In Parkinson's disease (PD) patients, visual misperceptions are a major problem within the non-motor symptoms. Pareidolia, i.e., the tendency to perceive a specific, meaningful image in an ambiguous visual pattern, is a phenomenon that occurs also in healthy subjects. Literature suggests that the perception of face pareidolia may be increased in patients with neurodegenerative diseases. We aimed to examine, within the same experiment, face perception and the production of face pareidolia in PD patients and healthy controls (HC). Thirty participants (15 PD patients and 15 HC) were presented with 47 naturalistic photographs in which faces were embedded or not. The likelihood to perceive the embedded faces was modified by manipulating their transparency. Participants were asked to decide for each photograph whether a face was embedded or not. We found that PD patients were significantly less likely to recognize embedded faces than controls. However, PD patients also perceived faces significantly more often in locations where none were actually present than controls. Linear regression analyses showed that gender, age, hallucinations, and Multiple-Choice Vocabulary Intelligence Test (MWT) score were significant predictors of face pareidolia production in PD patients. Montreal Cognitive Assessment (MoCA) was a significant predictor for pareidolia production in PD patients in trials in which a face was embedded in another region [F(1, 13) = 24.4, p = <0.001]. We conclude that our new embedded faces paradigm is a useful tool to distinguish face perception performance between HC and PD patients. Furthermore, we speculate that our results observed in PD patients rely on disturbed interactions between the Dorsal (DAN) and Ventral Attention Networks (VAN). In photographs in which a face is present, the VAN may detect this as a behaviourally relevant stimulus. However, due to the deficient communication with the DAN in PD patients, the DAN would not direct attention to the correct location, identifying a face at a location where actually none is present.

Parallel test versions require a comparable degree of difficulty and must capture the same characteristics using different items. This can become challenging when dealing with multivariate items, which are for example very common in language or image data. Here, we propose a heuristic to identify and select similar multivariate items for the generation of equivalent parallel test versions. This heuristic includes: 1. inspection of correlations between variables; 2. identification of outlying items; 3. application of a dimension-reduction method, such as for example principal component analysis (PCA); 4. generation of a biplot, in case of PCA of the first two principal components (PC), and grouping the displayed items; 5. assigning of the items to parallel test versions; and 6. checking the resulting test versions for multivariate equivalence, parallelism, reliability, and internal consistency. To illustrate the proposed heuristic, we applied it exemplarily on the items of a picture naming task. From a pool of 116 items, four parallel test versions were derived, each containing 20 items. We found that our heuristic can help to generate parallel test versions that meet requirements of the classical test theory, while simultaneously taking several variables into account.