ABSTRACT
OBJECTIVE: One type of test commonly used to assess auditory processing disorders (APD) is the Frequency Pattern Test, in which triads of pure tones of two different frequencies are presented, and participants are required to accurately report the sequence of tones, typically using a verbal response. The test is widely used clinically, but in its current format, is an under-exploited means of addressing some candidate processes, such as temporal ordering and frequency discrimination, which might be affected in APD. Here we describe a computer-based version of an auditory pattern perception test, the BirdSong Game, which was designed to be an engaging research tool for use with school-aged children. METHODS: In this study, 128 children aged 6-10 with normal peripheral hearing were tested. The BirdSong Game application was used to administer auditory sequential pattern tests, via a touch-screen presentation and response interface. A conditioning step was included prior to testing, in order to ensure that participants were able to adequately discriminate between the test tones, and reliably describe the difference using their own vocabulary. Responses were collected either verbally or manually, by having participants press cartoon images on the touch-screen in the appropriate sequence. The data was examined for age, gender and response mode differences. RESULTS: Findings on the auditory tests indicated a significant maturational effect across the age range studied, with no difference between response modes or gender. CONCLUSIONS: The BirdSong Game is sensitive to maturational changes in auditory sequencing ability, and the computer-based design of the test has several advantages which make it a potentially useful clinical and research tool.
Subject(s)
Auditory Perceptual Disorders/diagnosis , Hearing Tests/methods , Language Development Disorders/diagnosis , Speech Perception/physiology , Child , Computers , Female , Humans , MaleABSTRACT
We have merged two established anatomical terminologies with an evolving ontology of biological structure: the Foundational Model of Anatomy. We describe the problems we have encountered and the solutions we have developed. We believe that both the problems and solutions generalize to the integration of any legacy terminology with a disciplined ontology within the same domain.
Subject(s)
Anatomy/classification , Neuroanatomy/classification , Vocabulary, Controlled , Eponyms , Female , Humans , Language , Male , Software , Terminology as Topic , User-Computer InterfaceABSTRACT
The Foundational Model of Anatomy (FMA) is a frame-based ontology that represents declarative knowledge about the structural organization of the human body. Part-whole relationships play a particularly important role in this representation. In order to assure that knowledge-based applications relying on the FMA as a resource can reason about anatomy, we have modified and enhanced currently available schemes of meronymic relationships. We have introduced and defined distinct partitions for decomposing anatomical structures and attributed the part relationships in order to eliminate ambiguity and enhance specificity in the richness of meronymic relationships within the FMA.
Subject(s)
Anatomy/classification , Models, Anatomic , Vocabulary, Controlled , HumansABSTRACT
A logical and principled representation of cell types and their component parts could serve as a framework for correlating the various ontologies that are emerging in bioinformatics with a focus on cells and subcellular biological entities. In order to address this need we have extended the Foundational Model of Anatomy (FMA)1,2 from macroscopic to cellular and subcellular anatomical entities. The poster will provide a live demonstration of this implementation.
Subject(s)
Anatomy/classification , Cells/classification , Vocabulary, Controlled , Cells/cytology , Computational Biology , Humans , Internet , SoftwareABSTRACT
In order to meet the need for an expressive ontology in neuroinformatics, we have integrated the extensive terminologies of NeuroNames and Terminologia Anatomica into the Foundational Model of Anatomy (FMA). We have enhanced the FMA to accommodate information unique to neuronal structures, such as axonal input/output relationships.
