Psychological Research

, Volume 79, Issue 1, pp 28–41 | Cite as

On the time course of attentional focusing in older adults

  • Lisa N. JefferiesEmail author
  • Alexa B. Roggeveen
  • James T. Enns
  • Patrick J. Bennett
  • Allison B. Sekuler
  • Vincent Di Lollo
Original Article


Many sensory and cognitive changes accompany normal ageing, including changes to visual attention. Several studies have investigated age-related changes in the control of attention to specific locations (spatial orienting), but it is unknown whether control over the distribution or breadth of attention (spatial focus) also changes with age. In the present study, we employed a dual-stream attentional blink task and assessed changes to the spatial distribution of attention through the joint consequences of temporal lag and spatial separation on second-target accuracy. Experiment 1 compared the rate at which attention narrows in younger (mean age 22.6, SD 4.25) and older (mean age 66.8, SD 4.36) adults. The results showed that whereas young adults can narrow attention to one stream within 133 ms, older adults were unable to do the same within this time period. Experiment 2 showed that older adults can narrow their attention to one stream when given more time (266 ms). Experiment 3 confirmed that age-related changes in retinal illuminance did not account for delayed attentional narrowing in older adults. Considered together, these experiments demonstrate that older adults can narrow their attentional focus, but that they are delayed in initiating this process compared to younger adults. This finding adds to previously reported reductions in attentional dynamics, deficits in inhibitory processes, and reductions in posterior parietal cortex function that accompany normal ageing.


Attentional Blink Rapid Serial Visual Presentation Left Visual Field Rapid Serial Visual Presentation Stream Attentional Blink Magnitude 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) in the form of a Discovery grant to J.T. Enns, and a Discovery grant to V. Di Lollo. This work was also supported by a grant from the Canadian Institute of Health Research to P.J. Bennett and A.B. Sekuler. We thank Donna Waxman for her help recruiting and testing participants.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lisa N. Jefferies
    • 1
    Email author
  • Alexa B. Roggeveen
    • 2
  • James T. Enns
    • 3
  • Patrick J. Bennett
    • 4
  • Allison B. Sekuler
    • 4
  • Vincent Di Lollo
    • 5
  1. 1.School of Psychology and Exercise ScienceMurdoch UniversityMurdochAustralia
  2. 2.Faculty of Applied Health and Community StudiesSheridan CollegeOakvilleCanada
  3. 3.Department of PsychologyThe University of British ColumbiaVancouverCanada
  4. 4.Department of Psychology, Neuroscience, & BehaviourMcMaster UniversityHamiltonCanada
  5. 5.Department of PsychologySimon Fraser UniversityBurnabyCanada

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