Linguistic : Application of LSA to predict linguistic maturity and language disorder in children

In this chapter we will describe applications of latent semantic analysis to assess semantic linguistic maturity in children and how well the method can predict whether a child has developmental language disorder (DLD), based on orally produced narratives. Assessment of narrative ability in preschool years captures important cognitive, linguistic and socio-pragmatic skills and is an important prognostic tool for later academic success. We discuss the usefulness and potential of computerized methods to quantify semantics in research on typical and disordered language development. More importantly, we also discuss if and how such methods can be applied in clinical or educational contexts for diagnosis of language disorder in different populations, as an objective measure to complement standardized and decontextualized tests and assessment methods

Cognitive mechanisms of treatment in depression

Cognitive abnormalities are a core feature of depression, and biases toward negatively toned emotional information are common, but are they a cause or a consequence of depressive symptoms? Here, we propose a ‘cognitive neuropsychological' model of depression, suggesting that negative information processing biases have a central causal role in the development of symptoms of depression, and that treatments exert their beneficial effects by abolishing these biases. We review the evidence pertaining to this model: briefly with respect to currently depressed patients, and in more detail with respect to individuals at risk for depression and the effects of antidepressant treatments. As well as being present in currently depressed individuals, negative biases are detectable in those vulnerable for depression due to neuroticism, genetic risk, or previous depressive illness. Recent evidence provides strong support for the notion that both antidepressant drugs and psychological therapies modify negative biases, providing a common mechanism for understanding treatments for depression. Intriguingly, it may even be possible to predict which patients will benefit most from which treatments on the basis of neural responses to negative stimuli. However, further research is required to ascertain whether negative processing biases will be useful in predicting, detecting, and treating depression, and hence in preventing a chronic, relapsing course of illness