This study seeks to investigate the effects of manipulating aspects of a text-to-speech (TTS) voice on the learning and comprehension of a short passage, as well as detection of aspects of the passage such as its organization and key information. Pauses and pitch changes were used to demarcate this type of information. Participants listened to the passages and answered a series of cued and uncued recall questions to measure comprehension and learning, followed by a task to identify header structure. Preliminary results show trends that adding pauses might be beneficial, but more participants are needed to provide conclusive evidence. This study will contribute to the body of research surrounding technology adoption, assistive technology, and how to improve AI voices for the purposes of learning, as well as our understanding of how we process auditory information.
Human factors researchers and practitioners have awareness of, and access to, various sources covering methods commonly used in their field. However, human factors curriculum is underrepresented within high school education. In accordance with an effort to produce a website that is geared towards those with, at best, a limited understanding of the field, we wanted to also emphasize universal accessibility.
As automation becomes increasingly common in daily life the importance of understanding how we interact with automated systems increases, especially attribution of blame for accidents involving a human-automation team. The current research project looks at how humans attribute blame in an accident involving a human operator and a robot worker and is based on a previous study (Furlough et al., 2019). Participants will read two scenarios detailing an accident while being shown both the operator and robot. The robot’s appearance is manipulated to imply varying levels of automation, ranging from a simple robot to one with a human appearance. Results showed no significant effects, however, this research still has the potential to contribute to the understanding of interactions between humans and automated systems and could inform design in the future to facilitate a positive working environment with robots and humans.
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