Sex differences in the Simon task help to interpret sex differences in selective attention.

In the last decade, a number of studies have reported sex differences in selective attention, but a unified explanation for these effects is still missing. This study aims to better understand these differences and put them in an evolutionary psychological context. 418 adult participants performed a computer-based Simon task, in which they responded to the direction of a left or right pointing arrow appearing left or right from a fixation point. Women were more strongly influenced by task-irrelevant spatial information than men (i.e., the Simon effect was larger in women, Cohen's d = 0.39). Further, the analysis of sex differences in behavioral adjustment to errors revealed that women slow down more than men following mistakes (d = 0.53). Based on the combined results of previous studies and the current data, it is proposed that sex differences in selective attention are caused by underlying sex differences in core abilities, such as spatial or verbal cognition

Relative Validity Criteria for Community Mining Algorithms

Alberta Innovates Centre for Machine LearningNSERCFapespCNP

Relative validity criteria for community mining algorithms

[Extract] The recent growing trend in the data mining field is the analysis of structured/interrelated data, motivated by the natural presence of relationships between data points in a variety of the present-day applications. The structures in these interrelated data are usually represented using networks, known as complex networks or information networks; examples are the hyperlink networks of web pages, citation or collaboration networks of scholars, biological networks of genes or proteins, trust and social networks of humans, and much more. All these networks exhibit common statistical properties, such as power law degree distribution, small-world phenomenon, relatively high transitivity, shrinking diameter, and densification power laws (Newman 2010; Leskovec et al. 2005). Network clustering, a.k.a. community mining, is one of the principal tasks in the analysis of complex networks. Many community mining algorithms have been proposed in recent years (for a survey, refer to Fortunato 2010). These algorithms evolved very quickly from simple heuristic approaches to more sophisticated optimization-based methods that are explicitly or implicitly trying to maximize the goodness of the discovered communities. The broadly used explicit maximization objective is the modularity, first introduced by Newman and Girvan (2004)

The role of semantic processing in reading Japanese orthographies : an investigation using a script-switch paradigm

Research on Japanese reading has generally indicated that processing of the logographic script Kanji primarily involves whole-word lexical processing and follows a semantics-to-phonology route, while the two phonological scripts Hiragana and Katakana (collectively called Kana) are processed via a sub-lexical route, and more in a phonology-to-semantics manner. Therefore, switching between the two scripts often involves switching between two reading processes, which results in a delayed response for the second script (a script switch cost). In the present study, participants responded to pairs of words that were written either in the same orthography (within-script), or in two different Japanese orthographies (cross-script), switching either between Kanji and Hiragana, or between Katakana and Hiragana. They were asked to read the words aloud (Experiments 1 and 3) and to make a semantic decision about them (Experiments 2 and 4). In contrast to initial predictions, a clear switch cost was observed when participants switched between the two Kana scripts, while script switch costs were less consistent when participants switched between Kanji and Hiragana. This indicates that there are distinct processes involved in reading of the two types of Kana, where Hiragana reading appears to bear some similarities to Kanji processing. This suggests that the role of semantic processing in Hiragana (but not Katakana) reading is more prominent than previously thought and thus, Hiragana is not likely to be processed strictly phonologically. First Online: 08 November 2017</p