Evidence Based Complementary Intervention for Insomnia

Increasing scientific evidence point to a non-pharmacological complementary treatment for insomnia: white noise. Its presentation has been shown to induce sleep in human neonates and adults, probably by reducing the signal-to-noise ratio of ambient sound. White noise may be a simple, safe, cost-effective alternative to hypnotic medication in many psychiatric disorders, especially acute stress disorder and PTSD

Trust Strategies in Voiced-Agent Multiple-Component Home Automation

Trust is a critical factor in successful and productive human-automation interactions. When automation malfunctions, trust is negatively affected. The development of increasingly complex multiple-component systems, or those with a several autonomous elements, introduces even more ways for a system to err. One example is in smart home control systems where different subsystems may be controlled by different autonomous routines or rules. Multiple studies suggest that one error-prone component can lower user trust in the remaining components (the “pull down” effect). Other research suggests that certain types of information, when presented to the user, can reduce the strength of the pull-down effect by promoting heterogeneity of agents. The current study investigated the effectiveness of increasing the number of voiced agents within a system as a strategy for decreasing the strength of the pull down effect. Participants interacted with either a single- or four-agent system. A simulated smart home task required participants to adjust the lighting for several rooms of a house. Participants first completed a block with all reliable room lightings, and then a block with all but one reliable room lighting. Inconsistent with the current literature, the results did not reveal any pull down effect. In both agent conditions the presence of the unreliable room lighting did not decrease trust in the reliable room lightings. In the single-agent condition trust in the reliable room lightings increased between both reliability blocks. However, this trend was not seen with the four-agent condition. Future studies should investigate the effects of anthropomorphism, automation domain, and task characteristics on trust

Comparative study of analog and digital hearing aids

The purpose of the present study was to determine if objective and/or subjective differences between analog and digital hearing aids exist when blinding is utilized in the protocol and circuitry is controlled. Ten normal hearing and seven hearing impaired subjects were monaurally fitted with analog and digital hearing aids. Probe microphone measures were obtained at the plane of the tympanic membrane at two output levels (40 dB SPL and 70 dB SPL). Listener performance in quiet was evaluated via word recognition testing, listener performance in noise was evaluated via the Hearing in Noise Test, and listener preference was evaluated via a questionnaire. Results indicated similar performance for all objective and subjective tasks for both hearing aids with the exception of better performance in quiet at the 40 dB SPL presentation level with the analog hearing aid for the hearing impaired group. These results indicate that listeners performed as well or significantly better with the analog hearing aid than with the digital hearing aid. Furthermore, future investigation is recommended to evaluate the effectiveness of some features available on digital hearing aids that are not available on analog hearing aids, such as expansion and noise reduction

Adaptor Grammars for Unsupervised Paradigm Clustering

This work describes the Edinburgh submission to the SIGMORPHON 2021 Shared Task 2 on unsupervised morphological paradigm clustering. Given raw text input, the task was to assign each token to a cluster with other tokens from the same paradigm. We use Adaptor Grammar segmentations combined with frequency-based heuristics to predict paradigm clusters. Our system achieved the highest average F1 score across 9 test languages, placing first out of 15 submissions

Regularization or lexical probability-matching? How German speakers generalize plural morphology

Artificial language learning research has shown that, under some conditions, adult speakers tend to probability-match to inconsistent variation in their input, while in others, they regularize by reducing that variation. We demonstrate that this framework can characterize speaker behavior in a natural-language morphological inflection task: the lexicon can be used to estimate variation in speaker productions. In the task of German plural inflection, we find that speakers probability-match a lexical distribution conditioned on phonology, and largely disregard an alternative possible strategy of conditional regularization based on grammatical gender