bioRxivNeurosciencePreprint
A multi-modal approach to decomposing standard neuropsychological test performance: Symbol Search
Nicolas LangeriD, Erica J. HoiD, Andreas PedroniiD, Lindsay M. AlexanderiD, Enitan T. MarcelleiD, Kenneth SchusteriD, Michael Peter MilhamiD, Simon P. KellyiD
Abstract
Neuropsychological test batteries provide normed assessments of cognitive performance across multiple functional domains. Although each test emphasizes a certain component of cognition, a poor score can reflect many possible processing deficits. Here we explore the use of simultaneous eye tracking and EEG to decompose test performance into interpretable, components of cognitive processing. We examine the specific case of Symbol Search, a \"processing speed\" subtest of the WISC, which involves searching for the presence of either of two target symbols among five search symbols. To characterize the signatures of effective performance of the test, we asked 26 healthy adults to perform a computerized version of it while recording continuous EEG and eye tracking. We first established basic gaze-shifting patterns in the task, such as more frequent and prolonged fixation of each target than each search symbol, and longer search symbol fixations and overall trial duration for target-absent trials. We then entered multiple such metrics into a least absolute shrinkage and selection operator (LASSO) analysis, which revealed that short trial completion times were mainly predicted by longer initial fixations on the targets and fewer subsequent confirmatory saccades directed back to the targets. Further, the tendency to make confirmatory saccades was associated with stronger gamma-amplitude modulation by mid-frontal theta-phase in the EEG during initial target symbol encoding. Taken together, these findings indicate that efficient Symbol Search performance depends more on effective memory encoding than on general \"processing speed\".
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 2
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