Future time perspective: Dimensions of opportunities, life, and time are differentially associated with physical health, cognitive functioning, and well-being in old age.

: The present study investigated the multidimensional nature of the future time perspective scale and dimension-specific associations with measures of physical health, cognitive functioning, and well-being.: Using data from the Berlin Aging Study II ( = 1,038, age = 71 years, range = 61-88 years, 52% women), different models of future time perspective were compared using confirmatory factor analyses, and the best-fitting model was then used to explore dimension-specific associations with physical health, cognitive functioning, and well-being measures.: A model of future time perspective composed of a a and a was found to have the best fit.

Future time perspective: opportunities and limitations are differentially associated with subjective well-being and hair cortisol concentration.

Background: Future time perspective has been associated with subjective well-being, though depending on the line of research considered either an open-ended future time perspective or a limited future time perspective has been associated with high well-being. Most of this research however has conceptualized future time perspective as a one-dimensional construct, whereas recent evidence has demonstrated that there are likely at least two different underlying dimensions, a focus on opportunities and a focus on limitations. This project first seeks to replicate the two-dimensional structure of the Future Time Perspective Scale, and then examines the associations these dimensions may have with different measures of subjective well-being and a biological index of chronic stress.

Grapheme-color synaesthesia benefits rule-based Category learning.

Researchers have long suspected that grapheme-color synaesthesia is useful, but research on its utility has so far focused primarily on episodic memory and perceptual discrimination. Here we ask whether it can be harnessed during rule-based Category learning. Participants learned through trial and error to classify grapheme pairs that were organized into categories on the basis of their associated synaesthetic colors.