Ageing & Variability

Neural Underpinnings of Within-Person Variability in CognitiveFunctioning

Stuart W. S. MacDonaldUniversity of Victoria and Karolinska Institutet

Shu-Chen LiMax Planck Institute for Human Development

Lars BackmanKarolinska Institutet

Increased intraindividual variability (IIV), reflecting within-person fluctuations in behavioral perfor-mance, is commonly observed in aging as well as in select disorders including traumatic brain injury,schizophrenia, attention-deficit hyperactivity disorder (ADHD), and dementia. Much recent progress hasbeen made toward understanding the functional significance of IIV in cognitive performance (MacDonald,Nyberg, & Backman, 2006) and biological information processing (Stein, Gossen, & Jones 2005), withparallel efforts devoted to investigating the links between older adults’ deficient neuromodulation andtheir more variable neuronal and cognitive functions (Backman, Nyberg, Lindenberger, Li, & Farde,2006). Despite these advances in the study of IIV, there has been little empirical examination ofunderlying neural correlates and virtually no synthesis of extant findings. The present review summarizesthe accumulating empirical evidence linking age-related increases in IIV in cognitive performance toneural correlates at anatomical, functional, neuromodulatory, and genetic levels. Computational theoriesof neural dynamics (e.g., Li, Lindenberger, & Sikstrom, 2001) are also introduced to illustrate howage-related neuromodulatory deficiencies may contribute to increased neuronal noise and render infor-mation processing in aging neurocognitive systems to be less robust. The potential benefits of stochasticresonance and external noise are also discussed with respect to processing subthreshold stimuli (e.g., Li,von Oertzen, & Lindenberger, 2006). We conclude by highlighting important challenges and outstandingresearch issues that remain to be answered in the study of IIV.

Keywords: cognition, aging, within-person variability, brain, neuromodulation

Most cognitive and neuropsychological research has focused onmean differences in performance across persons. This orientationhas largely overshadowed research on performance variabilitywithin persons. This is a serious theoretical and practical oversightbecause, given large processing fluctuations, behavioral perfor-mance assessed from a single occasion is less accurate and repre-sentative. To the extent that variability in performance is small,mean-level differences provide useful predictive information.However, as within-person variability increases and representssystematic as opposed to random sources of error, performance

indexed from a single measurement occasion can result inflawed estimates of mean-group differences with implicationsfor both theory and practice (Hultsch & MacDonald, 2004;Hultsch, Strauss, Hunter, & MacDonald, 2008; Stuss & Binns,2008). On a related note, when within-person variability islarge, it poses considerable challenges for the estimation oflong-term cognitive change (e.g., Salthouse, 2007). Further,systematic and sizeable variability across measurements raisesquestions about the foundations of classical test theory and theutility of true score values (Lindenberger & von Oertzen, 2006).Thus, an exclusive emphasis on mean levels in the absence ofconsidering variability represents a critical oversimplificationof patterns of behavior that may lead to erroneous inference(Nesselroade, 2002). Recognition of these shortcomings hasgiven rise to models (e.g., diffusion models) that explain per-formance based on multiple distribution parameters, not justcentral tendency (e.g., Ratcliff, Van Zandt, & McKoon, 1999).Inclusion of variance parameters confers unique predictive in-formation about cognitive functioning independent of mean perfor-mance (e.g., West, Murphy, Armilio, Craik, & Stuss, 2002), withspecific analyses of variability facilitating discrimination amonggroups along multiple dimensions, such as patients with dementiaversus healthy controls or patients with dementia subtypes (Hultsch,MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000;Murtha, Cismaru, Waechter, & Chertkow, 2002).

Stuart W. S. MacDonald, Department of Psychology, University ofVictoria, Victoria, British Columbia, Canada, and Aging Research Center,Karolinska Institutet, Stockholm, Sweden; Shu-Chen Li, Center for Life-span Psychology, Max Planck Institute for Human Development, Berlin,Germany; Lars Backman, Aging Research Center, Karolinska Institutet.

This research was supported by grants from the Swedish Research Counciland Swedish Brain Power to Lars Backman, who was also supported by anAlexander von Humboldt Research Award. Shu-Chen Li was supported bygrants from the German Ministry of Education and Research and the MaxPlanck Society, and Stuart MacDonald was supported by a Scholar CareerInvestigator Award from the Michael Smith Foundation for Health Research.

Correspondence concerning this article should be addressed to StuartW. S. MacDonald, Department of Psychology, University of Victoria,P.O. Box 3050 STN CSC, Victoria BC V8W 3P5, Canada. E-mail:[email protected]

Psychology and Aging © 2009 American Psychological Association2009, Vol. 24, No. 4, 792–808 0882-7974/09/$12.00 DOI: 10.1037/a0017798

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The recent impetus toward examining variability in cognitivefunctioning, as both outcome and predictor, derives from a grow-ing literature showing performance fluctuations that (a) are neitherdue to random error nor measurement unreliability, (b) often sharea negative association with mean cognitive performance (e.g.,Hultsch & MacDonald, 2004; Hultsch et al., 2008), and (c) arelinked to theories explicating processing dynamics (e.g., Ratcliffet al., 1999) or the roles and potential neuronal sources of noise inaffecting information processing (e.g., Li, Lindenberger, & Sik-strom, 2001). Although the stability perspective has dominateddevelopmental research for years (Nesselroade, 2002), the currentfocus on variability is not new (see Cattell, 1957; Fisk & Rice,1955; Head, 1926; Wohlwill, 1973). For example, intraindividualvariability was a primary influence in formulating the state–traitanxiety distinction (Cattell & Scheier, 1961). The renewed vigorfor the study of variability likely reflects improved means andmethods for exploring performance fluctuations (Nesselroade &Salthouse, 2004). Within this literature, three primary indices ofvariability have been operationalized vis-a-vis the minimum con-ditions necessary to observe it with reference to a three-dimensional data box defined by persons, measures, and occasions(Cattell, 1966; Hultsch, MacDonald, & Dixon, 2002). Betweenpersons, variability has been operationalized as differences inperformance measured on a single task on a single occasion(referred to as individual differences).

Within persons, variability has been operationalized in twoways. The first concerns variability associated with measuringa single person on a single occasion across multiple tasks(referred to as intraindividual differences). For example,Holtzer, Verghese, Wang, Hall, and Lipton (2008) indexed thisform of within-person variability across a battery of neuropsy-chological tests to predict risk of incident dementia. The secondform of within-person variability is defined by the minimumcondition of measuring a single person on a single task acrossmultiple occasions (referred to as intraindividual variability orIIV). Such intraindividual fluctuations in performance havebeen indexed across multiple trials within a session or acrossmultiple sessions spanning longer intervals (e.g., hours, days,weeks) for a single measure (Li, Huxhold, & Schmiedek, 2004).

Analytical Perspectives in the Study of Variability

Developmentalists and aging theorists alike are inherently in-terested in change over time, and thus variability within personsand across occasions (i.e., IIV) has garnered particular attention forresearchers in these fields. To be sure, a thorough understanding ofthe wider field of variability research requires an appreciation ofthe distinctions between several analytical perspectives. To helporient the reader, we briefly will introduce how time scales, taskcharacteristics, and intraindividual dynamics are key factors toconsider in any operationalization of within-person variability.

Time Scales

One dichotomy that is fundamental to the study of variabilityconcerns differentiating ontogenetic versus microgenetic forms ofwithin-person change according to two criteria: permanence ofchange and time scale (Nesselroade, 1991). Intraindividual changerepresents a relatively slow and enduring process that unfolds

across months, years, or decades (e.g., progressive changes asso-ciated with development and aging, long-term learning, and skillacquisition) and has been referred to as becoming (Ford, 1987) ordeveloping (Li, Huxhold, & Schmiedek, 2004). In contrast, IIVrepresents relatively rapid and transient short-term change indexedacross minutes, days, or weeks for various types of behavior (e.g.,shifts in emotional state, variability in cognitive accuracy or pro-cessing speed, fluctuations in physical performance). The shortertime frame is said to reflect being (Ford, 1987) or functioning (Li,Huxhold, & Schmiedek, 2004), with indices of IIV variouslyreferred to as inconsistency, lack of processing robustness, wobble,and lability (for a review, see Hultsch et al., 2008). IIV can bedistinguished from more enduring intraindividual change. Al-though both variants of within-person change represent importantdevelopmental phenomena, recent research has been focused onIIV as a common component of aging-related cognitive decline, aswell as in relation to behavioral changes associated with neurode-generative diseases (e.g., Alzheimer’s disease) and other brain-related disorders (e.g., traumatic brain injury, schizophrenia). Con-sidering the diverse populations that exhibit increasing IIV andconcomitant cognitive deficits, more variable cognitive function-ing likely reflects a behavioral proxy for endogenous neuralchanges underlying impairment.

The time scale for measuring IIV itself is also important. Forexample, IIV indexed from moment to moment (e.g., fluctuationsin reaction time [RT] trials) versus day to day or week to week(e.g., fluctuations in mood) likely reflects different underlyingsources (e.g., Martin & Hofer, 2004; Rabbitt, Osman, Moore, &Stollery, 2001; Ram, Rabbitt, Stollery, & Nesselroade, 2005;Rocke, Li, & Smith, in press). In several studies, IIV has even beenexamined across different retest intervals for the same task in thesame sample. Rabbitt and colleagues (2001) found that RT vari-ability from trial to trial versus week to week was related, althoughindividual differences in within-session variability did not accountfor all the variance in between-session variability. Similarly,Hultsch and colleagues (Fuentes, Hunter, Strauss, & Hultsch,2001; Hultsch et al., 2000) reported that the magnitude of IIVacross trials is approximately twice as large as IIV across weeks.If a theoretical model assumes that endogenous sources underliecognitive variability (e.g., neural correlates such as changes in theefficiency of neurotransmitters), then IIV may be better capturedover short intervals (e.g., trial-to-trial fluctuations in RT tasks). Incontrast, exogenous modulators of variability (e.g., fatigue, per-ceived stress) may be better indexed over days or weeks.

Task Characteristics

Recent reviews have chiefly focused on the negative associationbetween IIV and cognitive performance (e.g., Hultsch et al., 2008;Luszcz, 2004; MacDonald, Nyberg, & Backman, 2006). However,variability is not always a harbinger of maladaptive functioning;rather, variability can be adaptive depending on specific taskcharacteristics. For example, performance variability in the childdevelopment literature is related to cognitive development (notimpairment), with such variability reflecting diverse strategy ex-ploration for complex tasks. Children who try a variety of strate-gies for complex tasks exhibit greater success. In contrast, moreconstrained tasks (including many of the response latency mea-sures examined in the cognitive aging literature on variability) are

793SPECIAL SECTION: NEURAL UNDERPINNINGS OF IIV

less amenable to multiple response strategies, and varying strate-gies to such tasks could be maladaptive. Siegler (1994) has re-ferred to such fluctuations in performance as the ebbing andflowing of new and old ways of thinking. Similar positive associ-ations between increased IIV and cognitive functioning have beenreported for older adults. Allaire and Marsiske (2005), for exam-ple, computed IIV across performance accuracy trials for threecognitive tasks that permitted implementation of new performancestrategies with practice. Results indicated that increased IIV waspositively correlated with level of task performance and the mag-nitude of practice-related gains. In contrast, IIV estimates derivedfrom cognitive tasks that provide little opportunity for strategicprocessing or practice-related gains (e.g., sensorimotor or percep-tual processing speed) are typically associated with maladaptiveoutcomes (e.g., Hultsch & MacDonald, 2004, Hultsch et al., 2008).Furthermore, depending on the domains of functioning (e.g., cog-nitive vis a vis affective), aging is not always associated withincreasing within-person variability. For instance, recently Rockeet al. (in press) reported that older adults exhibited less dailyfluctuations in positive and negative affect than young adults.

Subtypes of Variability

It is important to emphasize that variation could be operation-alized in numerous forms, even for the most basic cognitiveinformation processing tasks. Indices of variability can be indexedacross trials versus sessions or across all response latency trialsversus only correct responses and could even be examined in the

drift rate or diffusion coefficient across or within trials (cf. Ratcliffet al., 1999).

To better understand the characteristic nature of variability (e.g.,adaptive vs. maladaptive), Li, Huxhold, and Schmiedek (2004)proposed a taxonomy of intraindividual dynamics, reflecting vari-ations in an individual’s processes or performance over time thatare due to both endogenous and exogenous influences. For exam-ple, the classification of intraindividual dynamics allows for dis-tinctions to be drawn between adaptive versus maladaptive typesof variability (see Figure 1). Adaptive forms of variability aretypically observed to the extent that a task is amenable to strategyuse. These adaptive subtypes include plasticity (the functionalmalleability to obtain large learning gains following task expo-sure), diversity (exploratory behaviors and various strategies usedfor performing a complex task), and adaptability (ability to quicklyrecover peak functioning in the face of challenging task condi-tions). In contrast, continued fluctuation in performance subse-quent to mastering a given level of functioning indicates a lack ofprocessing robustness, defined by an increased ebb and flow inprocessing, and diminished stability in performance over briefintervals (Li, Huxhold, & Schmiedek, 2004, Li, Lindenberger, etal., 2004). It is this form of maladaptive IIV that characterizesvirtually all findings reported in the present review.

The Present Review

For the purposes of the present review, we focus on within-taskIIV reflecting intraindividual change in behavior that is relatively

Figure 1. Varieties of intraindividual dynamics. Adaptive forms of variability are typically observed for tasksamenable to strategy use. Adaptive subtypes include plasticity (the ability to exhibit learning gains following taskexposure), diversity (exploratory strategy use when performing a complex task), and adaptability (capacity toquickly recover peak functioning despite challenging task conditions). As a maladaptive form of variability, lackof processing robustness reflects continued performance fluctuations and diminished stability subsequent tomastering a given level of functioning. This form of maladaptive variability characterizes most findings reportedin the present review. From “Aging and Processing Robustness: Evidence From Cognitive and SensorimotorFunctioning,” by S.-C. Li, O. Huxhold, and F. Schmiedek, 2004, Gerontology, 50, p. 30. Copyright 2004 byKarger. Adapted with permission.

794 MACDONALD, LI, AND BÄCKMAN

rapid and transient (e.g., trial-to-trial fluctuations on RT tasks).Irrespective of analytical perspective (e.g., task type, time scale,behavior vs. brain), most of the empirical studies reviewed indexeda maladaptive form of IIV, largely derived from within-personstandard deviations (or related measures) computed across correct(i.e., accurate) trials of response latency tasks for various cognitiveoutcomes—a form of compromised processing robustness accord-ing to the Li, Huxhold, and Schmiedek (2004) taxonomy. Such IIVincreases characterize performance of older adults as well aspopulations with various conditions including dementia, head in-jury, schizophrenia, and attention-deficit hyperactivity disorder(ADHD). Although select evidence substantiates the hypothesizedneural underpinnings of increased IIV from both behavioral(Hultsch et al., 2008) and neuroimaging (Hedden & Gabrieli,2004; MacDonald, Nyberg, & Backman, 2006; Winterer et al.,2004) perspectives, there has been little examination of underlyingneural correlates and virtually no synthesis of extant findings. Webegin by reviewing variability findings from the life span litera-ture, as well as discussing potential origins of IIV. Next, wesummarize evidence linking age-related increases in IIV for cog-nitive functioning to neural correlates at anatomical, functional,neuromodulatory, and genetic levels. Although these distinct neu-ral correlates imply that increased IIV is multidetermined (for areview, see MacDonald et al., 2006), they may also reflect differ-ent levels of related phenomena. For example, fluctuations at thefunctional level (e.g., in blood oxygen–level dependent [BOLD]signal) may be directly linked to neuromodulation (e.g., dopaminebiomarkers). Finally, we conclude by emphasizing important chal-lenges and identifying key questions that remain to be answered inthe study of IIV.

IIV: A Life Span Perspective

IIV in cognitive performance across the life span is character-ized by a U-shaped function. Whereas IIV in cognitive functioningis initially high in childhood, it decreases through adolescence,only to reverse course with advancing age, where clear linksbetween increasing IIV and concomitant cognitive impairmentsare observed (e.g., Hultsch et al., 2002; Li, Lindenberger, et al.,2004; Rabbitt et al., 2001; Williams, Hultsch, Strauss, Hunter, &Tannock, 2005; Williams, Strauss, Hultsch, & Hunter, 2007). Thisdevelopmental pattern parallels the inverted U-shaped functionobserved for age-related changes in intellectual functioning (Li,Lindenberger, et al., 2004), with maturation and senescenceimposing age-specific constraints, particularly under execu-tively demanding conditions (Kray, Eber, & Lindenberger,2004; Li, Hammerer, Muller, Hommel, & Lindenberger, 2009).

The preponderance of data on IIV concerns older adults, withmost studies revealing clear age-related increases in IIV and con-comitant impairments in memory, attention, and language (e.g.,Anstey, 1999; Hultsch et al., 2000; Nesselroade & Salthouse,2004; Rabbitt et al., 2001). Such within-person fluctuations incognitive performance could correspond to as much as severaldecades of between-person age differences (Nesselroade & Salt-house, 2004). At cross section, IIV correlates negatively with levelof performance for diverse measures representing both fluid (e.g.,perceptual speed, working memory) and crystallized (e.g., vocab-ulary) abilities. The magnitude of IIV–cognition correlations isage- and task-dependent, with older adults exhibiting larger cor-

relations between IIV and cognitive performance regardless of taskcomplexity. In contrast, associations for young adults are attenu-ated (albeit still negative) and are typically only observed for selectmeasures characterized by higher cognitive demands (e.g., fron-tally based executive processes; Hultsch et al., 2002).

Several more stringent longitudinal assessments of the IIV–cognition association are available. For example, MacDonald,Hultsch, and Dixon (2003) examined longitudinal changes in IIVon four RT tasks administered over a 6-year period (three occa-sions) to 446 older adults from the Victoria Longitudinal Study(VLS). Findings showed that 6-year increases in IIV were associ-ated with 6-year cognitive decline for various cognitive outcomes(e.g., episodic memory, working memory), supporting claims thatbehavioral IIV is a predictor of cognitive aging. A key feature ofthis study was the proven link between change in IIV and cogni-tion at the within-person level. In an intriguing study, Lovden, Li,Shing, and Lindenberger (2007) examined the supposition thatvariability may represent both a precursor, signaling impendingdecline in cognitive performance (see Figure 2a), and outcome ofchange. Bivariate dual change score models were used to test thedynamic coupling between trial-to-trial RT variability for a mea-sure of perceptual speed (identical pictures) and change in meanlevels of performance for both perceptual speed and categoryfluency. Of particular interest was whether individual differencesin IIV at baseline preceded subsequent change in cognitive func-tioning. These models were applied to five-occasion 13-year lon-gitudinal data from the Berlin Aging Study (N � 447; 70–102years old at baseline). At baseline, individual differences in IIVacross RT trials for the perceptual speed task were coded into threegroups: individuals with scores greater than 0.5 standard devia-tions (SDs) above the mean (high-variability group), those withscores �0.5 SDs of the mean (median-variability group), and thosewith scores more than 0.5 SDs below the mean (low-variabilitygroup). Notably, individuals in the high-variability group showedaccelerated decline in category fluency relative to those in thelow-variability group (see Figure 2b). In contrast, individual dif-ferences in mean levels of category fluency at baseline did notreliably influence subsequent change in IIV. These findings sug-gest that shifts in variability precede changes in mean perfor-mance, provide arguably the most convincing developmental testof the variability hypothesis to date, and imply that theories ofcognitive aging should recognize and account for the developmen-tal cascade between senescent changes in variability and centraltendency.

Increased IIV in cognitive functioning has also been linked toimpairments and fluctuations in biomarkers of aging, includingsensory acuity and physiological functioning (Li, Aggen, Nessel-roade, & Baltes, 2001; MacDonald, Hultsch, & Bunce, 2006;Strauss, MacDonald, Hunter, Moll, & Hultsch, 2002), in accordwith interpretations of IIV as a behavioral indicator of centralnervous system (CNS) integrity. Other biomarkers such as forcedexpiratory volume and grip strength also account for considerablevariance in IIV and RT latency (Anstey, Dear, Christensen, &Jorm, 2005).

Additional behavioral findings linking variability to cognitiveperformance in the latter part of the life span indicate that IIV issystematically related to death (e.g., Shipley, Der, Taylor, &Deary, 2006). In one study, IIV uniquely predicted terminal cog-


nitive decline as many as 15 years prior to the death, with IIVincreasing every year closer to death (MacDonald, Hultsch, &Dixon, 2008). In this study, lower IIV scores also predicted theprobability of survival independent of age, cardiovascular health,and cognitive performance level, supporting the view that IIV is anearly behavioral marker of neurological dysfunction associatedwith impending death.

Origins of Increased IIV

Research on the origins of IIV remains sparse. At the cognitivelevel, postulated mechanisms include momentary lapses of atten-

tion (e.g., Bunce, Warr, & Cochrane, 1993) and failure to maintainexecutive control (e.g., West et al., 2002). Such accounts imply adisproportionately high number of very slow responses in the RTdistribution, a proposition supported by recent findings (e.g., Wil-liams et al., 2005). Although most extant research on IIV has reliedon such behavioral data, brain correlates have begun to be delin-eated in recent neuroscientific investigations. Closer inspection ofrelated literatures (life span developmental psychology, neuropsy-chology, neuroscience) reveals that increased IIV shares definitivelinks to numerous age- and non-age-related conditions, includingincreasing adult age, cognitive decline, impending death, ADHD,

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Figure 2. (a) Hypothetical depiction of level of intraindividual variability (IIV) as a precursor of cognitivechange for several individuals. (b) Longitudinal change in category fluency plotted as a function of performancevariability. At baseline, three variability groups (high, median, and low) were formed on the basis of individualdifferences in IIV across reaction time trials of a perceptual speed task. Individuals with IIV scores greater than0.5 standard deviations (SDs) above the mean were coded in the high-variability group, those with IIV scores�0.5 SDs of the mean were coded in the median-variability group, and those with IIV scores more than 0.5 SDsbelow the mean were coded in the low-variability group. Individuals who exhibited the most variability inperceptual speed at baseline also showed steeper longitudinal decline in category fluency, relative to individualsin the low- or median-IIV groups. From “Within-Person Trial-by-Trial Variability Precedes and PredictsCognitive Decline in Old and Very Old Age: Longitudinal Data from the Berlin Aging Study,” by M. Lovden,S.-C. Li, Y. L. Shing, and U. Lindenberger, 2007, Neuropsychologia, 45, p. 12. Copyright 2007 by Elsevier.Adapted with permission.


Parkinson’s disease, frontotemporal dementia, traumatic brain in-jury, and a specific allele (Val) of the catechol-O-methyl trans-ferase (COMT) gene (for reviews, see Hultsch et al., 2008;MacDonald, Nyberg, & Backman, 2006). Converging evidenceindicates that IIV in cognitive functioning is linked to these afore-mentioned outcomes independent of mean-level performance, un-derscoring the unique importance of variability (e.g., for promot-ing early detection of impending disease and improvingdifferential diagnosis). Thus, IIV is not solely the province ofaging at the behavioral level but rather reflects multiple endoge-nous brain correlates. For example, rapid changes in IIV from onemoment to the next in a cognitive task may reflect brain mecha-nisms, such as fluctuations in the connectivity of neuronal path-ways (e.g., Kelly, Uddin, Biswal, Castellanos, & Milham, 2008),and the efficacy of neurotransmitter systems (e.g., Backman et al.,2006). In the remaining sections of this review, we focus on linksbetween increased IIV in cognitive performance and key braincorrelates and speculate as to mechanisms underlying increasedvariability in behavior and brain alike.

Neural Correlates of IIV

Structural Brain Correlates

A growing body of evidence links structural brain correlates toIIV. Structural alterations associated with increased performancevariability include lesions to frontal gray matter (e.g., Sowell et al.,2003; Stuss, Murphy, Binns, & Alexander, 2003) as well aschanges to white matter, including volumetric decline, demyelina-tion, and hyperintensities that reflect abnormalities on magneticresonance imaging (MRI) scans due to age-related changes incerebral blood flow, vascular injury, or neurological conditions(e.g., Anstey et al., 2007; Britton, Meyer, & Benecke, 1991; Bunceet al., 2007; Walhovd & Fjell, 2007).

Lesions to frontal gray matter and associated increases in per-formance variability have a rich history in neuropsychology (e.g.,Goldstein, 1939; Head, 1926). With regard to structural correlates,an immediate question concerns whether elevated IIV exhibitsregional specificity. Neuropsychological evidence suggests thatthe frontal lobes (particularly the prefrontal cortex [PFC]) share astrong association with IIV. For example, Murtha and colleagues(2002) reported that persons with frontotemporal dementia weremore variable than those with Alzheimer’s disease for a similarlevel of disease severity, implying differential relevance of frontalversus medial temporal regions to elevated IIV. Stuss et al. (2003)examined whether frontal regions shared a stronger associationwith IIV and, if so, whether there were regional frontal variations.They examined 48 patients between 43 and 67 years old who hadbrain lesions due to various acute conditions including infarction,hemorrhage, and trauma (25 patients with frontal lesions, 11patients with nonfrontal lesions, and 12 control participants). Fourdifferent RT tasks were employed that required distinct levels offeature discrimination and identification: simple (respond to asingle shape); easy (four different shapes with one designated asthe target on 25% of the trials); complex (discrimination based onshape, color, and internal texture, with the target defined as acombination of these features); and redundant (also containingthree features, but the nontarget trials did not contain any targetfeatures). Consistent with expectations, those with circumscribed

frontal lesions were more variable than those with nonfrontallesions or controls. Moreover, for those with frontal lesions, adifferent profile of variability was observed depending on location.Individuals with lesions in right and left dorsolateral PFC, as wellas in the superior medial frontal cortex, exhibited the most pro-nounced variability, which increased with task difficulty. Thesefindings indicate that damage to the frontal lobes impairs stabilityof cognitive performance.

Given the well-documented associations between white matterintegrity and select cognitive deficits (e.g., processing speed, at-tention), potential links between white matter status and IIV havebeen examined in several recent studies. With increasing age, thecorpus callosum (CC) is known to atrophy, with the area of the CCpotentially reflecting an important indicator of white matter dis-ease related to demyelination or vascular insult (Pfefferbaum,Rosenbloom, Serventi, & Sullivan, 2002). In light of these asso-ciations, Anstey and colleagues (2007) investigated links amongthe CC area (traced on the mid-sagittal slice), IIV, and cognitivestatus. They examined differences in IIV across RT trials for bothsimple and choice RT tasks for two groups of adults who were60–64 years old: a healthy adult group (n � 432) and a groupcharacterized as having mild cognitive disorders (n � 57). For thenormative sample, no reliable association was observed betweenthe CC area and IIV; IIV accounted for less than 1% of thebetween-person variance in the CC area. However, individuals inthe mild cognitive disorders group who were more variable alsohad a smaller CC area, with IIV explaining as much as 12.7% ofthe variance. The fact that a stronger association between the CCarea and IIV was observed for the at-risk group suggests a role ofwhite matter alterations in both cognitive impairment and in-creased performance variability. Moreover, dividing the CC intothree distinct regions (anterior, intermediate, and posterior) yieldedthe largest associations between the anterior CC and IIV, consistentwith previously reported links among frontal structures, attention, andIIV (e.g., West et al., 2002).

Among potential explanations for the increased CC area–IIVlink in the mild cognitive disorders group, Anstey and colleagues(2007) invoked a concept based on biological limits of cognitiveprocessing (e.g., Baltes, Staudinger, & Lindenberger, 1999). Suchlimits may be defined in terms of brain reserve (i.e., the integrityof brain structure and neural circuitry) or in terms of cognitivereserve reflecting the ability to efficiently use existing brain struc-tures or to recruit alternate ones if necessary (Stern, 2002). Con-straints on brain or cognitive reserve may be due to numerousfactors including structural or functional changes. The reservepremise is supported by functional imaging studies showing pat-terns of individual differences in neural activation. With increasingtask difficulty, pathology-free individuals typically exhibit in-creased activation for the same brain regions that were activatedfor less demanding versions of the same task. However, there areclear individual differences in terms of these activation patterns;for a given level of task difficulty, individuals with greater reservecapacity exhibit less (i.e., more efficient) neural activation toachieve the same level of performance and could presumablyperform better than less skilled individuals at maximum exertion(e.g., Grady et al., 1996; Nyberg et al., 2003; Stern, 2002; Sternet al., 2003). Anstey and colleagues (2007) speculated that indi-vidual differences in the CC area in the mild cognitive disordersgroup could share a stronger association with deterioration in the


PFC that may, in turn, give rise to attentional deficits and increasedIIV (Andres, Parmentier, & Escera, 2006; Raz, Gunning-Dixon,Head, Dupuis, & Acker, 1998). Such an interpretation is consistentwith previous research showing that increased executive demandsresult in increased IIV in response latency (West et al., 2002).Individuals in the mild cognitive disorders group who were themost variable also had the most diminished brain reserve (in theform of a smaller CC area, likely indicating pathological whitematter alterations). This group’s increased IIV in response latencyfor a simple RT task could reflect such constraints to brain reservebut may also reflect lessened cognitive reserve (the greater burdenof structural brain changes may lead to failures of task-relevantprocessing or to deficiencies in basic cognitive resources). Eitherdiminished brain or cognitive reserve could exacerbate IIV incognitive performance.

A related study by Bunce and colleagues (2007) examined theassociation between white matter hyperintensities (WMHs) in var-ious brain regions (e.g., frontal, temporal, parietal) and increasedIIV across RT trials for 469 healthy community-dwelling adults(60–64 years old). Findings indicated that frontal WMHs werelinked to increased IIV but not to performance on other cognitivemeasures including global cognition, perceptual speed, and epi-sodic memory. In contrast, WMH in other brain regions did notshare a significant association with IIV. These patterns imply thatincreased WMH and the associated deterioration of neural path-ways in the frontal cortex play a key role in increased IIV observedfor older adults and those at risk of cognitive impairment.

In summary, lesions to frontal gray and white matter share arobust association with increased IIV. The developmental evolu-tion and involution of gray and white matter corresponds, at leastgrossly, to increasing and then decreasing intellectual functioning(Kray et al., 2004; Li, Lindenberger, et al., 2004) as well as todecreasing and then increasing IIV (MacDonald et al., 2003;Williams et al., 2005) across the life span. For example, thereported decreases in IIV from early childhood through adoles-cence (cf. Williams et al., 2005) may reflect systematic changes inbrain morphology, particularly in the frontal cortex (Gogtay et al.,2004). The reductions in gray matter density and synaptic pruningthat occur during adolescence and young adulthood (Sowell et al.,2003) may promote increased neural efficiency and decreasednoise in cognitive functioning that underlie the concomitant de-creases in IIV during development (Williams et al., 2005, 2007).Similarly, gray matter atrophy and increased neural noise in olderadults may underlie increased IIV (Raz et al., 2004; Sowell et al.,2003).

Life span changes in white matter volume, approximating aninverted U-shaped function, mirror closely the U-shaped life spanchanges observed for patterns of IIV (Gogtay et al., 2004; Sowellet al., 2003). Disconnectivity in associative pathways, whethercaused by immature or degraded white matter tracts, can alsoincrease variability. Such structural changes in performance mayresult in increased neural noise (e.g., due to white matter discon-nectivity), leading to less distinct cortical representations, poorercognitive performance, and increased performance variability. Ef-fectively, the neural system matures, levels off, and declines witha direct correspondence between increasing and then decreasingintellectual functioning to decreasing and then increasing IIV incognitive performance (MacDonald, Nyberg, & Backman, 2006).

Functional Brain Correlates

Functional MRI. As interest in performance variability as amarker of CNS integrity has increased (Hultsch & MacDonald,2004; Hultsch et al., 2008), functional brain imaging studies havebegun to emerge. Bellgrove, Hester, and Garavan (2004) con-ducted the first neuroscientific investigation of the functionalneuroanatomy of executive control in relation to IIV. A total of 42healthy participants, 18–46 years old, completed a Go/No-Goresponse inhibition task in an event-related functional magneticresonance imaging (fMRI) design. The Go/No-Go task presentedtwo letters, X and Y, in an alternating pattern (i.e., X Y X Y X . . .).Participants were instructed to press the response button for all Gotrials but to withhold responses to No-Go stimuli that interruptedthe alternating pattern (e.g., X Y X X). Findings indicated thatincreased IIV across Go trials of this response-inhibition task wasassociated with lower inhibitory success, slower responding, andincreased brain activity in left and right middle frontal regions.Individuals with increased IIV activated inhibitory regions to agreater extent, likely reflecting greater demands for executivecontrol to maintain task performance. Consistent with researchunderscoring important connections between the frontal lobes andincreased IIV (e.g., Stuss et al., 2003), these findings imply that IIVmay result from deficient attentional control (e.g., Bunceet al.,1993; Bunce, MacDonald, & Hultsch, 2004) and could serve toindex the efficiency of executive control processes (Bellgroveet al., 2004). Moreover, the findings are notable given that thefunctional correlates of IIV were robust for even a healthy youngto middle-aged sample.

In a related fMRI investigation in older adults, the functionalcorrelates of IIV were examined in relation to episodic retrievalsuccess (MacDonald, Nyberg, Sandblom, Fischer, & Backman,2008). IIV was computed across latency trials in a word recogni-tion task and then examined in relation to the magnitude andanatomical location of BOLD activations. The 19 older adults(70–79 years old) were presented with 80 words during an initialencoding session. During the subsequent retrieval session, bothbrain scans and response latencies were recorded. Lower variabil-ity across successful word retrieval trials was linked to better wordrecognition (r � �0.37), faster RTs (r � .66), and increasedBOLD activation (r � �0.49) in the supramarginal gyrus in theinferior parietal cortex, a structure implicated in sustained atten-tion, deep semantic encoding, and retrieval success (e.g., Cabeza &Nyberg, 2000; Shannon & Buckner, 2004; Wagner, Shannon,Kahn, & Buckner, 2005). In contrast, more variable individuals didnot recruit this area of inferior parietal cortex to the same extentand exhibited lower retrieval success. The observed links betweendecreasing IIV and parietal activations associated with retrievalsuccess support the hypothesis that behavioral IIV represents aproxy for neural integrity.

As a putative indicator of CNS efficiency, IIV may not onlyshare associations with activations in circumscribed brain regionsbut also be linked to activity across functional networks. In arecent investigation, Kelly et al. (2008) explored how IIV may bemodulated by competition between functional networks. One net-work of particular interest, the default-mode network, comprisesbrain regions that show coherent activity at rest (e.g., stimulus-independent thought) and are routinely deactivated duringattention-demanding cognitive tasks (Buckner & Vincent, 2007;


Raichle et al., 2001). An inability to suppress such default-modeactivity has been linked to attentional lapses (e.g., Weissman,Roberts, Visscher, & Woldorff, 2006), altered task-relevant acti-vation in PFC and anterior cingulate cortex, and various conditionsincluding Alzheimer’s disease, ADHD, and schizophrenia (e.g.,Buckner et al., 2005; Liang et al., 2006; Tian et al., 2006). As thesepatterns and conditions are also well-known correlates of IIV,Kelly and colleagues surmised that IIV and default mode activitymay be directly related. To pursue this idea, they used the Eriksenflankers task to examine whether increased IIV and associatedattentional lapses could be explained by competition between thedefault mode network (activations at rest) and regions of a task-relevant dorsal attentional network (activations during the flankerstask). Recent studies have shown that this competitive associationis represented in the brain as an antiphase (i.e., negative) correla-tion between activation in these two networks. Larger negativecorrelations are said to reflect better activity regulation betweenthe two networks, with the expectation that such larger antiphaseassociations would be linked to less IIV in performance. Active(flankers task) and resting state scans were obtained for 26 adults(mean age � 28.1 years). For each target network, the time seriesof BOLD activations were extracted, and then the correlation between

activations in each network was computed (see Figure 3a). Themagnitude of the negative association between spontaneous activ-ity in the default-mode network versus task-positive activity in theattentional network was subsequently correlated with IIV (coeffi-cient of variation) for congruent and incongruent RT trials in theflankers task. The findings indicated that as IIV for incongruentRT trials increased, the strong negative association between acti-vations in the default mode versus those in the attentional networksdiminished (see Figure 3b). These results indicate that increasingIIV is not only related to select anatomical regions of the brain butis also associated with compromised regulation and coordinationof neural networks.

Electroencephalography (EEG)/event-related potentials (ERP).Whole-brain functional activation techniques such as EEG havealso been used to link behavioral IIV in cognitive performance toevent-related oscillations in various EEG spectra. For example,psychometric intelligence is inversely correlated with event-related potential variability (averaged evoked potentials) in theparietal and temporal lobes (Barrett & Eysenck, 1994). Earlyresearch in this area also linked increased variability in P300latencies, an electrophysiological marker of various cognitive pro-cesses including attention and decision making (e.g., Sutton,

a

b

Figure 3. (a) Correlation between time series of blood oxygen level–dependent (BOLD) activations for thedefault mode network (activations at rest) and the dorsal attentional network (activations during the flankerstask). Competition between the default mode and dorsal attentional networks is represented in the brain as anantiphase association, with larger negative correlations reflecting better activity regulation between the twonetworks. TRs � repetition times. (b) Correlation between antiphase time series (see Figure 3a) and intraindi-vidual variability (IIV; coefficient of variation) for incongruent reaction time (RT) trials on the flankers task. AsIIV for incongruent RT trials increases, the strong negative association between activations in the default modeversus attentional network is weakened. These results suggest that IIV is associated with compromised regulationand coordination of neural networks. CV � coefficient of variation. From “Competition Between FunctionalBrain Networks Mediates Behavioral Variability,” by C. A. M. Kelly, L. Q. Uddin, B. B. Biswal, F. X.Castellanos, and M. P. Milham, 2008, NeuroImage, 39, p. 11. Copyright 2008 by Elsevier. Adapted withpermission.


1969), to old age and morbidity (Kugler, Taghavy, & Platt, 1993).More recently, increased prefrontal broadband noise in the theta-frequency band (6.0–8.0 Hz) for patients with schizophrenia wasassociated with increased BOLD signal in the dorsolateral PFCduring a working memory task, a pattern interpreted to reflectinefficient neural processing (Winterer & Weinberger, 2004). Thelink between increases in prefrontal EEG noise (i.e., activity nottime-locked to stimuli) during information processing and im-paired working memory in those with schizophrenia may reflectasynchronous field potential oscillations by cortical pyramidalneurons (Callicott et al., 2000).

In a recent study, Fjell, Rosquist, and Walhovd (in press) ex-amined instability at both the electrophysiological and behaviorallevels. Variability in P3a and P3b ERPs was indexed for a groupof 133 adults (20–88 years old) for a simple visual task measuringresponse latency (a visual oddball task composed of 210 stimuliwith a 10% probability for both targets and distractors). The P3aand P3b potentials are putative indicators of different attentionalprocesses; the former is related to attention switching and volun-tary allocation of attention as elicited by distractor stimuli (but seeSutton, 1969, who discussed numerous functional interpretationsof P3 potentials), with the latter thought to reflect stimulus eval-uation and controlled attentional processes (elicited by target stim-uli). IIV in response latencies for the simple visual task representsa third distinct source of information characterizing response se-lection and execution. The focus of this investigation was toidentify the locus of age-related cognitive instability in the infor-mation processing stream (e.g., at the level of stimulus evaluationvs. response execution). With increasing age of the participants,significant increases in variability were observed across RT trialsbut not in variability in P3a or P3b latency. In light of the patternsobserved, Fjell and colleagues (in press) concluded that age-relatedincreases in IIV seem to be localized to later stages of processing(e.g., response decision or execution, as indicated by age-relatedincreases in IIV) as opposed to earlier stages in the responsedecision stream (e.g., stimulus evaluation as indexed by P3b). Thisinterpretation is consistent with findings by West and colleagues(2002); in that study, young and older adults showed the samelevel of IIV for a low executive demand condition of the n-backtask, with the older group showing increased IIV as a function ofincreased executive demands.

Neuromodulatory Correlates

In addition to structural and functional brain changes, alterationsin select neurotransmitters, including those in the catecholamineand acetylcholine systems, may give rise to increased neural noise(see Backman et al., 2006, for review) that undergirds increasedIIV in cognitive performance. In particular, alterations in dopa-mine (DA) neuromodulation have been documented for selectpopulations who also exhibit increased behavioral IIV; these pop-ulations include not only older adults (Hultsch et al., 2002; Rabbittet al., 2001) but also children with ADHD (Castellanos & Tan-nock, 2002; Bellgrove, Gill, Hawi, Kirley, & Robertson, 2005) andpatients with schizophrenia (Manoach, 2003) and Parkinson’s dis-ease (Burton, Strauss, Hultsch, Moll, & Hunter, 2006). One work-ing hypothesis is that reduced DA activity increases neural noise,resulting in less distinct cortical representations manifest as de-creases in cognitive performance and increases in behavioral IIV

(Li, Lindenberger, & Sikstrom, 2001). In this section, we discussevidence from neurocomputational models, as well as recent em-pirical findings, that directly link DA dysregulation to increasedIIV.

Computational models. Various computational approacheshave been advanced to explain the mechanisms whereby dopami-nergic modulation affects cognition (see Rolls, Loh, Deco, &Winterer, 2008, for review). These attempts range from realisticbiophysical firing rate models of how D1 and D2 receptors affectthe stability of working memory representations (Durstewitz,Seamans, & Sejnowksi, 2000; see also Seamans & Yang, 2004, fora review) to more abstract models of dopaminergic effects on thedynamic connectivity between prefrontal cortex and basal ganglia(e.g., O’Reilly & Frank, 2006). Other models focus on the generalcomputational role of DA in affecting the signal-to-noise ratio ofneuronal signal transduction (e.g., Cohen & Servan-Schreiber,1992; Li, Lindenberger, & Sikstrom, 2001) or outcome-basedevaluation in reinforcement learning (see Montague, Hyman, &Cohen, 2004, for review).

Although the various computational approaches differ in levelof analysis and biophysical specificity, one basic assumptionshared by most theories is that dopaminergic modulation influ-ences the properties of neuronal representations of cognitive andperceptual events. For instance, a two-stage model of dopaminer-gic modulation of working memory aims at capturing the dynamicinteractions between DA and glutamate receptors in affecting theneuronal representations of memory items in the prefrontal cortex(Durstewitz et al., 2000). Specifically, when D2 receptor modula-tion predominates during the first stage, the PFC network issupposed to be in an exploratory state with multiple weak repre-sentations. However, in the second stage when D1 receptor mod-ulation predominates, heightened inhibitory mechanisms weed outweaker representations and enhance the representation of thestronger inputs (Seamans & Yang, 2004). This effect is nicelyparalleled by results from other models that aim at explicating thecomputational effects of dopaminergic modulation on the signal-to-noise ratio of information processing at a more molar level(Cohen & Servan-Schreiber, 1992; Li, Lindenberger, & Sikstrom,2001). In these models, DA is commonly assumed to facilitate theresponsivity in activity transmission both within and betweenmodules of a neural network. As the gain parameter of a neuralnetwork’s activation function is attenuated or increased to mimicdeficient or excessive dopaminergic modulation, respectively, thedistinctiveness of internal representations of stored perceptual ormemory items is reduced, resulting in impaired cognitive perfor-mance and increased behavioral IIV (e.g., Li, Lindenberger, &Sikstrom, 2001; Li & Sikstrom, 2002).

In vivo positron emission tomography (PET) research. Build-ing on these neurocomputational findings, investigators recentlyattempted to directly link a PET-derived in vivo marker of dopa-minergic neurotransmission (D2 binding potential) to IIV in cog-nitive performance (MacDonald, Cervenka, Farde, Nyberg, &Backman, 2009) in a group of middle-aged adults (N � 16; 41–65years old). D2 binding potential was derived for striatum and threeextrastriatal brain regions (orbitofrontal cortex [OFC], anteriorcingulate cortex [ACC], and hippocampus [HC]); selection of theregion of interest was based on extant findings suggesting thatfrontal and medial–temporal regions may differentially influenceIIV (Bellgrove et al., 2004; Murtha et al., 2002; Takahashi et al.,


2007). IIV in response latencies was indexed for measures ofepisodic memory and executive functioning on the basis of theestablished association these tasks share with IIV (see Hultsch etal., 2008). Finally, relative to other DA receptors (e.g., D1), D2binding may be especially relevant for the study of IIV. D2receptors serve a critical role in facilitating rapid shifts betweendifferent targets (cf. IIV), in contrast to D1 receptors, which havegreater relevance for tonic DA levels involved in maintaining aspecific cognitive set (e.g., Bilder, Volavka, Lachman, & Grace,2004; Cohen, Braver, & Brown, 2002). Given the key role of DAmodulation for cognitive performance and IIV in the neurocom-putational models, we hypothesized that decreased D2 bindingpotential would be linked to increased IIV.

Consistent with expectations, lower D2 binding in the ACC,HC, and OFC, but not in the striatum, was systematically linked toincreased IIV in both memory and executive functioning. Thisstudy is the first in which a direct link was found between in vivomeasures of DA function and behavioral measures of IIV incognitive performance. The findings complement the neuro-computational work of Li and colleagues (Li, Lindenberger, &Sikstrom, 2001; Li & Sikstrom, 2002), suggesting that DA dys-regulation alters the signal-to-noise ratio of neural informationprocessing, impairing neurons’ sensitivity to afferent signals, lead-ing to less distinct cortical representations, and ultimately resultingin increasing IIV and impaired cognitive functioning.

The MacDonald et al. (2009) data are also consistent withregional brain correlates identified in the neuropsychological andneuroscience literatures, reinforcing the importance of frontal re-gions as structural correlates of IIV (e.g., Bellgrove et al, 2004;Murtha et al., 2002; Stuss et al, 2003). The strongest associationwas observed between D2 binding in the ACC and IIV for anexecutive task that indexed set shifting. This implies that if theACC is not functioning optimally (e.g., due to reduced D2 bind-ing), the resulting diminished executive control coupled with theACC’s central processing role may lead to increased IIV in per-formance, particularly for executively demanding tasks (e.g., Westet al., 2002). These findings provide a nice extension of researchby Takahashi and colleagues (2007). Whereas they examined D2binding potential in select extrastriatal regions and found that D2binding in HC was associated with performance on episodic mem-ory tasks as well as frontally mediated executive tasks, our find-ings clearly show the relation of D2 binding in ACC and HC tomeasures of IIV in memory and executive tasks.

Genetic Correlates

In addition to neuromodulators and other brain-based correlates,emerging evidence indicates that IIV is under genetic influence aswell. The catechol-O-methyl transferase (COMT) enzyme de-grades DA in the frontal cortex, with carriers of the Val allele ofthe COMT gene having lower extracellular DA levels in prefrontalcortex than Met carriers due to elevated enzymatic activity (Wein-shilboum, Otterness, & Szumlanski, 1999). Stefanis and col-leagues (2005) examined whether IIV in cognitive performancevaried as a function of COMT allele (Val vs. Met) in a sample of521 men (mean age � 21 years). The relation of DA signaling(tonic vs. phasic) to performance on cognitive tasks is dependentupon the characteristics of the task itself. The Met allele, with itslower enzymatic activity, may differentially benefit performance

on cognitive tasks requiring cognitive stability and tonic levels ofDA activation but hamper performance on tasks that require cog-nitive flexibility and phasic activation. To examine this hypothesis,Stefanis and colleagues administered the continuous performancetest (identical pairs version), which requires participants to respondwhenever two rapidly flashed identical stimuli (e.g., four-digitnumbers), among the 300 stimuli pairs, appear over the course ofa 5-min trial. Findings indicated that Val carriers exhibited greaterIIV than Met carriers across trials of the continuous performancetest, thereby linking the COMT polymorphism and frontal DAactivity to IIV. The authors concluded that the greater performancestability for the Met genotype may be a consequence of increasedextracellular DA, serving to stabilize neural representations in theprefrontal cortex.

The link between genetic and neuromodulatory influences un-derscores that, although multidetermined, the neural and geneticcorrelates of IIV likely reflect different levels of related phenom-ena. Further emphasizing this point, percentage of change in theBOLD signal in the left PFC is also known to vary as a functionof the COMT genotype. Mattay and colleagues (2003) found thatfor a fixed level of n-back performance, participants who werehomozygous for the Val allele with less available DA in thesynaptic cleft exhibited increased BOLD activity in the PFC rel-ative to homozygous Met carriers. Moreover, this difference wasexacerbated with increasing executive task load (i.e., 2-back vs.1-back condition). The increased PFC activity in Val carriers wasinterpreted as a less efficient physiological response, perhaps dueto diminished neuronal signaling in the PFC (Li & Sikstrom, 2002;Stefanis et al., 2005). This interpretation is supported by otherneuroscientific findings showing that older adults nonselectivelyrecruit neural tissue in task-irrelevant brain regions during episodicand working memory performance (e.g., Cabeza, 2001; Logan,Sanders, Snyder, Morris, & Buckner, 2002). In synthesizing thesefindings, we note that a less efficient frontal response duringcognitive processing characterizes patients with schizophrenia,COMT Val carriers, and older adults—all groups displaying in-creased IIV in cognitive performance as well as alterations in theDA system. Although the aforementioned findings reveal somepromising associations between COMT and IIV, there is a clearneed for future research on the links between IIV and other genesthat influence DA functions, as well as other systems (e.g., neuro-transmitter, immune) altogether.

Key Issues and Future Research

Despite explosive growth in variability research in the pastdecade, a number of key challenges remain. In this final section,we address several important themes including measurement is-sues related to IIV, the potential adaptivity of neural noise, andseveral intriguing avenues for future research.

Measurement Issues and IIV

At the outset of this review, we introduced several analyticalperspectives (e.g., task characteristics, time scale) known to influ-ence observed patterns of performance variability. Although oftenan afterthought, the manner in which IIV is assessed may impactboth the magnitude and valence of associations between IIV andselect outcome measures. For example, there are several reasons


that age differences are more likely to be found for measures ofIIV indexed by response speed versus those indexed by responseaccuracy. Relative to accuracy-based measures, chronometric re-sponse latencies permit a more precise index of rapidly changinginternal processes (Gazzaniga, Ivry, & Mangun, 2002). If suchmoment-to-moment fluctuations represent the best time frame toassess efficacy of age-related cognitive functioning, then measuresof IIV over brief intervals likely confer the purest index. More-over, accuracy measures are often derived from complex tasks,which may be quite sensitive to strategy use (Hultsch et al., 2008);as noted, IIV for tasks amenable to strategy use may be positivelyrelated to other cognitive outcomes. Finally, latency measuresshould provide a more sensitive scale compared with indicators ofaccuracy due to measurement reliability. Accuracy-based cogni-tive measures often contain a limited number of items, potentiallyleading to unreliable estimates of variability. Recent findings sug-gest that, regardless of metric, a sufficient number of trials (�7)are required to reliably index IIV (Schmiedek, 2006). It is alsoimportant to keep in mind that relative to measures of IIV, thearithmetic mean has a greater proportion of systematic varianceavailable for association with cognitive outcomes. Thus, observeddifferences in the strength (but not valence) of association betweenmean versus IIV may reflect differences in reliability. If thereliability of IIV is lower than that of the mean, it is not surprisingthat it would share a comparatively weaker association with thesame cognitive outcome. Such measurement properties of variabil-ity represent important considerations as numerous researcherscollectively attempt to understand patterns observed in the extantliterature.

The Adaptive Potential of Noise: Stochastic Resonance

Although our focus has rarely strayed from the negative aspectsof IIV, the point that variability or noise is not always suboptimalis perhaps best illustrated in a phenomenon called stochastic res-onance (SR). SR denotes the fact that optimal levels of input (i.e.,externally introduced) noise are beneficial for detecting and trans-mitting weak signals in nonlinear physical and biological systems(see Moss, Ward, & Sannita, 2004, for review). A basic aspect ofSR (threshold SR) results from the interaction between a response(or activation) threshold, a subthreshold stimulus, and input noise.Consider an example of a hardly recognizable image of Big Ben,with most of its pixels degraded below a certain threshold of grayscale. In this case, with an optimal amount of random noise addedto the gray scale, the subthreshold image gradually emerges abovethe threshold and becomes visible. However, if more noise isadded, the image blurs again. Returning to the example of the brainand models of spike-generating neurons, a subthreshold signalcannot cross the threshold to produce action potentials, thus losingthe information content of the weak signal. However, the benefit ofadding noise in this case is to permit some spikes to cross thethreshold, thereby transmitting information about the signal.

Aging, Neuromodulation, and SR

At first glance, the notion of variability as a prime indicator orcause of age-related cognitive deficits conflicts with ample empir-ical and theoretical evidence showing the functional relevance andbenefit of SR noise. This apparent paradox leads to a pivotal

question: To what extent are the functions of synaptic noise andcharacteristics of SR altered in aging neurobiological systems aswell as in other conditions marked by suboptimal neuromodula-tion? Formal theories are helpful in guiding empirical research onthe interactive dynamics between synaptic noise as a regulatorymechanism of cortical neurons’ response properties and othermechanisms of neuronal gain control, such as neuromodulationand neuronal synchronization (e.g., Diesmann, Gewaltig, & Aert-sen, 1999). Models comparing systems with deficient neuromodu-lation (e.g., in the case of senescence or pathology) to intactsystems have generated insights about the interactions betweenneuromodulatory and synaptic noise gain control.

Using the stochastic gain-tuning model of neuromodulatoryaging (Li, Lindenberger, & Sikstrom, 2001) to simulate experi-mental effects observed in paradigms of subthreshold perceptualprocessing, we recently investigated how interactions between thebenefit of input noise and deficient neuromodulation jointly affectSR in older neurobiological systems (Li et al., 2006). Less efficientneuromodulation in old age was modeled by reducing the gainparameter (G) from optimal to suboptimal levels to yield activationfunctions with less steep slopes. Neural networks with attenuatedsystem gain modulation reacted to input noise less effectively,resulting in SR functions with lower and right-shifted peaks (seeFigure 4). These findings suggest that noise added to weak signalscontinues to yield beneficial effects in suboptimally modulatedsystems. However, in aging, the benefits of SR are affected in threemajor ways: (a) the absolute peak of SR is reduced, (b) the rangewithin which noise operates optimally is reduced, and (c) moreexternal noise is required to reach the peak of the SR function.

SR has recently attracted attention in research on aging becauseof its promising utility in attenuating the functional consequencesof older adults’ deficits in sensory and sensorimotor functions. For

Figure 4. Different levels of stochastic gain modulation that simulateage-related differences in neuromodulatory efficiency, result in stochasticresonance (SR) functions that differ in peak magnitude and the extent ofright shift of the peak. The bell-shaped SR function depicts the rise and fallof noise-enhanced benefits in signal processing. Neural networks withattenuated system gain parameter (G) react to input noise less effectively,yielding SR functions with lower and right-shifted peaks. Introducing noisecan be beneficial for subthreshold signals, but too much noise can obscurethe signal. E � expected value or mean. From “A NeurocomputationalModel of Stochastic Resonance and Aging,” by S.-C. Li, T. von Oertzen,and U. Lindenberger, 2006, Neurocomputing, 69, p. 1558. Copyright 2006by Elsevier. Adapted with permission.


instance, keeping one’s balance while standing is an importantbasic sensorimotor function that declines substantially during ag-ing (e.g., Huxhold, Li, Schmiedek, & Lindenberger, 2006) and isoften associated with serious negative consequences, such as falls.It has recently been shown that subthreshold electromechanicalnoise delivered through shoe insoles (Harry, Niemi, Priplata, &Collins, 2005) reduces older adults’ body sway while standing.

Important challenges remain if researchers are to fully under-stand the positive versus negative characteristics of variability andnoise. Research on SR clearly indicates that providing externalnoise into a system can improve signal processing (Li et al., 2006).Even white noise can produce an action potential for an otherwiseweak signal. However, as shown in Figure 4, introducing too muchnoise can also serve to obscure the signal. Additional research isrequired into how age-related increases in neuronal noise due toendogenous sources may be countered by the introduction ofexternal noise to augment signal processing. In contrast, manyexamples of the negative repercussions of noise were reviewed,showing clear links between increased variability and disadvan-taged characteristics (e.g., impaired cognitive functioning, smallerCC area). On the basis of the preponderance of data available, itseems that the maladaptive forms of IIV reflect changes in endog-enous brain mechanisms, such as white matter disconnectivity orDA dysregulation with increasing age. Such changes in brainstructure, function, and neuromodulation likely influence cellularprocesses that give rise to increased neural noise (i.e., reducedsignal to noise) and impaired cognitive representations, which issubsequently manifest as increased performance variability andimpaired cognitive functioning. For example, endogenous forms ofneural noise underlying increased behavioral variability couldbe due to a lack of coherence in neuronal firing patterns or touncoordinated firing within individual cells. Although somedata have linked the timing of neuronal firing to behavior (e.g.,Johansson & Birznieks, 2004), it remains to be determined whethercellular synchronization patterns map onto IIV in behavior. Alsorequired are single-cell studies in which across-trial variability infiring rate for individual cells is measured directly (see MacDonald,Nyberg, & Backman, 2006).

Future Research

With regard to IIV in behavior for select cognitive outcomes,most of the extant research is based upon an evaluation ofbetween-person differences in within-person performance fluctu-ations. Future researchers clearly would benefit from additionalwithin-person analyses of the intraindividual covariation betweenchange in IIV for a given task and mean level change for othercognitive outcomes (cf. Lovden et al., 2007). Such analyses willprovide a more stringent methodological assessment of whetherchange in IIV is temporally linked to change in mean performance.Future studies may explore within-person associations betweenrelevant features of a given task, such as the coupling betweenresponse accuracy (correct vs. incorrect trials) and the vicissitudesof response latency. As interest for this topic continues to grow, itis imperative to move from univariate to multivariate conceptual-izations of variability, which is akin to examining alterations in asingle local function versus alterations in the global organizationof numerous functions (e.g., how IIV across trials of a given RTtask is affected by, or may influence, shifts in cognitive resource

allocation or functional organization of the brain; Lindenberger &von Oertzen, 2006).

More mechanistic evidence on the brain–IIV relationship isneeded. Several important avenues of research could be pursuedthat would provide better understanding of neural mechanismsunderlying IIV, including experimental manipulation of IIV, aswell as the examination of IIV in the brain itself. With regard toexperimental approaches, manipulating brain correlates hypothe-sized to influence performance variability represents one methodto directly link IIV to brain function. This could involve pharma-cological manipulations of potential neural substrates of variabilitysuch as DA. For example, dopaminergic agonists or antagoniststhat respectively enhance or hamper neuromodulation could beadministered to examine the influence of DA signaling on IIVin cognitive performance. A strong correlative triad is welldocumented among DA, adult age, and cognitive performance(Backman et al., 2006). Given the hypothesized importance of DAto IIV, administering DA might decrease IIV in RT for olderadults, whereas an antagonist could increase IIV in the very samemeasure of RT for young adults.

Although recent investigations linking behavioral IIV in cogni-tive functioning to neural indicators represent an important firststep, a perhaps even more intriguing avenue of future investigationinvolves the direct examination of variability in the brain itself. Inlight of the hypothesis that endogenous sources underlie IIV incognitive functioning, brain-imaging techniques with excellenttemporal resolution should be used to precisely index neural vari-ability. One such direct approach would be to examine fluctuationsin the BOLD signal across individual trials. Technological ad-vances in event-related fMRI (e.g. high-field 4-Tesla MRI) makeit possible to resolve rapid changes in the hemodynamic signal(Yamaguchi, Hale, D’Esposito, & Knight, 2004). As a conse-quence, it is possible to compute an IIV index for rapid changesacross BOLD activation trials within individuals. Note that doingso can also lead to strong developmental tests regarding the natureof variability. Specifically, if increased behavioral IIV is a proxyfor impaired neural processes, then it follows that indices ofincreased brain IIV should be systematically associated with in-creased IIV in cognitive functioning. Following this logic, onecould conduct a stringent test of within-person coupling by exam-ining the time-varying covariation between IIV in behavior (e.g.,variability across discrete blocks of latency trials) and IIV in thebrain (e.g., variability in the BOLD signal for the correspondingbehavioral trials).

A similar logic applies to examining variability in electricalbrain activity as indexed with EEG, which provides even bettertemporal resolution than fMRI for indexing IIV in the brain.Recently, a novel demonstration of this approach was used toexamine the relation between variability of evoked electrical ac-tivity and performance on a cognitive task (face memory) forchildren (8–15 years old) and young adults (20–33 years old;McIntosh, Kovacevic, & Itier, 2008). Relative to the young adults,children exhibited increased behavioral IIV in response latenciesas well as poorer face memory. Notably, however, variability inthe brain signal itself increased with age, sharing a negativeassociation with both behavioral IIV and accuracy, consistent withprevious patterns observed for behavioral data in younger samples(cf. Siegler, 1994). McIntosh and colleagues (2008) argued that asthe brain continues to mature, such increased moment-to-moment


variability in EEG signals may reflect enhanced neural complexityand greater functional adaptivity (Gogtay et al., 2004). On thesurface, the findings by McIntosh and colleagues (2008) runcounter to the expectation that increased variability in the brainand behavior go hand in hand. However, upon closer scrutiny,these results are expected from the perspective of nervous systemnonlinear dynamics. Specifically, increased brain variability isadaptive to the extent that it facilitates the parsing of weak incom-ing signals (e.g., Destexhe & Contreras, 2006) or assists in theformation of functional networks (e.g., Fuchs, Ayali, Robinson,Hulata, & Ben-Jacob, 2007). At least from childhood through earlyadulthood, increased brain variability is indicative of enhancedneural complexity (e.g., increased integration of neuronal circuits,formation of new network connections), with corresponding ben-efits for cognitive adaptability. Clearly, it is of paramount interestto examine patterns of variability in older age groups and todetermine in particular whether brain variability for these individ-uals is positively or negatively correlated with cognitive function-ing (including behavioral IIV). As summarized earlier, variabilitycan confer both positive and negative benefits. In future research,investigators should not neglect the potential benefits of variabilityand the ways in which age-related differences in neuronal noisemay interact with processes by which external noise aids or ham-pers signal processing (e.g., SR and how the addition of noise tosystems can improve signal processing; Li et al., 2006).

In addition, it may be beneficial to include other neuroimagingtechniques such as event-related optical signal (EROS) as well astranscranial magnetic stimulation (TMS; see Gazzaniga et al.,2002). In EROS, optical fibers and infrared light are used to indexcerebral activity through measurement of the scattering propertiesof neurons. EROS has good spatial (millimeters) and temporal(milliseconds) measurement properties but cannot be used to indexsubcortical structures. In TMS, weak electric currents excite neu-rons and facilitate the study of brain connectivity, and thus it is apotentially interesting tool in light of findings linking IIV tonetwork competition (Kelly et al., 2008). Finally, multimodalassessments (e.g., combining fMRI and near-infrared specstros-copy) could help maximize temporal and spatial precision in thestudy of brain variability.

Summary

Extant evidence suggests that there are numerous neural under-pinnings of increased IIV in cognitive performance. Increased IIVmay be modulated by changes in gray matter density (e.g., due todevelopment, lesions, or neurodegeneration), changes in the integ-rity of white matter (e.g., disconnectivity in associative pathways,immature or degraded white matter tracts), and neuromodulatorychanges (e.g., age-related dysregulation of dopaminergic neuro-modulation). In addition, an emerging literature links IIV to ge-netic factors. Findings from neuropsychology and neurosciencealike underscore that increased IIV shares a robust association withthe frontal cortex (e.g., IIV is exacerbated for frontal vs. nonfrontallesions, and less efficient patterns of BOLD activation in frontalregions are linked to increased IIV). In fMRI research, inefficiencyis said to denote excessive activity for a given level of perfor-mance, presumably reflecting unfocused or unstable response cir-cuits (MacDonald, Nyberg, & Backman, 2006; Winterer & Wein-berger, 2004). These neurobiological underpinnings of increased

IIV likely reflect different levels of related phenomena, with somelevels exhibiting empirical links (e.g., DA, COMT genotype, andfMRI). Examining potential associations among these differentsources requires a novel fusion of existing methodologies (e.g., Li,Lindenberger, Nyberg, Heekeren, & Backman, in press). Whereasmolecular imaging techniques (e.g., PET) are well suited forinvestigating neuromodulatory correlates of IIV, fMRI techniquesare better suited for examining select brain regions and neuralcircuits linked to IIV. Pursuing multimodal imaging studies com-bining PET-derived DA markers and functional imaging dataduring cognitive activity for the same individuals will address thequestion of whether these neurobiological underpinnings jointlyinfluence IIV. An emerging literature suggests a link between DAactivity and the magnitude of the BOLD signal during cognitiveperformance (Landau, Lal, O’Neill, Baker, & Jagust, 2009;Nyberg et al., 2009; Schott et al., 2008). Of particular import in thepresent context, Landau et al. observed a link between a measureof striatal DA synthesis capacity and the load-dependent BOLDresponse during working memory in the left PFC, as well as toactivations in the supramarginal gyrus (a region frequently coac-tivated with PFC during working memory performance). Mac-Donald et al. (2008) reported that highly variable persons showedunderrecruitment of the supramarginal gyrus during episodic rec-ognition. These patterns suggest a linkage between dopaminergicneurotransmission, functional brain activation, and IIV that shouldbe further scrutinized in future research. Moreover, increased IIVin cognitive performance could have a bottom-up origin (e.g.,dysregulated DA neurotransmission in old age or in Parkinson’sdisease) or may be due to deficient top-down regulation by pre-frontal regions (e.g., following traumatic brain injury). Finally, it isimportant to emphasize that the concepts of noise and variabilityare not exclusively negative. Research on SR indicates that whitenoise added to a system can positively influence performance(Harry et al., 2005; Priplata et al., 2002), and brain variability inearly adulthood and behavioral variability for older adults mayreflect greater functional variability and neural complexity (McIn-tosh et al., 2008), as well as adaptability (Allaire & Marsiske,2005).

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Received November 7, 2008Revision received August 24, 2009

Accepted September 25, 2009 �

Call for Nominations

The Publications and Communications (P&C) Board of the American Psychological Associationhas opened nominations for the editorships of Experimental and Clinical Psychopharmacology,Journal of Abnormal Psychology, Journal of Comparative Psychology, Journal of CounselingPsychology, Journal of Experimental Psychology: General, Journal of Experimental Psychol-ogy: Human Perception and Performance, Journal of Personality and Social Psychology:Attitudes and Social Cognition, PsycCRITIQUES, and Rehabilitation Psychology for the years2012–2017. Nancy K. Mello, PhD, David Watson, PhD, Gordon M. Burghardt, PhD, Brent S.Mallinckrodt, PhD, Fernanda Ferreira, PhD, Glyn W. Humphreys, PhD, Charles M. Judd, PhD,Danny Wedding, PhD, and Timothy R. Elliott, PhD, respectively, are the incumbent editors.

Candidates should be members of APA and should be available to start receiving manuscripts inearly 2011 to prepare for issues published in 2012. Please note that the P&C Board encouragesparticipation by members of underrepresented groups in the publication process and would partic-ularly welcome such nominees. Self-nominations are also encouraged.

Search chairs have been appointed as follows:

● Experimental and Clinical Psychopharmacology, William Howell, PhD● Journal of Abnormal Psychology, Norman Abeles, PhD● Journal of Comparative Psychology, John Disterhoft, PhD● Journal of Counseling Psychology, Neil Schmitt, PhD● Journal of Experimental Psychology: General, Peter Ornstein, PhD● Journal of Experimental Psychology: Human Perception and Performance,

Leah Light, PhD● Journal of Personality and Social Psychology: Attitudes and Social Cognition,

Jennifer Crocker, PhD● PsycCRITIQUES, Valerie Reyna, PhD● Rehabilitation Psychology, Bob Frank, PhD

Candidates should be nominated by accessing APA’s EditorQuest site on the Web. Using yourWeb browser, go to http://editorquest.apa.org. On the Home menu on the left, find “Guests.” Next,click on the link “Submit a Nomination,” enter your nominee’s information, and click “Submit.”

Prepared statements of one page or less in support of a nominee can also be submitted by e-mailto Emnet Tesfaye, P&C Board Search Liaison, at [email protected].

Deadline for accepting nominations is January 10, 2010, when reviews will begin.


Documents

Ageing & Variability