Understanding the links between hearing impairment and dementia : development and validation of the social and emotional impact of hearing impairment (SEI-HI) questionnaire

Background The links between hearing impairment (HI) and dementia have been well documented, but factors mediating this relationship remain unknown. Major consequences of HI are social and emotional dysfunction, and as the risk of dementia increases linearly with the severity of HI, it is plausible that socio-emotional difficulties may play a role in this association. Objective The aim of this study was to develop and validate a tool to analyse levels of hearing-related disability, to investigate ultimately whether subjective disability contributes to risk of cognitive impairment compared with hearing thresholds alone. Methods Development and validation of the questionnaire, the Social and Emotional Impact of Hearing Impairment (SEI-HI), was conducted in four phases: (1) content; (2) scoring and outcomes; (3) validation; (4) feasibility in a sample of people with cognitive impairment. Results Considerable evidence was found for the internal and external reliability of the tool with high construct validity, concurrent validity and test-retest values of the SEI-HI questionnaire. A feasibility check on 31 patients with mild cognitive impairment or dementia showed the SEI-HI questionnaire was easy to administer and well-received. Conclusion The SEI-HI questionnaire is a relevant instrument to assess hearing-related disability which can be used in people with cognitive decline to assess further impact on risk of developing dementia

Sensorimotor input as a language generalisation tool: a neurorobotics model for generation and generalisation of noun-verb combinations with sensorimotor inputs

The paper presents a neurorobotics cognitive model explaining the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. The dataset used for training was obtained from object manipulation tasks with a humanoid robot platform; it includes 9 motor actions and 9 objects placing placed in 6 different locations), which enables the robot to learn to handle real-world objects and actions. Based on the multiple time-scale recurrent neural networks, this study demonstrates its generalisation capability using a large data-set, with which the robot was able to generalise semantic representation of novel combinations of noun-verb sentences, and therefore produce the corresponding motor behaviours. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. Further analyses of the learned network dynamics and representations also demonstrate how the generalisation is possible via the exploitation of this functional hierarchical recurrent network