Proofreading in Young and Older Adults: The Effect of Error Category and Comprehension Difficulty.

Proofreading text relies on stored knowledge, language processing, and attentional resources. Age differentially affects these constituent abilities: while older adults maintain word knowledge and most aspects of language comprehension, language production and attention capacity are impaired with age. Research with young adults demonstrates that proofreading is more attentionally-demanding for contextual errors which require integration across multiple words compared to noncontextual errors which occur within a single word. Proofreading is also more attentionally-demanding for text which is more difficult to comprehend compared to easier text. Older adults may therefore be impaired at aspects of proofreading which require production, contextual errors, or more difficult text. The current study tested these possibilities using a naturalistic proofreading task. Twenty-four young and 24 older adults proofread noncontextual (spelling) and contextual (grammar or meaning) errors in passages that were easier or more difficult to comprehend. Older adults were preserved at proofreading spelling errors, but were impaired relative to young adults when proofreading grammar or meaning errors, especially for difficult passages. Additionally, older adults were relatively spared at detecting errors compared to correcting spelling errors, in keeping with previous research. Age differences were not attributable to individual differences in vocabulary knowledge or self-reported spelling ability.This research was supported by a British Academy small grant (SG-35664) to M. Shafto. The author was supported during this research by a Junior Research Fellowship from Christ Church College, Oxford, UK.This is the final version of the article. It was first available from MDPI via http://dx.doi.org/10.3390/ijerph12111444

Relations between nonverbal cognitive ability and spoken language development : implications for deaf toddlers who use cochlear implants.

The first aim of this dissertation was to determine whether early deafness is related to children\u27s nonverbal cognitive abilities. Performance of a group of deaf infants were compared to that of same-aged hearing infants on visual sequence learning (VSL) and visual recognition memory (VRM) tasks. The hypothesis was that if deafness is negatively related to general cognitive ability, then the deaf infants would perform more poorly than same-aged hearing infants on the two tasks. There were no significant differences in VSL (n = 19) or VRM (n = 13) performance between the two groups (Chapter III). These results are inconclusive due to the small sample sizes, but importantly, there were individual infants in both groups who demonstrated learning on the two nonverbal tasks. The second aim was to determine whether VSL and VRM ability can provide predictive information about spoken language development. The results for the normal hearing 8.5-month-olds provide evidence for a significant relation between VSL ability and spoken language outcomes (Chapter IV). Specifically, it was found that sequence learning (thought to rely on procedural memory ability) may contribute to vocabulary and gestural development in normal-hearing infants. Further research with larger samples of infants is needed to determine whether procedural learning may be important for grammar acquisition. These results suggest that VSL ability may not be related to spoken language outcomes for deaf infants who use cochlear implants (Chapter V), although VRM ability may be (Chapter VI). If this pattern of results held up for a larger sample of deaf infants, this would suggest that the nonverbal cognitive abilities tapped in the VSL and VRM tasks are not critical for at least some aspects of spoken language development in deaf children who use cochlear implants, and that potential deficits in nonverbal cognitive ability are not necessarily associated with poorer spoken language ability in this population. In future research a larger sample of deaf infants should be recruited in order to clarify whether nonverbal cognitive skills are related to early deafness, and how those nonverbal skills might relate to spoken language development in this unique population

The coagulation-time of the blood in disease: some clinical records and considerations

It is now almost twenty years since Wright first drew attention to the value of measuring the rate of coagulation of the blood in disease. Since then much has been written upon the subject and many new methods of observation have been devised.Much of the work done in the earlier days was rather of the nature of physiological experiment: but indeed the results obtained by the various methods differed so widely and were so inconsistent even in themselves that there was little encouragement given for an advance to clinical observations. Later, with improved technique (most especially with a due regard to the knowledge of the effect of temperature as a factor in variation) much more reliable and consistent results were obtained and the advance to clinical problems was made with more assurance. Yet even now there are but few records in the literature of research into the coagulation time of the blood in disease. Haemophilia and purpura, it is true, have been studied in some detail, as also the assertions of Wright respecting the value of calcium salt administration (though in this last the examination has been chiefly, as by Addis'' along pharmacological lines) but in other diseases the records are few and imperfect. This comparative scantiness of clinical records has encouraged me to embark upon the series of observations here recorded. They have been arrived at by the uso of yet another method - a method borrowed from Drs. Dale and Laidlaw, as set forth by them in the Journal of Pathology and Bacteriology. ( This for brevity's sake I have termed the "Ball method"

Evolution of Bias in Human and Machine Learning Algorithm Interaction

Human algorithm interaction: people are now affected by the output of all types of machine learning algorithms. social media, blogs, social networks, and other services and applications. Motivation: ML algorithm relied on reliable labels from experts to build prediction. However, ML algorithm started to receive data from the more general population. The interaction leads to biased result which is caused by ingesting unchecked information from general population, such as biased samples and biased labels