OF MONOTONY IN ATC: EFFECTS OF TRAFFIC REPETITIVENESS. AND TRAFFIC DENSITY. Sonja Straussberger, Dirk Schaefer, Eurocontrol Experimental ...
A PSYCHOPHYSIOLOGICAL INVESTIGATION OF THE CONCEPT OF MONOTONY IN ATC: EFFECTS OF TRAFFIC REPETITIVENESS AND TRAFFIC DENSITY Sonja Straussberger, Dirk Schaefer, Eurocontrol Experimental Centre, France K. Wolfgang Kallus, Karl-Franzens-University Graz, Institute of Psychology, Department for Work-, Organizational and Environmental Psychology, Austria assessment of mental workload (ISO 10075) contributed to the unification of terminology at an international level [6]. Although criticized for its unsatisfactory theoretical status, it provides a framework for classifying and integrating a variety of phenomena. The basic assumption is that the totality of external influences a human operator is exposed to will result in individually perceived strain. Depending on individual and/or actual conditions, facilitating (e.g., activation, warming-up) or impairing effects (mental fatigue, fatigue-like-states, and satiation) may emerge.
Abstract The study described in this publication investigated the occurrence of a state of monotony depending on variation in task characteristics. 24 fully qualified Air Traffic Controllers between 22 and 47 years participated in an experiment with a 2 (repetitiveness) x 2 (dynamic density) x 2 (physical activity) x 2 (run) x 3/15 (interval within run)-mixed design. They were presented with 2 traffic scenarios with repetitive or non repetitive conflict patterns under high or low dynamic density. A variety of physiological measures (ECG, EOG, EDA, EEG, respiration), subjective reports during and after runs, and performance indicators were collected throughout the experimental session.
Monotony is seen as a slowly developing state of reduced activation [6]. It can arise as a consequence of activities that are repetitive, longlasting, of low stimulation or low difficulty. Mainly it is associated with feelings of sleepiness, fatigue, the task is perceived as uniform or boring. A reduced ability to react and adapt to changes can be connected with impaired and fluctuating performance. In his theory of monotony, Bartenwerfer [7], [8] emphasizes that two conditions contribute to the emergence of a state of monotony: continuous task engagement and a constrained task. Categorized as a fatigue-like state that may be caused by strain, it clearly needs to be distinguished from mental fatigue or other fatigue-like states such as mental satiation and low vigilance. Although similar in appearance, causes are different and thus the consequences for work design. The most important distinction to note here is that, unlike fatigue, the effects of monotony can be alleviated by changes in the operator’s task whilst recovery from fatigue effects requires physical and mental recuperation.
This paper reports results of physiological and subjective measures. Decreasing heart rate (HR) and increasing heart rate variability (HRV) support the aspect of deactivation as proposed by Bartenwerfer, while the pattern of subjective ratings appears more complex. Findings indicate that the nature of a state of monotony is affected by the initial state and may be reduced by increases in traffic density.
Introduction Research on monotony and related fields dates back until the early decades of the last century. Surprisingly, still little is known about the factors evoking or contributing to a state of monotony among Air Traffic Controllers (ATCO). This is even more interesting, as it is known that more incidents or accidents happen in situations of medium or low workload [1]. Considering not only periods of low traffic, the concept of monotony also has implications for the introduction of automation.
In Air Traffic Control the importance of monotony and boredom was stressed by several authors [e. g., 9]. Unfortunately, general interest remained scarce, even after publication of several literature reviews [10, 11]. Research from related fields helps to approach a quite unexplored field, but is limited by several factors. Firstly, in laboratory environments simple tasks or simulated driving were performed [12], [13]. Secondly, the nature of
A loose use of terms does not facilitate a better understanding of certain working conditions, as it is referred to in the context of boredom, monotony, underload or low vigilance [2], [3], [4], [5]. The introduction of a standard for the definition and
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assembly line work is different as it does not require highly qualified personnel and it imposes higher constraints on individuals. Thirdly, monitoring or maintaining vigilance is just one of the performance components in ATC. Additionally, the ATCO needs to continuously check, diagnose, decide and update his mental picture. For this reason, the comparability with studies of supervisory control or power plants is restricted to specific aspects.
behavioral indicators such as performance. Since it is known that psychophysiological indicators may vary significantly between and within individuals, an overall assessment is required to develop a more complete understanding.
Design The experiment involved a 2 x 2 x 2 x 2 x 3/15mixed design (Table 1). The independent variables included the between factors repetitiveness (repetitive vs. non repetitive traffic pattern), the sequence of dynamic density (low-high vs. high-low) and activity (with vs. without physical activity in break). Within factors were run (run 1 vs. run 2) and intervals within run (15 intervals for physiological variables; 3 intervals for subjective ratings).
As we can resume from previous studies, mental strain may depend on task characteristics, e.g., [12], individual factors, e.g., [14] and the situational context, e.g., [15], which may result in a state of monotony. One of the few studies undertaken with the intention to investigate monotony in ATC was a laboratory experiment conducted by Thackray et al. [16]. After performing simulated air traffic control radar tasks two extreme groups of eight participants were formed with low vs. high ratings of monotony and boredom during task completion. The high boredom-group showed greater increases in response times, HRV and strain plus greater decreases in attentiveness. Hoffmann and Lenert [17] administered a questionnaire that distinguishes between mental stress, fatigue, monotony, and satiation to 232 Austrian ATCOs. Towards the end of the shift there was a significant increase of fatigue, arising monotony and a change in satiation. With increasing complications during work, fatigue was consolidated, while monotony decreased.
Table 1. Experimental Design (sequence of Dynamic density (DD): l=low, h=high) Break Activity active non active Repetitiveness Repetitiveness repetitive non repetitive non repetitive repetitive Run 1 Run 2 n n total
DD
DD
DD
DD
DD
DD
DD
DD
l h 3
h l 3
l h 3
h l 3
l h 3
h l 3
l h 3
h l 3
24
A variety of measures was collected. On a physiological level, heart rate (HR) and heart rate variability (HRV), electroencephalogram (EEG), electrodermal activity (EDA), electrooculographic measures (EOG), respiration and movements were collected throughout the runs.
Methods The experiment described in this publication aims at investigating whether a psychophysiological pattern as described for the state of monotony by Bartenwerfer [7] can be observed among air traffic controllers depending on repetitiveness in task characteristics. Furthermore it was assumed that the complexity of the traffic itself may interfere with repetitiveness. As known for example from a study by Mogford et al. [18], the number of aircraft does not adequately reflect the difficulty of the work situation, but a variety of factors is contributing to the complexity of a situation. The concept of Dynamic Density (DD) [19] allows a better description of how a traffic situation develops over time as it continuously considers changes in any of the included variables.
During the scenarios, participants were asked to rate various aspects of feelings, attitudes, and emotions on a scale ranging from 1 (low) to 7 (high). The items consisted of those used in the study of Thackray et al. [16], which is attentiveness, fatigue, boredom, irritation, and strain. They were completed with concentration and motivation, as successfully used by Hagemann [20], and sleepiness. As fatigue and sleepiness describe a state of different intensity, this was recommended. The originally included item of monotony was not included in this scale, as it was supposed to attract too much attention on the goals of the experiment. This scale including the mentioned items will be referred to as TSI. Perceived workload during the run was rated after each run with the completion of NASA TLX [21]. This scale was complemented with items for feeling of monotony and situation awareness. Else, current mood was rated after runs using the UWIST Mood Adjective List [22]. The Scale of Feelings (SOF) [23] consists
The assessment of strain is based on the measurement of parameters along three dimensions: physiological variables, subjective ratings and
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of 4 subscales to assess stress, monotony, fatigue and satiation and was also administered after runs.
manipulation between high (h) and low (l) Dynamic Density was implemented with additionally required level changes of AC in the high DD condition. An evaluation of the scenarios took place in a pre-study.
On a behavioral level, task-performanceparameters and standardized after-task-performancetests of the Vienna Test System were collected. Additionally, questionnaires were introduced for the consideration of initial states and various personality aspects. This paper reports a first physiological and subjective data.
selection
Procedure The experiment included various sessions. The introductory session started with a general introduction and briefing, participants received extensive time to familiarize themselves with the simulation set-up and electrodes were applied. The main session started after 15 minutes of rest break and included 2 runs of 45 minutes with repetitive or non repetitive traffic scenarios of low or high DD and one additional run for 10 minutes to measure effects of an active rest break.
of
Participants The experiment was carried out at Maastricht Upper Area Control Centre. Participants were 24 fully qualified ATCOs (18 male, 6 female) from 10 European nations volunteering for participation in the experiment. Age ranged from 22 to 47 years. On the average, ATCOs have been fully licensed for 6 years (SD 5.5.).
Before and after runs, 3-min-baseline recordings were taken in a relaxed resting condition with closed eyes. Physiological measures were collected throughout the runs with Vitaport 3 Recorder (Temec Inc.). TSI-scales were filled in every 15 minutes during the scenario, TLX, UWIST and SOF was administered after each scenario. A debriefing concluded the session. One experimental session lasted approximately 5 hours 15 minutes.
Task The experiment was based on four traffic scenarios with medium traffic load (57 AC per hour). The semi-generic upper airspace created for this experiment (FL 250 – FL 600) involved a sector with arriving and departing traffic from a major airport. Participants worked on a standalone sector with 2 automatic feed sectors. To avoid social and communication influences, runs were conducted individually and controllers had to fulfill executive and planning roles. Pseudo-pilots were not included. The controller working position (CWP) included a 28”LCD monitor with keyboard and mouse for inputs; Short Term conflict Alert (STCA) was available and Reduced Vertical Separation Minimum (RVSM) for Europe applicable.
Results Statistical analyses employed a repeated measure MANOVA with repetitiveness and sequence of DD as between factors and run and intervals as within factors. Although DD was repeated within subjects, the sequence of DD (h-l vs. l-h) was included as a between-variable. The advantage of this procedure is a more precise estimation of statistical effects. An alpha level of .05 was used for statistical tests. Statistical relevance of DD is deducted from trend analysis. Results are reported according to the model of Descriptive Data Analysis [24], an approach suitable for multivariate analysis.
The scenarios included potential conflicts in constant 3-minute-intervals. Each conflict would result in a very close near-miss without the controller taking appropriate action. In repetitive traffic scenarios, participants were presented with equal potential conflict situations at the same crossing point. In non repetitive scenarios, participants were presented with potential conflict situations at varying crossing points throughout the sector. The constant conflict situation consisted of an aircraft in departure meeting a northbound incoming aircraft. The concept of DD was adapted to the needs, as the most important factors were kept constant in 3-minuteintervals (no. of AC, no. of level changes, routes, crossing points) over the course of a scenario. The
Physiological measures The analysis of physiological measures was based on 3-minutes-intervals as used for DD manipulation. The statistical analysis showed that baselinecorrected HR was lower for the repetitive group than for the non repetitive group (F1=4.406, p=.05). Mean HR decreased from the first to the second run (F1=17,68, p=.001). The course of HR is depicted in Figure 1.
3
8
6
6
mean HR (corr.)
mean HR (corr.)
8
4
2
4
2
Repetitivity
Repetitivity
0
0
non repetitive -2
non repetitive -2
repetitive 1
2
3
4
5
6
7
8
9
10 11
12
13 14
15
repetitive 1
2
3
Intervals: Run 1
4
5
6
7
8
9
10 11 12
13 14
15
Intervals: Run 2
Figure 1. Average corrected HR during first and second run for groups with non repetitive (n=12) and repetitive (n=12) traffic (Repetitiveness: F1=4.41, p=.05; Run: F1= 17.68, p=.001).
Table 2. Mean values (standard deviation) of TLX-ratings and results of statistical analysis Run 1 non repetitive l-h h-l
Run 2 repetitive l-h
h-l
non repetitive l-h h-l
repetitive l-h
h-l
Mental demand
58.00 (24.35)
56.50 (10.27)
22.00 (15.09)
27.67 (14.35)
61.50 (15.76)
41.00 (15.23)
36.67 (26.07)
21.67 (10.50)
Physical demand
12.33 (9.77)
7.83 (4.07)
7.17 (9.79)
7.33 (7.20)
15.17 (9.91)
7.83 (2.56)
16.33 (29.53)
11.67 (12.39)
Temporal demand
46.50 (23.32)
20.00 (25.00)
13.00 (15.54)
22.33 (15.27)
52.67 (21.09)
14.67 (7.47)
23.83 (23.74)
12.67 (7.92)
Effort a**
26.00 (24.94)
29.00 (21.95)
7.67 (7.23)
16.83 (11.14)
37.50 (29.98)
23.33 (22.98)
13.67 (12.74)
12.17 (8.23)
Performance a*, f*
49.83 (22.20)
38.50 (18.69)
12.17 (13.70)
19.83 (11.62)
50.67 (20.72)
32.50 (19.10)
26.17 (13.70)
17.33 (9.79)
Frustration
10.33 (4.59)
15.33 (11.27)
11.67 (18.10)
7.17 (9.11)
25.17 (14.91)
17.67 (13.06)
26.50 (32.45)
11.83 (4.26)
Monotony
10.33 (4.59)
15.33 (11.27)
11.67 (18.10)
7.17 (9.11)
25.17 (14.91)
17.67 (13.06)
26.50 (32.45)
11.83 (4.26)
Situation Awareness
67.00 (25.54)
71.83 (14.63)
83.17 (7.25)
80.50 (12.05)
63.83 (31.01)
73.50 (19.53)
82.00 (14.71)
77.50 (11.84)
Total Workload
251.0 (78.69)
209.2 (61.55)
158.3 (42.63)
167.5 (41.51)
267.7 (58.98)
190.3 (38.32)
215.8 (110.9)
163.0 (25.39)
a***, f**
a*, b*, d*, f*
e**, f*, g***
Note. a Main effects: Repetitivity (Rep), b Sequence of Dynamic Density (SequenceDD), c Run. Interaction effects: d Rep x SequenceDD, e Run x Rep, f Run x SequenceDD, g Run x Rep x SequenceDD p
