Language experience shapes the development of the mutual exclusivity bias

Halberda (2003) demonstrated that 17-month-old infants, but not 14- or 16-month-olds, use a strategy known as mutual exclusivity (ME) to identify the meanings of new words. When 17-month-olds were presented with a novel word in an intermodal preferential looking task, they preferentially fixated a novel object over an object for which they already had a name. We explored whether the development of this word-learning strategy is driven by children's experience of hearing only one name for each referent in their environment by comparing the behavior of infants from monolingual and bilingual homes. Monolingual infants aged 17–22 months showed clear evidence of using an ME strategy, in that they preferentially fixated the novel object when they were asked to "look at the dax." Bilingual infants of the same age and vocabulary size failed to show a similar pattern of behavior. We suggest that children who are raised with more than one language fail to develop an ME strategy in parallel with monolingual infants because development of the bias is a consequence of the monolingual child's everyday experiences with words.

3-5-man mutual zugzwangs in chess

This note reports the work of Wirth and Karrer in twin-sourcing all mutual zugzwang positions, mzugs, in 2-5-man endgames. This paper tabulates the mzug statistical data, gives examples of maximal mzugs and refers to a chess endgame website where further data is to be found.

Robustness of mutual information to inter-subject variability for automatic artefact removal from EEG

The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as Brain-Computer Interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of Mutual Information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a Support Vector Machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved.

The ethical challenges of ubiquitous healthcare

Ubiquitous healthcare is an emerging area of technology that uses a large number of environmental and patient sensors and actuators to monitor and improve patients’ physical and mental condition. Tiny sensors gather data on almost any physiological characteristic that can be used to diagnose health problems. This technology faces some challenging ethical questions, ranging from the small-scale individual issues of trust and efficacy to the societal issues of health and longevity gaps related to economic status. It presents particular problems in combining developing computer/information/media ethics with established medical ethics. This article describes a practice-based ethics approach, considering in particular the areas of privacy, agency, equity and liability. It raises questions that ubiquitous healthcare will force practitioners to face as they develop ubiquitous healthcare systems. Medicine is a controlled profession whose practise is commonly restricted by government-appointed authorities, whereas computer software and hardware development is notoriously lacking in such regimes.