The investigation was done with a heteroskedastic ordered logit model to account for age differences in unobserved variance.
Further, white matter integrity in these thalamocorticostriatal paths could statistically account for age differences in learning.
The results indicated that cautiousness measures accounted for age differences in omission errors but not in performance.
This cross-sectional study examined whether age differences in frontostriatal white matter integrity could account for age differences in reward learning in a community life span sample of human adults.
A generalized age-related slowing in the speed of information processing can currently account for age differences in the retrieval of letter-identity and semantic information from long-term memory.
Experiment 4 also indicates that older adults focus more on positive emotions than younger adults do when considering their options, and that this emotional shift can at least partially account for age differences in how much people are swayed by certainty in their choices.
These findings do not clarify, however, whether strategy differences can account for age differences in neural recruitment, or whether reduced neural recruitment forces adoption of simpler strategies.
To account for age differences in LTCF and community residents, we calculated age-adjusted relative risks (RRs) for death from infection with different pathogens by using Mantel-Haenszel methods.
Building on these findings, a diffusion tensor-imaging study revealed that the structural connectivity of the prefrontal cortex to the striatum could account for age differences in probabilistic reward learning (Samanez-Larkin, Levens, Perry, Dougherty, & Knutson, 2012).
We calculated incidence rates (IRs) per 100,000 person-years for each of the events of interest for each database and performed direct standardization using the WHO World Standard Population as reference to account for age differences when comparing the overall diagnosis rates (standardized IRs; SIRs) [ 19].
