Baum test

Abstract: The objective of the present study was to investigate the relationship between schizophrenia subtype and morphological characteristics of trees drawn in the Baum test. Subjects comprised the following three groups: 20 patients diagnosed with paranoid schizophrenia according to International Classification of Diseases (10th revision; ICD-10) criteria; 26 patients with non-paranoid schizophrenia according to ICD-10 criteria; and 53 healthy individuals. Differences in psychiatric symptoms as assessed using the Brief Psychiatric Rating Scale (BPRS) score were compared between patients with paranoid and non-paranoid schizophrenia. In addition, differences in two morphological characteristics of trees, namely trunk-to-crown ratio and trunk end opening, were compared between the three groups. No differences in psychiatric symptoms were identified between patients with paranoid and non-paranoid schizophrenia. Conversely, mean +/- SD trunk-to-crown ratio was 13.1 +/- 8.0 for patients with non-paranoid schizophrenia, 8.8 +/- 4.6 for patients with paranoid schizophrenia, and 5.4 +/- 3.4 for healthy individuals. Significant differences were identified between all three groups. Furthermore, mean trunk end opening was 0.80 +/- 0.7 for patients with paranoid schizophrenia, 0.38 +/- 0.6 for patients with non-paranoid schizophrenia, and 0.06 +/- 0.3 for healthy individuals. Again, significant differences were apparent between all three groups. These findings suggest that morphological differences in trees drawn in the Baum test can be observed between the two schizophrenia subtypes in terms of not only psychopathological interpretation, but also gestalt formation, as assessed on the basis of trees with collapsed gestalt or with some degree of gestalt. This suggests the possibility of multiple disorders at a physiological level. The present study confirmed that the Baum test can quantitatively assess facets of schizophrenia that existing scales such as BPRS are unable to analyze, and is useful for investigating brain function in patients with schizophrenia.

Abstract: In a pilot study, the possible influence of the inherited electroencephalogram (EEG) pattern on aspects of psychologic maturation was investigated. The twin sample consisted of 208 pairs, 110 monozygotic (MZ) and 98 dizygotic (DZ), mostly children and adolescents. The study showed a greater similarity between MZ compared with DZ twins in an immaturity score constructed from the Baum test (Koch 1976) and in a teacher's maturity scoring of essays. For both parameters, a correlation between occipital α-frequency and the test criterium could be demonstrated. This correlation was in the direction expected, and it was significant statistically. This result suggests an influence of genetically determined variation of brain maturation on interindividual variability of psychologic variation. Due to shortcomings of the methods used in the study, more specific conclusions cannot be drawn; the problem needs further elucidation using modern methods.

Abstract: One of the conditions for hiring workers in Indonesian companies is to pass a psychology test. Psychology test has many types of tests, one of which is the Baum test, a tree-drawing test. Baum test requires participants to draw a tree following the imagination of the participants. Later, the picture of the participant tree obtained from the Baum test will be assessed by psychologists as one of the labor assessment tools. This assessment is based on the shape of the tree crown, leaf shape, tree size on paper, etc. Currently, the assessment of the Baum test is still done manually by a psychologist. In order to help psychologists in assessing the Baum test, we propose an application in the form of a model that can study and classify tree images based on the tree size in the image by using the Convolutional Neural Network (CNN) method. CNN method allows computers to classify an object. The data are provided in the form of digital images of tree images from Baum testing that has been conducted. The data that has been collected will be used to train the model. The model used in this study has an architecture in the form of a convolution layer with a kernel size of 3×3, a pooling layer with a parameter size of 2×2, a dropout layer, a flatten layer, and a dense layer. The model was trained with 60 epochs and had a test and training data scale of 70:30. The model in this research has an accuracy of 74.07% in predetermined data testing and has an accuracy of 75% in new data testing.