Using Avatars to Study Social Cognition in Cross-cultural Psychology and High-functioning Autism

Over the last 20 years, virtual avatars have become a popular research tool in psychology and neuroscience for studying social cognition. As opposed to photographs or movie recordings of actual human beings, avatars allow for the precise control over all aspects of the stimulus, ranging from the avatar's gaze and movement behavior to its physical appearance, such as age, gender, or ethnicity (Vogeley & Bente, 2010). Additionally, avatars have made it possible to create interactive paradigms that enable the study of social interactions in real-time (Wilms et al., 2010). Benefitting from this recent development, the present thesis set out to use avatars to study social cognition in two areas: cross-cultural psychology and high-functioning autism. The unifying connection between the four studies combined in this thesis is that they all rely on specific advantages offered by avatars and that they could not have been conducted without them. As a whole, the aim of the present thesis is to advance knowledge in two major fields of social cognition—cross-cultural psychology and high-functioning autism—by using virtual avatars. Study 1 assessed the validity of social avatars as a research tool. Study 2 focused on cross-cultural differences in trust. Study 3 and Study 4 investigated whether two core abilities of social cognition—detection of direct gaze and perception of attractiveness—are impaired in individuals with high-functioning autism

Praktische Anwendung von Deep Learning: Klassifizierung der häufigsten Röntgenbilder in einem PACS mit Hilfe eines neuronalen Netzwerkes

Das Ziel dieser Arbeit war es, ein neuronales Netzwerk zur Klassifikation der häufigsten Kategorien von konventionellen Röntgenbildern (z.B.: Thorax ap, Abdomen in Seitenlage) zu entwickeln und anhand von internen und externen Daten zu validieren. Ein solches Netzwerk kann dabei helfen verschiedene radiologische Arbeitsabläufe zu verbessern. Hierzu wurden alle an unserem Institut erstellten Röntgenbilder aus dem Jahr 2017 (n = 71.274) aus dem PACS (Picture Archiving and Communication System) aufgerufen. Die 30 größten Kategorien (n = 58.291, 81,7% aller im Jahr 2017 erstellten Röntgenbilder) wurden dazu verwendet ein neuronales Netzwerk (MobileNet v1.0) mittels Transfer Learning zu trainieren und zu validieren. Die Kategorien der Röntgenbilder wurden anhand der DICOM-Metadaten extrahiert und an die Kategorien des WHO Manuals of Diagnostic Imaging angepasst. Zur unabhängigen, externen Validierung der Ergebnisse dienten Bilder von externen Krankenhäusern aus unserem PACS (n = 5324). In der internen Validierung betrug die Genauigkeit des Modells 90.3% (95%CI: 89.2–91.3%), In der externen Validierung betrug die Genauigkeit des Modells 94.0% (95%CI: 93.3–94.6%). Mit Hilfe von Daten nur einer Institution waren wir in der Lage ein neuronales Netzwerk zur Klassifikation der häufigsten Kategorien von Röntgenbildern zu trainieren. Das Netzwerk zeigte eine gute Generalisierbarkeit in den externen Daten und kann dazu verwendet werden Bilder deren Metadaten fehlen oder fehlerhaft sind in einem PACS zu organisieren bzw. eine Vorauswahl an Röntgenbildern zu treffen, so dass diese an spezialisierte neuronale Netzwerke zur Erkennung von Erkrankungen weitergeleitet werden können. Das neuronale Netzwerk kann auch dabei helfen andere radiologische Arbeitsabläufe zu optimieren (zum Beispiel: automatisierte Aufhängung von Röntgenbildern; Überprüfung, ob angefordertes und durchgeführtes Bild übereinstimmen). Das finale neuronale Netzwerk steht öffentlich zur Evaluation und Erweiterung zur Verfügung

Brief Report: Impression Formation in High-Functioning Autism: Role of Nonverbal Behavior and Stereotype Activating Information

Little is known about whether stereotypes influence social judgments of autistic individuals, in particular when they compete with tacit face-to-face cues. We compared impression formation of 17 subjects with high-functioning autism (HFA) and 17 age-, gender- and IQ-matched controls. Information about the profession of a job applicant served as stereotype activating information. The target person's nonverbal behavior was presented as a computer animation showing two virtual characters in interaction. Contrary to our hypothesis, HFA participants were as sensitive to nonverbal cues as controls. Moreover, HFA showed a tendency to evaluate persons more positively. This might indicate a routine HFA apply in impression formation in order to compensate for their deficit in intuitive understanding of nonverbal communication cues

Meet Joe Black

Online dating has become an important resource for building intimate relationships. Similarity and group membership have been found to be as important for online and off- line dating. Research on terror management theory has shown that both factors shield against death anxieties, indicating difficulties for dissimilar and intergroup couples. Yet, no study—so far—has investigated both factors simultaneously after mortality salience (MS). To close this gap, the current study presented German participants (N¼249) with a dating app that randomly assigned them to a MS or control condition. Afterward, a can- didate following a 2(group membership) ? 2(similarity) design was suggested. After MS, in contrast to the control group, similarity increased and dissimilarity decreased the Desire to Date in-group but not out-group member

Artificial intelligence abstracts from the European Congress of Radiology: analysis of topics and compliance with the STARD for abstracts checklist

Objectives To analyze all artificial intelligence abstracts presented at the European Congress of Radiology (ECR) 2019 with regard to their topics and their adherence to the Standards for Reporting Diagnostic accuracy studies (STARD) checklist. Methods A total of 184 abstracts were analyzed with regard to adherence to the STARD criteria for abstracts as well as the reported modality, body region, pathology, and use cases. Results Major topics of artificial intelligence abstracts were classification tasks in the abdomen, chest, and brain with CT being the most commonly used modality. Out of the 10 STARD for abstract criteria analyzed in the present study, on average, 5.32 (SD = 1.38) were reported by the 184 abstracts. Specifically, the highest adherence with STARD for abstracts was found for general interpretation of results of abstracts (100.0%, 184 of 184), clear study objectives (99.5%, 183 of 184), and estimates of diagnostic accuracy (96.2%, 177 of 184). The lowest STARD adherence was found for eligibility criteria for participants (9.2%, 17 of 184), type of study series (13.6%, 25 of 184), and implications for practice (20.7%, 44 of 184). There was no significant difference in the number of reported STARD criteria between abstracts accepted for oral presentation (M = 5.35, SD = 1.31) and abstracts accepted for the electronic poster session (M = 5.39, SD = 1.45) (p = .86). Conclusions The adherence with STARD for abstract was low, indicating that providing authors with the related checklist may increase the quality of abstracts