Intervening to alleviate word-finding difficulties in children: case series data and a computational modelling foundation

Abstract

We evaluated a simple computational model of productive vocabulary acquisition, applied to simulating two case studies of 7-year-old children with developmental word-finding difficulties across four core behavioural tasks. Developmental models were created, which captured the deficits of each child. In order to predict the effects of intervention, we exposed the computational models to simulated behavioural interventions of two types, targeting the improvement of either phonological or semantic knowledge. The model was then evaluated by testing the predictions from the simulations against the actual results from an intervention study carried out with the two children. For one child it was predicted that the phonological intervention would be effective, and the semantic intervention would not. This was borne out in the behavioural study. For the second child, the predictions were less clear and depended on the nature of simulated damage to the model. The behavioural study found an effect of semantic but not phonological intervention. Through an explicit computational simulation, we therefore employed intervention data to evaluate our theoretical understanding of the processes underlying acquisition of lexical items for production and how they may vary in children with developmental language difficulties.

Keywords:

• intervention
• word-finding difficulties
• connectionist modelling
• phonology
• semantics
• naming

Acknowledgements

We acknowledge the important contributions of our wider advisory group (including an educational psychologist and a young person with speech and language difficulties) on the design of the behavioural study, and of our Clinical Advisory Group (including Susan Ebbels, Kathleen Cavin, and Sarah Simpson) for shaping the therapies used and for more general advice. Mike Coleman constructed the reaction time (RT) version of the Picture Judgement Task. The randomization and the weighted statistics were kindly provided by David Howard, Newcastle University. University College London (UCL) MSc student Elisabeth Salt contributed to the analysis of the conversation data.

We are greatly indebted to the children who took part so willingly in the study and to their parents, teachers, and schools and to speech and language therapist Vivien Gibson who carried out some of the therapy.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Names have been changed for the purposes of anonymity.

Additional information

Funding

This research was supported by Economic and Social Research Council (ESRC) [grant number RES-062-23-2721].