F OCU S A R T I C L E

Cognitive and motivational selectivity in healthy aging

Liyana T. Swirsky | Julia Spaniol

Department of Psychology, RyersonUniversity, Toronto, Ontario, Canada

CorrespondenceLiyana T. Swirsky, Department ofPsychology, Ryerson University, Toronto,ON, Canada.Email: lswirsky@psych.ryerson.ca

Funding informationCanada Research Chairs, Grant/AwardNumber: 950-230178; Natural Sciences andEngineering Research Council of Canada,Grant/Award Number: RGPIN-2018-04455

AbstractNormal aging is associated with a reduction in the selectivity of cognitive processes

such as attention and memory. This loss of selectivity is attributed to diminished

inhibition and cognitive control mechanisms in older adults, which render them

more susceptible to distraction and more likely to attend to and encode irrelevant

information. However, motivational selectivity appears largely preserved in aging.

For example, older adults selectively avoid high-demand tasks, exhibit a positivity

bias in attention and memory, and show better memory for high-value compared to

low-value information. The aim of this review is to integrate these seemingly para-

doxical findings of reduced and preserved selectivity in aging, discuss potential

neural mechanisms, and propose questions for future research.

This article is categorized under:Neuroscience > CognitionPsychology > Development and Aging

KEYWORD S

cognitive control, inhibition, motivation

1 | INTRODUCTION

Contrary to the common stereotype held in many Western societies (Löckenhoff et al., 2009; Palmore, 1975), old age is notsynonymous with loss and decline. Four decades of research in psychology and allied sciences have shown that normal agingis associated with a complex pattern of gains and losses in cognitive and affective domains (e.g., Ebner & Fischer, 2014;Kensinger, 2009). Some abilities, such as working memory and processing speed, tend to decline with age, whereas others,such as semantic memory, everyday problem solving, and emotion regulation, are maintained or even improve with age(e.g., Glisky, 2007; Scheibe & Carstensen, 2010). In light of ongoing demographic changes (United Nations, 2017), there istremendous interest in fostering successful aging (Daffner, 2010) by capitalizing on older people's cognitive and affectivestrengths. A key theme in this context is selectivity, or the degree to which individuals are able to prioritize thoughts andbehaviors that are conducive to goal pursuit and well-being (e.g., Baltes & Baltes, 1990; Braver, 2012; Mather & Carstensen,2005). For the purposes of this review, selectivity is considered as a broad construct that encompasses both cognitive selectiv-ity and motivational selectivity. Cognitive selectivity relates to cognitive control and inhibition in the service of task goals(often imposed externally, e.g., in the form of task instructions). In contrast, motivational selectivity relates to hedonic goalsthat may reflect either chronically activated priorities or context-specific incentives. Furthermore, selectivity can manifestbehaviorally (i.e., in terms of biases in attention and memory) as well as neurally (e.g., in terms of neural recruitment and brainsignal variability).

Selectivity is central to several theoretical perspectives in the area of lifespan development (e.g., Baltes & Baltes, 1990;Carstensen, 1995; Hess, 2014), but separate lines of empirical research have yielded seemingly contradictory findings about

Received: 28 February 2019 Revised: 8 May 2019 Accepted: 15 May 2019

DOI: 10.1002/wcs.1512

WIREs Cogn Sci. 2019;10:e1512. wires.wiley.com/cogsci © 2019 Wiley Periodicals, Inc. 1 of 12https://doi.org/10.1002/wcs.1512

aging and selectivity (e.g., Lustig, Hasher, & Zacks, 2008; Reed, Chan, & Mikels, 2014). The goal of this review is to synthe-size and reconcile these disparate findings, and to propose a framework for future research on aging and selectivity.

Selectivity can be defined at the level of behavior, cognitive-affective processes, and underlying neurobiological mecha-nisms. At the behavioral level, selectivity relates to preferences for particular stimuli, tasks, and activities over others. Forexample, older adults may demonstrate selectivity by spending more time looking at positive than at negative faces, and theymay preferentially seek out environments that help them to maintain a positive mood (Carstensen, Isaacowitz, & Charles,1999; Hess, 2014; Mather & Carstensen, 2003). Cognitive selectivity is facilitated through goal-directed attention, cognitivecontrol, and memory. For example, selective attention involves attending to target stimuli while ignoring irrelevant distractorstimuli, thereby supporting the encoding of important information into long-term memory (Gazzaley & Nobre, 2012; Rowe,Valderrama, Hasher, & Lenartowicz, 2006). At the neural level, selectivity refers to the specificity with which neural resourcesare engaged during cognitive tasks (Park & Reuter-Lorenz, 2009). For example, neuroimaging studies have shown age-relatedincreases in functional connectivity (i.e., correlated activity) between task-relevant and task-irrelevant brain regions, consistentwith a pattern of neural dedifferentiation or reduced selectivity with age (Voss et al., 2008).

Interestingly, different aspects of selectivity show different adult-developmental trajectories. Age-related reductions inselectivity are primarily apparent in the context of tasks involving inhibition (Hasher, Zacks, & May, 1999) or other forms ofcognitive control (Braver & Barch, 2002), visual attention (Madden, 2007), and associative memory (Naveh-Benjamin, 2000;Old & Naveh-Benjamin, 2008). The tenor of this literature is that aging is associated with increased distractibility and areduced ability to prioritize goal-relevant over irrelevant stimuli in the environment (Amer, Campbell, & Hasher, 2016; Car-lson, Hasher, Connelly, & Zacks, 1995). Consistent with behavioral findings of reduced cognitive selectivity, neuroimagingstudies have revealed an age-related reduction in the engagement of task-specific neural resources (Park & Reuter-Lorenz,2009). That is, older adults tend to show less activation in task-specific regions compared to younger adults, along withincreased activation in regions that are not task-specific (Campbell, Grady, Ng, & Hasher, 2012; Nashiro, Sakaki, Braskie, &Mather, 2017). In sharp contrast to these findings, lifespan theories of resource availability and motivation postulate preservedor even enhanced selectivity with age. Resource-based theories propose that older adults seek to minimize losses (e.g., by giv-ing up formerly important goals perceived as unattainable) and to maximize performance on important tasks, while aiming toconserve limited cognitive resources (Baltes & Baltes, 1990; Hess, 2014). In line with these perspectives, findings in themotivated-cognition literature suggest preserved motivational selectivity with age. For example, older adults are able to focuson information that supports their emotion regulation goals (Carstensen et al., 1999; Mather & Knight, 2005), and to prioritizehigh-value over low-value stimuli during intentional long-term memory encoding (Castel et al., 2016; Spaniol, Schain, &Bowen, 2014). The current review aims to integrate these disparate findings to propose a coherent framework for futureresearch on aging and selectivity.

2 | AGE-RELATED REDUCTION IN COGNITIVE SELECTIVITY

Cognitive control—an umbrella term for executive functions that facilitate goal-directed behavior (Botvinick & Braver,2015)—is the ability to flexibly represent, maintain, and update internal goals (Braver & West, 2011). One function of cogni-tive control that is critical for attentional and mnemonic selectivity is inhibitory control. Inhibitory control refers to the abilityto focus on task-relevant, target information while suppressing task-irrelevant, competing information (Hasher et al., 1999).According to the inhibitory deficit hypothesis (Hasher & Zacks, 1988) aging is associated with a general reduction in inhibi-tory function, such that older adults are less able to ignore irrelevant stimuli alongside target stimuli and to restrain highlypracticed responses in favor of a less frequent but appropriate response (Butler & Zacks, 2006; Healey, Hasher, & Campbell,2013). This inhibitory deficit is reflective of an age-related decrease in selectivity and control that may underlie many of theage differences in cognitive performance (Lustig et al., 2008). For example, older adults are slower on visual search tasks(Madden et al., 2014), have better implicit memory for distractor stimuli (Rowe et al., 2006), and encode incidental associa-tions between targets and distractors (Campbell, Hasher, & Thomas, 2010).

While inhibitory control demonstrates an age-related decline, more automatic stimulus-driven control processes are rela-tively preserved in older adults (Braver & West, 2011). In Braver's (2012) dual-mechanisms of control theory, proactivecontrol refers to top-down, goal-driven control (cognitively demanding) whereas reactive control refers to bottom-up,stimulus-driven control (less demanding). The dual-mechanisms of control theory explains the inhibitory deficit in older adultsas an impairment in proactive control capacity, accompanied by spared reactive control capacity (Braver, 2012). Because ofthis asymmetry, older adults are particularly vulnerable to the influence of automatic, stimulus-driven distractions (Park &Reuter-Lorenz, 2009). The age-related shift from proactive to reactive control has also been used to explain compensatory

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neural activity in older adults. That is, over-recruitment of brain regions in older adults may be interpreted as a signature oflate-acting reactive control in the absence of preparatory control (Grady, 2012).

It should be noted that the reduction in selectivity resulting from compromised cognitive control in older adults is notinherently maladaptive. Amer et al. (2016) highlight potential benefits associated with loosened cognitive control, includingbetter performance on tasks that require use of distraction, implicit retrieval, learning regularities from one's environment andcreative problem solving. On the flip side, older adults do tend to show deficits on tasks that require distraction suppression,selective attention, intentional retrieval, and working memory (i.e., updating the contents in mind and deleting no-longer-relevant contents).

One phenomenon that exemplifies the occasional benefits of reduced cognitive control in older adults is hyper-binding(Campbell et al., 2010; Campbell & Hasher, 2018; Campbell, Trelle, & Hasher, 2014; Weeks, Biss, Murphy, & Hasher,2016). Because older adults are less able to suppress irrelevant information alongside relevant information, they are morelikely to encode and later recall that irrelevant information (Biss, Rowe, Weeks, Hasher, & Murphy, 2018; Healey et al.,2013). In addition to better memory for previous distractors, older adults' lack of selective attention at encoding results in asso-ciations between co-occurring targets and distractors. In laboratory studies of hyper-binding, participants complete a selective-attention task during which they must attend to a series of target stimuli (e.g., faces) while ignoring distractor stimuli(e.g., names superimposed on the faces). Later, participants receive a seemingly unrelated memory test, in which they mustlearn and recall a list of stimulus pairs (e.g., face-name associations). Half of these pairings are maintained from the selective-attention task, while the other half is rearranged. Older adults, but not younger adults, demonstrate better recall for maintainedpairs, relative to rearranged pairs. Thus, hyper-binding due to reduced inhibitory control may counteract the age-related asso-ciative memory deficit seen in tasks that explicitly require the formation and retrieval of new associations (Naveh-Benja-min, 2000).

2.1 | Neural substrates of age-related decrease in cognitive selectivity

According to the frontal theory of cognitive aging (West, 1996), age-related deterioration of the frontal lobes is primarilyresponsible for age differences in cognitive performance. While this localized perspective is consistent with findings of bothearlier-onset and greater-magnitude decline (Hartley, 1993; Shimamura & Jurica, 1994) in frontally mediated cognitive controlabilities compared to non-frontal abilities, contemporary perspectives emphasize the role of distributed brain networks in cog-nitive aging (Greenwood, 2000). From a network perspective, a prominent candidate for age-related change is thefrontoparietal control network (Ptak, 2012). This network encompasses regions required for task engagement and selectiveattention, including the dorsolateral prefrontal cortex (PFC) and posterior parietal cortex (Ptak, 2012; Vincent, Kahn, Snyder,Raichle, & Buckner, 2008; see also Kennedy & Mather, 2019). The frontoparietal control network is consistently activatedduring attention-based tasks, facilitating the integration of goal representations from the dorsolateral PFC with sensory inputand spatial mapping information from the posterior parietal (Ptak, 2012). Reduced activation and functional connectivitywithin the frontoparietal control network in older adults have been linked to processing of and interference from distractorsthat must be suppressed to achieve task goals (Campbell et al., 2012).

The frontoparietal control network relies critically on PFC function for top-down regulation of task-relevant posterior andsub-cortical regions (Braver & West, 2011). The interaction between the dorsolateral PFC and the dopaminergic midbrain iscritical for cognitive control mechanisms that facilitate inhibition and resultant selectivity of attention and memory (Braveret al., 2014; Braver & Barch, 2002). Normal aging is associated with structural and functional change in the PFC, with a well-documented decrease in size and dopaminergic transmission (Harada, Love, & Triebel, 2013). These changes in PFC structureand connectivity likely contribute to age-related reductions in selectivity from reduced cognitive control.

Despite the age-related decline of the PFC, a common finding in the neuroimaging literature is that the PFC is over-recruited in older adults relative to younger adults (for a review, see Grady, 2012). The age-related shift from posterior, earlyprocessing regions to anterior, higher processing regions, also known as “posterior–anterior shift in aging” (Davis, Dennis,Daselaar, Fleck, & Cabeza, 2008), is hypothesized to reflect compensatory recruitment, at least when paired with improvedcognitive performance (see Cabeza et al., 2018). The over-activation of prefrontal regions in older adults has been associatedwith age differences in cognitive control that may account for reduced proactive (goal-directed) control and selective attention(Paxton, Barch, Racine, & Braver, 2008). Echoing this finding, evidence from event-related potentials suggests that age-related over-recruitment and under-recruitment may be linked to reactive and proactive cognitive control in older adults,respectively (Kopp, Lange, Howe, & Wessel, 2014). Whether reduced selectivity of neural recruitment indeed drives age-related changes in the balance of cognitive-control modes remains a question for future research.

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Anterior over-recruitment may reflect reactive compensation when coupled with enhanced or maintained performance inolder adults compared with younger adults (Cabeza et al., 2018). When coupled with reduced performance, however, over-recruitment may indicate dedifferentiation (Grady, 2008, 2012). According to this perspective, aging is associated withreduced functional specificity of brain activity such that task-irrelevant regions are recruited in addition to task-specific regionsseen in younger adults (Rajah & D'Esposito, 2005). This lack of selectivity of neural responses thus reflects a loss of effi-ciency that may be relevant to age-related decline in cognitive control and inhibition processes discussed above.

A second large-scale brain network relevant to age-related decreases in cognitive control and attentional inhibition is thedefault-mode network, which includes the medial PFC, posterior cingulate cortex, inferior parietal lobule, lateral temporal cor-tex, and the hippocampal formation (Buckner, Andrews-Hanna, & Schacter, 2008). Relative to younger adults, older adultsdemonstrate reduced task-induced deactivation of this network (Spreng & Turner, 2013). Deactivation of the default-modenetwork is associated with cognitive performance during tasks that require cognitive control, such as visual attention tasks thatinclude distraction. Reduced deactivation of the default-mode network has been linked to distractibility and mind wanderingin younger adults (Chadick & Gazzaley, 2011; Mason et al., 2007). This suggests that weakened default-mode network sup-pression may be a causal factor underlying increased distractibility in older adults. Turner and Spreng (2015) recently pro-posed the default-executive coupling hypothesis of aging (DECHA), according to which age-related changes in activation ofthe frontoparietal control network and the default-mode network, as well as age-related increases in the coupling betweenthese networks, represent an age-related shift from reliance on fluid cognition (frontoparietal control network-dependent) tocrystallized cognition (default-mode network-dependent; Spreng et al., 2018; Turner & Spreng, 2015).

The neuroimaging evidence reviewed up to this point has focused on studies that analyzed age-related differences in themean functional magnetic resonance imaging (fMRI) signal. An emerging literature suggests that intraindividual fMRI signalvariability—traditionally dismissed as “brain noise”—also shows systematic relations to age and cognitive performance. Spe-cifically, fMRI signal variability is positively associated with external task demands and behavioral task performance, andnegatively associated with age (Grady & Garrett, 2018). Whether age differences in brain signal variability mediate age differ-ences in cognitive selectivity is an important question for future work.

Taken together, older adults exhibit changes in regional activation and in network connectivity patterns. Specifically, thePFC undergoes significant structural and functional change, and age-related changes in the activity of the frontoparietal con-trol network and the default-mode network point toward a reduction of neural selectivity. This loss of selectivity in neuralrecruitment and connectivity may be explained by compensation, dedifferentiation, or a shift toward stronger reliance oncontext-free—or “semanticized” (Spreng et al., 2018)—representations in aging.

3 | AGE-RELATED STABILITY IN MOTIVATIONAL SELECTIVITY

The loss of selectivity resulting from changes in mechanisms of inhibition and control can be contrasted with evidence of pre-served or increased selectivity associated with motivational processes. Older adults exhibit “motivational selectivity” at thelevel of task selection and in terms of within-task prioritization of specific type of input. These findings are best viewedthrough the lens of lifespan theories of motivation, discussed next, which emphasize the interplay between motivational fac-tors (e.g., goals, accountability) and age-related reductions in critical resources (e.g., time, energy).

3.1 | Lifespan theories of motivation

The selection, optimization, and compensation framework (Freund & Baltes, 1998), as well as more recent theories such associoemotional selectivity theory (Carstensen et al., 1999) and selective engagement theory (Hess, 2014), view selectivity as amotivated adaptation to growing resource constraints in aging. A core assumption shared by these theories is that internal(e.g., processing speed, health) and external (e.g., time, relationships) resources become depleted with age and that thisresource depletion leads to changes in motivational priorities.

According to the selection, optimization, and compensation framework, successful aging is achieved through a combina-tion of selective goal setting, resource allocation, and mobilization of compensatory strategies (Baltes & Baltes, 1990). Ratherthan seeking new developmental gains, older adults shift their focus toward maintaining resources and avoiding losses(Freund & Baltes, 1998). Selective engagement theory (Ennis, Hess, & Smith, 2013; Hess, 2006, 2014) has its roots in theselection, optimization, and compensation framework, but it places greater emphasis on age-related shifts in the costs and ben-efits of cognitive engagement. Compared to younger adults, older adults may reserve effort for costly tasks that “matter”(i.e., tasks that have greater self-relevance). The emphasis on the importance of resources in selectivity is supported by the

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finding that older adults with higher cognitive and physical resources are more intrinsically motivated to engage in effortfultasks than older adults with reduced resources (Queen & Hess, 2017).

Socioemotional selectivity theory differs from selective engagement theory in its emphasis on the central role of emotionregulation. By this account, the amount of time one expects to have left to live is the critical resource. Reductions in expectedtime horizon lead older adults to prioritize maintenance of positive affect in the present over knowledge-acquisition that maybenefit a future self (Carstensen et al., 1999). The chronically activated goal of maintaining positive affect has a broad influ-ence on cognition, including the domains of memory and attention (for a review, see Reed & Carstensen, 2012).

It is worth noting that selective engagement theory and socioemotional selectivity theory address complementary mecha-nisms of selectivity. For example, due to rising costs of cognitive engagement, older adults may be hesitant to engage withnew technologies such as video chat apps. At the same time, emotion regulation goals may boost the cost–benefit ratio associ-ated with learning the new technology, because interacting with distant loved ones is likely to promote positive emotions. Thatis, both theories describe motivational influences on selectivity that influence engagement, either separately or in concert.

Taken together, lifespan theories of motivation emphasize the influence of resources, motivation, and goals on task engage-ment and on information processing. While they differ with respect to the mechanisms of selectivity that they postulate, theyconverge on the idea that motivational selectivity remains influential throughout the adult lifespan.

3.2 | Empirical findings on aging and motivation

Motivation can give rise to selectivity via chronically activated goals or via acute, task-specific incentives. Intrinsic motiva-tional factors, such as accountability, appear to play an important role for older adults. In contrast to extrinsic rewards, such asmoney, accountability drives effort by enhancing self-relevance of task performance and providing satisfaction via task com-pliance. For example, in one study (Hess, Germain, Swaim, & Osowski, 2009), younger and older participants were randomlyassigned to a high- or a low-accountability condition. All participants completed a social judgment task in which they decided,based on brief behavioral descriptions, which of the two hypothetical people was more qualified for a job. Participants werealso asked to provide a written justification for their choice. Later, they were surprised with a recognition test in which theyhad to discriminate between old and new behavior descriptions. In the high-accountability condition, participants' justificationsfor their qualification judgments were read by a co-participant. Older adults in the accountability condition showed bettermemory performance than their counterparts in the no accountability condition. This effect of accountability was absent inyounger adults (Hess et al., 2009). Furthermore, consistent with predictions of the selective engagement theory (Hess, 2006,2014), age differences in memory performance were reduced under high accountability, suggesting that accountabilityincreased older adults' willingness to engage and expend cognitive resources (Hess et al., 2009).

In another study (Smith & Hess, 2015), the impact of accountability on older adults' cognitive effort was replicated using amemory search task of differing degrees of difficulty. Participants viewed an array of letters on a screen and decided whetherthe target letter on the next screen had been part of the previous array. Difficulty was manipulated by varying the size of thearray. Under high accountability, older adults' performance at higher levels of task difficulty was higher than under lowaccountability (Smith & Hess, 2015). Finally, a more recent study (Hess, Growney, O'Brien, Neupert, & Sherwood, 2018)demonstrated that intrinsic motivation can counteract the costs associated with engaging in a cognitively demanding memorytask. Cognitive cost was operationalized as degree of effort, which in turn was measured through systolic blood pressure.Intrinsic motivation was assessed via self-report. Older adults reported higher levels of engagement and demonstrated bettermemory performance when intrinsically motivated, even in the face of high costs. Overall, these findings support the predic-tions of selective engagement theory, whereby age-related increases in selectivity are a by-product of reduced resources andheightened engagement thresholds in aging.

Another major motivational drive, as predicted by socioemotional selectivity theory, is the motivation to attain or maintainpositive emotion. The oft-reported empirical pattern by which information processing biases become more positive with age isreferred to as the age-related positivity effect (Mather & Carstensen, 2005; Reed et al., 2014). This inclination differentiatesolder adults from younger adults, whose information processing bias tends to favor negative stimuli (Baumeister, Bratslavsky,Finkenauer, & Vohs, 2001; Carstensen & DeLiema, 2018; Lobue, 2009). Both younger and older adults thus demonstratemotivational selectivity on the basis of emotional valence, albeit in opposite directions.

Similarly, both younger and older adults are sensitive to differences in the reward value of stimuli, and show enhanced atten-tion to and memory for information encountered during anticipation of reward or in temporal proximity to reward feedback(e.g., monetary incentive; Mather & Schoeke, 2011; Spaniol et al., 2014; Williams, Kudus, Dyson, & Spaniol, 2018). Further-more, stimuli designated as “high-value” are better recalled than their “low-value” counterparts, a phenomenon referred to as

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value-directed encoding (Castel, Benjamin, Craik, & Watkins, 2002). Value-directed encoding is preserved in older adults, consis-tent with the idea that metacognitive control over encoding processes in the service of value and reward is intact in aging (Castelet al., 2002, 2016; Castel, Balota, & McCabe, 2009). Why does the impact of reward and value on attention and memory notincrease with age? This may reflect an overshadowing of older adults' chronic emotion-regulation goals by acute task-relatedgoals. Indeed, a meta-analysis (Reed et al., 2014) shows that one of the key predictions of socioemotional selectivity theory—theage-related positivity effect in attention and memory—tends to be observed primarily under naturalistic, unconstrained conditions(e.g., “view the images as if you were watching television”). Value and reward signals likely undermine this unconstrainedprocessing mode in older adults.

Another factor closely related to motivation is arousal. Arousal is of particular interest when affect serves as a source of moti-vation, as postulated by socioemotional selectivity theory. According to the arousal biased competition (ABC) theory (Mather &Sutherland, 2011), arousal increases processing of high-priority information while impairing processing of low-priority informa-tion, thus increasing the selectivity of information processing. Priority can emerge from sensory properties of stimuli (“bottom-up”) or from current goals (“top-down”). Most experimental tests of ABC theory have been conducted using negatively valencedarousal stimuli (Sutherland & Mather, 2012, 2015), but a recent study employed both negative and positive stimuli, with similarresults (Sutherland & Mather, 2018), suggesting that the critical dimension affecting attentional selectivity is arousal and notvalence. Few studies have tested the ABC theory in older adults. The first study found a similar pattern of arousal-biased compe-tition in younger and older adults (Sutherland & Mather, 2015). However, more recently, Lee et al. (2018) reported that arousalenhanced visual attention in older adults regardless of perceptual salience, such that the link between arousal and selectivity wasweakened in older compared to younger adults. Similarly, Durbin, Clewett, Huang, and Mather (2018) demonstrated reducedmemory selectivity in older adults who had encoded relevant and irrelevant stimuli in high-arousal states. Collectively, the litera-ture to date suggests that arousal may enhance selectivity in younger but not in older adults.

Taken together, it appears that sensitivity to motivational incentives remains intact in older adults, influencing task andengagement and leading to increased selectivity via bolstered cognitive control which directs resources to goal-relevant stim-uli. Arousal may be a limiting influence on motivated selectivity in older adults. That is, although motivation enhances top-down control to facilitate goal-directed attentional selectivity, arousal may bias selective attention in favor of bottom-upprocesses guided by stimulus features.

3.3 | Neural substrates of motivational selectivity in aging

Areas of the brain most associated with reward and emotion are the dopaminergic midbrain structures and the amygdala,respectively. Consistent with preserved emotional and motivational processing, these areas undergo relatively little changewith age (unlike the PFC, e.g., Mather, 2016). Thus, motivational selectivity in older adults likely reflects dopaminergic mod-ulation of areas responsible for top-down cognitive control (e.g., the dorsolateral PFC; Braver et al., 2014). It should be notedthat there is well-documented dopaminergic decline with aging (e.g., Bäckman, Nyberg, Lindenberger, Li, & Farde, 2006; Li,Lindenberger, & Sikström, 2001), which is reflected in age-related decrements in reward-based learning (e.g., Eppinger, Häm-merer, & Li, 2011; Worthy, Cooper, Byrne, Gorlick, & Maddox, 2014). This appears to contradict the above-mentioned find-ings of preserved reward-based modulation of attention and memory in older adults, which also depend on dopaminergicneurotransmission. As recently noted by Karrer, Josef, Mata, Morris, and Samanez-Larkin (2017), more research is needed toelucidate why some dopamine-dependent cognitive functions are more compromised in aging than others.

The exact mechanism through which motivation enhances selectivity is unknown but one hypothesis is that reward-motivatedselectivity may result from strengthened task-relevant representations coded and maintained in the frontoparietal control network(Etzel, Cole, Zacks, Kay, & Braver, 2016; Yee & Braver, 2018). In this way, motivation acts to enhance dampened networkfunction and connectivity associated with aging. Recently, the affective integration motivation (AIM) framework was proposedto describe how affective and motivational processes shape decisions in younger and older adults (Samanez-Larkin & Knutson,2015). According to AIM, older adults' selective sensitivity to gains (and reduced sensitivity to losses; Samanez-Larkin et al.,2007) is linked to relatively preserved nucleus accumbens activity, which relies primarily on dopaminergic transmission, as wellas relatively disrupted anterior insula activity, which relies on both dopaminergic and noradrenergic transmission.

Considering their role in supporting motivation and arousal, both the dopaminergic and noradrenergic systems are likelycontributors to the preservation of selectivity in older adults. Arousal is linked to the locus-coeruleus–norepinephrine (LC–NE) system in the brain (Mather & Harley, 2016), and given arousal's proposed role in top-down selectivity, it is likely thatthe LC–NE influences motivated action, in conjunction with changes in dopaminergic transmission. The recent GlutamateAmplifies Noradrenergic Effects (GANE) theory suggests that selective attention under arousal is partly a result of

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noradrenergic modulation of activity in the frontoparietal control network (Mather, Clewett, Sakaki, & Harley, 2016). How-ever, as mentioned, recent findings point to a reduction in this link in older adults. Thus, the decreased attentional selectivityin older adults may be due to diminished modulation of the frontoparietal control network by the LC–NE system (Lee et al.,2018). The GANE model also posits a fundamental role of glutamate in determining where NE from the LC ends up (Matheret al., 2016; Sutherland & Mather, 2018). According to GANE, brain regions with the most activity at the time of arousal-related upregulation of the LC–NE will receive more NE than less active regions, resulting in exaggerated NE modulation(Mather, 2016; Mather et al., 2016). The consequences of differentially distributed NE are twofold: (a) enhancement of activecortical representations (perceptually salient, goal-relevant) and (b) suppression of relatively inactive cortical representations(non-salient, task-irrelevant). While this pattern of simultaneous excitation and inhibition leads to enhance attentional selectiv-ity in younger adults, it may underlie the lack of selectivity seen in older adults. Despite intact enhancement of important stim-uli by arousal, the lack of selectivity is due to a reduction in the inhibitory process by which irrelevant stimuli are suppressed(Lee et al., 2018). This has been demonstrated for memory and attentional selectivity (Durbin et al., 2018; Sutherland &Mather, 2015). For example, negative arousal enhances younger adults’ memory for task-relevant pictures and inhibition oftask-irrelevant pictures (Clewett, Huang, Velasco, Lee, & Mather, 2018); the opposite is true for older adults, whose memoryselectivity for task-relevant over irrelevant pictures is reduced under threat (i.e., arousal; Durbin et al., 2018).

Overall, preserved motivational selectivity in aging is linked to preserved structural and functional integrity of amygdalaand of the ventral striatum. Motivation–cognition interactions that help buffer declines in cognitive control may reflect dopa-minergic modulation of the frontoparietal control network. However, arousal, which relies on noradrenergic transmission,may reduce selectivity in older adults despite its role in enhancing selectivity in younger adults.

4 | SYNTHESIS

After surveying the literature on divergent trajectories of selectivity in neurocognitive aging, the following conclusions can bedrawn. Selectivity of attention and memory guided by top-down control, including inhibitory mechanisms, decline with age(see Figure 1). This reduction in goal-driven selectivity may be exacerbated by states of arousal, which render older adults lessable to prioritize target information in the face of distraction. Reduced cognitive control and reduced arousal-dependent selec-tivity are linked to age-related changes in dopaminergic, noradrenergic, and glutamergic neurotransmission as well as tochanges in prefrontal activation and in frontoparietal control network connectivity patterns.

In contrast to the broadening of information processing that results from reduced cognitive control, older adults exhibitnarrowing in terms of tasks in which they choose to engage (see Figure 1). Older adults opt to engage effortfully in tasks thatare “worth it” because they are deemed meaningful, self-relevant, or enjoyable. Older adults also opt for activities that helpstabilize their emotional well-being. Motivation through value, reward, and affect upregulates cognitive control and inhibitory

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Sources of decline in cognitive

selectivity with aging:

Sources of stability/increase in

motivational selectivity withaging:

Emotion regulation

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Cognitive control

Behavioral age effects: Behavioral age effects:

Selective attention Positivity effect

Selective engagement inRT to target in visual searchSuppression of distraction

Candidate neural substrates:PFC structure and functionFPCN modulation by DA & NE

De-coupling of DMN and FPCN

Key: : increase relative to younger adults,younger adults

: decrease relative to younger adults, =: stable relative to

Candidate neural substrates:

memory selectivity)

Hyper-binding & associativedeficit (

InhibitionArousal Intrinsic motivation

Reward sensitivity

CognitiveSelectivity

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meaningful tasks= Reward-modulated attention

and memory

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structure and function

FPCN modulation (DA-driven?)

FIGURE 1 An overview of factors that modulate age-related changes in cognitive and motivational selectivity,examples at the level of information-processing/behavior,and potential neural mechanisms. Factors contributing toage-related decreases in selectivity include a reduction incognitive control, reduction in inhibition, and increases inarousal. Factors that contribute to preserved or increasedmotivational selectivity include emotion regulation, intrinsicmotivation and reward-based motivation. DA, dopamine;DMN, default-mode network; FPCN, frontoparietal controlnetwork; NE, norepinephrine; PFC, prefrontal cortex

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processes to yield enhanced selective attention and memory. In this way, motivational manipulations may be a feasible, nonin-vasive solution to maintain and enhance cognitive selectivity in older adults.

In the broader picture, it appears that a multitude of factors influence cognitive performance in older adults and that “losses”are not set in stone. While older adults may be susceptible to distraction and less able to hone in on important information in gen-eral, intact motivational drives can be leveraged to enhance these abilities when appropriate for the goals of the current task.Motivation–cognition interactions thus form an exciting avenue of research in cognitive aging, with practical implications forolder adults' cognitive outcomes. Particular promise lies in the combination of cognitive-affective, behavioral, and neural levelsof analysis. For example, phenomena observed at the neural level can help constrain theorizing about cognitive and motivationalprocesses involved in selectivity, as well as about behavior resulting from these processes. Furthermore, in our view, cognitiveand motivational selectivity are intricately interwoven. For example, emotion-regulation goals may dictate which activities olderadults engage in, while reduced inhibition may affect older adults' performance within these activities. On the flip side, reducedinhibitory control may compromise older adults' ability to demonstrate selective task engagement.

5 | FUTURE DIRECTIONS

An important question for future work in the area of cognitive aging is whether older adults' intact motivational selectivity cancounteract age-related losses in cognitive selectivity due to reduced cognitive control. If motivation can be harnessed toimprove inhibitory functions required for selective attention, motivated older adults should perform better on tasks that requirehigh levels of cognitive control. A methodological concern, however, is how best to motivate older adults in laboratory tasks,especially when considering lifespan theories of motivation that shed light on difficulties associated with inducing motivationeffectively in both younger and older adults.

Relatedly, the question arises about the range of utility of motivation for performance enhancement. While motivation mayenhance selectivity, successful performance will also depend on the nature of the task. In other words, motivation may not beuniversally beneficial for older adults as certain tasks benefit from more diffuse attention and loosened top-down control(Biss, Ngo, Hasher, Campbell, & Rowe, 2013; Weeks et al., 2016). The key question, then, is under which circumstancesmotivation helps versus hinders older adults' performance.

Another challenge lies in the specification of the neural mechanisms underlying motivational selectivity, and their interac-tions with neural mechanisms of cognitive control. How do age-related changes in network activity and connectivity give riseto the age-related reduction in selectivity, and how are they modulated by motivational states? Moreover, while dopamine andnorepinephrine are undoubtedly central to these dynamics, what are their unique contributions to motivational selectivity inolder adults? How does older adults' reduced selectivity under arousal interact with their preserved motivational selectivity?

Following the theme of neural mechanisms, how might age-related decline in the selectivity of neural activity relate toreduced cognitive-behavioral selectivity in older adults? Reduced cognitive selectivity in older adults parallels reduced neuralselectivity described by dedifferentiation, whereas motivational selectivity in older adults is more consistent with upregulationof PFC described by compensation. Future studies could test hypotheses of dedifferentiation and compensation while contra-sting conditions associated with reduced and enhanced behavioral selectivity in older adults.

Relatedly, how might motivation influence whole-brain network connectivity dynamics? For example, can motivationalmanipulations influence age-related coupling patterns described by the DECHA model? If motivational states enhance task-relevant prefrontal engagement and/or default-mode network disengagement, this may represent a feasible target for interven-tion. Lastly, findings of age-related changes in network coupling also lead to new behavioral predictions. In particular,intrinsic motivation associated with the activation of idiosyncratic, self-relevant goals (as opposed to extrinsic incentives suchas financial reward) may have more impact on selectivity for older adults than for younger adults. This prediction followsfrom the assumption that intrinsic motivation relies on access to “semanticized” representations—a context in which the age-related increase in the cross-talk between default-network and frontoparietal control networks should help rather than hindercognitive performance (Grady, Sarraf, Saverino, & Campbell, 2016).

6 | CONCLUSION

Human aging is characterized by a shifting balance of gains and losses. This review has focused on age-related changes inselectivity, the degree to which individuals succeed at prioritizing thoughts and actions that are relevant to their goals. Age-related losses in the area of cognitive control and inhibition lead to age-related reductions in selectivity, whereas extrinsic

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motivation, intrinsic motivation, and emotion regulation in aging help preserve the capacity for selective thought and behaviorin older adults. Considering the complex interplay of motivational and cognitive factors in the real world, more research isneeded to understand the neural underpinnings of motivated cognition, as well as the individual, social, and environmentalfactors that support selectivity in everyday life.

CONFLICT OF INTEREST

The authors have declared no conflicts of interest for this article.

RELATED WIREs ARTICLE

CISDA: Changes in integration for social decisions in aging

ORCID

Liyana T. Swirsky https://orcid.org/0000-0002-4857-1684

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How to cite this article: Swirsky LT, Spaniol J. Cognitive and motivational selectivity in healthy aging. WIREs CognSci. 2019;10:e1512. https://doi.org/10.1002/wcs.1512

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