Investigating a Computational Explanation of the Black Hole Illusion

ABSTRACT

Objective

We investigated the role of Perrone’s algorithm in the Black Hole Illusion (BHI). After analyzing the algorithm and identifying two of its predictions, we empirically tested them with two on-line experiments.

Background

In 1983, Perrone proved that in daylight conditions it is possible to compute the descent angle using a ratio of retinal distances corresponding to the runway and surrounding context. Using the algorithm in nighttime conditions, with just the visible runway, pilots would overestimate the descent angle, leading to the BHI.

Method

Mathematical analysis indicates the algorithm predicts a large BHI; perhaps too large if there are no mitigating factors. As Perrone noted, the BHI illusion magnitude should be affected by runway width; we also found that some conditions predict a reverse BHI (pilots should underestimate their descent angle). In our experiments, participants observed a cockpit view of a runway during five seconds of steady approach. In a subsequent still image, participants indicated where they believed the plane would land if it continued its flight path. We measured the accuracy of the landing positions for various runway widths and various background contexts.

Results

The experiments did not show a BHI for any conditions; so the experiments do not validate the model predictions.

Conclusion

Based on our analyses, Perrone’s algorithm does not provide an adequate explanation of the Black Hole Illusion.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Disclaimer

Investigating a computational explanation of the Black Hole Illusion uses the Unreal® Engine. Unreal® is a trademark or registered trademark of Epic Games, Inc. in the United States of America and elsewhere. Unreal® Engine, Copyright 1998 – 2021 Epic Games, Inc. All rights reserved.