Birte Schiffhauer

Intrinsic Default Mode Network Connectivity Predicts Spontaneous Verbal Descriptions of Autobiographical Memories during Social Processing

Neural systems activated in a coordinated way during rest, known as the default mode network (DMN), also support autobiographical memory (AM) retrieval and social processing/mentalizing. However, little is known about how individual variability in reliance on personal memories during social processing relates to individual differences in DMN functioning during rest (intrinsic functional connectivity). Here we examined 18 participants’ spontaneous descriptions of autobiographical memories during a 2 h, private, open-ended interview in which they reacted to a series of true stories about real people’s social situations and responded to the prompt, “how does this person’s story make you feel?” We classified these descriptions as either containing factual information (“semantic” AMs) or more elaborate descriptions of emotionally meaningful events (“episodic” AMs). We also collected resting state fMRI scans from the participants and related individual differences in frequency of described AMs to participants’ intrinsic functional connectivity within regions of the DMN. We found that producing more descriptions of either memory type correlated with stronger intrinsic connectivity in the parahippocampal and middle temporal gyri. Additionally, episodic AM descriptions correlated with connectivity in the bilateral hippocampi and medial prefrontal cortex, and semantic memory descriptions correlated with connectivity in right inferior lateral parietal cortex. These findings suggest that in individuals who naturally invoke more memories during social processing, brain regions involved in memory retrieval and self/social processing are more strongly coupled to the DMN during rest.

Welcome to the Future – How Naïve Users Intuitively Address an Intelligent Robotics Apartment

The purpose of this Wizard-of-Oz study was to explore the intuitive verbal and non-verbal goal-directed behavior of naive participants in an intelligent robotics apartment. Participants had to complete seven mundane tasks, for instance, they were asked to turn on the light. Participants were explicitly instructed to consider nonstandard ways of completing the respective tasks. A multi-method approach revealed that most participants favored speech and interfaces like switches and screens to communicate with the intelligent robotics apartment. However, they required instructions to use the interfaces in order to perceive them as competent targets for human-machine interaction. Hence, first important steps were taken to investigate how to design an intelligent robotics apartment in a user-centered and user-friendly manner.

Get One or Create One: the Impact of Graded Involvement in a Selection Procedure for a Virtual Agent on Satisfaction and Suitability Ratings

N = 86 participants were either confronted with a predefined virtual agent, or could select a virtual agent from predefined sets of six or 30 graphical models, or had the opportunity to self-customize the agent’s appearance more freely. We investigated the effect of graded user involvement in the selection procedure on their ratings of satisfaction with the agent and perceived task suitability. In a second step, we explored the psychological mechanism underlying this effect. Statistical analyses revealed that satisfaction with the chosen virtual agent increased with the degree of participants’ involvement in terms of more choice, but not in terms of self-customization. Furthermore, we show that this effect was driven by the perceived likeability, attractiveness, and competence of the agent. We discuss implications of our results for the development of a virtual agent serving as a virtual assistant in a smart home environment.

Let the User Decide! User Preferences Regarding Functions, Apps, and Interfaces of a Smart Home and a Service Robot

In an online survey, we studied user expectations and preferences for functions and apps in the context of a smart apartment. Furthermore, we explored which type of interface users would choose for an interaction with the smart apartment. Equally important, we investigated users’ acceptance of a service robot in the smart home. Results showed high levels of acceptance for both, the smart apartment and the robot, although the preferred interface for the apartment was context dependent. We discuss implications of the current survey and highlight key aspects to be taken into consideration when developing innovation technology for the home context.