Refine
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
Institute
- Medizin (1)
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.
Focusing on the examples of multiple degree modification, this paper argues that the class of degree expressions in English is syntactically and semantically diverse, subdivided both according to the semantic effects of its members and according to the extent to which they permit, and participate in, multiple layers of modification. We argue that these two factors are linked, and result in (at least) a three-way distinction between ˋtrue degree morphemes', which map gradable adjectives to properties of individuals and combine with their arguments in a Head-Specifier structure; ˋintensifiers', which are syntactic and semantic modifiers of properties constructed out of gradable adjectives; and ˋscale modifiers', which are also syntactic and semantic modifiers, but which combine with ˋbare' gradable adjectives (relations between individuals and degrees) rather than properties formed out of gradable adjectives.