AKADEMIN FÖR TEKNIK OCH MILJÖ
Avdelningen för bygg-, energi- och miljöteknik
Alarm signals, can a change of siren speed capture human attention?
Tomas Hansson 2017
Uppsats, Avancerad nivå (magisterexamen), 30 hp Miljöpsykologi
Masterprogram i miljöpsykologi
Handledare: John Marsh Examinator: Robert Ljung
1 Preface
I want to thank my supervisor John Marsh for his great help in writing this study. I also want to thank Robin Liljenberg for his help in data collection. And last a thanks to Robert Ljung and the students at Gävle University that participated in the study.
2 Abstract
An effective alarm system is a critical part of many different types of jobs. It is also important that the alarm signal can capture human attention and convey appropriate urgency. In the current study the effect of siren sounds with or without unexpected, deviant sounds represented by a change of speed (a temporal deviant) were tested to evaluate if such change could successfully capture attention. The results showed that distraction was more pronounced when the deviant within the sound was a change from fast to slow as compared with slow to fast. Therefore, an alarm signal using a temporal deviant – changing from fast to slow—can be effective in capturing human attention and might be factored into the design of alarm systems.
Keywords: Alarm system, Irrelevant sound, Deviant, Orienting response
Innehåll
Introduction ... 3
Hypothesis ... 5
Method ... 5
Result ... 7
Discussions ... 8
Reference ... 10
3
Introduction
To minimize accidents, good working alarm systems are critical. Today alarm systems are in almost all electronic devices. Therefore, it is important that operators can interpret the alarm system signals correctly (Ljungberg et al., 2012). The alarm needs to be logical to understand for the operator. When the alarm signal sounds the operator should act with the correct urgency, and the operator should also know what the alarm signal means (Ljungberg et al., 2012). Furthermore, it is important that the alarm only sounds when there is a real problem. Otherwise operators will start to become unsure of the validity of the alarm signal. If the alarm sounds too often, and it is a false alarm, this will create a cry wolf effect (Breznitz, 1983). To mitigate the cry wolf effect alarm systems need to be reliable. If that is not possible, then a system wherein the operators easily can cross check if the alarms are true could help in to reduce mistakes from false alarms (Manzey, Gerard & Wiczorek, 2014).
Auditory alarms are popular to use because they are easily manufactured and they can be fitted in small devices (Edworthy & Hellier, 2005). The problems start, however, when operators should know what alarm in equipment it is that sounds. Thanks to their small size, devices can have multiple alarms in them. In hospitals, for example,
equipment can possess up to 20 alarms (Edworthy & Hellier, 2005; Sanderson, Wee, Seah, & Lacherez, 2006). Research has shown that it is hard for operators to remember more than six to seven alarm signals (Sanderson et al., 2006). Wolfman, Miller and Volanth (1996) suggest that electronic devices should have a maximum of four to six alarm signals. Because as above mentioned it is hard for humans to learn more than six to seven alarm signals (Patterson, 1985; Wolfman et al., 1996). Wolfman et al. (1996) also suggest that too many alarm signals can be confused with each other making it harder for the operator to analyse what the alarm signal concerns. Another problem with alarm systems is that they can often worsen human performance even after their onset (Wolfman et al., 1996). When many alarms sound they can disrupt cognition and have an adverse effect on human cognition. On explanation as to why human performance is worse after the onset of an alarm signal can be interpreted within the duplex account of distraction (Hughes, 2014). Hughes (2014) suggests that auditory distraction has two
4 distinct forms. The first form is “interference by process” whereby individuals
unconsciously, process sounds in the environment, and this processing competes with similar internal task processes participants use to undertake the focal task. The second form of distraction is “attentional capture," here the sound captures worker's attention and therefore, cause loss of focus on the task at hand (Vachon, Labonté, & Marsh, 2017). For optimal task performance, workers need to be able to concentrate on the task at hand, but at the same time they must scan the environment for potential hazards. If many alarms sound within an environment, then lower task performance will result (Parmentier et al., 2010). Research also suggests that the orienting response is involved in worsening human performance, because when humans hear sounds, they
automatically attempt to address whether the sounds are dangerous, important or simply noise. The orienting response is a composite of head movements, physiological changes (heart rate slowing) and pupil responses (e.g., dilation) and these are associated with the attention being captured from a focal task (Brandt, 2011).
Auditory stimuli that are unrelated to a visual task (and hence “irrelevant sound”), are known to disrupt human performance of a focal task (Stokes & Arnell, 2012; Gabriel et al, 2012; Wetzel, 2015). The disruption happens even when the subjects are told to ignore the sound and, can lower human performance up to 30% (Beaman & Jones, 1997, Cowan 1988). How large the effect is, is dependent on the character of the sound.
A tone that is continually repeated (4 4 4 4), is less disruptive then a sound containing different tones or stimuli (4 7 1 5), this is called the “changing-state effect” (Beaman &
Jones, 1997; Röer, Bell & Buchner, 2013; Hughes et al, 2005). Lacherez, Donaldson and Burt (2016) tested the idea that if an alarm signal was learnt first, then the effect of the sound on memory could be reduced. However, they found that even when the participants received a training period before the memory tests, they were still impaired to the same degree by the learnt irrelevant sounds compared to sounds that were novel in the context of the experiment. The result from this study suggest that alarms are not less annoying or easier to ignore when operators have learned the acoustics of the signal beforehand.
In contrast to changing-state sounds, deviant irrelevant sounds are those that are unexpected given the context provided by the preceding part of the sound stimulation, such as the b in the following sequence: aaaaaaaabaaa. Prior studies have shown that
5 deviant sounds are more disruptive to human performance in tests involving memory for visually presented items over the short-term (serial recall) than normal changing- state sounds (e.g., Vachon, Hughes & Jones, 2012; Hughes, Vachon & Jones, 2005).
The deviant sounds can comprise of unexpected changes in pitch, intensity or speed.
When humans detect a deviation it is harder for them to concentrate on their visual task since the deviant produces an orienting response, capturing attention away from the visual task (Parmentier & Hebro, 2013).
Hypothesis
As mentioned in the foregoing, deviant sounds disrupt human performance. Therefore alarm system sequences that use deviant sounds should be more disruptive to human performance than non-deviant alarm system sequences. If the alarm signal is composed of deviant sound, then they might be expected to capture more attention when used as an alarm signal, than alarm sequences that comprise no deviants. This prediction is tested within the study undertaken here. In the current study the effects of siren-type sounds with and without a deviant as represented by a change in speed, were compared in relation to how effective they were at disrupting human serial recall performance.
The change in speed is in essence a temporal deviant that is known to capture attention (Hughes et al., 2005) and it would be expected that both changing from fast to slow and from slow to fast should capture attention relative to no change. Of interest is whether one form of change is more distracting than the other. This would hold implications for embedding change within alarm signals.
Method
Participants
The study consisted of 56 students from the university of Gävle, which were recruited by the university of Gävle’s TimeCenter webbpage, and random drop-ins. Due to equipment and program errors data were lost for for six of the participants and as such the final number of participants were thus 50 (26 males, and 24 females; age range = 20-39 (mean age = 22). In return for participating in the study, participants received two cinema tickets.
6 Apparatus and Material
To be remembered items: Eprime 2.0 was used. Random presentation of eight digits from the set, 1-8 digits were shown on a computer screen. Each digit was shown for 500 ms and no digits were presented twice in a given list. Participants whore headphones throughout the study and to-be-ignored alarm siren sounds were presented over headphones. The alarm-siren consisted of two different tones, recorded at 400-300hz.
These tones were edited to two speeds: 800ms for the slow and 200ms for fast. Tones were presented in an alternating fashion with half of the sequences beginning with the 400hz tone and half with the 300hz tone. During presentation between the fourth and fifth visual to-be-remembered item, there was a temporal deviant within the trial.
Temporal deviant was either the siren changing from fast to slow, or from slow to fast.
There were also control trials whereby the siren stayed presented at a fast, or slow rate throughout the entire presentation of the trial. When that happened the sound stayed at that ms till the next deviation.
Experimental design
The study was conducted in a classroom in the university of Gävle. Via to-be-ignored sound, participants were presented with two different types of deviation, fast to slow and slow to fast, but only one deviation could be present at a given time in the study.
There were also pure fast and pure slow trials in which there was no deviant.
Participants performed 90 trials. The previous deviation acted standard deviation until the next deviation occurred. Serial recall performance was dependent variable- whereby participants were required to remember the correct order of the eighth randomly
presented digits on each trials.
Procedure
The participants were instructed to sit in front of a computer screen while wearing headphones. The participants were instructed to ignore sounds they could hear in the headphones and that instructions about the test were going to appear on their computer screens. Participants initiated the tests themselves. It took approximately 50 minutes to finish. They were free to abort the test if they desired. All participants took part via their own free will.
7
Result
To score points in the test participants needed to put place a digit in the position in which it was presented. The participants responses were therefore, scored with strict serial recall standards: a chosen digit had to be in the same place in which it was presented during list presentation to be scored as correct. Figure 1 shows serial recall performance in each of the 4 [2(Sound Speed) × 2 (Deviant)] conditions. The results appear to show that the fast-slow deviant produced disruption relative to the fast standard. However, the slow-fast deviant appears to fail to produce any disruption as compared with the slow standard.
Fig.1 Showing proportion correct recall of steady slow, fast and deviant slow-fast, fast- slow.
A 2 (Sound Speed: Fast vs Slow) × 2 (Deviant: Deviant Present vs. Deviant Absent) repeated measures Analysis of Variance (ANOVA) revealed no main effect of Sound Speed F(1, 49) = .04, MSE = .004, p = .84, η2p = .001. However, there was a main effect of Deviant, F(1, 49) = 13.96, MSE = .003, p < .001, η2p = .22,. There was also a
marginally significant interaction Sound Speed and Deviant, F(1, 49) = 4.03, MSE = .012, p = .05, η2p = .076. Simple effects analysis (Least Significant Difference) was undertaken to decompose the significant interaction. This demonstrated a significant difference between fast with no deviant and fast with a deviant (e.g., changing from fast to slow) p < .001, 95% CI [.022, .068]). However, there was no significant difference
0,4 0,45 0,5 0,55 0,6 0,65 0,7
Slow Fast Deviant Slow-Fast Deviant Fast-Slow
Proportion Correct Recall
Sound Condition
8 between slow with and without deviant (e.g., changing from slow to fast) p = .2, 95% CI [-.008, .035]). Further analysis showed that performance for fast without deviant was marginally better than performance for slow without deviant, p = .09, 95% CI [-.03, .002]). However, performance for slow with a deviant (e.g., changing to fast) was no different from performance for fast with a deviant (e.g., changing to slow), p = .25, 95%
CI [-.012, .047])
Discussion
An effective alarm signal needs to be able to capture an operator’s attention. Therefore, it is important that the signal is composed of sounds that is optimal for the requirement to capture an individuals’ attention when it is fixed on a given task. As earlier research has shown, deviant sound can capture attention while changing-state sounds typically do not (Roeber, Berti, Widmann & Schröger, 2005; Boll & Berti, 2009; Kirmse, Jacobsen & Schröger, 2009; Vachon et al., 2017). Therefore, the goal of this study was to investigate if deviant sound embedded in an alarm signal would make it effective at capturing attention, as indexed by disruption of task performance. Moreover, the property of temporal deviants in capturing attention was investigated. In the current study deviation could either be a change of speed of a to-be-ignored siren from fast to slow, or from slow to fast. The degree to which the deviant captured attention was gauged by the disruption it produced to serial recall performance.
The results showed that when the deviant was a change from fast to slow, serial recall was impaired compared to the condition with a fast presentation of alternating tones (e.g., a siren). However, when the deviation was slow to fast no deviation effect was observed (e.g., no difference between this condition and the slow only standard). This shows that the temporal deviation effect had a disruption over and above the changing- state nature of the to-be-ignored sound (Hughes et al., 2005). However, the deviation effect was not equal between the different deviant sounds. Deviant fast to slow sound yielded a larger impairment than slow to fast sounds. Why did the deviant comprising fast to slow impair human performance more than the other two types of sounds?
Vachon et al. (2017) suggest that deviant tasks numbers, or random geometry figures, affect humans the same regardless of the task at hand. However, the results from this study show that, when deviant sounds are used, they affect participants differently
9 depending on the characteristics of the sounds. This suggests that deviant sounds yield different effect depending on the acoustic properties of the sounds. Earlier research has found that sound with higher speeds conceive more urgency in participants which could explain why fast-slow deviant sounds give rise to greater disruption of serial recall (Hollander & Wogalter, 2000; Hellier, Edworthy, Weedon, Walters, & Adams, 2002).
Parmentier and Kefauver (2015) argue that irregular deviant sounds are harder for the brain to predict. If the sound is regular Parmentier and Kefauver (2015) argue that the brain can predict the characteristics of the sounds because they are slower. For the irregular deviant sounds the characteristics of the sound may change too fast for the brain to predict what is coming next and therefore gives rise to worse performance on tests. This could add to the explanation of why deviant fast-slow give rise to poorer performance than deviant slow-fast. But more research is needed to study why deviant fast-slow is more disruptive to human performance than slow to fast.
Recall rate in the study was about 60-65 % which is lower than the recall rates in Hughes et al., 2005). This could be due to the presence of extraneous variables that could not be controlled in the experiment (e.g. temperature, background noise, ventilation).
The findings in this study could be used to develop better alarm signals. The results suggest that using deviant fast-slow alarm signals should capture attention to a greater extent than no deviation or slow-fast deviations within alarm signal. The results show that an alarm signal consisting of slow-fast changes capture less attention then an alarm signal consisting of deviant fast to slow sounds. By using deviant sound in alarm signals, better alarm systems that are capable of capturing human attention can be devised. However, one must be cautious not to switch all alarm systems to involve deviant sounds because low priority alarms that do not necessarily require responses may give rise to undesired drops in human performance (Parmentier et al., 2010).
Moreover, if different equipment has many different signals, then workers may experience poorer performance if all alarm signals contain a deviant. This is since Parmentier et al., (2010) showed that if many alarm signals sound then lower task performance will result. Therefore, alarm system designers and engineers need to weigh up the potential benefits for installing attentionally capturing properties in alarms with
10 the negative effects that those alarm signals will have for human performance including productivity. With this in mind, perhaps the fast to slow attribute should be reserved for high priority alarms that require an immediate response to avoid severe consequences.
To further study if an alarm signal consisting of a deviant sound such as a temporal change, is effective to use in alarm systems, the presence of such alarms should be studied in the context of different job environments (e.g. open plan offices, healthcare, factories, and so on). Another study could be undertaken to see if an orienting response is present when the sound conveys deviant alarm signals. This is because a
physiological response to a deviant may occur without a behavioural consequence.
Measuring heart rate or galvanic skin response changes in relation to the presence of a deviant might be a more sensitive measure that the behavioural data produced by the serial recall task alone. Effective alarm signals should be those that produce more pronounced psychological responses to deviant events within the alarm.
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