Impact Factor 4.504

The Frontiers in Neuroscience journal series is the 1st most cited in Neurosciences

Mini Review ARTICLE

Front. Aging Neurosci., 27 July 2017 | https://doi.org/10.3389/fnagi.2017.00246

Attentional Orienting and Dorsal Visual Stream Decline: Review of Behavioral and EEG Studies

  • Department of Psychology, University of Auckland, Auckland, New Zealand

Every day we are faced with an overwhelming influx of visual information. Visual attention acts as the filtering mechanism that enables us to focus our limited neural resources, by selectively processing only the most relevant and/or salient aspects of our visual environment. The ability to shift attention to the most behaviorally relevant items enables us to successfully navigate and interact with our surroundings. The dorsal visual stream is important for the rapid and efficient visuospatial orienting of attention. Unfortunately, recent evidence suggests that the dorsal visual stream may be especially vulnerable to age-related decline, with significant deterioration becoming evident quite early in the aging process. Yet, despite the significant age-related declines to the dorsal visual stream, the visuospatial orienting of attention appears relatively well preserved in older adults, at least in the early stages of aging. The maintenance of visuospatial orienting of attention in older adults appears to be facilitated by the engagement of compensatory neural mechanisms. In particular, older adults demonstrate heightened activity in the frontal regions to compensate for the reduced activity in the posterior sensory regions. These findings suggest that older adults are more reliant on control processes mediated by the anterior regions of the frontoparietal attention network to compensate for less efficient sensory processing within the posterior sensory cortices.

Introduction

Everything from playing tennis to simply walking down the street require the successful orienting of attention from one location to the next. This process of shifting attention is referred to as the visuospatial orienting of attention. A process which relies on an interacting network of structures, which include regions of the lateral prefrontal cortex (Corbetta and Shulman, 2002, 2011; Corbetta et al., 2008; Schall, 2009; Vandenberghe and Gillebert, 2009; Asplund et al., 2010), working in conjunction with the dorsal visual stream (Siegel et al., 2008; Lambert and Shin, 2010; Marrett et al., 2011; Capilla et al., 2014) to guide the deployment of attentional resources. The involvement of dorsal visual stream processing in visuospatial orienting is consistent with both Goodale and Milner’s (1992) duplex model of vision which suggests that the dorsal visual stream is responsible for visually guided actions (including eye movements; Milner and Goodale, 2008), and with the premotor theory of attention which suggests that there is considerable overlap in the neural mechanisms responsible for the overt and covert orienting of attention (Rizzolatti et al., 1987). Unfortunately, recent evidence suggests that the dorsal visual stream may be vulnerable to age-related declines from relatively early on in the aging process (Langrová et al., 2006). This mini-review article examines the impact of these age-related declines on the visuospatial orienting of attention.

Age-Related Decline of The Dorsal Visual Stream

Recent studies suggest that the dorsal visual stream may be vulnerable to age-related atrophy and decline due to its cellular composition. The dorsal visual stream which extends from area V1 to the dorsolateral occipital cortex (Maunsell, 1987), and regions of the posterior parietal cortex (Kravitz et al., 2011), receives input primarily from the magnocellular layers of the lateral geniculate nucleus (LGN; Celesia and DeMarco, 1994; Grill-Spector and Malach, 2004). In contrast, the ventral visual stream which extends from area V1 to the inferior temporal cortex (Kravitz et al., 2011), receives input from both the magnocelluar and parvocellular layers (Celesia and DeMarco, 1994; Grill-Spector and Malach, 2004) of the LGN. Braddick et al.’s (2003) dorsal stream vulnerability hypothesis argues that, since magno cells are larger than parvo cells, and neurons with larger cell bodies and axon diameters are more susceptible to damage, magno cells are more susceptible to degeneration. In addition, losses in the magnocellular pathway may be more readily apparent as there are far fewer magno cells than parvo cells (Skottun, 2000). Thus, even when similar numbers of neurons are lost in both the magnocellular and parvocellular pathways, functional declines may be more apparent in the magnocellular pathway. Since the dorsal visual stream’s primary input comes from magno cells (Celesia and DeMarco, 1994), the dorsal visual stream is in turn, more susceptible to degeneration (Braddick and Atkinson, 2011). Although Braddick’s proposal of dorsal visual stream vulnerability was based on an examination of dorsal visual stream deficits in early childhood development; it does highlight the possibility that the dorsal visual stream may be more susceptible to decline in general.

Evidence of age-related dorsal visual stream declines has come from studies examining the age-related declines to neural structures. In a study by Ziegler et al. (2012), 547 participants between the ages of 19–86 years old were recruited, to perform a large-sample cross-sectional voxel-based morphometry (VBM) study to examine reductions in gray matter volume with advancing age. The results revealed that the dorsal visual stream exhibited substantially larger reductions in gray matter volume, compared to the ventral visual stream and the primary visual areas. These declines have been localized to the superior parietal cortex (Driscoll et al., 2009), as well as, the superior and inferior parietal gyri (Crivello et al., 2014; Fjell et al., 2014), all of which are crucial for the visuospatial orienting of attention (Corbetta and Shulman, 2002). Moreover, in elderly subjects, neural declines in the parietal regions (Resnick et al., 2003; Driscoll et al., 2009; Thambisetty et al., 2010; Crivello et al., 2014; Fjell et al., 2014) are compounded by a reduction in cerebral blood flow to the region (Martin et al., 1991), resulting in less efficient processing within the dorsal visual stream.

Impact of Dorsal Visual Stream Declines (Behavioral)

This mini-review article will focus primarily on the impact of these declines on the age-related changes to performance within the spatial cueing paradigm. In these studies, a cue elicits a shift of attention to its location (in the case of a peripheral cue; Eriksen and Hoffman, 1974; Theeuwes, 1991; Rafal and Henik, 1994; Yantis and Hillstrom, 1994; Oonk and Abrams, 1998), or to the location signaled by the cue stimulus (in the case of a central cue; Jonides, 1981; Shepherd and Müller, 1989; Cheal and Lyon, 1991; Theeuwes, 1991; Friesen et al., 2004). Subsequently, a target appears in either the location signaled by the cue stimulus (valid trial), or at an un-cued location (invalid trial). Participants are tasked with making a response to the onset of the target stimulus. The cueing benefit is indexed by a decrease in reaction times when targets appear in the location signaled by the cue stimulus and is believed to reflect the engagement of attentional resources at the target location (e.g., Jonides, 1981; Shepherd and Müller, 1989; Theeuwes, 1991; Yantis and Hillstrom, 1994; Friesen et al., 2004). Conversely, the cueing cost is indexed by an increase in reaction times when the target appears at an un-cued location, and is believed to reflect the disengagement and shifting of attentional resources from the invalidly cued location to the target location (Posner, 1980).

It appears that despite the declines to the dorsal visual stream, the visuospatial orienting of attention appears relatively well-preserved in older adults (Nissen and Corkin, 1985; Hartley et al., 1990). In spite of the slower reaction times seen in older adults, the magnitude of cueing benefit for both peripherally (Folk and Hoyer, 1992, Experiment 1; Greenwood et al., 1993; Olk and Kingstone, 2009) and centrally (Greenwood et al., 1993; Lincourt et al., 1997; Curran et al., 2001; Lorenzo-López et al., 2002; Olk and Kingstone, 2009) presented cues is similar for both younger and older adults. However, some studies have noted an increase in cueing costs following invalidly cued targets (Hartley et al., 1990; Greenwood and Parasuraman, 1994). Taken together, these results suggest that aging may be selectively associated with reduced efficiency in disengaging and shifting attentional resources from one location to the next, while the ability to effectively utilize the cues to guide the deployment of attentional resources remains intact.

One potential shortcoming of the aforementioned studies is that in these studies participants are required to make a detection or discrimination response to a target in an otherwise empty screen. In contrast, visual scenes are typically cluttered and we need to be able to rapidly identify a relevant item (target) from surrounding items (distractors). A process which has typically been studied using the visual search paradigm (Treisman and Gelade, 1980). In these studies, participants are required to identify a target that differs from surrounding items by a single feature (feature search) or by a conjunction of features (conjunction search; Treisman and Gelade, 1980). Results indicate that aging appears to selectively impair conjunction search while feature search remains intact (Plude and Doussard-Roosevelt, 1989). An elegant study by Greenwood and Parasuraman (1999) examined if the selective age-related impairment in conjunction search could in part be accounted for by an age-related reduction in the ability to flexibly expand and contract the focus of attention. To do so, they employed the use of precues that indicated the location of an upcoming target with a varying degree of precision (element-size precue: highlights a single possible location; column-size precue: highlights a column of possible locations; array-size precue: highlights a whole array of potential locations). The results indicated that age-related impairments in conjunction search can be alleviated by the use of precise and valid precues. Although this benefit was somewhat smaller in the oldest group of participants (above 76 years old). They suggest that aging impairs the ability to flexibly expand and contract the focus of attention and that elderly participants are more reliant on the precues to adjust the scale of their attention. But in the oldest participants the ability to utilize the precues to adjust the scale of attention is reduced. These findings suggest that although the visuospatial orienting of attention remains relatively well preserved in older adults, its flexibility is somewhat reduced.

Impact of Dorsal Visual Stream Declines (EEG)

Additionally, some studies have also employed the use of electroencephalographic (EEG) recordings in order to examine the electrophysiological correlates of attention shifts. These event-related potential (ERP) studies have most commonly focused on the P1 (80–130 ms) and the N1 (140–200 ms) components in relation to processing of target stimuli (Mangun and Hillyard, 1991; Wright et al., 1995). These studies demonstrate that shifting attention to a particular location of the visual field, increases the amplitude of P1 and N1 components (Mangun and Hillyard, 1991; Eimer, 1994; Luck et al., 1994; Mangun, 1995; Anllo-Vento et al., 1998) evoked by stimuli within the attended location. The amplification of these components appears to involve a selective enhancement of the signal to noise ratio of stimuli within the attended area, thereby strengthening the perceptual representation of stimuli located within that region (see Carrasco, 2011, for review; Heinze et al., 1990, 1994). Consequently, stimuli within the attended area are more rapidly detected; giving rise to more rapid response times when targets appear at the cued location (valid trials) compared to when targets appear at un-cued locations (invalid trials). Results from ERP studies examining the age-related changes to the underlying neural substrates of visuospatial orienting closely parallel the results of behavioral studies. These studies demonstrate that despite delayed latency of the P1 (80–130 ms) and N1 (140–200 ms) components, the augmentation of these early components elicited by attended relative to unattended targets is similar across both younger and older adults (Yamaguchi et al., 1995; Curran et al., 2001; Nagamatsu et al., 2011). With some studies showing linear correlations between the latency of ERP components and the mean reaction times of elderly participants (Li et al., 2013), which further bolsters the proposal that reductions in transmission efficiency of neural signals may account for slowed visuospatial orienting in elderly subjects (Hong and Rebec, 2012).

More recently, researchers have begun to focus on the neural activity elicited during the cue-target interval; much of this work has focused upon examining the modulation of prestimulus alpha activity along the fronto-occipital axis during the cue-target interval (Foxe et al., 1998; Worden et al., 2000; Babiloni et al., 2006; Rihs et al., 2009; Foxe and Snyder, 2011). Alpha-band desynchronization of the contralateral fronto-occipital axis appears to increase perceptual sensitivity by causing a baseline shift in the sensitivity of the neurons representing the to-be-attended location (Sauseng et al., 2005; Rihs et al., 2007; Siegel et al., 2008; Capotosto et al., 2009; Kelly et al., 2009; Capilla et al., 2014), while a concurrent alpha-band synchronization of the ipsilateral fronto-occipital axis inhibits processing of unattended regions (Kelly et al., 2006; Klimesch et al., 2007; Rihs et al., 2007, 2009; Capotosto et al., 2009; Foxe and Snyder, 2011; Bengson et al., 2012). The lateralization of prestimulus alpha reflects the top-down attentional modulation of neural processing in the visual cortices, and is referred to as proactive attentional control (Braver, 2012). It is termed proactive as it reflects the ability of the attentional system to bias perceptual processing in favor of an upcoming target before it is presented. In contrast, reactive attentional control refers to resolving interference between target and potentially distracting information at later stages of the processing hierarchy (Geerligs et al., 2014).

Recent studies indicate that the level of alpha power lateralization during the cue-target interval is significantly reduced in older adults (Vaden et al., 2012; Hong et al., 2015; Li and Zhao, 2015), and this reduction is most prominent along parietal-occipital regions (Zanto et al., 2011; Deiber et al., 2013). Specifically, older adults showed significant reductions in the level of event-related synchronization of prestimulus alpha (ipsilaterally) along these sites (Karrasch et al., 2004; Deiber et al., 2010; Vaden et al., 2012), which is consistent with earlier reports suggesting that older adults face significant difficulty with distractor suppression (Gazzaley et al., 2008; Schmitz et al., 2010; Haring et al., 2013). The decreased modulation of prestimulus alpha is proposed to be the result of the overall decline in alpha power in older adults, which renders the modulation of prestimulus alpha a less efficient means of attentional control (Vaden et al., 2012; Deiber et al., 2013; Hong et al., 2015; Li and Zhao, 2015). Older adults may compensate for this deficit with stronger early engagement of motor areas (Deiber et al., 2013), and an increase in reactive control (Paxton et al., 2008; Geerligs et al., 2014) mediated by the anterior nodes of the frontoparietal attention network (De Fockert et al., 2009; Schmitz et al., 2010; Haring et al., 2013; Li et al., 2013; Geerligs et al., 2014). These findings suggest that the age-related deterioration of parietal-occipital regions impairs elderly participants’ ability to engage in the proactive attentional biasing of early sensory regions and leads to an increased reliance on more reactive control strategies.

Additionally, while the majority of research into the patterns of age-related cerebral reorganization have centered on intra-hemispheric patterns of reorganization, there is also extensive evidence to support inter-hemispheric patterns of reorganization (Cabeza et al., 1997; Madden et al., 1999; Tulving et al., 1994; Reuter-Lorenz et al., 2000; Cappell et al., 2010). It has been proposed that with the progressive age-related deterioration of specialized neural networks, the high metabolic costs of inter-hemispheric communication (Bullmore and Sporns, 2012; Liang et al., 2013) are outweighed by the benefits to behavioral performance (Banich, 1998; Cabeza, 2002). Although most studies of age-related asymmetry reduction have focused predominantly on the bilateral recruitment of the prefrontal cortices (Madden et al., 1999; Reuter-Lorenz et al., 2000), there is also evidence for age-related asymmetry reduction within the parietal cortices (Garavan et al., 1999). This implies that while the control of spatial attention may be strongly right lateralized in young adults (Corbetta et al., 1993; Foxe et al., 2003; Thiebaut de Schotten et al., 2011), older adults may maintain their performance on visuospatial orienting tasks by the bilateral recruitment of the posterior parietal cortex. Evidence for the hemispheric asymmetry reduction in visuospatial attention comes from studies demonstrating age-related attenuation of pseudoneglect in healthy older adults (Schmitz and Peigneux, 2011; Benwell et al., 2014; Learmonth et al., 2017). Pseudoneglect refers to the consistent attentional bias to the left visual field that is typically observed in healthy young adults (Bowers and Heilman, 1980; Voyer et al., 2012), and is believed to be due to the right hemisphere dominance for visuospatial processing (Waberski et al., 2008; Cavézian et al., 2012). Learmonth et al. (2017) demonstrated the typical leftward attentional bias in young adults was coupled with greater activity over the right parieto-occipital regions, and this lateralization was absent in older adults (whom also failed to show a leftward attentional bias). These results suggest that in older adults’ deterioration of parieto-occipital regions may lead to compensatory increases in activity from homologous regions within the opposite hemisphere.

Conclusion

In summary, these studies suggest that in spite of the early age-related declines of the dorsal visual stream the visuospatial orienting of attention remains relatively well preserved, at least in the earlier stages of aging. This maintenance appears to be facilitated by the engagement of compensatory neural mechanisms; which is consistent with both the compensation-related utilization of neural circuits hypothesis (CRUNCH; Reuter-Lorenz and Cappell, 2008), and the scaffolding theory of aging and cognition (STAC; Park and Reuter-Lorenz, 2009), which propose that the age-related deterioration of the frontoparietal attention network will result in the engagement of compensatory neural mechanisms to support the performance of visual attention tasks. Although the engagement of compensatory neural mechanisms enables older adults to shift attention in the face of widespread structural deterioration (Schneider-Garces et al., 2010; Vallesi et al., 2011; Shafto et al., 2012; Meunier et al., 2014), it results in slower and less efficient task performance (Park and Reuter-Lorenz, 2009; Meunier et al., 2014; Reuter-Lorenz and Park, 2014). Furthermore, progressive deterioration of the frontoparietal network will result in an increased reliance on compensatory neural mechanisms, but at the same time increasing levels of atrophy and structural deterioration also limits the brain’s capacity for reorganization (Burke and Barnes, 2006). As age-related atrophy proceeds, the brain will eventually reach its limits for functional reorganization and result in the more apparent attentional declines seen in the later stages of old age (80 years and above; Daffner et al., 2011).

Author Contributions

The mini review was written by ETS-L with assistance and feedback from AJL.

Funding

Funding Body: Marsden Fund of New Zealand. Project Number: 3711736. Project Title: Sight unseen: penetrating the enigma of unconscious vision.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Anllo-Vento, L., Luck, S. J., and Hillyard, S. A. (1998). Spatio-temporal dynamics of attention to color: evidence from human electrophysiology. Hum. Brain Mapp. 6, 216–238. doi: 10.1002/(sici)1097-0193(1998)6:4<216::aid-hbm3>3.0.co;2-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Asplund, C. L., Todd, J. J., Snyder, A. P., and Marois, R. (2010). A central role for the lateral prefrontal cortex in goal-directed and stimulus-driven attention. Nat. Neurosci. 13, 507–512. doi: 10.1038/nn.2509

PubMed Abstract | CrossRef Full Text | Google Scholar

Babiloni, C., Vecchio, F., Bultrini, A., Luca Romani, G., and Rossini, P. M. (2006). Pre- and poststimulus alpha rhythms are related to conscious visual perception: a high-resolution EEG study. Cereb. Cortex 16, 1690–1700. doi: 10.1093/cercor/bhj104

PubMed Abstract | CrossRef Full Text | Google Scholar

Banich, M. T. (1998). The missing link: the role of interhemispheric interaction in attentional processing. Brain Cogn. 36, 128–157. doi: 10.1006/brcg.1997.0950

PubMed Abstract | CrossRef Full Text | Google Scholar

Bengson, J. J., Mangun, G. R., and Mazaheri, A. (2012). The neural markers of an imminent failure of response inhibition. Neuroimage 59, 1534–1539. doi: 10.1016/j.neuroimage.2011.08.034

PubMed Abstract | CrossRef Full Text | Google Scholar

Benwell, C. S. Y., Thut, G., Grant, A., and Harvey, M. (2014). A rightward shift in the visuospatial attention vector with healthy aging. Front. Aging Neurosci. 6:113. doi: 10.3389/fnagi.2014.00113

PubMed Abstract | CrossRef Full Text | Google Scholar

Bowers, D., and Heilman, K. M. (1980). Pseduneglect: effects of hemispace on tactile line bisection task. Neuropsychologia 18, 491–498. doi: 10.1016/0028-3932(80)90151-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Braddick, O., and Atkinson, J. (2011). Development of human visual function. Vision Res. 51, 1588–1609. doi: 10.1016/j.visres.2011.02.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Braddick, O., Atkinson, J., and Wattam-Bell, J. (2003). Normal and anomalous development of visual motion processing: motion coherence and ‘dorsal-stream vulnerability’. Neuropsychologia 41, 1769–1784. doi: 10.1016/s0028-3932(03)00178-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Braver, T. S. (2012). The variable nature of cognitive control: a dual mechanisms framework. Trends Cogn. Sci. 16, 106–113. doi: 10.1016/j.tics.2011.12.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Bullmore, E., and Sporns, O. (2012). The economy of brain network organization. Nat. Rev. Neurosci. 13, 336–349. doi: 10.1038/nrn3214

PubMed Abstract | CrossRef Full Text | Google Scholar

Burke, S. N., and Barnes, C. A. (2006). Neural plasticity in the ageing brain. Nat. Rev. Neurosci. 7, 30–40. doi: 10.1038/nrn1809

PubMed Abstract | CrossRef Full Text | Google Scholar

Cabeza, R. (2002). Hemispheric asymmetry reduction in older adults: the Harold model. Psychol. Aging 17, 85–100. doi: 10.1037//0882-7974.17.1.85

PubMed Abstract | CrossRef Full Text | Google Scholar

Cabeza, R., Grady, C. L., Nyberg, L., McIntosh, A. R., Tulving, E., Kapur, S., et al. (1997). Age-related differences in neural activity during memory encoding and retrieval: a positron emission tomography study. J. Neurosci. 17, 391–400.

PubMed Abstract | Google Scholar

Capilla, A., Schoffelen, J. M., Paterson, G., Thut, G., and Gross, J. (2014). Dissociated α-band modulations in the dorsal and ventral visual pathways in visuospatial attention and perception. Cereb. Cortex 24, 550–561. doi: 10.1093/cercor/bhs343

PubMed Abstract | CrossRef Full Text | Google Scholar

Capotosto, P., Babiloni, C., Romani, G., and Corbetta, M. (2009). Frontoparietal cortex control spatial attention through modulation of anticipatory alpha rhythms. J. Neurosci. 29, 5863–5872. doi: 10.1523/JNEUROSCI.0539-09.2009

PubMed Abstract | CrossRef Full Text | Google Scholar

Cappell, K. A., Gmeindl, L., and Reuter-Lorenz, P. A. (2010). Age differences in prefontal recruitment during verbal working memory maintenance depend on memory load. Cortex 46, 462–473. doi: 10.1016/j.cortex.2009.11.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Carrasco, M. (2011). Visual attention: the past 25 years. Vision Res. 51, 1484–1525. doi: 10.1016/j.visres.2011.04.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Cavézian, C., Valadao, D., Hurwitz, M., Saoud, M., and Danckert, J. (2012). Finding centre: ocular and fMRI investigations of bisection and landmark task performance. Brain Res. 1437, 89–1103. doi: 10.1016/j.brainres.2011.12.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Celesia, G. G., and DeMarco, P. J. Jr. (1994). Anatomy and physiology of the visual system. J. Clin. Neurophysiol. 11, 482–492.

PubMed Abstract | Google Scholar

Cheal, M., and Lyon, D. R. (1991). Central and peripheral precuing of forced-choice discrimination. Q. J. Exp. Psychol. A. 43, 859–880. doi: 10.1080/14640749108400960

PubMed Abstract | CrossRef Full Text | Google Scholar

Corbetta, M., Miezin, F. M., Shulman, G. L., and Petersen, S. E. (1993). A PET study of visuospatial attention. J. Neurosci. 13, 1202–1226.

PubMed Abstract | Google Scholar

Corbetta, M., Patel, G., and Shulman, G. L. (2008). The reorienting system of the human brain: from environment to theory of mind. Neuron 58, 306–324. doi: 10.1016/j.neuron.2008.04.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Corbetta, M., and Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215. doi: 10.1038/nrn755

PubMed Abstract | CrossRef Full Text | Google Scholar

Corbetta, M., and Shulman, G. L. (2011). Spatial neglect and attention networks. Annu. Rev. Neurosci. 34, 569–599. doi: 10.1146/annurev-neuro-061010-113731

PubMed Abstract | CrossRef Full Text | Google Scholar

Crivello, F., Tzourio-Mazoyer, N., Tzourio, C., and Mazoyer, B. (2014). Longitudinal assessment of global and regional rate of grey matter atrophy in 1,172 healthy older adults: modulation by sex and age. PLoS One 9:e114478. doi: 10.1371/journal.pone.0114478

PubMed Abstract | CrossRef Full Text | Google Scholar

Curran, T., Hills, A., Patterson, M. B., and Strauss, M. E. (2001). Effects of aging on visuospatial attention: an ERP study. Neuropsychologia 39, 288–301. doi: 10.1016/s0028-3932(00)00112-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Daffner, K. R., Sun, X., Tarbi, E. C., Rentz, D. M., Holcomb, P. J., and Riis, J. L. (2011). Does compensatory neural activity survive old age? Neuroimage 54, 427–438. doi: 10.1016/j.neuroimage.2010.08.006

PubMed Abstract | CrossRef Full Text | Google Scholar

De Fockert, J. W., Ramchurn, A., van Velzen, J., Bergström, Z., and Bunce, D. (2009). Behavioral and ERP evidence of greater distractor processing in old age. Brain Res. 1282, 67–73. doi: 10.1016/j.brainres.2009.05.060

PubMed Abstract | CrossRef Full Text | Google Scholar

Deiber, M., Ibañez, V., Missonnier, P., Rodriguez, C., and Giannakopoulos, P. (2013). Age-associated modulations of cerebral oscillatory patterns related to attentional control. Neuroimage 82, 531–546. doi: 10.1016/j.neuroimage.2013.06.037

PubMed Abstract | CrossRef Full Text | Google Scholar

Deiber, M. P., Rodriguez, C., Jaques, D., Missonnier, P., Emch, J., Millet, P., et al. (2010). Aging effects on selective attention-related electroencephalographic patterns during face encoding. Neuroscience 171, 173–186. doi: 10.1016/j.neuroscience.2010.08.051

PubMed Abstract | CrossRef Full Text | Google Scholar

Driscoll, I., Davatzikos, C., An, Y., Wu, X., Shen, D., Kraut, M., et al. (2009). Longitudinal pattern of regional brain volume change differentiates normal aging from MCI. Neurology 72, 1906–1913. doi: 10.1212/wnl.0b013e3181a82634

PubMed Abstract | CrossRef Full Text | Google Scholar

Eimer, M. (1994). An ERP study on visual spatial priming with peripheral onsets. Psychophysiology 31, 154–163. doi: 10.1111/j.1469-8986.1994.tb01035.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Eriksen, C. W., and Hoffman, J. E. (1974). Selective attention: noise suppression or signal enhancement? Bull. Psychon. Soc. 4, 587–589. doi: 10.3758/bf03334301

CrossRef Full Text | Google Scholar

Fjell, A. M., Westlye, L. T., Grydeland, H., Amlien, I., Espeseth, T., Reinvang, I., et al. (2014). Accelerating cortical thinning: unique to dementia or universal in aging? Cereb. Cortex 24, 919–934. doi: 10.1093/cercor/bhs379

PubMed Abstract | CrossRef Full Text | Google Scholar

Folk, C. L., and Hoyer, W. J. (1992). Aging and shifts of visual spatial attention. Psychol. Aging 7, 453–465. doi: 10.1037//0882-7974.7.3.453

PubMed Abstract | CrossRef Full Text | Google Scholar

Foxe, J. J., McCourt, M. E., and Javitt, D. C. (2003). Right hemisphere control of visuospatial attention: line-bisection judgements evaluated with high-density electrical mapping and source analysis. Neuroimage 19, 710–726. doi: 10.1016/s1053-8119(03)00057-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Foxe, J. J., Simpson, G. V., and Ahlfors, S. P. (1998). Parietal-occipital 10 Hz activity reflects anticipatory state of visual attention mechanisms. Neuroreport 9, 3929–3933. doi: 10.1097/00001756-199812010-00030

PubMed Abstract | CrossRef Full Text | Google Scholar

Foxe, J. J., and Snyder, A. C. (2011). The role of alpha-band brain oscillations as a sensory suppression mechanism during selective attention. Front. Psychol. 2:154. doi: 10.3389/fpsyg.2011.00154

PubMed Abstract | CrossRef Full Text | Google Scholar

Friesen, C. K., Ristic, J., and Kingstone, A. (2004). Attentional effects of counter predictive gaze and arrow cues. J. Exp. Psychol. Hum. Percept. Perform 30, 319–329. doi: 10.1037/0096-1523.30.2.319

PubMed Abstract | CrossRef Full Text | Google Scholar

Garavan, H., Ross, T. J., and Stein, E. A. (1999). Right hemispheric dominance of inhibitory control: an event-related functional MRI study. Proc. Natl. Acad. Sci. U S A 96, 8301–8306. doi: 10.1073/pnas.96.14.8301

PubMed Abstract | CrossRef Full Text | Google Scholar

Gazzaley, A., Clapp, W., Kelley, J., McEvoy, K., Knight, R. T., and D’Esposito, M. (2008). Age-related top-down suppression deficit in the early stages of cortical visual memory processing. Proc. Natl. Acad. Sci. U S A 105, 13122–13126. doi: 10.1073/pnas.0806074105

PubMed Abstract | CrossRef Full Text | Google Scholar

Geerligs, L., Saliasi, E., Maurits, N. M., Renken, R. J., and Lorist, M. M. (2014). Brain mechanisms underlying the effects of aging on different aspects of selective attention. Neuroimage 91, 52–62. doi: 10.1016/j.neuroimage.2014.01.029

PubMed Abstract | CrossRef Full Text | Google Scholar

Goodale, M. A., and Milner, A. D. (1992). Separate visual pathways for perception and action. Trends. Neurosci. 15, 20–25. doi: 10.1016/0166-2236(92)90344-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Greenwood, P. M., and Parasuraman, R. (1994). Attentional disengagement deficit in nondemented elderly over 75 years of age. Aging Cogn. 1, 188–202. doi: 10.1080/13825589408256576

CrossRef Full Text | Google Scholar

Greenwood, P. M., and Parasuraman, R. (1999). Scale of attentional focus in visual search. Percept. Psychophys. 61, 837–859. doi: 10.3758/bf03206901

PubMed Abstract | CrossRef Full Text | Google Scholar

Greenwood, P. M., Parasuraman, R., and Haxby, J. V. (1993). Changes in visuospatial attention over the adult lifespan. Neuropsychologia 31, 471–485. doi: 10.1016/0028-3932(93)90061-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Grill-Spector, K., and Malach, R. (2004). The human visual cortex. Annu. Rev. Neurosci. 27, 649–677. doi: 10.1146/annurev.neuro.27.070203.144220

PubMed Abstract | CrossRef Full Text | Google Scholar

Haring, A. E., Zhuravleva, T. Y., Alperin, B. R., Rentz, D. M., Holcomb, P. J., and Daffner, K. R. (2013). Age-related differences in enhancement and suppression of neural activity underlying selective attention in matched young and old adults. Brain Res. 1499, 69–79. doi: 10.1016/j.brainres.2013.01.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Hartley, A. A., Kieley, J. M., and Slabach, E. H. (1990). Age differences and similarities in the effects of cues and prompts. J. Exp. Psychol. Hum. Percept. Perform. 16, 523–537. doi: 10.1037//0096-1523.16.3.523

PubMed Abstract | CrossRef Full Text | Google Scholar

Heinze, H. J., Luck, S. J., Mangun, G. R., and Hillyard, S. A. (1990). Visual event-related potentials index focused attention within bilateral stimulus arrays. I. Evidence for early selection. Electroencephalogr. Clin. Neurophysiol. 75, 511–527. doi: 10.1016/0013-4694(90)90138-a

PubMed Abstract | CrossRef Full Text | Google Scholar

Heinze, H. J., Mangun, G. R., Burchert, W., Hinrichs, H., Scholz, M., Münte, T. F., et al. (1994). Combined spatial and temporal imaging of brain activity during visual selective attention in humans. Nature 372, 543–546. doi: 10.1038/372543a0

PubMed Abstract | CrossRef Full Text | Google Scholar

Hong, S. L., and Rebec, G. V. (2012). A new perspective on behavioral inconsistency and neural noise in aging: compensatory speeding of neural communication. Front. Aging Neurosci. 4:27. doi: 10.3389/fnagi.2012.00027

PubMed Abstract | CrossRef Full Text | Google Scholar

Hong, X., Sun, J., Bengson, J. J., Mangun, G. R., and Tong, S. (2015). Normal aging selectively diminishes alpha lateralization in visual spatial attention. Neuroimage 106, 353–363. doi: 10.1016/j.neuroimage.2014.11.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Jonides, J. (1981). “Voluntary versus automatic control over the mind’s eye’s movement,” in Attention and Performance IX, eds J. B. Long and A. D. Baddeley (Hillsdale, NJ: Erlbaum), 187–203.

Karrasch, M., Laine, M., Rapinoja, P., and Krause, C. M. (2004). Effects of normal aging on event-related desynchronization/synchronization during a memory task in humans. Neurosci. Lett. 366, 18–23. doi: 10.1016/j.neulet.2004.05.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Kelly, S. P., Gomez-Ramirez, M., and Foxe, J. J. (2009). The strength of anticipatory spatial biasing predicts target discrimination at attended locations: a high-density EEG study. Eur. J. Neurosci. 30, 2224–2234. doi: 10.1111/j.1460-9568.2009.06980.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Kelly, S. P., Lalor, E. C., Reilly, R. B., and Foxe, J. J. (2006). Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention. J. Neurophysiol. 95, 3844–3851. doi: 10.1152/jn.01234.2005

PubMed Abstract | CrossRef Full Text | Google Scholar

Klimesch, W., Sauseng, P., and Hanslmayr, S. (2007). EEG alpha oscillations: the inhibition–timing hypothesis. Brain Res. Rev. 53, 63–88. doi: 10.1016/j.brainresrev.2006.06.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Kravitz, D. J., Saleem, K. S., Baker, C. I., and Mishkin, M. (2011). A new neural framework for visuospatial processing. Nat. Rev. Neurosci. 12, 217–230. doi: 10.1038/nrn3008

PubMed Abstract | CrossRef Full Text | Google Scholar

Lambert, A. J., and Shin, M. J. (2010). The hare and the snail: dissociating visual orienting from conscious perception. Vis. Cogn. 18, 829–838. doi: 10.1080/13506281003693569

CrossRef Full Text | Google Scholar

Langrová, J., Kuba, M., Kremlácek, J., Kubová, Z., and Vit, F. (2006). Motion-onset VEPs reflect long maturation and early ageing of visual motion processing system. Vision Res. 46, 536–544. doi: 10.1016/j.visres.2005.06.024

PubMed Abstract | CrossRef Full Text | Google Scholar

Learmonth, G., Benwell, C. S. Y., Thut, G., and Harvey, M. (2017). Age-related reduction of hemispheric lateralisation for spatial attention: an EEG study. Neuroimage 153, 139–151. doi: 10.1016/j.neuroimage.2017.03.050

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, L., Gratton, C., Fabiani, M., and Knight, R. T. (2013). Age-related frontoparietal changes during the control of bottom-up and top-down attention: an ERP study. Neurobiol. Aging 34, 477–488. doi: 10.1016/j.neurobiolaging.2012.02.025

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, L., and Zhao, D. (2015). Age-related inter-region EEG coupling changes during the control of bottom-up and top-down attention. Front. Aging Neurosci. 7:223. doi: 10.3389/fnagi.2015.00223

PubMed Abstract | CrossRef Full Text | Google Scholar

Liang, X., Zou, Q., He, Y., and Yang, Y. (2013). Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the brain. Proc. Natl. Acad. Sci. U S A 110, 1929–1934. doi: 10.1073/pnas.1214900110

PubMed Abstract | CrossRef Full Text | Google Scholar

Lincourt, A. E., Folk, C. L., and Hoyer, W. J. (1997). Effects of aging on voluntary and involuntary shifts of attention. Aging Neuropsychol. Cogn. 4, 290–303. doi: 10.1080/13825589708256654

CrossRef Full Text | Google Scholar

Lorenzo-López, L., Doallo, S., Vizoso, C., Amenedo, E., Holguín, S. R., and Cadaveira, F. (2002). Covert orienting of visuospatial attention in the early stages of aging. Neuroreport 13, 1459–1462. doi: 10.1097/00001756-200208070-00022

PubMed Abstract | CrossRef Full Text | Google Scholar

Luck, S. J., Hillyard, S. A., Mangun, G. R., and Gazzaniga, M. S. (1994). Independent attentional scanning in the separated hemispheres of split-brain patients. J. Cogn. Neurosci. 6, 84–91. doi: 10.1162/jocn.1994.6.1.84

PubMed Abstract | CrossRef Full Text | Google Scholar

Madden, D. J., Turkington, T. G., Provenzale, J. M., Denny, L. L., Hawk, T. C., Gottlob, L. R., et al. (1999). Adult age differences in functional neuroanatomy of verbal recognition memory. Hum. Brain Mapp. 7, 115–135. doi: 10.1002/(sici)1097-0193(1999)7:2<115::aid-hbm5>3.0.co;2-n

PubMed Abstract | CrossRef Full Text | Google Scholar

Mangun, G. R. (1995). Neural mechanisms of visual selective attention. Psychophysiology 32, 4–18. doi: 10.1111/j.1469-8986.1995.tb03400.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Mangun, G. R., and Hillyard, S. A. (1991). Modulations of sensory-evoked brain potentials indicate changes in perceptual processing during visual-spatial priming. J. Exp. Psychol. Hum. Percept. Perform. 17, 1057–1074. doi: 10.1037//0096-1523.17.4.1057

PubMed Abstract | CrossRef Full Text | Google Scholar

Marrett, N. E., de-Wit, L. H., Roser, M., Kentridge, R. W., Milner, A. D., and Lambert, A. J. (2011). Testing the dorsal stream attention hypothesis: electrophysiological correlates and the effects of ventral stream damage. Vis. Cogn. 19, 1089–1121. doi: 10.1080/13506285.2011.622729

CrossRef Full Text | Google Scholar

Martin, J., Friston, K. J., Colebatch, J. G., and Frackowiak, R. S. J. (1991). Decreases in regional cerebral blood flow with normal aging. J. Cereb. Blood Flow Metab. 11, 684–689. doi: 10.1038/jcbfm.1991.121

PubMed Abstract | CrossRef Full Text | Google Scholar

Maunsell, J. H. R. (1987). “Physiological evidence for two visual subsystems,” in Matters of Intelligence, ed. L. M. Vaina (Netherlands: Reidel Dordrecht), 59–87.

Google Scholar

Meunier, D., Stamatakis, E. A., and Tyler, L. K. (2014). Age-related functional reorganization, structural changes, and preserved cognition. Neurobiol. Aging 35, 42–54. doi: 10.1016/j.neurobiolaging.2013.07.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Milner, A. D., and Goodale, M. A. (2008). Two visual systems re-viewed. Neuropsychologia 46, 774–785. doi: 10.1016/j.neuropsychologia.2007.10.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Nagamatsu, L. S., Carolan, P., Liu-Ambrose, T. Y., and Handy, T. C. (2011). Age-related changes in the attentional control of visual cortex: a selective problem in the left visual hemifield. Neuropsychologia 49, 1670–1678. doi: 10.1016/j.neuropsychologia.2011.02.040

PubMed Abstract | CrossRef Full Text | Google Scholar

Nissen, M. J., and Corkin, S. (1985). Effectiveness of attentional cueing in older and younger adults. J. Gerontol. 40, 185–191. doi: 10.1093/geronj/40.2.185

PubMed Abstract | CrossRef Full Text | Google Scholar

Olk, B., and Kingstone, A. (2009). A new look at aging and performance in the antisaccade task: the impact of response selection. Eur. J. Cogn. Psychol. 21, 406–427. doi: 10.1080/09541440802333190

CrossRef Full Text | Google Scholar

Oonk, H. M., and Abrams, R. A. (1998). New perceptual objects that capture attention produced inhibition of return. Psychon. B. Rev. 5, 510–515.

PubMed Abstract | Google Scholar

Park, D. C., and Reuter-Lorenz, P. (2009). The adaptive brain: aging and neurocognitive scaffolding. Annu. Rev. Psychol. 60, 173–196. doi: 10.1146/annurev.psych.59.103006.093656

PubMed Abstract | CrossRef Full Text | Google Scholar

Paxton, J. L., Barch, D. M., Racine, C. A., and Braver, T. S. (2008). Cognitive control, goal maintenance, and prefrontal function in healthy aging. Cereb. Cortex 18, 1010–1028. doi: 10.1093/cercor/bhm135

PubMed Abstract | CrossRef Full Text | Google Scholar

Plude, D. J., and Doussard-Roosevelt, J. A. (1989). Aging, selective attention and feature integration. Psychol. Aging 4, 98–105. doi: 10.1037//0882-7974.4.1.98

PubMed Abstract | CrossRef Full Text | Google Scholar

Posner, M. I. (1980). Orienting of attention. Q. J. Exp. Psychol. 32, 3–25. doi: 10.1080/00335558008248231

PubMed Abstract | CrossRef Full Text | Google Scholar

Rafal, R., and Henik, A. (1994). “The neurology of inhibition: integrating controlled and automatic processes,” in Inhibitory Processes in Attention, Memory, and Language, eds D. Dagenbach and T. H. Carr (San Diego, CA: Academic Press), 1–51.

Google Scholar

Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., and Davatzikos, C. (2003). Longitudinal magnetic resonance imaging studies of older adults: a shrinkingbrain. J. Neurosci. 23, 3295–3301.

PubMed Abstract | Google Scholar

Reuter-Lorenz, P. A., and Cappell, K. A. (2008). Neurocognitive aging and the compensation hypothesis. Curr. Dir. Psychol. Sci. 17, 177–182. doi: 10.1111/j.1467-8721.2008.00570.x

CrossRef Full Text | Google Scholar

Reuter-Lorenz, P. A., Jonides, J., Smith, E. E., Hartley, A., Miller, A., Marshuetz, C., et al. (2000). Age differences in the frontal lateralization of verbal and spatial working memory revealed by PET. J. Cogn. Neurosci. 12, 174–187. doi: 10.1162/089892900561814

PubMed Abstract | CrossRef Full Text | Google Scholar

Reuter-Lorenz, P., and Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychol. Rev. 24, 355–370. doi: 10.1007/s11065-014-9270-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Rihs, T. A., Michel, C. M., and Thut, G. (2007). Mechanisms of selective inhibition in visual spatial attention are indexed by α-band EEG synchronization. Eur. J. Neurosci. 25, 603–610. doi: 10.1111/j.1460-9568.2007.05278.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Rihs, T. A., Michel, C. M., and Thut, G. (2009). A bias for posterior α-band power suppression versus enhancement during shifting versus maintenance of spatial attention. Neuroimage 44, 190–199. doi: 10.1016/j.neuroimage.2008.08.022

PubMed Abstract | CrossRef Full Text | Google Scholar

Rizzolatti, G., Riggio, L., Dascola, I., and Umiltá, C. (1987). Reorienting attention across the horizontal and vertical meridians: evidence in favour of a premotor theory of attention. Neuropsychologia 25, 31–40. doi: 10.1016/0028-3932(87)90041-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Sauseng, P., Klimesch, W., Stadler, W., Schabus, M., Doppelmayr, M., Hanslmayr, S., et al. (2005). A shift of visual spatial attention is selectively associated with human EEG alpha activity. Eur. J. Neurosci. 22, 2917–2926. doi: 10.1111/j.1460-9568.2005.04482.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Schall, J. D. (2009). “Frontal eye fields,” in Encyclopedia of Neuroscience, eds M. D. Binder, N. Hirokawa and U. Windhorst (Berlin: Springer-Verlag GmbH), 1635–1638.

Google Scholar

Schmitz, T. W., Cheng, F. H. T., and De Rosa, E. (2010). Failing to ignore: paradoxical neural effects of perceptual load on early attentional selection in normal aging. J. Neurosci. 30, 14750–14758. doi: 10.1523/JNEUROSCI.2687-10.2010

PubMed Abstract | CrossRef Full Text | Google Scholar

Schmitz, R., and Peigneux, P. (2011). Age-related changes in visual pseudoneglect. Brain Cogn. 76, 382–389. doi: 10.1016/j.bandc.2011.04.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Schneider-Garces, N. J., Gordon, B. A., Brumback-Peltz, C. R., Shin, E., Lee, Y., Sutton, B. P., et al. (2010). Span, CRUNCH, and beyond: working memory capacity and the aging brain. J. Cogn. Neurosci. 22, 655–669. doi: 10.1162/jocn.2009.21230

PubMed Abstract | CrossRef Full Text | Google Scholar

Shafto, M., Randall, B., Stamatakis, E. A., Wright, P., and Tyler, L. K. (2012). Age-related neural reorganization during spoken word recognition: the interaction of form and meaning. J. Cogn. Neurosci. 24, 1434–1446. doi: 10.1162/jocn_a_00218

PubMed Abstract | CrossRef Full Text | Google Scholar

Shepherd, M., and Müller, H. J. (1989). Movement versus focusing of visual attention. Percept. Psychophys. 46, 146–154. doi: 10.3758/bf03204974

PubMed Abstract | CrossRef Full Text | Google Scholar

Siegel, M., Donner, T. H., Oostenveld, R., Fries, P., and Engel, A. K. (2008). Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention. Neuron 60, 709–719. doi: 10.1016/j.neuron.2008.09.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Skottun, B. C. (2000). The magnocellular deficit theory of dyslexia: the evidence from contrast sensitivity. Vision Res. 40, 111–127. doi: 10.1016/s0042-6989(99)00170-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Thambisetty, M., Wan, J., Carass, A., An, Y., Prince, J. L., and Resnick, S. M. (2010). Longitudinal changes in cortical thickness associated with normal aging. Neuroimage 52, 1215–1223. doi: 10.1016/j.neuroimage.2010.04.258

PubMed Abstract | CrossRef Full Text | Google Scholar

Thiebaut de Schotten, M., Dell’Acqua, F., Forkel, S. J., Simmons, A., Vergani, F., Murphy, D. G. M., et al. (2011). A lateralized brain network for visuospatial attention. Nat. Neurosci. 14, 1245–1246. doi: 10.1038/nn.2905

PubMed Abstract | CrossRef Full Text | Google Scholar

Theeuwes, J. (1991). Exogenous and endogenous control of attention: the effects of visual onsets and offsets. Percept. Psychophys. 49, 83–90. doi: 10.3758/bf03211619

PubMed Abstract | CrossRef Full Text | Google Scholar

Treisman, A., and Gelade, G. (1980). A feature-integration theory of attention. Cogn. Psychol. 12, 97–136. doi: 10.1016/0010-0285(80)90005-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Tulving, E., Kapur, S., Craik, F. I., Moscovitch, M., and Houle, S. (1994). Hemispheric encoding/retrieval asymmetry in episodic memory: positron emission tomography findings. Proc. Natl. Acad. Sci. U S A 91, 2016–2020.

PubMed Abstract | Google Scholar

Vaden, R. J., Hutcheson, N. L., McCollum, L. A., Kentros, J., and Visscher, K. M. (2012). Older adults, unlike younger adults, do not modulate alpha power to suppress irrelevant information. Neuroimage 63, 1127–1133. doi: 10.1016/j.neuroimage.2012.07.050

PubMed Abstract | CrossRef Full Text | Google Scholar

Vallesi, A., McIntosh, A. R., and Stuss, D. T. (2011). Overrecruitment in the aging brain as a function of task demands: evidence for a compensatory view. J. Cogn. Neurosci. 23, 801–815. doi: 10.1162/jocn.2010.21490

PubMed Abstract | CrossRef Full Text | Google Scholar

Vandenberghe, R., and Gillebert, C. R. (2009). Parcellation of parietal cortex: convergence between lesion-symptom mapping and mapping of the intact functioning brain. Behav. Brain Res. 199, 171–182. doi: 10.1016/j.bbr.2008.12.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Voyer, D., Voyer, S. D., and Tramonte, L. (2012). Free-viewing laterality tasks: a multi-level meta-analysis. Neuropsychology 26, 551–567. doi: 10.1037/a0028631

PubMed Abstract | CrossRef Full Text | Google Scholar

Waberski, T. D., Gobbelé, R., Lamberty, K., Buchner, H., Marshall, J. C., and Fink, G. R. (2008). Timing of visuo-spatial information processing: electrical source imaging related to line bisection judgements. Neuropsychologia 46, 1201–1210. doi: 10.1016/j.neuropsychologia.2007.10.024

PubMed Abstract | CrossRef Full Text | Google Scholar

Worden, M. S., Foxe, J. J., Wang, N., and Simpson, G. V. (2000). Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex. J. Neurosci. 20:RC63.

PubMed Abstract | Google Scholar

Wright, M. J., Geffen, G. M., and Geffen, L. B. (1995). Event related potentials during covert orientation of visual attention: effects of cue validity and directionality. Biol. Psychol. 41, 183–202. doi: 10.1016/0301-0511(95)05128-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Yamaguchi, S., Tsuchiya, H., and Kobayashi, S. (1995). Electrophysiologic correlates of age effects on visuospatial attention shift. Cogn. Brain Res. 3, 41–49. doi: 10.1016/0926-6410(95)00017-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Yantis, S., and Hillstrom, A. P. (1994). Stimulus-driven attentional capture: evidence from equiluminant visual objects. J. Exp. Psychol. Hum. Percept. Perform. 20, 95–107. doi: 10.1037//0096-1523.20.1.95

PubMed Abstract | CrossRef Full Text | Google Scholar

Zanto, T. P., Pan, P., Liu, H., Bollinger, J., Nobre, A. C., and Gazzaley, A. (2011). Age-related changes in orienting attention in time. J. Neurosci. 31, 12461–12470. doi: 10.1523/JNEUROSCI.1149-11.2011

PubMed Abstract | CrossRef Full Text | Google Scholar

Ziegler, G., Dahnke, R., Jäncke, L., Yotter, R. A., May, A., and Gaser, C. (2012). Brain structural trajectories over the adult lifespan. Hum. Brain Mapp. 33, 2377–2389. doi: 10.1002/hbm.21374

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: aging, visuospatial orienting of attention, compensation, dorsal visual stream, magnocellular pathway

Citation: Sciberras-Lim ET and Lambert AJ (2017) Attentional Orienting and Dorsal Visual Stream Decline: Review of Behavioral and EEG Studies. Front. Aging Neurosci. 9:246. doi: 10.3389/fnagi.2017.00246

Received: 16 May 2017; Accepted: 14 July 2017;
Published: 27 July 2017.

Edited by:

Christos Frantzidis, Aristotle University of Thessaloniki, Greece

Reviewed by:

James Danckert, University of Waterloo, Canada
Christopher S. Y. Benwell, University of Glasgow, United Kingdom

Copyright © 2017 Sciberras-Lim and Lambert. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Evatte T. Sciberras-Lim, esci911@aucklanduni.ac.nz