1. Where words meet numbers: Comprehension of measurement unit terms in posterior cortical atrophy.
- Author
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González, Aida Suárez, Hoffman, Paul, and Crutch, Sebastian
- Subjects
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UNITS of measurement , *ATROPHY , *COMPREHENSION , *PARIETAL lobe , *OCCIPITAL lobe - Abstract
Units of measurement (e.g., metre, week, gram) are critically important concepts in everyday life. Little is known about how knowledge of units is represented in the brain or how this relates to other forms of semantic knowledge. As unit terms are intimately connected with numerical quantity, we might expect knowledge for these concepts to be supported by parietally-mediated representations of space, time and magnitude. We investigated knowledge for measurement units in patients with posterior cortical atrophy (PCA), who display profound impairments of spatial and numerical cognition associated with occipital and parietal lobe atrophy. Relative to healthy controls, PCA patients displayed impairments for a range of unit-based knowledge, including the ability to specify the dimension which a unit refers to (e.g., grams measure mass), to select the appropriate units to measure everyday quantities (e.g., grams for sugar) and to determine the relative magnitudes of different unit terms (e.g., gram is smaller than kilogram). In most cases, their performance was also significantly poorer than a patient control group diagnosed with typical Alzheimer's disease. Our results suggest that impairment to systems that code numerical and spatial magnitudes has an effect on non-numerical verbal knowledge for measurement units. Units of measurement appear to lie at the intersection of the brain's verbal and numerical semantic systems, making them a critical class of concepts in which to investigate how magnitude-based codes contribute to verbal semantic representation. • Unit-based knowledge is impaired in PCA. • Deficits on coding of numerical magnitudes may impact non-numerical verbal knowledge. • Unit of measurement may be a tool to help elucidate magnitude-based semantic representations. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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