Effect of focus on verbal working memory: Critical role of the focus word in reading

The effect of focus on working memory was investigated with the reading span test (RST). In two experiments, the span scores and the number of intrusion errors were compared between the focused RST the and the nonfocused RST. Focus word was defined as the most important word for comprehending a sentence. For the focused RST, the target word to be maintained was the focus word for the sentence. In the nonfocused RST, however, the target word was not the focus word for the sentence. The results of both experiments showed that RST span scores were higher for the focused RST than for the nonfocused RST, and intrusion errors were found to increase for the nonfocused RST. In Experiment 2, the effect of focus was compared between high-span and low-span subjects. An effect of sentence length was also investigated. The result showed that low-span subjects were more affected than were high-span subjects by whether the word to be remembered was the focus word. The effect of sentence length was not confirmed. These findings suggest that the low-span subjects had deficits in their ability to establish and/or inhibit mental focus when faced with conflict situations in reading.     

Clustering preference data in the presence of response-style bias

Preference data, such as Likert scale data, are often obtained in questionnaire-based surveys. Clustering respondents based on survey items is useful for discovering latent structures. However, cluster analysis of preference data may be affected by response styles, that is, a respondent's systematic response tendencies irrespective of the item content. For example, some respondents may tend to select ratings at the ends of the scale, which is called an ‘extreme response style’. A cluster of respondents with an extreme response style can be mistakenly identified as a content-based cluster. To address this problem, we propose a novel method of clustering respondents based on their indicated preferences for a set of items while correcting for response-style bias. We first introduce a new framework to detect, and correct for, response styles by generalizing the definition of response styles used in constrained dual scaling. We then simultaneously correct for response styles and perform a cluster analysis based on the corrected preference data. A simulation study shows that the proposed method yields better clustering accuracy than the existing methods do. We apply the method to empirical data from four different countries concerning social values.