Broadscale dampening of uncertainty adjustment in the aging brain
Kosciessa, J. Q.; Mayr, U.; Lindenberger, U.; Garrett, D. D.
Show abstract
0.The ability to prioritize among input features according to relevance enables adaptive behaviors across the human lifespan. However, relevance often remains ambiguous, and such uncertainty increases demands for dynamic control. While both cognitive stability and flexibility decline during healthy ageing, it is unknown whether aging alters how uncertainty impacts perception and decision-making, and if so, via which neural mechanisms. Here, we assess uncertainty adjustment across the adult lifespan (N = 100; cross-sectional) via behavioral modelling and a theoretically informed set of EEG-, fMRI-, and pupil-based signatures. On the group level, older adults show a broad dampening of uncertainty adjustment relative to younger adults. At the individual level, older individuals with more young-like neural responses also showed better maintained cognitive control. Our results highlight neural mechanisms whose maintenance plausibly enables flexible task-set, perception, and decision computations across the adult lifespan.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior 96%
- Reconfigurations of cortical manifold structure during reward-based motor learning 96%
- Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex 96%
Similar papers in this journal
- Higher rostral locus coeruleus integrity is associated with better memory performance in older adults 97%
- Cortical recycling in high-level visual cortex during childhood development 95%
- White matter connections of human ventral temporal cortex are organized by cytoarchitecture, eccentricity, and category-selectivity from birth 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.