. Within a timewindow of 2500 ms, participants had to react as fast as possible to target tones or inhibit the reaction in case of non-target tones. Errors in target 3
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Fig. 3. Overview of the used driving simulator (left) and scenario, which comprises inner city and rural roads, as well as highways (right).
To ensure a habituation to the simulator and to avoid end effects, we first considered the IOP5–15 – as most commonly did – in the time-window of 5 to 15 min. To ensure more realistic values of driving performance, we additionally used a more objective way by focusing on the time of adaptation (e.g., Joshi et al., 2017). Therefore, gradients of the IOPs are graphically displayed and considered over the time. As soon as IOP stagnated or decreased, participants had adapted to the system. IOPadapt. was calculated only for participants who adapted to the simulator, by considering driving performance after adaptation.
tones and errors in non-target tones were summarized for quantifying inhibition. 3.2.8. Driving simulator Driving performance was investigated in a static driving simulator, consisting of a close-to-production vehicle of the compact class. The simulated environment was projected on the walls in front of and beside the simulator. The view to the rear was displayed on a monitor placed behind the simulator. Small screens were placed at the side mirrors to make the simulated environment as realistic as possible. The scenario comprised a virtual area of 3 × 3 km and a total distance of 70 km consisting of inner-city areas, rural routes as well as highways, where participants had to interact with other road users (Maas, 2017) (see also Fig. 3). We used no pre-defined routes or car-following tasks. Furthermore, we asked the participants to freely choose where and how to drive. A more technical description of the simulator can be found elsewhere (Maas et al., 2014; Schweig et al., 2018). Driving performance was quantified by using the index of performance (IOP), calculated by Joshi's algorithm (Joshi et al., 2017). While previous simulator studies mostly investigated driving performance by considering only single driving parameter, the IOP contains numerous criteria that provide information on driving performance (steer behavior, activity of the pedals, lane drifts), which are considered in the IOP to the same extent.
3.3. Procedure Before starting, participants were informed about the study and the possibility that they could end at any time without reprisal. Subsequently, the physician tested vision and hearing but also cardio-vascular impairments (blood pressure and electrocardiogram). Furthermore, participants where asked for a history of neurological, psychological, and orthopedic disorders. People above 60 years were tested with the ‘DemTect-Test’. In the following (T0), participants completed the D2 test as well as the TMT (B). After 10 minutes' time to rest, participants were instructed how to drive within the simulator (e.g., that the simulator is a vehicle with automatic gearshift) and informed again that they can stop whenever they feel sick or they want to cancel for another reason. Maximum time of driving was 25 min. Within
Fig. 4. Experimental procedure of the three measurement times. The study was conducted over three measurement times to rule out participants' fatigue. Due to the large sample, the time intervals between single measurement times are relatively high. 4
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Table 1 Correlations between driving performance (1, 2) and measures of specific attentional domains of visual selective attention (3), auditory selective attention (7), visual divided attention (4), switching attentional demands (5, 6), switching between attributes (9), switching between rules (12), and vigilance (8), as well as inhibition (11) and working memory (10).
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
IOP5–15 IOPadapt. Single demand: selective Single demand: divided Switching demands: selective Switching demands: divided Oddball D2 TMT-B Digit span Go/NoGo-task MCST
M
SD
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
0.31 1.62 457.07 664.34 473.65 648.58 448.77 149.94 75.08 5.20 10.07 14.07
0.84 0.52 59.31 93.90 58.98 87.18 68.39 46.40 28.64 1.52 4.97 9.84
– 0.246 −0.109 0.024 −0.146 −0.057 −0.038 0.068 0.055 −0.095 0.021 0.157
– 0.152 0.218 0.157 0.084 0.204 −0.244 0.053 −0.204 −0.006 0.196
– 0.613** 0.736** 0.685** 0.522** 0.130 0.213* −0.015 0.226* 0.133
– 0.556** 0.747** 0.471** 0.331** 0.307** −0.156 0.371** 0.243**
– 0.681** 0.622** 0.149 0.210* −0.012 0.273** 0.141
– 0.523** 0.309** 0.275** −0.120 0.335** 0.175
– 0.174 0.247** −0.114 0.278** 0.046
– 0.483** −0.317** 0.428** 0.345**
– −0.415** 0.432** 0.439**
– −0.374** −0.239**
– 0.358**
Note. Sample size of all correlations with IOPadapt. was N = 40. * p < .05. ** p < .001.
attentional domains together with inhibition and working memory also revealed no significant effects. Furthermore, we found no relation between age and driving performance. However, some aspects need to be further discussed in this context. The present measures comprise relatively simple stimuli, such as objects (e.g., star, triangle), single numbers, or letters. However, previous studies that report relations between cognitive functions and driving ability used more complex tasks. For example, the mentioned Useful Field of View test, which also focusses on the attentional demands of selective and divided attention (Ball et al., 1993). Within this test, participants are asked – for example – 1) to divide their attention across a series of tasks when driving or 2) to identify relevant information from visual clutter or distractions (such as finding an item on a crowded supermarket shelf). Furthermore, most findings on relations between driving performance and attention, inhibition, as well as working memory come from on-road driving studies (e.g., Richardson and Marottoli, 2003). Although, simulators provide the opportunity to examine relevant topic highly standardized as well as cost and time efficient, the transferability of findings to real scenarios is discussed controversial (see, Engström et al., 2005; Törnros, 1998). Therefore, Hallvig et al. (2013) state that a generalization from simulators to real driving must be made with caution. Especially in the context of attention, the question arises whether previously reported significant correlations between neurophysiological tests and driving performance in the simulator, actually refer to driving performance or other aspects such as adaptation (see Brandtner et al., 2019, for further discussion). Another aspect – which aggravates the comparability of distinct driving studies – concerns the fact that each study uses its own parameters for describing participants driving performance. While some use solely single parameters, such as steering wheel movement (Thiffault and Bergeron, 2003) or lane excursions (Rumschlag et al., 2015), others used numerous variables for considering driving performance. For example, Chaparro et al. (2005) used variables of detection and identification of road signs, detection and avoidance of large low-contrast road hazards, gap judgment, lane keeping, and time to complete the course for describing participants driving performance. In the present study, driving performance is described by means of the Index of Performance (Joshi et al., 2017), which constitutes parameters of steer behavior, activity of the pedals, as well as lane drifts, which are considered to the same extent. In addition to driving performance the selected routes seem to further explain differences between existing driving studies, as well as the findings at hand. The effects of the kind of driving scenario is shown by longer reaction times to secondary stimuli with increased complexity of the driving scenario (driving straights, intersections, overtaking maneuvers) (Cantin et al., 2009). Although the authors identified age-effects under all conditions, the most complex driving scenario yielded a disproportionate increase. In addition, Horberry et al. (2006a) report
T1 participants performed the MCST, the Go/NoGo-task, and the digit span task (backwards). During T2 participants performed the SwAD-task as well as the oddball task. The present study was implemented in a larger project. Therefore, the time between the individual measurement times was relatively long (see Fig. 4). 3.4. Statistical analysis The statistical analyses were carried out by SPSS 25.0 for Windows (IBM SPSS Statistics). Prior to mean calculations of SwAD and oddball measures, reaction times of each block that exceeded two standard deviations of participants' mean of the respective block were identified as outliers. This concerned less than two reaction times per condition and participant. Mean reaction times for each block were calculated. We used Pearson correlations to investigate associations between the driving performance scores (IOP5– 15, IOPadapt.) and measures of attentional demands, inhibition, and working memory. Multiple hierarchical moderated regression analyses were conducted in order to test the hypothesized three-way interaction between the three predictors, namely measures of attentional demands (single demand: selective, single demand: divided, switching demands: selective, switching demands: divided, Oddball, D2, TMT-B, MCST), inhibition (Go/NoGo-task), and working memory (Digit span) on driving performance (IOP5–15). All predictors were mean centered. Furthermore, Pearson correlations between measures of driving performance and age were tested as post-hoc analysis. 4. Results Means of the IOP5–15 did not significantly correlate with specific attentional domains, as well as inhibition and working memory. Only 40 individuals adapted to the simulator. Additional analyzes for those individuals revealed no significant relations between IOPadapt and the used measures. Descriptive and test statistics for the correlations are presented in Table 1. None of the three-way interaction models explain a significant proportion of variance, all F(7, 122) ≤ 1.47, all ps ≥ 0.183 (see also Table 2). Posthoc, we correlated age with both scores of driving performance. Here, no correlations were found for the IOP5–15: r = −0.076, p = .406 and the IOPadapt.: r = −0.142, p = .384. 5. Discussion The present study tested correlations between driving performance and different attentional domains, inhibition, as well as working memory, in a relatively large sample. In contrast to any previous assumptions, we found no significant correlations between driving performance and the used measures. The tested three-way interactions for the respective 5
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Table 2 The β-coefficients and test statistics for the multiple hierarchical moderated regression analyses. Predictor
β
t
p
Predictor
Single demand: selective Single demand: selective Go/NoGo Digit span Single demand: selective × Digit span Go/NoGo × Digit span Single demand: selective × Go/NoGo Single demand: selective × Go/NoGo × Digit span
−0.149 −1.424 0.010 0.094 −0.089 −0.821 0.065 0.554 0.044 0.450 0.056 0.526 −0.063 −0.535 F(7, 115) = 0.557, p = .789
β
t
p
Oddball 0.157 0.925 0.413 0.581 0.654 0.600 0.594
Oddball Go/NoGo Digit span Oddball × Digit span Go/NoGo × Digit span Oddball × Go/NoGo Oddball × Go/NoGo × Digit span
−0.108 −1.010 −0.002 −0.018 −0.080 −0.790 0.019 0.166 0.059 0.618 0.130 1.221 −0.129 −1.167 F(7, 115) = 0.566, p = .782
0.315 0.986 0.431 0.869 0.538 0.225 0.246
Single demand: divided Go/NoGo Digit span Single demand: divided × Digit span Go/NoGo × Digit span Single demand: divided × Go/NoGo Single demand: divided × Go/NoGo × Digit span
Single demand: divided 0.022 0.209 −0.016 −0.151 −0.088 −0.825 −0.063 −0.566 0.048 0.464 −0.055 −0.521 0.010 0.088 F(7, 115) = 0.243, p = .974
0.835 0.880 0.411 0.573 0.644 0.603 0.930
D2 Go/NoGo Digit span D2 × Digit span Go/NoGo × Digit span D2 × Go/NoGo D2 × Go/NoGo × Digit span
D2 0.042 0.378 −0.033 −0.302 −0.060 −0.531 −0.086 −0.718 0.068 0.603 −0.001 −0.005 −0.058 −0.467 F(7, 115) = 0.315, p = .946
0.706 0.763 0.596 0.474 0.548 0.996 0.641
Switching demand: selective Go/NoGo Digit span Switching demand: selective × Digit span Go/NoGo × Digit span Switching demand: selective × Go/NoGo Switching demand: selective × Go/NoGo × Digit span
Switching demand: selective −0.179 −1.646 0.031 0.297 −0.073 −0.685 0.027 0.227 0.058 0.602 −0.028 −0.252 −0.008 −0.063 F(7, 115) = 0.640, p = .722
0.103 0.767 0.495 0.821 0.548 0.801 0.950
TMT-B Go/NoGo Digit span TMT-B × Digit span Go/NoGo × Digit span TMT-B × Go/NoGo TMT-B × Go/NoGo × Digit span
TMT-B 0.003 0.023 0.046 0.414 −0.160 −1.386 −0.152 −1.239 0.120 1.015 0.123 0.962 0.239 1.692 F(7, 115) = 0.742, p = .636
0.982 0.679 0.168 0.218 0.312 0.338 0.093
Switching demand: divided Go/NoGo Digit span Switching demand: divided × Digit span Go/NoGo × Digit span Switching demand: divided × Go/NoGo Switching demand: divided × Go/NoGo × Digit span
Switching demand: divided −0.041 −0.402 0.040 0.381 −0.092 −0.831 −0.070 −0.603 0.019 0.187 −0.261 −2.450 0.156 1.304 F(7, 115) = 1.20, p = .308
0.688 0.704 0.407 0.547 0.852 0.016 0.195
MCST Go/NoGo Digit span MCST × Digit span Go/NoGo × Digit span MCST × Go/NoGo MCST × Go/NoGo × Digit span
MCST 0.129 1.276 −0.045 −0.439 −0.102 −0.952 −0.129 −1.247 0.131 1.277 0.178 1.815 0.041 0.366 F(7, 115) = 1.474, p = .183
0.204 0.662 0.343 0.215 0.204 0.072 0.715
present study focused on basic neuropsychological measures, we rather suggest that future studies should address this topic by using more applied tasks. However, along with previous findings it should be noted that it is highly relevant to differentiate between specific attentional domains, such as selective attention or divided attention. In terms of design and implementation, we recommend to additionally consider basic findings on specific attentional domains such as modality and spatiality effects but also more applied findings from related fields. However, more applied research on specific attentional domains is necessary to get a better understanding of – for example – the effects of in-vehicle technologies on inattention and crash risk. Especially in terms of designing traffic safety programs the relations between specific attentional domains and driving performance should be also focused from a more applied perspective in future research.
lower mean speed on a highway drive in older compared to younger participants. Furthermore, Vardaki and Karlaftis (2011) identified better freeway driving performance in people who were more familiar with driving on streets of higher speed, which underpins the hypothesis that older people are more likely to have problems on these roads, since they usually drive on roads with lower speeds. Within the study at hand, we used a scenario consisted of inner city and country roads, but also a freeway. We did not use predefined routes or a car-following task (in order to ensure a most realistic possible scenario). Therefore, participants were able to choose how and where to drive. However, it must be assumed that especially people who felt uneasy or had increased problems, chose more simple routes, performed little turning or overtaking maneuvers, and drove at a lower speed, which must be considered as limitation of the study. Further limitations or possible biases need to be discussed regarding the present sample. Although the large sample characterizes the present study it also carries the risk of biases. As discussed at the beginning of the discussion we did not specifically tested for vision and motor functions. Furthermore, the sample is dominated by males, whereas females are underrepresented and thus limits the results. Although the age distribution covers a wide range from 23 to 89 years, there is a shift towards older participants. In contrast to previous findings, the increased age of the sample did not lead to larger effects.
7. Conclusion Based on present findings, it can be summarized that it is not possible to predict simulator driving performance based on commonly used neurophysiological tests of specific domains. Along with previous findings we suggest using rather context-specific tasks than basic neuropsychological measures to quantify specific domains, in order to predict peoples' driving performance.
6. Future research
Funding
Following up on the findings of the present study one might assume to refrain from a domain-specific consideration in future research. Since the
The study presented here was carried out within the framework of the research project ‘ALFASY – Altersgerechte Fahrerassistenzsysteme’ (‘Age6
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Transportation Research Interdisciplinary Perspectives 3 (2019) 100077 Hunt, L., Morris, J.C., Edwards, D., Wilson, B.S., 1993. Driving performance in persons with mild senile dementia of the Alzheimer type. J. Am. Geriatr. Soc. 41 (7), 747–753. https://doi.org/10.1111/j.1532-5415.1993.tb07465.x. Joshi, S.S., Maas, N., Schramm, D., 2017. A vehicle dynamics based algorithm for driver evaluation. 11th International Conference on Intelligent Systems and Control (ISCO). https:// doi.org/10.1109/isco.2017.7856028. Kalbe, E., Kessler, J., Calabrese, P., Smith, R., Passmore, A.P., Brand, M.A., Bullock, R., 2004. DemTect: a new, sensitive cognitive screening test to support the diagnosis of mild cognitive impairment and early dementia. Int. J. Geriatr. Psychiatry 19 (2), 136–143. https:// doi.org/10.1002/gps.1042. Kessler, J., Calabrese, P., Kalbe, E., Berger, F., 2000. DemTect: Ein neues Screening-Verfahren zur Unterstützung der Demenzdiagnostik. Psycho 26, 343–347. 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based Driver Assistance Systems’). This project was funded by the European Regional Development Fund (ERDF).
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Transportation Research Interdisciplinary Perspectives journal homepage: https://www.journals.elsevier.com/transportation-researchinterdisciplinary-perspectives
Driving performance and specific attentional domains ⁎
Magnus Liebherr a, , Stephanie Antons a, Stephan Schweig b, Niko Maas b, Dieter Schramm b, Matthias Brand a a b
University of Duisburg-Essen, Department of General Psychology: Cognition, Duisburg, Germany University of Duisburg-Essen, Department of Mechatronics, Duisburg, Germany
A R T I C L E
I N F O
Article history: Received 6 September 2019 Received in revised form 17 November 2019 Accepted 17 November 2019 Available online 28 November 2019 Keywords: Driving simulator Attention Age Inhibition Working memory
A B S T R A C T
Converging evidence from numerous previous studies highlights the relevance of attention in driving. However, these studies mostly conclude from respective situations or use complex tests that tap into further cognitive processes. Aiming a better understanding of specific attentional domains, we investigated the relation between visual selective attention, auditory selective attention, visual divided attention, switching attentional demands, switching between attributes, switching between rules, vigilance and driving performance in a driving simulator. Furthermore, we tested three-way interaction effects with respective attentional domains, inhibition and working memory. In the present study, 123 participants completed a driving scenario as well as commonly used measures of attention (SwAD-task, Oddball-task, MCST, TMT-B, D2), inhibition (Go/NoGo-task), and working memory (visual digit-span-task). Findings indicate no correlations between the tested attentional domains and driving performance. Furthermore, we found no interaction effects with the attentional domains and the two factors of inhibition and working memory on simulator driving performance. The present findings suggest no possibility to transfer findings from specific attentional domains, as well as the used measures for inhibition, and working memory to peoples' simulator driving performance. Along with previous findings we suggest using rather context-specific tasks than basic neuropsychological measures to quantify specific attentional domains, in order to predict peoples' driving performance.
1. Introduction Converging evidence from numerous previous studies highlights the relevance of attention for driving performance and accidental rate (e.g., Horberry et al., 2006a; Young and Regan, 2007). For example, Klauer et al. (2006) report an accidental rate based on inattention of 80%. Especially long glances inside the vehicle are frequently pointed out as related to crash involvements (Horrey and Wickens, 2007; Klauer et al., 2006; Wikman et al., 1998). The magnitude of this problem is likely to increase with increasing popularity of in-vehicle technologies (Lerner and Boyd, 2004; Strayer et al., 2003a, 2003b). In past, different attentional domains have been reported to be of outmost relevance for successfully driving. For example, selective attention is frequently discussed as important ability and its limitation is associated with an increased crash risk as well as a decreased driving performance (e.g., De Raedt and Ponjaert-Kristoffersen, 2000; Lundqvist et al., 2000; Richardson and Marottoli, 2003). In this context, previous studies report relations between visual selective attention (Baldock et al., 2007; Richardson and Marottoli, 2003) as well as auditory selective attention (Arthur Jr and Doverspike, 1992) and on-road driving performance as well as accidental rate. ⁎ Corresponding author. E-mail address: [email protected]. (M. Liebherr).
http://dx.doi.org/10.1016/j.trip.2019.100077 2590-1982/©2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Since talking on a phone while driving became increasingly popular, the attentional domain of divided attention is frequently discussed in driving studies (e.g., Becic et al., 2010; McCartt et al., 2006; Strayer and Drews, 2007; Strayer and Drews, 2004; Strayer et al., 2003a, 2003b; Strayer and Johnston, 2001). For example, Strayer and Johnston (2001) show that cell phone conversations but not simply holding a cell phone or listening to audiobooks interferes with detection of traffic lights. Additional eye-tracking data indicate a reduced attention to foveal information when talking on a cell phone (Strayer and Drews, 2007). In addition, studies show a relation between deficits in divided attention of older adults and poorer performance on a driving test (De Raedt and Ponjaert-Kristoffersen, 2000), increased crash involvement (Owsley et al., 1998), as well as a reduced recognition of road signs (Chaparro et al., 2005). Along with selective and divided attention, the ability of task switching or switching attention is frequently discussed to be relevant in driving. For example, findings demonstrate a relation between measures of switching attention and drivers' crash risk (Elander et al., 1993; Mihal and Barrett, 1976). Furthermore, it is argued that slower reaction times to targets in hand-held and hands-free (through earphones) conditions compared to hands-free (through loudspeakers) condition are based on a required shifting of attention between different spaces (Fagioli and Ferlazzo, 2006; Ferlazzo et al., 2008). Hunt et al. (1993) found that performance in a simple paper/pencil
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3. Methods
test of switching attention significantly correlates with driving performance in healthy elderly as well as people with mild senile dementia. A further attentional domain which seems to play an important role in driving is vigilant attention (Mackworth, 1957). Findings from driving studies indicate affected driving performance during periods of hypo-vigilance (Larue et al., 2011). Furthermore, participants show increased crash risk on monotony roads due to lapses of vigilance (Larue et al., 2011; Thiffault and Bergeron, 2003). Schmidt et al. (2007) demonstrate a linear degradation of reaction times, P300-amplitude and parietal alpha power during a three-hour monotonous daytime driving. In order to test attention in a driving setting, Ball et al. (1993) developed the Useful Field of View test (UFOV), which comprises three subtests of processing speed, divided attention, and selective attention (Ball et al., 1993; Ball and Rebok, 1994; Owsley, 1994; Owsley et al., 1991; Owsley et al., 1995). Ball et al. (1993) report a high sensitivity (89%) and specificity (81%) of the test for predicting older drivers' crash risk. More recently, Clay et al. (2005), demonstrate in their meta-analysis a large effect size (Cohen's d = 0.945) for the relationship between the UFOV test and negative driving outcomes in the elderly. To sum up, previous driving studies commonly highlight the relevance of attentional processes. However, these studies mostly conclude from the respective driving situations and the corresponding driving behavior on attentional processes. Furthermore, studies which used most commonly neuropsychological tests either focused on age and disease effects or not domain specific. Although the UFOV test addresses two of the respective domains of attention, it taps into further visual and cognitive processing.
3.1. Participants In the present study, 123 participants (age: min-max: 23–89, M = 57.39 years, SD = 15.61; 31 women) completed a driving scenario as well as commonly used measures of several subdomains of attention, inhibition, and working memory. Exclusion criteria were neurological or cardio-vascular diseases as well as impairments in the ability to see or hear, which was assessed/tested by a physician, prior to the study. Additionally, participants above 60 years were excluded as soon as they showed first signs of dementia (DemTect < 9) (Kalbe et al., 2004; Kessler et al., 2000). In order to participate in the study, the people had to drive actively, at least 3000 km/year. Participants signed written informed consent prior to the investigation. The study was approved by a local ethics committee. 3.2. Measures 3.2.1. Switching Attentional Demands Task (visual selective attention, visual divided attention, switching attentional demands) The Switching Attentional Demands Task (SwAD-task) constitutes a paradigm for measuring selective and divided attention, but more important the process of switching between these demands (Liebherr et al., 2019). Participants have to manually respond on different visually presented stimuli. Stimuli in all conditions include numbers (1–9) and shapes (triangle, rhombus, rectangle, circle, star, octagon), which are presented simultaneously. In four blocks of selective attention, participants were asked to respond either on a specific number (e.g., two) or shape (e.g., rhombus), and to ignore other stimuli. Under divided attention, participants had to respond to a target-number (e.g., seven) and a target-shape (e.g., star) with two different buttons. All other shapes and numbers should be ignored. The switching condition comprises four blocks of selective and four of divided attention which are presented alternately. Each block – whether single or switching demand – includes a total of 26 trials (presented for 250 ms), with five to eight target stimuli/block. Interstimulus intervals are randomized between 500 ms and 2300 ms, in which a fixation-cross is presented in the middle of the screen. Maximum response time is set to 1800 ms after each stimulus (see Fig. 2). Response time and error rate are measured.
2. The current study The present study aims to test the relation between domain specific attentional abilities and driving performance in a simulator. To investigate the respective attentional domains as specific as possible, we applied tests of visual selective attention, auditory selective attention, visual divided attention, switching attentional demands, switching between attributes, switching between rules, and vigilance. Since numerous behavioral as well as neurophysiological studies highlight a close relationship between attention and processes of inhibition as well as working memory (e.g., Awh et al., 2006; Awh and Jonides, 2001; Booth et al., 2003; Chun, 2011; Conway et al., 2001; Lijffijt et al., 2009), we tested three-way interaction effects with the respective attentional domains and the two factors of inhibition and working memory (see Fig. 1). We additionally considered the process of aging by focusing on a wide range of age.
3.2.2. Oddball Task (auditory selective attention) Previously, the oddball task has been used in an uncountable number of behavioral and neurophysiological studies in both patients and healthy participants (e.g., Fichtenholtz et al., 2004; Huettel and McCarthy, 2004; Squires et al., 1975). In the study at hand, we used a modified version of
Fig. 1. Three-way interactions are tested for respective attentional domains with inhibition and working memory. 2
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Fig. 2. Schematic overview of the SwAD-task – Differentiation between selective and divided attention depends on instructions. After stimulus presentation for 250 ms, participants have a maximum time of 1800 ms to respond. During this period the screen is black. After responding, the randomized ISI, represented by a white cross in the middle of the screen, starts until the next stimulus is presented.
an auditory oddball task to measure selective attention. After a short training trial (6 stimuli, 2 target/4 non-target), participants were asked to react on 20 target tones and to ignore 90 non-target tones. Stimuli were presented in the same length (200 ms) and volume as well as in a randomized order (interstimulus interval randomized between 500 ms and 1000 ms). Within a time-window of 2000 ms after stimulus presentation participants had to react to target tones as fast as possible by pressing the space bar. Simultaneously to the auditory oddball task, we presented a video of a car ride on a monitor. Participants were asked to simply watch the video without video-specific action. Error rate and reaction time were measured.
3.2.5. D2 (vigilance) The D2 test was used to measure vigilance (Brickenkamp, 1978). The test comprises 14 rows, each with 47 “p” and “d” characters with one to four dashes above and/or below each letter, on a single sheet of paper (Brickenkamp and Zillmer, 1998). Participants were asked to identify all “d” with two dashes, regardless of whether the dashes are above or below the “d”, or one is above, and one is below the “d”. The time for each row is limited to 20 s with no break in between. Test performance was quantified by counting the errors participants made (both the right ones who were not recognized and the wrong ones who were marked).
3.2.3. The Trail Making Test (switching between attributes) Switching between different attributes was assessed by the Trail Making Test Part B (TMT-B) (Reitan, 1958; Reitan and Wolfson, 1995). The test includes a sheet of paper with encircled numbers (1−13) and letters (A–L). Participants were asked to connect numbers and letters alternating in numerical, respectively alphabetical order, starting with one, and ending with 13. We measured the time, participants needed to complete the task. Once the participants made an error, the experimenter made them aware of it, what is reflected in the required time.
3.2.6. Visual digit span task backwards (working memory) Working memory capacity was measured by a visual digit span task (backwards) (Ramsay and Reynolds, 1995). The computerized task comprises sequences of digits, one digit presented after the other. The number of digits in a sequence increase after every second, from two to a maximum of nine digits. Participants were asked to repeat the presented sequence backwards by pressing the numbers on the keyboard, directly after its presentation. As soon as a sequence has been answered incorrectly twice or the nine digits are completed successfully, the task ends. The maximum number of digits was used to quantify working memory capacity (see also Hilbert et al., 2014).
3.2.4. Modified Card Sorting Test (switching between rules) The Modified Card Sorting Test (MCST) (Nelson, 1976) was used to measure switching between rules (e.g., Miyake et al., 2000). Within the test, participants need to categorize options, detect rules and maintain sets. The task comprises different decks of cards, each card with a certain number (1–4) of shapes (triangle, square, star, or circle) in a certain color (green, yellow, blue, or red). Participants need to sort each appearing card to one of the four target cards, although they do not know which rule to apply (sorting rules: by shape, color, or number of shapes). Participants were provided with feedback (visual and acoustic) in order to find the correct sorting rule. Once the participants have sorted six cards correctly, the rule changed. Performance was quantified by the number of perseverative errors.
3.2.7. Go/NoGo-task (inhibition) We measured inhibition with a recently developed auditory Go/NoGo task (Wegmann et al., 2017). During four blocks participants were faced with four different tones, one selected as go- and one as nogo-stimuli. Each block comprises five go-stimuli and five nogo-stimuli. The stimuli were presented in a random order with two go-stimuli not consecutive. We changed go- and nogo-stimuli randomly during the four blocks, but all stimuli had the same length (1150 ms) and volume. Within a timewindow of 2500 ms, participants had to react as fast as possible to target tones or inhibit the reaction in case of non-target tones. Errors in target 3
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Fig. 3. Overview of the used driving simulator (left) and scenario, which comprises inner city and rural roads, as well as highways (right).
To ensure a habituation to the simulator and to avoid end effects, we first considered the IOP5–15 – as most commonly did – in the time-window of 5 to 15 min. To ensure more realistic values of driving performance, we additionally used a more objective way by focusing on the time of adaptation (e.g., Joshi et al., 2017). Therefore, gradients of the IOPs are graphically displayed and considered over the time. As soon as IOP stagnated or decreased, participants had adapted to the system. IOPadapt. was calculated only for participants who adapted to the simulator, by considering driving performance after adaptation.
tones and errors in non-target tones were summarized for quantifying inhibition. 3.2.8. Driving simulator Driving performance was investigated in a static driving simulator, consisting of a close-to-production vehicle of the compact class. The simulated environment was projected on the walls in front of and beside the simulator. The view to the rear was displayed on a monitor placed behind the simulator. Small screens were placed at the side mirrors to make the simulated environment as realistic as possible. The scenario comprised a virtual area of 3 × 3 km and a total distance of 70 km consisting of inner-city areas, rural routes as well as highways, where participants had to interact with other road users (Maas, 2017) (see also Fig. 3). We used no pre-defined routes or car-following tasks. Furthermore, we asked the participants to freely choose where and how to drive. A more technical description of the simulator can be found elsewhere (Maas et al., 2014; Schweig et al., 2018). Driving performance was quantified by using the index of performance (IOP), calculated by Joshi's algorithm (Joshi et al., 2017). While previous simulator studies mostly investigated driving performance by considering only single driving parameter, the IOP contains numerous criteria that provide information on driving performance (steer behavior, activity of the pedals, lane drifts), which are considered in the IOP to the same extent.
3.3. Procedure Before starting, participants were informed about the study and the possibility that they could end at any time without reprisal. Subsequently, the physician tested vision and hearing but also cardio-vascular impairments (blood pressure and electrocardiogram). Furthermore, participants where asked for a history of neurological, psychological, and orthopedic disorders. People above 60 years were tested with the ‘DemTect-Test’. In the following (T0), participants completed the D2 test as well as the TMT (B). After 10 minutes' time to rest, participants were instructed how to drive within the simulator (e.g., that the simulator is a vehicle with automatic gearshift) and informed again that they can stop whenever they feel sick or they want to cancel for another reason. Maximum time of driving was 25 min. Within
Fig. 4. Experimental procedure of the three measurement times. The study was conducted over three measurement times to rule out participants' fatigue. Due to the large sample, the time intervals between single measurement times are relatively high. 4
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Table 1 Correlations between driving performance (1, 2) and measures of specific attentional domains of visual selective attention (3), auditory selective attention (7), visual divided attention (4), switching attentional demands (5, 6), switching between attributes (9), switching between rules (12), and vigilance (8), as well as inhibition (11) and working memory (10).
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
IOP5–15 IOPadapt. Single demand: selective Single demand: divided Switching demands: selective Switching demands: divided Oddball D2 TMT-B Digit span Go/NoGo-task MCST
M
SD
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
0.31 1.62 457.07 664.34 473.65 648.58 448.77 149.94 75.08 5.20 10.07 14.07
0.84 0.52 59.31 93.90 58.98 87.18 68.39 46.40 28.64 1.52 4.97 9.84
– 0.246 −0.109 0.024 −0.146 −0.057 −0.038 0.068 0.055 −0.095 0.021 0.157
– 0.152 0.218 0.157 0.084 0.204 −0.244 0.053 −0.204 −0.006 0.196
– 0.613** 0.736** 0.685** 0.522** 0.130 0.213* −0.015 0.226* 0.133
– 0.556** 0.747** 0.471** 0.331** 0.307** −0.156 0.371** 0.243**
– 0.681** 0.622** 0.149 0.210* −0.012 0.273** 0.141
– 0.523** 0.309** 0.275** −0.120 0.335** 0.175
– 0.174 0.247** −0.114 0.278** 0.046
– 0.483** −0.317** 0.428** 0.345**
– −0.415** 0.432** 0.439**
– −0.374** −0.239**
– 0.358**
Note. Sample size of all correlations with IOPadapt. was N = 40. * p < .05. ** p < .001.
attentional domains together with inhibition and working memory also revealed no significant effects. Furthermore, we found no relation between age and driving performance. However, some aspects need to be further discussed in this context. The present measures comprise relatively simple stimuli, such as objects (e.g., star, triangle), single numbers, or letters. However, previous studies that report relations between cognitive functions and driving ability used more complex tasks. For example, the mentioned Useful Field of View test, which also focusses on the attentional demands of selective and divided attention (Ball et al., 1993). Within this test, participants are asked – for example – 1) to divide their attention across a series of tasks when driving or 2) to identify relevant information from visual clutter or distractions (such as finding an item on a crowded supermarket shelf). Furthermore, most findings on relations between driving performance and attention, inhibition, as well as working memory come from on-road driving studies (e.g., Richardson and Marottoli, 2003). Although, simulators provide the opportunity to examine relevant topic highly standardized as well as cost and time efficient, the transferability of findings to real scenarios is discussed controversial (see, Engström et al., 2005; Törnros, 1998). Therefore, Hallvig et al. (2013) state that a generalization from simulators to real driving must be made with caution. Especially in the context of attention, the question arises whether previously reported significant correlations between neurophysiological tests and driving performance in the simulator, actually refer to driving performance or other aspects such as adaptation (see Brandtner et al., 2019, for further discussion). Another aspect – which aggravates the comparability of distinct driving studies – concerns the fact that each study uses its own parameters for describing participants driving performance. While some use solely single parameters, such as steering wheel movement (Thiffault and Bergeron, 2003) or lane excursions (Rumschlag et al., 2015), others used numerous variables for considering driving performance. For example, Chaparro et al. (2005) used variables of detection and identification of road signs, detection and avoidance of large low-contrast road hazards, gap judgment, lane keeping, and time to complete the course for describing participants driving performance. In the present study, driving performance is described by means of the Index of Performance (Joshi et al., 2017), which constitutes parameters of steer behavior, activity of the pedals, as well as lane drifts, which are considered to the same extent. In addition to driving performance the selected routes seem to further explain differences between existing driving studies, as well as the findings at hand. The effects of the kind of driving scenario is shown by longer reaction times to secondary stimuli with increased complexity of the driving scenario (driving straights, intersections, overtaking maneuvers) (Cantin et al., 2009). Although the authors identified age-effects under all conditions, the most complex driving scenario yielded a disproportionate increase. In addition, Horberry et al. (2006a) report
T1 participants performed the MCST, the Go/NoGo-task, and the digit span task (backwards). During T2 participants performed the SwAD-task as well as the oddball task. The present study was implemented in a larger project. Therefore, the time between the individual measurement times was relatively long (see Fig. 4). 3.4. Statistical analysis The statistical analyses were carried out by SPSS 25.0 for Windows (IBM SPSS Statistics). Prior to mean calculations of SwAD and oddball measures, reaction times of each block that exceeded two standard deviations of participants' mean of the respective block were identified as outliers. This concerned less than two reaction times per condition and participant. Mean reaction times for each block were calculated. We used Pearson correlations to investigate associations between the driving performance scores (IOP5– 15, IOPadapt.) and measures of attentional demands, inhibition, and working memory. Multiple hierarchical moderated regression analyses were conducted in order to test the hypothesized three-way interaction between the three predictors, namely measures of attentional demands (single demand: selective, single demand: divided, switching demands: selective, switching demands: divided, Oddball, D2, TMT-B, MCST), inhibition (Go/NoGo-task), and working memory (Digit span) on driving performance (IOP5–15). All predictors were mean centered. Furthermore, Pearson correlations between measures of driving performance and age were tested as post-hoc analysis. 4. Results Means of the IOP5–15 did not significantly correlate with specific attentional domains, as well as inhibition and working memory. Only 40 individuals adapted to the simulator. Additional analyzes for those individuals revealed no significant relations between IOPadapt and the used measures. Descriptive and test statistics for the correlations are presented in Table 1. None of the three-way interaction models explain a significant proportion of variance, all F(7, 122) ≤ 1.47, all ps ≥ 0.183 (see also Table 2). Posthoc, we correlated age with both scores of driving performance. Here, no correlations were found for the IOP5–15: r = −0.076, p = .406 and the IOPadapt.: r = −0.142, p = .384. 5. Discussion The present study tested correlations between driving performance and different attentional domains, inhibition, as well as working memory, in a relatively large sample. In contrast to any previous assumptions, we found no significant correlations between driving performance and the used measures. The tested three-way interactions for the respective 5
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Table 2 The β-coefficients and test statistics for the multiple hierarchical moderated regression analyses. Predictor
β
t
p
Predictor
Single demand: selective Single demand: selective Go/NoGo Digit span Single demand: selective × Digit span Go/NoGo × Digit span Single demand: selective × Go/NoGo Single demand: selective × Go/NoGo × Digit span
−0.149 −1.424 0.010 0.094 −0.089 −0.821 0.065 0.554 0.044 0.450 0.056 0.526 −0.063 −0.535 F(7, 115) = 0.557, p = .789
β
t
p
Oddball 0.157 0.925 0.413 0.581 0.654 0.600 0.594
Oddball Go/NoGo Digit span Oddball × Digit span Go/NoGo × Digit span Oddball × Go/NoGo Oddball × Go/NoGo × Digit span
−0.108 −1.010 −0.002 −0.018 −0.080 −0.790 0.019 0.166 0.059 0.618 0.130 1.221 −0.129 −1.167 F(7, 115) = 0.566, p = .782
0.315 0.986 0.431 0.869 0.538 0.225 0.246
Single demand: divided Go/NoGo Digit span Single demand: divided × Digit span Go/NoGo × Digit span Single demand: divided × Go/NoGo Single demand: divided × Go/NoGo × Digit span
Single demand: divided 0.022 0.209 −0.016 −0.151 −0.088 −0.825 −0.063 −0.566 0.048 0.464 −0.055 −0.521 0.010 0.088 F(7, 115) = 0.243, p = .974
0.835 0.880 0.411 0.573 0.644 0.603 0.930
D2 Go/NoGo Digit span D2 × Digit span Go/NoGo × Digit span D2 × Go/NoGo D2 × Go/NoGo × Digit span
D2 0.042 0.378 −0.033 −0.302 −0.060 −0.531 −0.086 −0.718 0.068 0.603 −0.001 −0.005 −0.058 −0.467 F(7, 115) = 0.315, p = .946
0.706 0.763 0.596 0.474 0.548 0.996 0.641
Switching demand: selective Go/NoGo Digit span Switching demand: selective × Digit span Go/NoGo × Digit span Switching demand: selective × Go/NoGo Switching demand: selective × Go/NoGo × Digit span
Switching demand: selective −0.179 −1.646 0.031 0.297 −0.073 −0.685 0.027 0.227 0.058 0.602 −0.028 −0.252 −0.008 −0.063 F(7, 115) = 0.640, p = .722
0.103 0.767 0.495 0.821 0.548 0.801 0.950
TMT-B Go/NoGo Digit span TMT-B × Digit span Go/NoGo × Digit span TMT-B × Go/NoGo TMT-B × Go/NoGo × Digit span
TMT-B 0.003 0.023 0.046 0.414 −0.160 −1.386 −0.152 −1.239 0.120 1.015 0.123 0.962 0.239 1.692 F(7, 115) = 0.742, p = .636
0.982 0.679 0.168 0.218 0.312 0.338 0.093
Switching demand: divided Go/NoGo Digit span Switching demand: divided × Digit span Go/NoGo × Digit span Switching demand: divided × Go/NoGo Switching demand: divided × Go/NoGo × Digit span
Switching demand: divided −0.041 −0.402 0.040 0.381 −0.092 −0.831 −0.070 −0.603 0.019 0.187 −0.261 −2.450 0.156 1.304 F(7, 115) = 1.20, p = .308
0.688 0.704 0.407 0.547 0.852 0.016 0.195
MCST Go/NoGo Digit span MCST × Digit span Go/NoGo × Digit span MCST × Go/NoGo MCST × Go/NoGo × Digit span
MCST 0.129 1.276 −0.045 −0.439 −0.102 −0.952 −0.129 −1.247 0.131 1.277 0.178 1.815 0.041 0.366 F(7, 115) = 1.474, p = .183
0.204 0.662 0.343 0.215 0.204 0.072 0.715
present study focused on basic neuropsychological measures, we rather suggest that future studies should address this topic by using more applied tasks. However, along with previous findings it should be noted that it is highly relevant to differentiate between specific attentional domains, such as selective attention or divided attention. In terms of design and implementation, we recommend to additionally consider basic findings on specific attentional domains such as modality and spatiality effects but also more applied findings from related fields. However, more applied research on specific attentional domains is necessary to get a better understanding of – for example – the effects of in-vehicle technologies on inattention and crash risk. Especially in terms of designing traffic safety programs the relations between specific attentional domains and driving performance should be also focused from a more applied perspective in future research.
lower mean speed on a highway drive in older compared to younger participants. Furthermore, Vardaki and Karlaftis (2011) identified better freeway driving performance in people who were more familiar with driving on streets of higher speed, which underpins the hypothesis that older people are more likely to have problems on these roads, since they usually drive on roads with lower speeds. Within the study at hand, we used a scenario consisted of inner city and country roads, but also a freeway. We did not use predefined routes or a car-following task (in order to ensure a most realistic possible scenario). Therefore, participants were able to choose how and where to drive. However, it must be assumed that especially people who felt uneasy or had increased problems, chose more simple routes, performed little turning or overtaking maneuvers, and drove at a lower speed, which must be considered as limitation of the study. Further limitations or possible biases need to be discussed regarding the present sample. Although the large sample characterizes the present study it also carries the risk of biases. As discussed at the beginning of the discussion we did not specifically tested for vision and motor functions. Furthermore, the sample is dominated by males, whereas females are underrepresented and thus limits the results. Although the age distribution covers a wide range from 23 to 89 years, there is a shift towards older participants. In contrast to previous findings, the increased age of the sample did not lead to larger effects.
7. Conclusion Based on present findings, it can be summarized that it is not possible to predict simulator driving performance based on commonly used neurophysiological tests of specific domains. Along with previous findings we suggest using rather context-specific tasks than basic neuropsychological measures to quantify specific domains, in order to predict peoples' driving performance.
6. Future research
Funding
Following up on the findings of the present study one might assume to refrain from a domain-specific consideration in future research. Since the
The study presented here was carried out within the framework of the research project ‘ALFASY – Altersgerechte Fahrerassistenzsysteme’ (‘Age6
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based Driver Assistance Systems’). This project was funded by the European Regional Development Fund (ERDF).
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