A language-familiarity effect for speaker discrimination without comprehension

The influence of language familiarity upon speaker identification is well established, to such an extent that it has been argued that “Human voice recognition depends on language ability” [Perrachione TK, Del Tufo SN, Gabrieli JDE (2011) Science 333(6042):595]. However, 7-mo-old infants discriminate speakers of their mother tongue better than they do foreign speakers [Johnson EK, Westrek E, Nazzi T, Cutler A (2011) Dev Sci 14(5):1002–1011] despite their limited speech comprehension abilities, suggesting that speaker discrimination may rely on familiarity with the sound structure of one’s native language rather than the ability to comprehend speech. To test this hypothesis, we asked Chinese and English adult participants to rate speaker dissimilarity in pairs of sentences in English or Mandarin that were first time-reversed to render them unintelligible. Even in these conditions a language-familiarity effect was observed: Both Chinese and English listeners rated pairs of native-language speakers as more dissimilar than foreign-language speakers, despite their inability to understand the material. Our data indicate that the language familiarity effect is not based on comprehension but rather on familiarity with the phonology of one’s native language. This effect may stem from a mechanism analogous to the “other-race” effect in face recognition

A cleft care workshop for speech and language pathologists in resource-limited countries : the participants' experiences about cleft care in Uganda and satisfaction with the training effect

Objectives: workshops and specialized training programs are often inaccessible for speech and language pathologists (SLPs) based in resource-limited countries given the lack of supply, the long travel distances and the excessive participation fees. To stimulate life-long learning opportunities for all, this study described and measured the effect of a free, two-day cleft care workshop for SLPs in Uganda. The workshop included different topics related to the assessment and treatment of children with a cleft of the palate with or without a cleft of the lip (CP +/- L). Methods: The participants who presented during the two-day course were asked to complete a pre- and postworkshop questionnaire to evaluate their satisfaction. The pre-workshop form also included some questions concerning cleft care in Uganda. Both the pre- and post-workshop forms included three visual analogue scales to investigate the evolution of the participants' estimation of their knowledge regarding speech in patients with a CP +/- L and to assess the changes in their self-confidence in the diagnosis and treatment of this population. Results: seventeen SLPs completed the pre- and post-workshop questionnaires. In general, the participants were highly satisfied with the different themes covered in the program. After the training course, the participants rated their general knowledge about CP +/- L and their self-confidence in the diagnosis and treatment of children with a CP +/- L significantly higher than before the workshop. Conclusion: the vast majority of the SLPs reported that cleft care was not easily accessible in Uganda. The most commonly reported obstacle for cleft care was a lack of knowledge about this matter in the SLPs themselves highlighting the importance of the organization of additional education opportunities. The participants reported a significantly higher level of self-confidence in diagnosing and treating children with a CP +/- L after the workshop. The content of this workshop can form the basis for future learning opportunities for SLPs based in resource-limited countries

Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data