Mobile Phone use: Reaction Times

The purpose of this study was to determine the effects of divided attention upon response time. Participants consisted of 51 female and 10 male students from the University of Canberra, ranging in age from 19- 60 years (M = 24.95, SD = 7.99). Participants were asked to complete a spatial cueing task while using their mobile phone to either send text messages or make phone calls. Data was collected using the universities computers on the program Cog Lab 2.0. Results revealed that the text and talk conditions for all task types (neutral, valid, and invalid) had significantly slower reaction times than the control condition. The text group showed significantly slower reaction times than the talk group. Furthermore, the control group showed that the reaction times for the valid tasks was significantly faster than the neutral, and significantly faster for the valid than invalid tasks. These results do support previous research and literature in the area of mobile phone use while driving.

The use of mobile phones has grown over the last five years, with over 21.26 million users in Australia alone (White, Hyde, Walsh & Watson, 2010). Despite increasing evidence that mobile phone use while driving presents risks; drivers still engage in this behaviour. A self- report study on mobile phone use while driving in Australia, found that 43 percent of mobile phone owners use their phones while driving to answer their calls, followed by making calls 36 percent, reading text messages 27 percent, and sending text messages 18 percent. Approximately a third of these drivers used hand free units, indicating that most Australian drivers use hand held mobile phones while driving (White & Watson, 2010).

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The impairment potential of mobile phone usage while driving has been the focus of various behavioural and experimental studies. Although these studies differ in the extent of behavioural changes found, most researchers agree that there is a significant negative effect on different aspects of driving performance. The most common aspects are the withdrawal of attention and slower reaction times (Reed & Green, 1999). The impact of driving while using a mobile phone on reaction time is often explained with reference to a phenomenon commonly referred to as inattentional blindness or change blindness (Strayer, Drews & Johnston, 2003), wherein a person who is focusing attention on one particular task will fail to notice an unexpected stimulus even while directly looking at it (Simons & Chabris, 1999).

Strayer and Johnston (2003), determined that drivers conversing on a hands free mobile phone were more likely than drivers not using mobile phones to fail to notice traffic signals and respond slower to brake lights. As a result drivers were more likely to cause rear end accidents and less likely to be able to recall detailed information about specific visual stimuli (Strayer et al., 2003). These researchers also found this behaviour in participants who fixated their vision, suggesting that mobile phone conversations may induce inattentional blindness in the context of driving. However, Strayer Johnston (2003) considered that because they used a high- fidelity driving simulator that these results were conclusive of real life driving. These results may not be accurate in real life scenarios were participants would be driving on real roads with real vehicles.

Beede Kass, (2006) also used a driving simulator to measure the impact of a conversation task on a hands free mobile phone and a signal detection task while driving. Results suggested driving performance in terms of traffic violations, was significantly impaired while participants converse on the hands free unit and overall performance in the signal detection task were low. Finally they found an interaction between the mobile phone conversation and a signal detection task in measures of speed, speed variability, reaction time and attention lapses (Beede & Kass, 2006).

However, drivers that are not subjected to distracting tasks may also fail to notice important features of the traffic environment. That is, even when scanning different parts of the visual scene appropriately, there is a risk that important features will be missed in unattended areas (Simons & Chabris, 1999). In considering the phenomena of inattentional blindness, it is worth reiterating a key modifier, unexpected events. Generally, the occurrence of these inattentional failures seems to be reduced when the observer anticipates the object. Therefore, the unexpected events seem to be the most problematic. In the context of traffic, these may be somewhat harder to define quantitatively because these events can take on many different forms (Simons & Chabris, 1999).

A study conducted by Posner, Snyder Davidson, (1980) using a spatial cueing task, looked into the theory of expected versus unexpected events. They believe that participants’ responses to cued targets are usually faster and sometimes more accurate than responses to uncued targets. Results from the study conducted by Posner et al., (1980) suggest that participants were faster when the cue appeared in the same location (valid) and slowest when the cue appeared opposite the indicated cue (invalid). Posner, Snyder and Davidson, (1980) interpreted these results as showing that participants shifted their attention to the location of the target prior to its appearance. Equally, when participants were expecting the cue to appear in the opposite area, participants shifted attention to the wrong location. However, it may be possible to describe these results as being due to participants’ anticipation of the target position, or even chance.

Alternatively, Simons & Chabris (1999) provided a review of experiments in which participants focusing on visual tasks fail to notice unexpected visual stimuli, and present their own seminal explanation of the phenomenon. Results suggest that the probability of noticing the unexpected object depended on the similarity of the particular object within the display and the difficulty of the task. Simons & Chabris (1999) add that the spatial proximity of the object to attended location did not affect the detection, suggesting that participants attend to objects and events, not positions (Simons et al., 1999). However, this study did not explore whether individual differences in noticing, take place from differences in the ability to perform the primary task.

Strayer, Drews & Crouch (2006) compared drivers using mobile phones to drunk drivers, concludingthat when controlling for driving difficulty and time on task, mobile-phone drivers exhibited a greater impairment than intoxicated drivers. Results of this study found that the reaction time of drivers using a mobile phone were slower by 8.4 percent relative to drivers who neither had consumed alcohol nor were using phones. Also drivers using mobile phones were actually more likely to have a rear- end crash than drivers who had consumed alcohol (Strayer & Crouch, 2003). The impact of using a hands free phone on driving performance was not found to differ from the impact of using a hand held phone, which researchers suggested was due to the withdrawal of attention from the processing of information in the driving environment while engaging in mobile phone conversation (Strayer et al., 2003). However, the measures used for the two impairments mentioned above, are quite unusual. Mobile phone impairment is associated with the diversion of attention and is temporary, while the impairment from alcohol persists for longer periods of time. Furthermore, while mobile phone users have some kind of control (e.g. pausing a conversation) drivers who are intoxicated cannot do much to control their performance.

Studies that have looked at the effects of texting while driving have also suggested a negative impact on driver’s performance (Drews, Yazdani, Celeste, Godfrey & Cooper, 2009). Research by Drews & Cooper (2009) found a lack of response time in participants who used their mobile phones to send text messages while driving on a simulator. They concluded the texters in the driving simulator had more crashes, responded more slowly to the brake lights of cars in front of them- and showed more impairment in forward and sideways control than drivers who talked on their mobile phones while driving. (Drews et al. also found that text messaging participant’s longest eyes off the road duration was over six seconds. At 55mph this equates to a driver travelling the length of a football field without looking at the roadway.

In summary, the purpose of this study is to explore the effects of divided attention on response time. To achieve this purpose, this study aims to measure response times in the neutral, valid, and invalid conditions of a spatial cueing task, while participants use their mobile phones to talk or text. Based on both theory and past research, it is hypothesised that the control group will have significantly faster reaction times over all groups (text and talk). It was also hypothesised that the reaction times for the control group across all task types (valid, invalid, and neutral) would be significantly different. More specifically, it was predicted that the task type for the valid condition would be faster than the neutral task, and significantly faster for the valid than the invalid task. It was hypothesised that there would be a significant difference between participant’s reaction times within the talk group across all three conditions (valid, invalid, and neutral) in contrast to the text group. More specifically it was predicted that the reaction times for the talk group will be significantly faster overall compared to the text group.

Method
Participants

The participants of this study consisted of 61 graduate and undergraduate students of the unit cognitive psychology, from the University of Canberra (51 female and 10 male). Ages ranged from 19 to 60 years (M = 24.95, SD = 7.99). Participants were allocated a condition based on their tutorial group. Tutorial one were allocated to the text condition, this group included 20 participants of which two performed the control condition due to non- availability of a mobile phone. Tutorial two participants were allocated to the talk condition, this group included 18 participants, of which one participant did the control condition. Tutorial three and four participants were allocated to the control condition, this group included 24 participants, of which three participants did the text condition. One participant was excluded from the study, as they did not record their mean response times.

Materials

All 61 participants were given a spatial cueing task on the universities computer during class tutorials. Participants used the computer program Cog Lab 2.0 to view and complete the cueing task. Each participant was given an instruction sheet as per his / her tutorial group. Participants within the text and talk condition used their own personal mobile phone.

Procedure

Student participants were divided into three groups as arranged by their tutorial time and group. These groups comprised of three conditions text, talk, and control. While in tutorials participants were given an instruction sheet and told to follow the instructions as per their group category (text, talk, or control). In order to maintain confidentiality participants were asked to select and record a code name. They were than asked to give their age, gender, and identify the group they had been assigned to.

Each group of participants were given a set of instructions that were unique to their own group. The text group were told to complete the spatial cueing exercise while writing and sending three text messages. They were instructed not to answer their phone or talk to anyone else during the experiment. The talk group were instructed to make a series of short calls or one long call while taking part in the experiment. They were also told not to answer the phone or talk to any one else in the room. The control group were given instructions to focus only on the experiment and give it the same attention they would if driving a car on a busy road. They were told not to talk on the phone, message, or talk to anyone else in the room. Participants were then asked to complete the spatial cueing task on the computer (Cog Lab 2.0) per their assigned group.

Design

Variables: The independent variable in this study was the mobile phone = 3 levels, the dependant variable was response time.

Results

Effect of Condition on Reaction Time

Mean reaction times for the Text group were slower than for the Talk group, and those for the Talk group were slower than the Control group. Mean reaction times for each condition on the Neutral, Valid and Invalid tasks are shown below in Figure 1.

Figure 1. Mean reaction time for control, text and talk conditions across neutral, valid and invalid spatial cueing tasks.

A Kruskal-Wallis ANOVA indicated a significant difference in reaction times across Control (Mean Rank = 15.0), Talk (Mean Rank = 31.3), and Text (Mean Rank = 48.3) conditions, H(2,61) = 38.60, p < .005, Cohen's f = 1.33.

The significance level was reset to p = .02 using a Bonferroni correction. A Mann-Whitney U tests indicated that the Text group (Mean Rank = 33.48 for Neutral task, Mean Rank = 33.95 for Valid task, Mean Rank = 33.0 for Invalid task, n = 21) had significantly slower reaction times than the Control group (Mean Rank = 12.48 Neutral task, Mean Rank = 12.04 Valid task, Mean Rank = 12.91 Invalid task, n = 23), U = 11.0, z = -5.416;U = 1.0, z = -5.181; U = 21.0, z = -5.651; (corrected for ties), p < .005 for all comparisons. The effect sizes were large across the Neutral, Valid and Invalid tasks, r = 0.82, r = 0.85, r = 0.78 respectively.

Follow-up Mann-Whitney U tests indicated that the Talk group (Mean Rank = 28.59, Mean Rank = 29.24, Mean Rank = 28.18, n = 17) also had significantly slower reaction times than the Control group (Mean Rank = 14.52 Neutral task, Mean Rank = 14.04 Valid task, Mean Rank = 14.83 Invalid task, n = 23), U = 58.0, z = -3.762; U = 47.0, z = -4.063; U = 65.0, z = -3.57; (not corrected for ties) , p < .005 for all comparisons. The effect size were medium to large across the three tasks (Neutral, Valid, Invalid), r = 0.59, r = 0.64, r = 0.56 respectively.

Follow-up Mann-Whitney U tests indicated the Text group (Mean Rank = 25.81, Mean Rank = 26.86, Mean Rank = 26.05, n = 21) had significantly slower reaction times than the Talk group (Mean Rank = 11.71 Neutral task, Mean Rank = 10.41 Valid task, Mean Rank = 11.41 Invalid task, n = 17), U = 46.0, z = -3.89; U = 24.0, z = -4.536; U = 41.0, z = -4.037; (not corrected for ties), p < .005 for all comparisons. The effect sizes were large across the three tasks, r = 0.63, r = 0.74, r = 0.65.

Effect of Task Type on Reaction Time

A Friedman ANOVA showed there was a significant difference in reaction times across task type for the control group, I‡2(2) = 24.09, p < .005. Follow-up pairwise comparisons with the Wilcoxon Signed Rank test and Bonferroni adjustment of I± = .02 showed that the reaction time for Valid tasks (Mean Rank = 1.17) was significantly faster than for Neutral tasks (Mean Rank = 2.30), T = 248, z = -3.346 (based on positive ranks), and significantly faster for Valid (Mean Rank = 1.17) than Invalid tasks (Mean Rank = 2.52) T = 24, z = -3.467 (based on negative ranks), p = .001 in both cases. These effects can be described as large, r = 0.70 and r = 0.72 respectively. There was no significant difference between Invalid and Neutral tasks for this group. Finally, a Friedman ANOVA showed no significant difference in reaction times across task type for the Text group, I‡2(2) = 3.524, p = .172, or the Talk group, I‡2(2) = 0.118, p = .943.

Discussion

This study explored the effects of divided attention on response time. The results of the Kruskal-Wallis ANOVA did show a significant difference between reaction times across all three conditions (control, talk and text). However this analysis leaves the ambiguous situation of not knowing which condition/s differed more so than others. A second analysis was performed, this revealed that response times for the text group across all task types (valid, invalid, and neutral) were significantly slower than the control group, the effect was large. Results also revealed that the response times for the talk group across all task types were significantly slower than the control group; the effect was medium to large. These results are consistent with the first hypothesis. Previous studies much more scientific than ours, conducted in vehicle simulators have also found a significant relationship between similar aspects of texting, talking, and driving. However, drawing comparisons between this studies results and past studies results, issues arise over the current studies methods.

This study was not employed in a driving simulator, nor was the task undertaken in a real driving environment or vehicle. Participant simply sat in front of a computer in a class room where they were told to imagine driving a car on a busy road. There is no possible way this would accurately represent actual driver duties or a real driving environment. The sample size is also quite questionable and would not represent the current driving population. A future benefit for this study would be to create a more legitimate driving environment and increase the sample size.

The results of the fourth analysis also supported the hypothesis; these results showed the text group to have significantly slower reaction times than the talk group across all task types, the effect was large. Results are also consistent with past research on texting, driving and mobile phone use. Although, this study was not performed in a real or simulated driving environment these results were expected because texting required the participants to remove their eyes and attention away from the computer screen. However, these results only indicated a difference between reaction times, they do not suggest where the difference lies or how much interference can be attributed to the manual manipulation of the phone (e.g. texting), or how much can be attributed to the demands placed on attention by the phone conversation. A benefit to future studies would be to measure each one of these underlining factors separately and then compare those with other activities commonly engaged during driving.

The last analysis showed there was a significant difference in reaction times across task type for the control group. More specifically results showed reaction time for valid tasks to be significantly faster than for neutral tasks, and significantly faster for the valid than the invalid. These effects were described as large. This result also supports the hypothesis and the previous study conducted by Posner and Davidson, (1980). However, most spatial cueing experiments including this one have been concerned with the effect of directing attention on the detection of stimuli. Little has been done on the influence of visual attention on higher-level cognitive tasks, i.e., where a response would involve making a decision between two or more alternatives (Johnston, McCann & Remington, 1995). According to Johnston et al. (1995) responding to a higher-level cognitive task and detecting a stimulus may only be the first stage or a single process in a series of mental procedures involved in the response. Directing attention to the location of the stimulus might result in faster detection of the stimulus. It may be beneficial for this study and others like it to explore this theory more comprehensively.

References

Beede, K. E., & Kass, S. T. (2006). Engrossed in conversation: The impact of cell phones on simulated driving performance. Accident Analysis & Prevention, 38, 415-421. Retrieved from http://www.Canberra.edu.au/library

Drews, F. A., Yazdani, H., Celeste, N., Godfrey, Cooper, J. M., & Strayer, D. L. (2009). Text Messaging during simulated driving. Journal of Human Factors and Ergonomics Society, 51, 762-770.

Johnston, J. C., McCann, R. S., & Remington, R. W. (1995). Chronometric evidence for two types of attention. Journal of Psychological Sciences, 6, 365-386.

Posner, M. I., Snyder, R. R., & Davidson, B. J. (1980). Attention and the detection of signals, Journal of Experimental Psychology, 109, 160-174.

Reed, M. P., & Green, P. A. (1999). Comparison of driving performance on-road and in a low-cost simulator using a concurrent telephone dialling task. Ergonomics, 42, 1015-1037.

Simons, D. J., & Chabris, C. F. (1999). Gorillas in our midst: Sustained inattentional blindness for dynamic events. Perception, 28, 1059-1074.

Strayer, D. L., Drews, F. A., & Crouch, D. J. (1999). A comparison of the cell phone driver and the drunk driver. Journal of Human Factors and Ergonomics Society, 48, 381-391.

Strayer, D. L., Drews, F. A., & Johnston, W. A. (2003). Cell phone- induced failures of visual attention during simulated driving. Journal of Experimental Psychology, 9, 23-32.

White, K. M., Hyde, M. K., Walsh, S. P., & Watson, B. (2010). Mobile phone use while driving: An investigation of the beliefs influencing drivers’ hands- free and hand- held mobile phone use. Journal of Traffic Psychology and Behaviour, 13, 9-20. Retrieved from http://www. canberra.edu.au/library

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