Peripheral vision, perceptual asymmetries and visuospatial attention in young, young-old and oldest-old adults

Muiños, Mónica; Palmero, Francisco; Ballesteros, Soledad

doi:10.1016/j.exger.2015.12.006

Cited by 15 publications

(10 citation statements)

References 49 publications

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“…An alternative point of view is that under appropriate control, processing speed accounts for most age-related differences in executive deficits (Verhaeghen and Cerella, 2002 ). For example, a study of three groups of participants, with mean ages of 22, 70, and 85 years, respectively, showed common perceptual and orienting attention patterns, and differences were observed for processing speed only (Muiños et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting Age From Behavioral Test Performance for Screening Early Onset of Cognitive Decline

Statsenko

Habuza

Charykova

et al. 2021

Front. Aging Neurosci.

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Background: Neuronal reactions and cognitive processes slow down during aging. The onset, rate, and extent of changes vary considerably from individual to individual. Assessing the changes throughout the lifespan is a challenging task. No existing test covers all domains, and batteries of tests are administered. The best strategy is to study each functional domain separately by applying different behavioral tasks whereby the tests reflect the conceptual structure of cognition. Such an approach has limitations that are described in the article.Objective: Our aim was to improve the diagnosis of early cognitive decline. We estimated the onset of cognitive decline in a healthy population, using behavioral tests, and predicted the age group of an individual. The comparison between the predicted (“cognitive”) and chronological age will contribute to the early diagnosis of accelerated aging.Materials and Methods: We used publicly available datasets (POBA, SSCT) and Pearson correlation coefficients to assess the relationship between age and tests results, Kruskal-Wallis test to compare distribution, clustering methods to find an onset of cognitive decline, feature selection to enhance performance of the clustering algorithms, and classification methods to predict an age group from cognitive tests results.Results: The major results of the psychophysiological tests followed a U-shape function across the lifespan, which reflected the known inverted function of white matter volume changes. Optimal values were observed in those aged over 35 years, with a period of stability and accelerated decline after 55–60 years of age. The shape of the age-related variance of the performance of major cognitive tests was linear, which followed the trend of lifespan gray matter volume changes starting from adolescence. There was no significant sex difference in lifelong dynamics of major tests estimates. The performance of the classification model for identifying subject age groups was high.Conclusions: ML models can be designed and utilized as computer-aided detectors of neurocognitive decline. Our study demonstrated great promise for the utility of classification models to predict age-related changes. These findings encourage further explorations combining several tests from the cognitive and psychophysiological test battery to derive the most reliable set of tests toward the development of a highly-accurate ML model.

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Section: Discussionmentioning

confidence: 99%

Predicting Age From Behavioral Test Performance for Screening Early Onset of Cognitive Decline

Statsenko

Habuza

Charykova

et al. 2021

Front. Aging Neurosci.

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“…While the allocation of covert attention to the periphery (a cognitive process) is likely to be decreased in older adults, it seems that sensory phenomena, such as crowding in the peripheral visual field, remain unchanged in aging (Astle et al, 2014). Similarly, Muiños et al (2016) found that the cost of detecting eccentric targets compared to foveal targets was not different for older and younger adults. The difference between (most of) these studies appears to be whether a central task and a peripheral task are competing with one another.…”

Section: Introductionmentioning

confidence: 93%

“…Adding a secondary central task to visual search affects peripheral performance of older adults to a much greater extent than the younger adults' performance (Ball et al, 1988;Sekuler et al, 2000), which could be due to said decrease in peripheral attention but also to impaired divided attention abilities in older adults. On the other hand, other studies have shown no decline in peripheral visual function (Astle, Blighe, Webb, & McGraw, 2014;Hommel et al, 2004;Muiños, Palmero, & Ballesteros, 2016). While the allocation of covert attention to the periphery (a cognitive process) is likely to be decreased in older adults, it seems that sensory phenomena, such as crowding in the peripheral visual field, remain unchanged in aging (Astle et al, 2014).…”

Section: Introductionmentioning

confidence: 95%

Reliance on central vs. peripheral vision for visual search in younger and older adults

Laurin¹,

Ouerfelli-Éthier²,

Pisella³

et al. 2020

Preprint

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Older adults show declines performing visual search, but their nature is unclear. We propose that it is related to greater attentional reliance on central vision. To investigate this, we tested how occluding central vision would affect younger and older adults in visual search. Participants (14 younger, M = 21.6 years; 16 older, M = 69.6 years) performed pop-out and serial search tasks in full view and with different sized gaze-contingent artificial central scotomas (no scotoma, 3°, 5° or 7° diameter).In pop-out search, older adults showed longer search times for peripheral targets during full viewing. Their reaction times, saccades and fixation durations also increased as a function of scotoma size, contrary to younger adults. These declines may reflect a relative impairment in peripheral visual attention for global processing in aging.In serial search, despite older adults being generally slower, we found no difference between groups in reaction time increases for eccentric targets and for bigger scotomas. These results may come from the difficulty of serial search, in which both groups used centrally limited attentional windows.We conclude that older adults allocate more attentional resources towards central vision compared to younger adults, impairing their peripheral processing primarily in pop-out visual search.

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“…The onset time of IOR is about 300 ms following cue onset (for a meta-analysis, see Samuel and Kat, 2003), however, it also varies with task difficulty (e.g., Lupiáñez et al, 1997; Lupiáñez et al, 2001; see Klein, 2000, for a discussion) and reflects the flexibility of executive control. For instance, the onset time of IOR is much later in older adults, whose executive functions are declining (e.g., Langley et al, 2011; Muiños et al, 2016; Li et al, 2018). The delayed onset of IOR is likely the result of slow (or failed) disengagement of attention from the cue (Lupiáñez et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Children With Mathematical Learning Difficulties Are Sluggish in Disengaging Attention

Zhang

Fu²,

Xue

et al. 2019

Front. Psychol.

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Mathematical learning difficulties (MLD) refer to a variety of deficits in math skills, typically pertaining to the domains of arithmetic and problem solving. The present study examined the time course of attentional orienting in MLD children with a spatial cueing task, by parametrically manipulating the cue-target onset asynchrony (CTOA). The results of Experiment 1 revealed that, in contrast to typical developing children, the inhibitory aftereffect of attentional orienting – frequently referred to as inhibition of return (IOR) – was not observed in the MLD children, even at the longest CTOA tested (800 ms). However, robust early facilitation effects were observed in the MLD children, suggesting that they have difficulties in attentional disengagement rather than attentional engagement. In a second experiment, a secondary cue was introduced to the cueing task to encourage attentional disengagement and IOR effects were observed in the MLD children. Taken together, the present experiments indicate that MLD children are sluggish in disengaging spatial attention.

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Peripheral vision, perceptual asymmetries and visuospatial attention in young, young-old and oldest-old adults

Cited by 15 publications

References 49 publications

Predicting Age From Behavioral Test Performance for Screening Early Onset of Cognitive Decline

Predicting Age From Behavioral Test Performance for Screening Early Onset of Cognitive Decline

Reliance on central vs. peripheral vision for visual search in younger and older adults

Children With Mathematical Learning Difficulties Are Sluggish in Disengaging Attention

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