This proposed study will examine the effects of aging on reward-based associative learning and generalization and how these relationships vary by common genetic polymorphisms. The ability to acquire new skills and apply those experiences to predict positive outcomes in novel situations is essential at all ages, enabling people to make critical economic decisions or social judgments. For example, people often select stocks based on wins and losses in the market, and the integration of these reinforcement outcomes over multiple experiences can help when navigating new investments, such as retirement planning. Similarly, this proposal focuses on frontostriatal-based learning (i.e., acquiring new associations from feedback) and hippocampal- based generalization (i.e., context-dependent transfer). These processes remain relatively understudied in cognitive aging, which is surprising given their relevance to adults of all ages. Our focus on the dissociation between learning and generalization is particularly relevant to older adults because these processes call on different brain regions that are differentially affected by healthy aging. Here, 175 younger and 175 older adults will complete a variety of learning and generalization tasks that have been validated to assess frontostriatal and hippocampal function. Older adults are predicted to show greater age-related deficits on learning than generalization, adding to data that healthy aging affects the striatal system more than the hippocampus (Aim 1). Consistent with evidence of heterogeneity of cognitive function in healthy aging, we will also show that some seniors do better than others during learning and/or generalization. Because genetic polymorphisms can reveal important individual differences in cognitive function, this study will also examine genetic components that may support individual differences in learning and generalization;notably, the dopamine transport gene (DAT1), which is most abundant in the striatum, and the gene coding for Brain-Derived Neurotrophic Factor (BDNF) that has highest expression in the hippocampus. It is expected that a functional polymorphism in DAT1 will predict individual differences in frontostriatal learning, whereas a functional polymorphism in BDNF will predict individual differences in hippocampal generalization (Aim 2.1). Finally, this study will examine whether healthy aging modulates these genetic effects on cognitive function, by testing a recent hypothesis that aging magnifies the functional significance of genetic variants on cognition (Aim 2.2). It is predicted that genotypic differences on cognition will be larger in older than younger adults. The identification of cognitively relevant genes, and age-related differences therein, brings promise to refine knowledge about neurobiological mechanisms of cognition and may help to explain heterogeneity of cognitive function in old age. Understanding the mechanisms underlying individual differences in vulnerability to cognitive decline may, in turn, inform cognitive training and pharmacological treatment programs aimed at maximizing cognitive functioning in old age. These goals are increasingly important as the number of older adults in the world population steadily rises.
The ability to learn new skills and generalize that knowledge is essential at all ages, but especially among older adults who must continually adapt to new people, environments and technologies;thus, it is important to characterize how and why these processes change with age. This research will show how genetic polymorphisms contribute to individual differences in learning and generalization of reward-based associations in younger and older adults, revealing important individual differences in vulnerability to cognitive decline. This understanding, in turn, will help in tailoring personalized cognitive training and pharmacological treatment programs aimed at maximizing cognitive function as a function of a person's age, genotype and cognitive phenotype.
|Sojitra, Ravi B; Lerner, Itamar; Petok, Jessica R et al. (2018) Age affects reinforcement learning through dopamine-based learning imbalance and high decision noise-not through Parkinsonian mechanisms. Neurobiol Aging 68:102-113|
|Schuck, Nicolas W; Petok, Jessica R; Meeter, Martijn et al. (2018) Aging and a genetic KIBRA polymorphism interactively affect feedback- and observation-based probabilistic classification learning. Neurobiol Aging 61:36-43|
|Petok, Jessica R; Myers, Catherine E; Pa, Judy et al. (2018) Impairment of memory generalization in preclinical autosomal dominant Alzheimer's disease mutation carriers. Neurobiol Aging 65:149-157|