<p dir="ltr">The Black Hole Illusion (BHI) is a nighttime aviation landing illusion where pilots overestimate their descent angle. The BHI occurs when only the outline of the runway is visible to pilots, usually at night with little illumination of the environment. This illusion is dangerous, as it causes pilots to perceive themselves at a high descent angle; and they compensate by flying lower, resulting in a possible crash into the ground or obstacles before the runway. A common interpretation of the BHI is that it represents a perceptual illusion, where the descent angle is misperceived. We quantitatively analyzed two different perceptual theories that predict pilots perceived descent angle during the BHI experience; and we also quantitatively analyzed another perceptual theory to apply during nighttime approaches to alleviate the disorientation experienced from the BHI. Of the first two theories, Perrone's algorithm (Perrone, 1983) predicts that the magnitude of the illusion should vary with runway width/length in nighttime conditions, compared to no illusion and no effect of runway width/length in daylight conditions. On the other hand, the eye-level algorithm (adapted from the work in Galanis, Jennings, and Beckett (1998) and Robinson, Williams, and Biggs (2020)) predicts that there should be no effect of runway width/length in either nighttime or daylight conditions. The last algorithm, the focus of expansion algorithm (adapted from the theory of Gibson (1950) and Gibson (1966)), details a way that pilots can obtain the landing position of their aircraft without estimating their angle of descent, thereby alleviating possible disorientation experienced during nighttime approaches. Additionally, we conducted three empirical studies: The first two aimed at testing Perrone's algorithm and the eye-level algorithm; and the third aimed at testing the focus of expansion algorithm. Across the first two empirical studies, we did demonstrate a BHI for the nighttime evaluations of descent angle; but the data did not support either algorithm. In the third empirical study, the data did not support the focus of expansion algorithm; however, we found that participants were more accurate with estimating the aircraft's landing position when the landing position was closer to the beginning of the runway. Overall, we conclude that the BHI may reflect general disorientation in conditions with limited information.</p>

<p></p><p>A large body of literature suggests that cognitive abilities are important determinants for training and job performance, including flight performance. The associations between measures of ability tests and job performance have been the focus of many empirical studies, resulting in an overall conclusion that general mental ability, <i>g</i>, is the main source of prediction, while other narrower abilities have limited power for predicting job performance. Despite the attention given to cognitive ability-flight performance relationships, their associations have not been fully understood at the broad construct level, and most extant literature focused on the relations at the observed scores level. Thus, the present dissertation study was designed to contribute to the progression of this understanding by examining the relations between cognitive abilities and flight training performance, using data from four U.S. Air Force (USAF) pilot samples. For comparison, one navigator and one air battle manager sample were also analyzed. The data were obtained from correlation matrices of prior investigations and analyzed via structural equation modeling (SEM) procedures. </p> <p> Four studies are reported in the thesis: (1) preliminary study, (2) primary validation study, (3) cross-validation study, and (4) cross-occupation validation study. The preliminary study assessed the test battery used in the subsequent predictive studies. The primary validation study introduced a bifactor predictive SEM model for testing the influence of cognitive abilities in predicting pilot performance. The cross-validation study assessed the consistency of the predictive model suggested in the primary validation study, using three additional pilots’ samples. The cross-occupation validation study compared the predictive model using data from three aviation-related occupations (flying, navigation, air battle management). Ability factors were extracted from scores of pilot applicants on the Air Force Officer Qualifying Test (AFOQT), the USAF officers’ primary selection test battery, whereas the flight performance scores were obtained from pilot records during the flight training program.</p> <p> In addition to the <i>g</i> factor, <i>verbal ability, quantitative ability, spatial ability, perceptual speed ability, and aviation-related acquired knowledge </i>are the six latent cognitive ability factors investigated in the reported studies. Pilot performance measures were modeled either as observed or latent variables covering ratings of academic and hands-on flying performance in different phases of the training program. The studies of this thesis established that (1) general ability contributes substantially to the prediction models; however, it is not the only important predictor, (2) aviation-related acquired knowledge is the most robust predictor of pilot performance among the abilities examined, with a role even exceeding that of <i>g</i>, (3) perceptual speed predicted pilot performance uniquely in several occasions, while verbal, spatial, and quantitative abilities demonstrated trivial incremental validity for hands-on pilot performance beyond that provided by the <i>g</i> measure, and (4) the relative importance of cognitive abilities tends to vary across aviation occupations.</p><br><p></p>