Abstract
CONCLUSION
The Bayesian process model increased the informativity of Verbal Learning test data, by showing uncertainty of the traditional sum score measurements as well as how the underlying processes differed between populations. Additionally, the model grants insight into underlying memory processes for individuals and how these processes vary within and between them.
RESULTS
Analysis of the sum score indicated that patients scored lower than controls on immediate recall but found no difference for delayed recall. The process model analysis indicated a small difference between patients and controls in immediate retrieval from both the partially learned and learned states, with no differences in learning or delayed retrieval processes. Individual-level analysis shows considerable uncertainty for sum scores. Sum scores were more certain than single trials. Retrieval parameters also showed less uncertainty than learning parameters.
INTRODUCTION
Verbal memory is a complex and fundamental aspect of human cognition. However, traditional sum-score analyses of verbal learning tests oversimplify underlying verbal memory processes. We propose using process models to subdivide memory into multiple processes, which helps in localizing the most affected processes in impaired verbal memory. Additionally, the model can be used to address score and process variability. This study aims to investigate the effects of cancer and its treatment on verbal memory, as well as provide a demonstration of how process models can be used to investigate the uncertainty in neuropsychological test scores.
METHOD
We present an investigation of memory process scores in non-CNS cancer survivors (n = 184) and no-cancer controls (n = 204). The participants completed the Amsterdam Cognition Scan (ACS), in which classical neuropsychological tests are digitally recreated for online at-home administration. We analyzed data from the ACS equivalent of a Verbal Learning Test using both traditional sum scores and a Bayesian process model.