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Computer based analyses offer a possibility for objective methods to assess semantic-linguistic quality of narratives at the text level. The aim of the present study is to investigate whether a semantic language impairment index (SELIMI) based on latent semantic analysis (LSA) can discriminate between children with developmental language disorder (DLD) and children with typical language development. Spoken narratives from 54 children with DLD and 54 age matched controls with typical language development were summarized in a semantic representation generated using LSA. A statistical model was trained to discriminate between children with DLD and children with typical language development, given the semantic vector representing each individual child's narrative. The results show that SELIMI could distinguish between children with DLD and children with typical language development significantly better than chance and thus has a potential to complement traditional analyses focussed on form or on the word level.

We propose a method to quantify "semantic linguistic maturity" (SELMA) based on a high dimensional semantic representation of words created from the co-occurrence of words in a large text corpus. The method was applied to oral narratives from 108 children aged 4;0-12;10. By comparing the SELMA measure with maturity ratings made by human raters we found that SELMA predicted the rating of semantic maturity made by human raters over and above the prediction made using a child's age and number of words produced. We conclude that the semantic content of narratives changes in a predictable pattern with children's age and argue that SELMA is a measure quantifying semantic linguistic maturity. The study opens up the possibility of using quantitative measures for studying the development of semantic representation in children's narratives, and emphasizes the importance of word co-occurrences for understanding the development of meaning.

Traditionally, election polls have asked for participants' own voting intentions. In four elections, we previously found that we could improve predictions by asking participants how they thought their social circles would vote. A potential concern is that the social-circle question might predict results less well in elections with larger numbers of political options because it becomes harder to accurately track how social contacts plan to vote. However, we now find that the social-circle question performs better than the own-intention question in predicting two elections with many political parties: The Netherlands' 2017 general election and the Swedish 2018 general election. [ABSTRACT FROM AUTHOR]

Although legal contexts are subject to biased reasoning and decision making, to identify and test debiasing techniques has largely remained an open task. We report on experimentally deploying the technique ''giving reasons pro et contra'' with professional (N = 239) and lay judges (N = 372) at Swedish municipal courts. Using a mock legal scenario, participants assessed the relevance of an eyewitness's previous conviction for his credibility. On average, both groups displayed low degrees of bias. We observed a small positive debiasing effect only for professional judges. Strong evidence was obtained for a relation between profession and relevance-assessment: Lay judges seemed to assign a greater importance to the prior conviction than professional judges did. We discuss challenges for future research, calling other research groups to contribute additional samples. [ABSTRACT FROM AUTHOR]

This article discusses the concept of Bayes factors as inferential tools that can serve as an alternative to null hypothesis significance testing in the day-to-day work of developmental researchers. A Bayes factor indicates the degree to which data observed should increase (or decrease) the credibility of one hypothesis in comparison to another. Bayes factor analyses can be used to compare many types of models but are particularly helpful when comparing a point null hypothesis to a directional or nondirectional alternative hypothesis. A key advantage of this approach is that a Bayes factor analysis makes it clear when a set of observed data is more consistent with the null hypothesis than the alternative. Bayes factor alternatives to common tests used by developmental psychologists are available in easy-to-use software. However, we note that analysis using Bayes factors is a less general approach than Bayesian estimation/modeling, and is not the right tool for every research question. [ABSTRACT FROM PUBLISHER]