Computer-Assisted Noise Pareidolia Tests Through Patient Emulation

Pareidolia is a psychiatric phenomenon similar to visual illusions or hallucinations, serving as a distinguishing feature of psychiatric disorders. The so-called noise pareidolia test is a typical diagnosis method for pareidolia. In the test, ambiguous noise-like images are shown to patients, to see if patients misunderstand them as faces. However, the existing testing method is very time-consuming for both patients and experts. Furthermore, it has low accuracy in distinguishing some psychiatric disorders with similar characteristics. This research presents a machine learning-based framework to assist in assessing the pareidolia phenomenon. This is a challenging task because analyzing psychiatric disorders primarily involves observing behavioral changes. This is in contrast to other medical imaging tasks which rely on physically visible changes. Our framework emulates the behavior of patients in noise pareidolia tests. To achieve this, we fine-tune face detection models on patient data to misunderstand ambiguous patterns in the same way a patient would do. Then, we can compare new patients to the behavior of existing patients to assess the pareidolia phenomenon, through two classification approaches: a distance function-based approach and a multilayer perceptron-based approach. The proposed framework is evaluated on the data from 48 real patients. The result shows a performance superior to baseline methods in distinguishing pareidolia from other similar visual illusions. Moreover, our framework introduces two sampling methods to reduce the number of images shown to the patient while reaching a comparable high accuracy, improving the efficiency and making the framework feasible for a clinical test.