Results
Bednarek et al. carried out a study to examine how field dependance effected susceptibility to a false horizon illusion1. Furthermore, they looked at how secondary functions requiring pilot attention effected susceptibility to the false horizon illusion. Succumbing to a false horizon was seen as an indication of spatial disorientation (SD).
The first part of the experiment is shown in Illustration 1 and depicts susceptibility to a false horizon illusion by showing comparisons for the control and experimental conditions. The control condition figures are totals of the three participant groups Field Independent (FI), Field Intermediate (FINT) and Field Dependent (FD) termed ‘FID’. In the experimental condition the means have been tabulated separately under each group. The three variables measured under both conditions were Asymmetry of Heading (AH), Variability of Heading (VH) and Average Flight Course (AV).
| Comparison of Flight Profiles of FID pilots under a False Horizon | |||||||
|---|---|---|---|---|---|---|---|
| Control Cond | Experimental Condition (mean) | ||||||
| FID (mean) | FI | FINT | FD | ||||
| Asymmetry of Heading (AH) | -0.20 | 0.12 | 0.28 | 0.30 | |||
| Variability of Heading (VH) | 0.01 | 0.011 | 0.019 | 0.016 | |||
| Average Flt Course (AV) | 298.23 | 310.93 | 307.64 | 308.35 | |||
Multiple comparisons of the AH and VH data were carried out and in the case of the tabulated AH data, supported that FI flight profile accuracy was much greater than FD (p < .05). For the VH data, there were no significant differences between FD and FI participants (p > .05). But FINT participants showed significant differences to FI participants (p < .01). The key factor shown in this illustration is that of AH, in which FI participants showed the least asymmetry in comparison to FINT and FD. The AV data showed that all pilots experienced spatial disorientation as the course heading was supposed to be 300deg. The sloping cloud (false horizon) produced a righthand deviation of approximately 10deg. There were no significant differences in the AV data across cognitive styles (p > .05).
The second part considered the effect the secondary functions of attention and working memory efficacy had on flight course under a false horizon condition. These variables were subjected to a multiple linear regression and an exploratory analysis to produce a model that was used to compare FD, FI and FINT pilots. The results were moderately significant for FD and FINT pilots but FI results were not statistically significant. Whilst the article does indicate potential attention and working memory efficacy predictors for FD and FINT pilots these results could only explain 22% and 44% or variances respectively. The results simply showed that there are multiple individual differences that influence false horizon susceptibility.
During the experimental task in the simulator FI participants clearly displayed greater accuracy under a false horizon condition, however, the regression results were not significant for FI pilots (p > .05) in providing reliable predictors. Additionally the experimental results showed that all participants succumbed to the false horizon, the FI participants just did it in a much more coordinated manner. This conclusion support previous research in that FI pilots are less susceptible to spatial disorientation.
Methods
Research approach
This was a positivist study that looked at how cognitive processes effected the ability of Field Independent Dependent (FID) pilots to react to a false horizon in a simulated situation.
Sample
The study comprised of 66 participants, with a mean age of 32 years and 1017 flying hours respectively. This sample was made up of 24 airplane pilots and 42 helicopter pilots. Gender was not detailed. The sample was identified from a military pilot population with the use of a Witkins Test. The population size was not mentioned. This testing method resulted in 66 participants comprising of 22 FI, 22 FNT and 22 FD pilots. Specific gender and aircraft type backgrounds for the respective FDI groups were not provided.
Design
This research was conducted using a repeated measures design, followed by a multiple linear regression analysis of the cognitive variables.
Variables
Independent Variables
- preferred cognitive style, FI, FINT or FD
- selective and divided attention
- efficiency of selective attention and working memory (WM) capacity
- WM capacity, speed of processing, and WM updating
During the experimental task on the flight simulator the following dependent variables were recorded:
- variability in heading
- asymmetry of heading
- average flight course
Materials
FI, FINT and FD participants were identified by a Witkins Test. The cognitive variables of selective and divided attention were obtained first with a Divided Attention (DIVA) Task. Second, efficiency of selective attention and working memory capacity were obtained through a Switching Attention Task (SWATT). Third, WM capacity, speed of processing and WM updating were obtained through a Memory Attention Task (MMATT).
Variability in heading and the asymmetry of heading were calculated mathematically as a product of various flight parameters recorded in the Hyperion Simulator as a means to quantify the level of spatial disorientation the participant was experiencing.
Procedure
FI, FINT and FD participants were first selected with the Witkins Test. Participants then completed the cognitive testing and two weeks later the two simulator flight tasks consisting of the control task (no visual illusion) and experimental task (false horizon illusion).
Data analysis
First the flight profiles from the control and false horizon conditions were compared against FI, FINT and FD preference. This was tabulated and displayed graphically to indicate susceptibility to the false horizon illusion.
Second a multiple regression analysis of the attention (DIVA Test) and WM efficacy (MMATT) data was performed to mathematically predict changes of heading in the false horizon condition tested earlier.
Generalization potential
The data from this research could be used to assist in military pilot selection as it reinforces existing data that FI individuals will be less susceptible to SD. In particular the multiple regression data provided an accurate predictor for SD. The information would also be of use in flight deck design as it provides an indiction of what type of tasks contribute to SD, thus allowing systems engineers to deconflict these tasks with critical flight profiles. There is potential in the future if a pilots FID is known then the computer system on the aircraft could be designed to limit tasks that will decrease attentional and WM capacity to a critical level.
Want to know more?
- Original article's abstract
- This link provides access to the original's abstract and a paid option to the full article.
Contributors to this page
Authors / Editors
Dean J Bishop (2013). Massey University, New Zealand. Dean Bishop
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