@inproceedings{stille2017,
  title={Neural modeling of developmental lexical disorders},
  author={Catharina Marie Stille and Trevor Bekolay and Bernd J. Kröger},
  booktitle={Bernstein Conference 2017},
  year={2017},
  organization={Council of Ontario Universities},
  doi={10.12751/nncn.bc2017.0126},
  abstract={Standardized tests exist for the diagnostics of
    developmental lexical disorders, but it is still
    difficult to associate the resulting behavior of a
    child while speaking with functional deficits in the
    child´s brain. The mental lexicon is part of the
    speech and language knowledge repository of
    individuals. It enables humans to produce as well as
    to understand speech. The computational frameworks
    we used for implementing a model of the mental
    lexicon and speech processing are the NEF (Neural
    Engineering Framework, Eliasmith et al. 2012,
    Eliasmith 2013) and the SPA (Semantic Pointer
    Architecture, Eliasmith et al 2012, Stewart &
    Eliasmith 2014). These frameworks allow modeling of
    large scale neural networks, comprising sensory,
    motor and cognitive components. The modeled task is
    the WWT 6-10 (Word range and Word Retrieval Test,
    see Glück 2011), which comprises 95 items and is a
    picture naming and word comprehension task. In case
    of incorrect answers semantic and phonological cues
    are also given in order to facilitate word
    production. A major goal of this study is to
    introduce a quantitative neurocomputational model
    for lexical storage as well as for lexical
    retrieval. A further goal of this study is to
    associate neural dysfunctions with deficits in
    speech behavior. Concretely, the deficits of
    interest are in lexical storage and lexical
    access. The dysfunctions introduced here are the
    lesioning of specific neural SPA-buffers and of
    specific neural connections between these
    buffers. Based on the behavioral data given by the
    WWT, we are now able to associate functional neural
    deficits with symptomatic behavioral data. This
    allows us to identify potential dysfunctions at
    neural level for word retrieval and word storage.}
}