text:
- Original article
- Open Access
Talking to your car can drive you to distraction
© The Author(s) 2016
- Received: 7 June 2016
- Accepted: 27 September 2016
- Published: 14 November 2016
This research examined the impact of in-vehicle information system (IVIS) interactions on the driver's cognitive workload; 257 subjects participated in a weeklong evaluation of the IVIS interaction in one of ten different model-year 2015 automobiles. After an initial assessment of the cognitive workload associated with using the IVIS, participants took the vehicle home for 5 days and practiced using the system. At the end of the 5 days of practice, participants returned and the workload of these IVIS interactions was reassessed. The cognitive workload was found to be moderate to high, averaging 3.34 on a 5-point scale and ranged from 2.37 to 4.57. The workload was associated with the intuitiveness and complexity of the system and the time it took participants to complete the interaction. The workload experienced by older drivers was significantly greater than that experienced by younger drivers performing the same operations. Practice did not eliminate the interference from IVIS interactions. In fact, IVIS interactions that were difficult on the first day were still relatively difficult to perform after a week of practice. Finally, there were long-lasting residual costs after the IVIS interactions had terminated. The higher levels of workload should serve as a caution that these voice-based interactions can be cognitively demanding and ought not to be used indiscriminately while operating a motor vehicle.
- Cognitive workload
- Cognitive distraction
- Driving
- Driver distraction
- Aging
- Divided attention
- Multitasking
Motor vehicle crashes are the leading cause of accidental injury deaths in the US (National Safety Council White, 2010). Distractions of one kind or another have been observed in 66 % of cases within the 6 s preceding a crash (Carney, McGehee, Harland, Weiss, & Raby, 2015). Voice-activated features may seem to be a natural solution to driver distraction, allowing drivers to keep their eyes on the road and their hands on the steering wheel. In fact, many newer-model vehicles come equipped with voice-activated systems that allow drivers to adjust climate control, select music, place and receive phone calls, send and read textual messages, and interact with social media. The impact of these voice-based commands on the motorist's cognitive workload is unknown; however, voice-command laboratory surrogates with perfect reliability have been shown to produce surprisingly high levels of workload (Strayer, Cooper, Turrill, Coleman, & Hopman, 2015a, Strayer et al., 2015b). Moreover, older adults are likely to have greater difficulty using these voice-based systems and, ironically, they are more likely to purchase new vehicles equipped with these features (Sivak, 2013). Finally, the effect of practice with these voice-command systems has not been fully explored and it is unknown if the higher levels of cognitive workload abate with extended practice. These issues take on greater significance given a large segment of the driving public may use these systems in the coming years.
In order to allow drivers to maintain their eyes on the forward roadway, nearly every vehicle sold in the US and Europe can now be optionally equipped with an in-vehicle information system (IVIS). Using voice commands, drivers can access functions as varied as voice dialing, music selection, GPS destination entry, and even climate control. Voice-activated features would seem to be a natural evolution in vehicle safety that requires little justification. However, a large and growing body of literature cautions that auditory/vocal tasks may have unintended consequences that adversely affect traffic safety (e.g., Bergen, Medeiros-Ward, Wheeler, Drews, & Strayer, 2013).
The National Highway Traffic Safety Administration (NHTSA) is in the process of developing voluntary guidelines to minimize driver distraction created by electronic devices in the vehicle. There are three planned phases to the NHTSA guidelines. The phase 1 guidelines, entered into the Federal Register on March 15, 2012, address visual–manual interfaces for devices installed by vehicle manufactures. The phase 2 guidelines, scheduled for release sometime in 2016, will address visual–manual interfaces for portable and aftermarket electronic devices. Phase 3 guidelines will address voice-based auditory interfaces for devices installed in vehicles and for portable aftermarket devices.
Currently, there are no unified regulations regarding the use of wireless technology in the vehicle—the NHTSA phase 1 guidelines are voluntary and it is unknown whether any of the currently available vehicles meet these guidelines. With the explosive growth in technology, the problem of driver distraction is poised to become much more acute.
Benchmarking cognitive distraction
Our prior research provided a benchmark for the cognitive workload associated with common in-vehicle activities (Strayer et al., 2015a, 2015b; see also Cooper, Ingebretsen, & Strayer, 2014; and Strayer, Turrill, Coleman, Ortiz, & Cooper, 2014). In our studies, we developed and validated a cognitive distraction scale based on converging operations from the laboratory, driving simulator, and an instrumented vehicle driven in a residential section of Salt Lake City. Our research shows that the distraction potential can be reliably measured, that cognitive workload systematically varies as a function of the secondary task performed by the driver, and that some activities, particularly newer voice-based interactions in the vehicle, are associated with surprisingly high levels of mental workload.
We obtained workload ratings attributable to cognitive sources by comparing seven different concurrent tasks with a "single-task" condition where the drivers did not perform any concurrent secondary-task activity (Strayer et al., 2015b). The seven tasks were listening to the radio, listening to a book on tape, talking to a passenger, talking on a hands-free cell phone, talking on a hand-held cell phone, interacting with a simple voice messaging system, and a cognitively demanding Operation Span (OSPAN) task that was used for calibration.1 In our distraction scale, the non-distracted single-task driving anchored the low-end (category 1) and the mentally demanding OSPAN task anchored the high-end (category 5) of the scale. Using this method, we found that activities such as listening to the radio or an audio book were not very distracting. Other activities, such as conversing with a passenger or talking on a hand-held or hands-free cell phone, were associated with moderate increases in cognitive distraction. Finally, activities such as using a speech-to-text system to send and receive short text or e-mail messages produced a surprisingly high level of cognitive distraction.
The speech-to-text system that we evaluated in the laboratory is noteworthy because the speech-recognition portion of the system was perfectly reliable and there was no requirement to review, edit, or correct garbled translations. In our research protocol, perfect speech recognition was implemented using a "Wizard-of-Oz" paradigm (Kelley, 1983; Lee, Caven, Haake, & Brown, 2001), in which the participant's speech was secretly entered into the computer by the experimenter with no transcription errors. Consequently, drivers did not need to take their eyes off the road or their hands off the steering wheel when making these voice-based interactions. Nevertheless, this "best case" speech-to-text e-mail/text message system received a category 3 rating on the cognitive distraction scale.
In our 2014 research (Strayer et al., 2014), we examined voice-based interactions in greater detail. We found that just listening to voice messages without the possibility of generating a reply was associated with a cognitive workload rating comparable to that of conversing on a cell phone (i.e., category 2). However, when drivers composed replies to these messages, the workload rating increased to category 3 on the cognitive distraction scale. Like our earlier testing, this laboratory-based system was perfectly reliable. We also found no systematic difference between the natural (i.e., human) and synthetic (i.e., computerized) delivery of the messages. This latter finding suggests that there is little to be gained by improving the quality of the synthetic speech, at least with regard to the driver's mental workload.
Our 2014 research also evaluated Apple's intelligent personal assistant, Siri, to send and receive text messages, update Facebook or Twitter, and to modify and review calendar appointments. To create a completely hands-free version of the interaction, a lapel microphone was clipped to the participant's collar and they activated Siri with the command "Hey Siri" at which point a researcher manually activated the device. Drivers neither looked at nor made physical contact with the iPhone during these interactions. Even so, the workload ratings for these interactions exceeded category 4 on our workload scale. Moreover, there were two crashes in the driving simulator study when participants were using Siri.
The primary difference between our laboratory-based speech-to-text system and the Siri-based interactions was the reliability of the system (see also Strayer et al., 2015a). Siri was error-prone, producing different responses to seemingly identical commands. In other circumstances, Siri required exact phrasing to accomplish specific tasks and subtle deviations from that phrasing would result in failure. Moreover, when there was a failure to properly dictate a message, Siri required starting the interaction over since there was no way to modify/edit a message or command. For these reasons and others, voice-based interactions using an intelligent personal assistant such as Siri were significantly more mentally demanding than conversing on a cell phone.
Research objectives and experimental overview
The current research addresses several important issues related to the assessment of cognitive workload in the vehicle. First, our prior research examined drivers who were in their mid-20s (e.g., the average age of participants in the Strayer et al. (2015b) study was 23). This younger cohort tends to be more tech-savvy than an older population: it is unclear how demanding older drivers will find these voice-based interactions. This issue gains importance because drivers between the ages of 55 and 64 years are the most likely to purchase new vehicles equipped with voice-command technology to control infotainment and other vehicle functions (Sivak, 2013). In fact, laboratory studies have documented substantially greater costs of multitasking for older adults (e.g., Hartley & Little, 1999; Kramer & Larish, 1996; McDowd & Shaw, 2000); therefore, it is likely that the workload scale developed in our prior research is a conservative estimate of the cognitive workload experienced by older drivers interacting with these voice-based systems.
Second, our prior research examined the driver's cognitive workload soon after they had been introduced to the vehicle, with minimal training (i.e., 15 minutes or less) using the vehicle and the IVIS. The old adage "practice makes perfect" suggests that extended practice with the IVIS may reduce or even eliminate the interference caused by these voice-based interactions. For practice to be effective, however, the system needs to be intuitive and error free with a consistent mapping between input–output operations (e.g., Shiffrin & Schneider, 1977). Because many of the systems that are currently available tend to be complex and error prone, with inconsistent behavior (e.g., Cooper et al., 2014), there are limits on how much improvement can be expected with extended practice.
Our study recruited male and female drivers between the ages of 21 and 70 years to participate in a weeklong evaluation of IVIS interactions in one of ten different model-year 2015 automobiles. After familiarization with the vehicle, participants were trained on how to interact with the voice-based system to perform common IVIS tasks (e.g., dialing, radio tuning). Following this initial orientation, they were tested on the IVIS interactions using the method that we developed to assess cognitive workload in the vehicle (e.g., Strayer et al., 2015b). Participants then took the vehicle home for 5 days and practiced interacting with the IVIS. At the end of 5 days of practice, participants returned and were retested on the cognitive workload of these same IVIS interactions. This design allowed us to evaluate the effects of age and practice on IVIS interactions.
Following approval from the institutional review board (IRB), participants were recruited by word of mouth and flyers posted on the University of Utah campus. They were compensated $250 upon completion of the weeklong study. Data were collected from July 4th of 2014 through June 18th of 2015.
Distribution of age and gender for each of the vehicles used in the experiment
Vehicle model
21–34 years
35–53 years
54–70 years
Buick LaCrosse
Chevy Equinox
Chevy Malibu
Chrysler 200c
Ford Taurus
Hyundai Sonata
Mazda 6
Nissan Altima
Toyota 4Runner
VW Passat
Prior to participation in the research, the University of Utah's Division of Risk Management ran a Motor Vehicles Record report on each prospective participant to ensure a clean driving history (e.g., no at-fault accidents in the past five years) and eligibility to be registered as a University driver. In addition, following University of Utah policy, each participant was required to complete a 20-minute online defensive driving course and pass the certification test. Participants reported between 5 and 55 years of driving experience (x̄ = 28 years). Additionally, participants reported driving an average of 160 miles per week. All participants were recruited from the greater Salt Lake area and spoke well-articulated English.
Materials and equipment
Ten 2015 model-year vehicles equipped with automatic transmissions were used in this research (see Table 1 for a complete breakdown of the different vehicles used in the study). In each vehicle, voice-based interactions with the IVIS were initiated with the press of a button located on the steering wheel and ended either automatically or with a second press of the button, depending on the vehicle and function. Each of the ten vehicle systems allowed drivers to complete contact calling and number dialing tasks through a Bluetooth-paired cellphone.
Dual-Vision XC cameras, manufactured by Rosco Vision Systems, were installed in the vehicles by a qualified technician. Cameras were mounted under the rear view mirror, providing a view of the forward roadway and of the driver's face. An infrared illuminator was installed in each vehicle for nighttime video recording. The cameras also included an embedded GPS system. Cameras were set to automatically begin recording audio, video, and GPS data as soon as the vehicle ignition was turned on by the driver and to stop recording when the vehicle ignition was turned off. Video data were recorded at 3.5 frames per second at standard VGA resolution.
During the first day of the study (session 1) and on the last day of the study (session 2), participants wore a head-mounted Detection Response Task (DRT) device that consisted of an LED light mounted to a flexible arm that was connected to a headband, a micro-switch attached to the participant's left or right thumb (the switch was attached to the hand opposite that of the vehicle's steering wheel voice-activation button), and a dedicated microprocessor to handle all stimulus timing and response data. The light was positioned in the periphery of the participant's left eye (approximately 15° to the left and 7.5° above the participant's left eye) so that it could be seen while looking at the forward roadway but did not obstruct the view of the driving environment. The stimulus presentation configuration adhered to the International Standards Organization (ISO) standard 17488 with red LED stimuli configured to flash every 3–5 s. Data were collected using an Asus Transformer Book T100s with quad-core Intel® Atom™ processors running at 1.33 GHz.
An auditory version of the OSPAN task, developed by Watson and Strayer (2010), was used to induce a high workload baseline during testing. This task required participants to recall single syllable words in serial order while solving mathematical problems. In the auditory OSPAN task, participants were asked to remember a series of two to five words that were interspersed with math-verification problems (e.g., given "[3/1] − 1 = 2?"—"cat"—"[2 × 2] + 1 = 4?"—"box"—RECALL, the participant should have answered "true" and "false" to the math problems when they were presented and recalled "cat" and "box" in the order in which they were presented when given the recall probe). In order to standardize presentation for all participants, a prerecorded version of the task was created and played back during testing.
- 1.
How mentally demanding was the task?
- 2.
How physically demanding was the task?
- 3.
How hurried or rushed was the pace of the task?
- 4.
How successful were you in accomplishing what you were asked to do?
- 5.
How hard did you have to work to accomplish your level of performance?
- 6.
How insecure, discouraged, irritated, stressed, and annoyed were you?
A study facilitator was assigned to each participant for the duration of the data collection session. Facilitators were trained to precisely administer the research procedure and adhered to a scripted evaluation protocol. Additionally, facilitators were responsible for ensuring the safety of the driver, providing in-car training, and delivering task cues to participants.
Before the study began, participants filled out an IRB approved consent form and a brief intake questionnaire to assess basic characteristics of phone and driving usage and experience. Participants were then familiarized with the controls of the instrumented vehicle, adjusted the mirrors and seat, and were informed of the tasks that would be completed while driving. The first portion of training involved an introduction to the DRT device. Participants were fitted with the device and were instructed on its functionality. Once comfortable with the general procedure, they were allowed to practice with the DRT device until they felt comfortable with its usage. In most cases, participants were comfortable with the functionality of the device within a couple of minutes. Participants were provided training on the functionality of the IVIS system and asked to complete a series of contact calling, number dialing, and radio tuning tasks until they reached proficiency. Participants then completed a three-minute orientation for each of the tasks in the IVIS condition and a three-minute orientation of the OSPAN task while the vehicle was parked. A practice loop within a parking lot was completed in order to familiarize the participant with the handling of the vehicle.
Next, participants completed one circuit around the 2.7 mile driving loop, located in the Avenues section of Salt Lake City, UT, in order to become familiar with the route itself. The route provided a suburban/residential driving environment and contained seven all-way controlled stop signs, one two-way stop sign, and two stoplights. Given the restricted usage characteristics of the roadway, traffic remained relatively consistent during testing. After the practice drive, participants began the experimental portion of the study. In total, participants drove the vehicle for approximately 20 minutes before the initial data collection began.
Six tasks were given to participants during the IVIS condition of the study; each involved the use of the vehicle's unique voice-activated infotainment system. The tasks were initiated once participants reached pre-specified locations that were chosen to allow participants approximately 1.5 minutes to complete each task. If the participant was unable to complete a task before the next task was to begin, they were told to abandon that first task and move on to the new one.
Task 1: "Call from your contacts Joel Cooper"
Task 2: "Tune your radio to 98.3 FM"; once completed, "Tune your radio to 1320 AM"
Task 2b (for the Nissan and Volkswagen vehicles): "Call from your contacts Chris Hunter"
Task 3: "Dial your own phone number"
Task 4: "Tune your radio to 1160 AM"; once completed, "Tune your radio to 90.1 FM"
Task 4b (for the Nissan and Volkswagen vehicles): "Dial your own phone number"
Task 5: "Call from your contacts Amy Smith at work"
Task 6: "Dial your own phone number"
Participants were then familiarized with the specific requirements of the upcoming condition and were told that their task was to follow the route previously practiced while complying with all local traffic rules, including obeying a 25 miles per hour (mph) speed limit. Throughout each of the three experimental conditions (single-task, IVIS, and OSPAN), the driver performed the DRT task. Each of the conditions required the driver to complete one loop of the 2.7-mile course and the order of the conditions was counterbalanced across participants. The OSPAN condition induced a continuous secondary-task load, whereas the task load in the IVIS condition was intermittent. Any driving sections with turns were excluded from the DRT and video analyses to minimize the potential of a manual confound.
At the conclusion of the first day of testing (session 1), participants were given a logbook to document their interactions with the IVIS during the ensuing 5 days. Participants were encouraged to practice using the IVIS system on their own time with special emphasis given to contact calling, number dialing, and radio station selection. Once familiar with the journaling and instructions for the week, participants took the research vehicle home and began the practice portion of the study. Following the five-day practice interval, participants returned on the last day for evaluation (session 2). The data collection protocol for session 2 was identical to that of session 1 except that the extensive IVIS training was no longer necessary.
The core experimental design was a 3 (Age) × 10 (Vehicle) × 3 (Condition) × 2 (Session) split-plot factorial. Age was a between-subject factor and included three age groups, 21–34, 35–53, and 54–70 years.2 Vehicle was also a between-subject factor and included ten 2015 model-year vehicles: a Buick LaCrosse with IntelliLink, a Chevy Equinox with MyLink, a Chevy Malibu with MyLink, a Chrysler 200c with Uconnect, a Ford Taurus with Sync MyFord Touch, a Hyundai Sonata with Blue Link, a Mazda 6 with Connect, a Nissan Altima with NissanConnect, a Toyota 4Runner with Entune, and a Volkswagen Passat with Car-Net. Condition was a three-level within-subject factor (single-task, IVIS, and OSPAN conditions). Session was also a within-subject factor and refers to the first day of testing (session 1) and the last day of testing (session 2) that were separated by 5 days of practice with the IVIS system. The three conditions in each session were performed in a counterbalanced order across participants. Interactions with the IVIS involved two number dialing tasks, two contact calling tasks, and four radio tuning tasks, with the exception that participants driving the Nissan and Volkswagen vehicles completed three number dialing tasks and three contact calling tasks because these vehicles did not support vocal radio tuning. Additionally, because the DRT analysis allowed for a differentiation between on-task performance (i.e., the time when participants were actively engaged in the IVIS interactions) and off-task performance (i.e., the period of time between IVIS tasks when the driver was not interacting with the IVIS but rather was driving as in the single-task condition), Condition had four factors (i.e., single-task, IVIS off-task, IVIS on-task, and OSPAN) when assessing the effects of IVIS interactions on DRT performance.
Dependent measures
Cognitive workload was determined by a number of performance measures. These measures were derived from the DRT task, subjective reports, and analysis of video recorded during the experiment.
DRT MANOVA. An overall analysis that statistically combined the effects of RT and hit rate (see the "Hit rate" section below).
DRT RT. Defined as the sum of all valid RTs to the DRT task divided by the number of valid RTs.
DRT hit rate. Defined as the number of valid responses divided by the total number of stimuli presented during each condition.
DRT residual costs. To evaluate the residual effects of secondary task interactions on DRT RT, performance in the off-task segments of the drive was sorted into 3-s bins relative to the time that the off-task interval began. For example, a DRT event occurring 5 s after the end of an IVIS interaction would be sorted into the second bin.
Subjective: NASA TLX. Defined as the response on a 21-point scale for each of the six subscales of the TLX (Mental, Physical, Temporal, Performance, Effort, and Frustration).
Subjective: Intuitiveness and complexity. Defined as the response on a 21-point scale to questions on task intuitiveness (i.e., "how intuitive, usable, and easy was it to use the system") and complexity (i.e., "how complex, difficult, and confusing was it to use the system").
Video: Task completion time. Task completion time was defined as the time from the moment participants first pressed the voice activation button to the time that the same button was pressed to terminate a task, or in the case of radio tuning, the moment when the system accurately carried out the requested task. Task completion time reflects the average task duration across the six tasks in the IVIS condition.
Video: Glance location. Defined as the percentage of all visual glances that fell within the forward roadway, the dashboard region, or the right, left, and rear-view mirrors.
Video: Practice frequency. Defined as the count of IVIS voice interactions during the 5-day practice session where participants practiced using the voice assistant to call a contact, dial a number, tune the radio, or engage in other voice tasks.
Detection Response Task
The DRT is inversely related to the workload in the driving task (e.g., Strayer et al., 2015a, 2015b). Thus, increases in RT and decreases in hit rate are indicative of an increase in the workload experienced by the driver. As can be seen in Figs. 1 and 2, RT increased and hit rates decreased as a function of the experimental condition and the age of the participant. Additionally, the age-related differences observed in the single-task baseline were amplified in the IVIS-1 condition. Perusal of Figs. 3 and 4 shows that RT decreased and hit rates increased with practice and the practice effects observed in the single-task baseline were exacerbated in the IVIS-1 condition.
The DRT data were first analyzed using a 3 (Age) × 10 (Vehicle)3 × 4 (Condition) × 2 (Session) MANOVA that included both reaction time and hit rate as dependent variables.4 There were significant main effects of Age (F(4, 454) = 14.07, p < 0.001, η2 = 0.110), Condition (F(6, 1362) = 164.86, p < 0.001, η2 = 0.421), and Session (F(2, 226) = 48.61, p < 0.001, η2 = 0.301). In addition, Condition interacted with Age (F(12, 1362) = 8.15, p < 0.001, η2 = 0.067), Vehicle (F(54, 1362) = 1.53, p = 0.009, η2 = 0.057), and Session (F(6, 1362) = 12.54, p < 0.001, η2 = 0.052). None of the other effects were significant.
Reaction time
The reaction time data from the DRT were analyzed using a 3 (Age) × 10 (Vehicle) × 4 (Condition) × 2 (Session) Analysis of Variance (ANOVA). The analysis revealed significant main effects of Age (F(2, 227) = 31.71, p < 0.001, η2 = 0.218), Condition (F(3, 681) = 894.29, p < 0.001, η2 = 0.798), and Session (F(1, 227) = 84.65, p < 0.001, η2 = 0.272). In addition, as can be seen in Fig. 1, Condition interacted with Age (F(6, 681) = 15.75, p < 0.001, η2 = 0.122), Vehicle (F(27, 681) = 2.00, p = 0.002, η2 = 0.074), and Session (F(3, 681) = 16.62, p < 0.001, η2 = 0.068). None of the other effects were significant.
Hit rate
The hit rate data from the DRT task were analyzed using a 3 (Age) × 10 (Vehicle) × 4 (Condition) × 2 (Session) ANOVA. The analysis revealed significant main effects of Age (F(2, 227) = 17.87, p < 0.001, η2 = 0.136), Condition (F(3, 681) = 129.15, p < 0.001, η2 = 0.363), and Session (F(1, 227) = 53.61, p < 0.001, η2 = 0.191). In addition, as shown in Fig. 2, Condition interacted with Age (F(6, 681) = 7.94, p < 0.001, η2 = 0.065), Vehicle (F(27, 681) = 1.87, p = 0.005, η2 = 0.069), and Session (F(3, 681) = 12.44, p < 0.001, η2 = 0.052). None of the other effects were significant.
The Condition × Age interaction (Figs. 1 and 2) indicates that the costs of the IVIS interactions were greater for older adults than for younger adults. RT increased with age by 18.2 % in the single-task condition and by 29.7 % in the IVIS-1 condition. A similar analysis of hit rates found a decrease with age of 2.1 % in the single-task condition and of 8.5 % in the IVIS-1 condition. This interaction was also found in the log transformed RT data (F(2, 227) = 10.17, p < 0.001, η2 = 0.071).5
The Condition × Session interaction (Figs. 3 and 4) indicates that the effects of practice were more pronounced when participants were using the IVIS than when they were in the single-task condition. RT decreased with practice by 3.5 % in the single-task condition and by 9.0 % in the IVIS-1 condition. A similar comparison on hit rates found an increase with practice of 1.4 % in the single-task condition and of 5.7 % in the IVIS-1 condition.
The MANOVA reported above found a significant Condition × Vehicle interaction that requires additional analyses for clarity in the interpretation. The interaction could be due to difficulties operating the vehicle, workload differences with the IVIS interactions, or a combination of the two. To discriminate between these interpretations, we created a composite of the DRT measures obtained from the IVIS-1 condition by taking the weighted average of the z-transformed RT and hit rate data. This transformation was necessary because RT and hit rate are on different scales and the result was a score that was centered at 0 and the standard deviation was 1.6 A similar procedure was used to compute the single-task and OSPAN composite scores.
Residual costs
The residual costs took a significant amount of time to dissipate. In fact, the data indicate that off-task performance reflects a mixture of "single-task" performance and the persistent costs associated with the IVIS interactions from the immediately preceding on-task period. One way to contextualize these residual costs is to use logic underlying the workload scale developed by Strayer et al. (2015b) to estimate, based solely on the DRT RT data, when the cognitive workload would reach a category 4 level (approximately 6 s), when it would reach a category 3 level (approximately 9 s), and when it would reach a category 2 level (approximately 15 s). The residual costs are notable because of their magnitude, their duration, and the fact that they are obtained even when there is no active switch to perform another task. They appear to reflect the lingering act of disengaging from the cognitive processing associated with the IVIS task and fully re-engaging attention to the driving environment. From a practical perspective, the data indicate that just because a driver terminates a voice-based interaction does not mean that they are no longer impaired. Indeed, the residual costs are at a category 3 level of impairment 9 s after the IVIS interaction had terminated. At the 25 mph speed limit in our study, drivers would have traveled over the length of a football field during this 9-s interval.
The six sub-scales of the NASA TLX were analyzed using a 3 (Age) × 10 (Vehicle) × 3 (Condition) × 2 (Session) ANOVA. There were significant main effects of Vehicle (F(54, 1362) = 1.47, p = 0.016, η2 = 0.055), Condition (F(12, 900) = 72.10, p < 0.001, η2 = 0.490), and Session (F(6, 222) = 28.51, p < 0.001, η2 = 0.435). In addition, Condition interacted with Age (F(24, 1880) = 2.46, p < 0.001, η2 = 0.032), Vehicle (F(108, 2724) = 1.60, p < 0.001, η2 = 0.060), and Session (F(12, 900) = 3.36, p < 0.001, η2 = 0.043). The Session × Vehicle (F(54, 1362) = 1.36, p = 0.045, η2 = 0.051) and the Session × Age × Vehicle interactions were also significant (F(108, 1362) = 1.30, p = 0.025, η2 = 0.094). None of the other effects were significant.
As with the DRT analysis described above, there was a significant Condition × Vehicle interaction in the TLX data, which is an analysis with parallel structure to the DRT. We created a composite of the TLX measures obtained from the IVIS condition by taking the weighted average of the z-transformed sub-scales of the TLX. A similar transform was used to compute the single-task and OSPAN composite scores. As with the DRT analysis, a main effect of the Vehicle holding constant any differences in single-task workload ratings (using ANCOVA) would indicate that subjective workload of the IVIS interactions differed as a function of Vehicle.
Video analysis
Three performance measures were derived from analysis of the video. These were task completion time, glance location, and practice frequency.
Task completion time
Glance location
A simplified 3 (Glance location) × 3 (Condition) repeated measures ANOVA was conducted on the data presented in Fig. 14. Both the main effect of Glance location (F(2, 856) = 126.17, p < 0.001, η2 = 0.983) and the Glance location × Condition interaction were significant (F(4, 856) = 52.9, p < 0.001, η2 = 0.198). Performing the voice tasks with the IVIS led to a reduction in the glance time to the mirrors and forward roadway with a corresponding increase in glance time to the dashboard displays. Similarly, performing the OSPAN task led to a reduction in the glance time to mirrors and dashboard displays with a corresponding increase in glance time to the forward roadway. Given that the primary task was to drive the vehicle and that the secondary tasks were primarily cognitive in nature, it is not surprising that drivers maintained their eyes on the forward roadway the majority of the time.
Practice frequency
The cognitive distraction scale
Step 1: For each dependent measure, the standardized scores were computed using zi = (xi − X)/SD, where X refers to the overall mean and SD refers to the pooled standard deviation.
Step 2: For each dependent measure, the standardized condition averages were computed by collapsing across subjects.
Step 3: The standardized averages were computed with an equal weighting for secondary (i.e., DRT performance) and subjective (i.e., NASA TLX performance) metrics. The measures within each metric were also equally weighted. For example, the secondary task workload metric was comprised of an equal weighting of the measures DRT RT and DRT hit rate.
Step 4: The standardized mean differences were range-corrected so that the non-distracted single-task condition had a rating of 1.0 and the OSPAN task had a rating of 5.0
The objective of the current research was to examine the effect of IVIS interactions on the cognitive workload experienced by drivers across the age range. We selected voice-based tasks that could be performed with no visual component and only a minimal button press to initiate. As such, the tasks were primarily cognitive in nature (i.e., aside from the initial button press on the steering wheel, there was no requirement for visual or manual interaction). We explored several interrelated questions concerning the cognitive workload of these voice-based tasks. First, how demanding are these IVIS interactions? How do they compare to other common in-vehicle activities such as talking on a cell phone? Does the workload differ for the different vehicles? If they differ, what is the basis for the difference? Second, laboratory studies have found that older adults exhibit greater costs when multitasking. Do these age-related differences hold for real-world interactions while operating a motor vehicle? Third, does practice eliminate any age-related or vehicle-related differences in cognitive workload? If it does, how much practice is necessary? We address each of these issues in the following paragraphs.
First, using the IVIS to complete common tasks (e.g., voice dialing, contact calling, and radio tuning) was associated with a significant increase in the cognitive workload of the driver compared to the single-task condition. The overall workload ratings associated with IVIS interaction averaged 3.34 on our 5-point scale and ranged from 2.37 to 4.57; this reflects a moderate to high level of cognitive workload. These cognitive workload ratings were associated with the intuitiveness and complexity of the IVIS and the time it took participants to complete the interaction. Systems that scored lower in cognitive workload were rated as being more intuitive and less complex and their system interactions required a shorter time to complete. By contrast, systems that were higher in cognitive workload were rated as being less intuitive and more complex and their system interactions required a greater time to complete. Importantly, our analyses were able to dissociate the differential workload associated with operating the vehicle (i.e., in the single-task condition) from the workload associated with IVIS interactions. We performed ANCOVAs that held constant single-task performance and found significant effects of the IVIS interaction. That is, the cognitive workload ratings are associated with the IVIS and not the operation of the vehicle.
Third, practice improved performance for all conditions; however, the practice effects were greater as the task complexity increased. This was revealed in the Condition × Session interactions, where the effects of practice were more pronounced in the on-task IVIS condition than in the single-task condition. For example, RT decreased with 5 days of practice by 3.5 % in the single-task condition and by 9.0 % in the on-task segments of the IVIS condition. A similar comparison of hit rates found an increase with practice of 1.4 % in the single-task condition and of 5.7 % in the on-task IVIS condition. However, even after 5 days of practice, there were still large costs associated with IVIS interactions. A fine-grained analysis that focused on performance after 5 days of practice still found large differences between the single-task condition and on-task segments of the IVIS condition (F(2, 226) = 336.17, p < 0.001, η2 = 0.748). Compared to the single-task condition, RT increased by 41.8 % and hit rates decreased by 8.5 % when participants performed IVIS interactions (Fig. 17).
Practice effects for all of human learning are known to be negatively accelerated (i.e., the Power Law of Learning), such that the biggest improvements occur early in training (Newell & Rosenbloom, 1981; see also Heathcote, Brown, & Mewhort, 2000). This implies that any additional practice with IVIS interactions will have diminishing returns compared to what was observed after 5 days of practice. It appears that the impairments from using the IVIS cannot be easily practiced away. Moreover, neither the Condition × Session × Vehicle interactions (all p values ≥0.482) nor the Condition × Session × Age × Vehicle interactions (all p values 0.137) were significant. This is important because it indicates that the relative ordering of the IVIS systems was not altered with practice. IVIS interactions that were easy on the first day were also easy after 5 days of practice, and those IVIS interactions that were difficult on the first day were relatively difficult to perform after 5 days of practice.
Vehicle differences
Our findings indicated that there were significant differences in the cognitive workload of the IVIS systems. The Chevy Equinox system had the lowest rating on the cognitive workload scale and the Mazda 6 system had the highest rating on the cognitive workload scale. Interestingly, the Chevy Equinox system rated highest (i.e., best) on intuitiveness, had one of the lowest ratings on complexity, and took one of the shortest time to complete (as measured by the time on task). By contrast, the Mazda 6 system rated the lowest on intuitiveness, highest on complexity, and had the second longest time to complete. This pattern is noteworthy because the intuitiveness, complexity, and time on task measures were not included in the derivation of the cognitive workload scale. Nevertheless, they converge on the same interpretation of the driver's experience. A general principle that has emerged from this research is that robust, intuitive systems with lower levels of complexity and shorter task durations tend to have lower cognitive workload than more rigid, error-prone, time-consuming systems.
The analysis of workload using the on/off task DRT data found that "on-task" performance was associated with surprisingly high levels of workload (i.e., averaging 3.34 on our 5-point scale). The higher level of workload should serve as a caution that these voice-based interactions can be very mentally demanding and ought not to be used indiscriminately while operating a motor vehicle. It is likely that the intuitiveness, complexity, and timing demands associated with the IVIS interactions are the reason for the increased level of cognitive workload.
Residual costs
Interestingly, the off-task DRT performance provided evidence of persistent interference following the IVIS interactions. Despite the fact that the participants were not interacting with the system in any way, there were residual costs associated with the prior interaction. These residual costs are notable for their magnitude (in the seconds immediately following an interaction, the impairments are similar to that observed with OSPAN). These costs are also notable for their duration, lasting up to 27 s after an interaction had been completed. To put this in context, at 25 mph a vehicle would have traveled 988 feet before the residual costs had completely dissipated. These findings have implications for self-regulatory strategies, such as choosing to dial or send a text message at a stoplight, because the costs of these interactions are likely to persist when the light turns green. The residual costs are likely related to the driver reestablishing situational awareness of the driving environment that was lost during the IVIS interaction (Fisher & Strayer, 2014; Strayer & Fisher, 2016).
The voice-based interactions evaluated in the current study were designed to be completed using simple voice commands. However, like others (e.g., Reimer et al., 2014), we found that many participants routinely glanced at the displays during interactions. Additionally, we found that interactions with the voice-based systems changed the frequency of glances to the forward roadway and side and rear-view mirrors. Based on these findings, it is increasingly evident that natural visual scanning behavior is fundamentally coupled to cognitive processing demands. Quite simply, it is incorrect to assume that talking to your car is an "eyes-free" activity.
The momentary cognitive workload ratings associated with IVIS interaction averaged 3.34 on our 5-point scale and ranged from 2.37 to 4.57. These findings reflect a moderate to high level of cognitive workload. The workload ratings were associated with the intuitiveness and complexity of the IVIS and the time it took participants to complete the interaction.
The momentary cognitive workload experienced by older drivers performing the IVIS interactions was significantly greater than that experienced by younger drivers. In fact, the age-related differences that were observed in the single-task condition doubled when participants interacted with the IVIS. Practice does not eliminate the interference caused by IVIS interactions. IVIS interactions that were easy on the first day were also easy after 5 days of practice and those interactions that were difficult on the first day were still relatively difficult to perform after 5 days of practice.
There were differences in the cognitive workload of the different IVIS systems over and above any differences associated with simply driving the vehicles. We found that robust, intuitive systems with lower levels of complexity and shorter task durations tend to have lower cognitive workload than more rigid, error-prone, time-consuming systems.
There were long-lasting residual costs after IVIS interactions had terminated. At 3 s after the IVIS interaction had completed, the cognitive workload was a category 5 level; at 6 s the workload was at a category 4 level; at 9 s the workload was at a category 3 level; at 15 s the workload was at a category 2 level; and the workload reached a category 1 level after 27 s.
The OSPAN task was originally developed to measure working memory capacity (e.g., Engle, 2012). An auditory version of the OSPAN task was developed for use in the vehicle by Watson and Strayer (2010).
The analyses reported below show the same pattern as when age is treated as a continuous variable rather than a categorical variable.
The Vehicle condition codes for all data collected in each vehicle. Thus, a significant effect of Vehicle would reflect general differences in performance associated with driving the vehicle and not differences in the IVIS interface. Differences in the IVIS interfaces are seen in the effect of Condition and the Condition × Vehicle interaction.
A preliminary analysis that included gender as a factor found that males responded, on average, 45 ms faster than females (p < 0.001); however, gender did not interact with any of the other factors (all p values >0.200); hence we collapsed across this variable for all additional analyses.
Interpretation of the Condition × Age interaction is complicated by the possibility that age-related differences in dual-task performance may simply reflect an increase that is proportional to the age-related slowing observed in the single-task baseline (e.g., Lindholm & Parkinson, 1983). However, a log transformation of the RT data makes proportional changes additive, whereas an interaction would provide evidence that the dual-task costs were greater for older adults (Madden, 2001). The significant Condition × Age interaction found in the log-transformed RT data indicates that the dual-task costs experienced by older adults are greater than would be expected by the proportional increase.
The z-transformation is a linear transformation whereby the transformed mean is 0 and the standard deviation is 1. The resulting data are centered and differences between the conditions are proportional to the differences before the transformation (i.e., an ANOVA performed on the z-transformed data is identical to an analysis on the raw data).
These data rule out speed–accuracy tradeoffs as an explanation of the age-related differences in IVIS interaction. For both RT and accuracy measures, older adults' performance was impaired to a greater extent than that of younger adults.
This research was supported by a grant from the AAA Foundation for Traffic Safety.
Authors' contributions
DLS and JMC designed the study. JT, JRC, and RJM refined the data collection protocol and collected the data. DLS, JC, JT, JRC, and RHJ were involved in the different phases of the data analysis. DLS, JMC, JT, JRC, and RJH contributed to the written manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors' Affiliations
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text:
THE second season of Joss Whedon's Dollhouse will premiere on Friday, September 25th @ 9 p.m. EST/PST on FOX. Series creator and executive producer Joss Whedon will write and direct the episode that will set the stage for season two.
In Dollhouse, Eliza Dushku stars as Echo, an "Active" in a highly illegal and underground group of individuals whose personalities have been wiped clean so that they can be imprinted with any number of new personas. Confined between missions to a secret facility known as the "Dollhouse," the Actives are hired by the wealthy, powerful and connected to wholly become – with mind, personality and physiology – whomever the client wants or needs them to be. Whether imprinted to be a lover, an assassin, a corporate negotiator or a best friend, the Actives know no other life than the specific engagements they are in at the time – or do they?
Dollhouse is produced by 20th Century Fox Television. The series was created by Joss Whedon, who also serves as executive producer, writer and director. Tim Minear is an executive producer, while Tara Butters, Michele Fazekas and David Solomon are co-executive producers. Additionally, series star Eliza Dushku serves as a producer. The show also stars Tahmoh Penikett, Olivia Williams, Harry Lennix, Fran Kranz, Dichen Lachman and Enver Gjokaj.
Tags: 20th Century Fox Television, David Solomon, Dichen Lachman, Dollhouse, Eliza Dushku, Entertainment, Enver Gjokaj, Fox TV, Fran Kranz, Harry Lennix, Joss Whedon, Michele Fazekas, Olivia Williams, Tahmoh Penikett, Tara Butters, Tim Minear

subset: commoncrawl


text:
How do you count words? Ashley Wagner discusses the question as part of our video on the number of words in the English language.
Definición de -tor en Español:
- 1
Sufijo de origen latino que forma nombres y adjetivos a partir de verbos con el significado de 'persona que realiza la acción, agente'.
redactor; interventor; inventor
Further reading
'Compliment' or 'complement': what's the difference?Read more
A lot of people get compliment and complement confused: they're pronounced in the same way and have very similar spellings but they have completely different meanings.
Should you capitalize the word "Internet"?Read more
The question of whether the word internet should be capitalized is so passionately debated and rife with controversy that it has its own Wikipedia article.
14 ways to hugFind out more
We've delved into the Historical Thesaurus of the Oxford English Dictionary to come up with some historical substitutions for hug as a verb.


text:
Business is booming in the Garden City
TOOWOOMBA businessman Graham Strang knows all-to-well the direct benefits the booming energy and resources sector is bringing to city businesses.
A report released this month by the Queensland Resources Council shows the energy and resources sector is responsible, either directly or indirectly, for injecting $1.035 billion into the Toowoomba economy annually.
Furthermore, the report found a staggering $429 million was spent in Toowoomba as a direct result of purchasing goods and services from local businesses.
Mr Strang, who owns and operates Hip Pocket Workwear and Safety, said his business had benefited enormously from the energy and resources sector.
"We are a family owned and operated business that was established in 2008," Mr Strang said.
"One of the main reasons for establishing the business was we recognised the growth and opportunities available.
"In four years, we have gone from a turnover of $250,000 to $2.8 million annually," he said.
Mr Strang said the continued growth of the business could largely be attributed to the booming energy and resources sector.
"We started off with four employees and now as of this week we employ 11," he said.
"They are all local employees so therefore they spend their wages in Toowoomba.
"Furthermore, we use local businesses to work with us to achieve our business needs and deadlines."
Reader poll
Have you benefited from the mining boom?
This poll ended on 11 January 2013.
Current Results
This is not a scientific poll. The results reflect only the opinions of those who chose to participate.
Toowoomba Chamber of Commerce and Industry president Andrew Wielandt believes the strength of the local economy is the envy of state, if not the nation.
"We are the envy of Queensland, if not the nation, based on the strength of our economy and the growth opportunities it presents," Mr Wielandt said.
"It reinforces the fact that not only is Toowoomba a base for other sectors, but also it now has this spoke (mining and resources sector) in its wheel.
"It is really great news. It just goes to show how strong the Toowoomba economy is," he said.
Toowoomba and Surat Basin Enterprise CEO Shane Charles said that although the energy and resources sector figure was impressive, the contribution of the agriculture sector should not be overlooked.
"The agriculture sector injected $663.8 million into the Toowoomba economy last year," Mr Charles said.
"The agriculture sector has a much bigger multiplier factor that the energy and recourses sector does.
"So we are talking about a $2.4 billion driver, both directly and indirectly, to the Toowoomba economy," he said.
Mr Charles said next year there would be a significant ramping-up of activity in the mining and resources sector and Toowoomba was positioned perfectly to cash-in.


text:
"Fishing this creek in Cochrane, I have learned myself that this creek is one that many people overlook. There is no walleye in the creek, the species that call..." Jumpingpound Creek
"Correction there is walleye in this lake. I know this because I have caught them. When I was younger I've heard of brown trout where stoney creek flows in, but have..." Burntstick Lake
"There are no catfish, lake trout or chinook salmon. There are rainbow trout, perch and suckers...." Alford Lake
"There are no catfish, brown trout or northern pike. There are rainbow trout,perch and suckers...." Phyllis Lake


text:
More than fighting fires
Regina Fire & Protective Services has been an integral part of Regina and area since 1882. In the early days all that was expected of the fire brigade was to fight fires. Today, we have expanded our services to include:
- Emergency Dispatch Centre: The Dispatch Centre is the communications core for all fire incidents. It is responsible for receiving and dispatch of emergency information, communication link to emergency scenes and maintenance of fire communications equipment. Four full-time dispatchers are supported by a pool of 16 Firefighter Dispatchers who are required to maintain dispatch skills.
- Public Education: Regina Fire & Protective Services delivers a Fire Safety Education Program to help residents understand that they have the ability to prevent fires.
- Automated External Defibrillators (AED) and Naloxone Kits: Regina Fire & Protective Services, in cooperation with the Regina Qu'Appelle Health Region, maintains Automated External Defibrillators (AED) and Naloxone Kits on all of the Fire & Protective Services' 13 front-line fire trucks.
- Emergency Medical: Fire & Protective Services responds to emergencies involving life-threatening injuries and victims who are not breathing and unconscious. Firefighters are trained as Level 1 First Responders.
- Hazardous Materials Response Team: This team responds to hazardous material emergencies.
- Jaws of Life: The Jaws of Life program involves the rescue of accident victims primarily from their vehicles.
- Water & Ice Rescue: This rescue team is trained in surface water and ice rescue.
- Technical Rescue Team: This team is trained in general rescue techniques and rope rescue.
- Inspection Program
Regina Fire & Protective Services' diversification of services is maintained by a fleet of 18 front line apparatus that includes pumper, rescue and ladder trucks. Firefighters who are primarily assigned to apparatus for fire suppression services also staff the rescue units as required.