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The Pair Test: A computerised measure of learning and memory.

Sarah BuckFilipa BastosTorsten BaldewegFaraneh Vargha-Khadem
Published in: Behavior research methods (2021)
There is increasing interest in the assessment of learning and memory in typically developing children as well as in children with neurodevelopmental disorders. However, neuropsychological assessments have been hampered by the dearth of standardised tests that enable direct comparison between distinct memory processes or between types of stimulus materials. We developed a tablet-based paired-associate learning paradigm, the Pair Test, based on neurocognitive models of learning and memory. The aims are to (i) establish the utility of this novel memory tool for use with children across a wide age range, and (ii) examine test validity, reliability and reproducibility of the construct. The convergent validity of the test was found to be adequate, and higher test reliability was shown for the Pair Test compared to standardised measures. Moderate test-retest reproducibility was shown, despite a long time interval between sessions (14 months). Moreover, the Pair Test is able to capture developmental changes in memory, and can therefore chart the developmental trajectory of memory and learning functions across childhood and adolescence. Finally, we used this novel instrument to acquire normative data from 130 typically developing children, aged 8-18 years. Age-stratified normative data are provided for learning, delayed recall and delayed recognition, for measures of verbal and non-verbal memory. The Pair Test thus provides measures of learning and memory accounting for encoding, consolidation and retrieval processes. As such, the standardised test results can be used to determine the status of learning and memory in healthy children, and also to identify deficits in paediatric patients at risk of damage to the neural network underlying mnemonic functions.
Keyphrases
  • working memory
  • young adults
  • emergency department
  • traumatic brain injury
  • depressive symptoms
  • oxidative stress
  • neural network
  • electronic health record
  • deep learning
  • patient reported outcomes