A model-based analysis of the impairment of semantic memory

Factor structure and clinical applicability of new semantic tasks in Alzheimer's disease and aphasia

Semantic tasks are frequently used when examining language functions in patients with acquired disorders such as Alzheimer's disease (AD) and aphasia. Little is known about the possible covariation between different types of tasks or their factor structure in healthy adults. Additionally, few studies have examined semantic task performances in different patient groups. The aims of this data-driven study were to examine the factor structure in a wide range of semantic tasks in healthy older adults, the possible differences in factor variables between healthy controls, patients with AD and patients with stroke aphasia, as well as the clinical applicability of tasks in differentiating the two patient groups from controls. Participants included 59 healthy older adults, 13 patients with AD and 14 patients with aphasia. The results indicated a four-factor solution for the semantic task variables: (1) the Semantic association factor, (2) the Time factor, (3) the Verbal factor and (4) the Synonym factor. The Verbal factor was the only distinguishing factor between the two patient groups. Three factors reliably discriminated between the controls and the AD patients, and the Verbal factor reliably discriminated between the controls and the aphasia patients. In addition, a few single task variables showed outstanding discrimination for both patient groups. This study supports the notions of semantic tasks tapping into more than one cognitive subcomponent and a more general semantic impairment in AD than in aphasia. In clinical assessment, choosing appropriate semantic tasks is crucial in order to reliably detect the characteristics of the impairment.

Bayesian Modeling of the Mnemonic Similarity Task Using Multinomial Processing Trees

The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings. We develop new cognitive models, based on the multinomial processing tree framework, for two versions of the MST. The models are implemented as generative probabilistic models and applied to behavioral data using Bayesian graphical modeling methods. We demonstrate how the combination of cognitive modeling and Bayesian methods allows for flexible and powerful inferences about performance on the MST. These demonstrations include latent-mixture extensions for identifying individual differences in decision strategies, and hierarchical extensions that measure fine-grained differences in the ability to detect lures. One key finding is that the availability of a "similar" response in the MST reduces individual differences in decision strategies and allows for more direct measurement of recognition memory.