Does initial performance variability predict dual-task optimization with practice in younger and older adults?

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Experimental Aging Research: An International Journal Devoted to the Scientific Study of the Aging Process Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/uear20

Does Initial Performance Variability Predict Dual-Task Optimization with Practice in Younger and Older Adults? a

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Tilo Strobach , Denis Gerstorf , François b

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Maquestiaux & Torsten Schubert

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Department of Psychology, Humboldt University Berlin, Berlin, Germany b

Université Paris-Sud, Paris, France Published online: 13 Dec 2014.

To cite this article: Tilo Strobach, Denis Gerstorf, François Maquestiaux & Torsten Schubert (2015) Does Initial Performance Variability Predict Dual-Task Optimization with Practice in Younger and Older Adults?, Experimental Aging Research: An International Journal Devoted to the Scientific Study of the Aging Process, 41:1, 57-88, DOI: 10.1080/0361073X.2015.978210 To link to this article: http://dx.doi.org/10.1080/0361073X.2015.978210

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Experimental Aging Research, 41: 57–88, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 0361-073X print/1096-4657 online DOI: 10.1080/0361073X.2015.978210


DOES INITIAL PERFORMANCE VARIABILITY PREDICT DUAL-TASK OPTIMIZATION WITH PRACTICE IN YOUNGER AND OLDER ADULTS? Tilo Strobach and Denis Gerstorf Department of Psychology, Humboldt University Berlin, Berlin, Germany

François Maquestiaux Université Paris-Sud, Paris, France

Torsten Schubert Department of Psychology, Humboldt University Berlin, Berlin, Germany Background/Study Context: The variability associated with reaction time (RT) is sometimes considered as a proxy for inefficient neural processing, particularly in old age and complex situations relying upon executive control functions. Here, it is examined whether the amount of variability exhibited early in practice can predict the amount of improvement with later practice in dual-task performance, and whether the predictive power of variability varies between younger and older adults. Methods: To investigate the relationship between variability and practice-related improvement, RT mean and variability data are used, obtained from an experiment in which younger and older adults performed two tasks in single-task and dual-task conditions across seven practice sessions. These RT and variability data were related to the single-task and dual-task practice benefits. These benefits were computed as follows: dual-task/single-task RTs at the beginning of practice minus Received 7 April 2013; accepted 7 December 2013. François Maquestiaux is now at Psychology Department, Université de Franche-Comté, Besançon. Address correspondence to Tilo Strobach, Department of Psychology, Humboldt University Berlin, Rudower Chaussee 18, 12489 Berlin, Germany. E-mail: [email protected]


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T. Strobach et al. dual-task/single-task RTs at the end of practice. Results: In both age groups, dual-task processing was speeded up with practice and variability associated with the means was reduced. Most important, independent of mean RTs, variability allowed predicting dual– task practice benefit in both age groups under specific conditions. Conclusion: These findings suggest that the relationship between performance variability and executive control functions under some specific conditions. Implications of these results for models of practiced dual tasks are discussed.

Intraindividual variability denotes transient and rapid performance fluctuations that occur over different time scales and different age groups (i.e., trials, seconds, minutes, hours, days, weeks; Nesselroade, 1991; Ram & Gerstorf, 2009). In the context of reaction time (RT) performance, intraindividual variability indicates some inconsistencies in how fast people perform a task (MacDonald, Hultsch, & Dixon, 2003; Ram, Rabbitt, Stollery, & Nesselroade, 2005) and is the outcome of fluctuating task performance (Slifkin & Newell, 1998). These fluctuations, in turn, have been repeatedly linked to a number of key outcomes of successful aging (e.g., Strauss, MacDonald, Hunter, Moll, & Hultsch, 2002), to stable between-person differences over time (Hultsch, MacDonald, Hunter, Levy-Bencheton, & Strauss, 2000; Rabbitt, Osman, Moore, & Stollery, 2001; Ram et al., 2005), across cognitive tasks (Fuentes, Hunter, Strauss, & Hultsch, 2001; Hultsch et al., 2000), and across domains of functioning (Li, Aggen, Nesselroade, & Baltes, 2001). However, several questions with regard to intraindividual variability remained open. For instance, can RT variability predict later plasticity in cognitive functioning (Lövdén, Bäckman, Lindenberger, Schaefer, & Schmiedek, 2010), particularly in those plastic changes that occur following a repetition-based intervention of training or practice? What are the boundary conditions of RT variability in predicting cognitive plasticity? How do these boundary conditions vary according to whether the individuals are younger adults or older adults? To investigate these boundary conditions, we contrast (a) situations in which the processing capacity is largely available with (b) situations in which this capacity is greatly reduced (e.g., Kahneman, 1973; Treisman & Gelade, 1980). This availability and reduction of capacity is operationalized in situations in which only one task is performed (low processing demand in this single task) and in situations in which two simultaneous tasks (high processing demand in this dual task). The close examination of the relationships between variability and later practice-related cognitive plasticity has the potential to provide information that may serve at identifying those persons who might benefit


Predicting Optimized Dual-Task Performance

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most from cognitive interventions as well as those who might benefit less. Detecting precociously low-benefit individuals may help identify, particularly among older adults, those at risk of facing large cognitive declines later on (West, 1996). For instance, patients with a vascular dementia (Inasaridze, Foley, Logie, & Della Sala, 2009) or at an early stage of Alzheimer’s disease (Crossley, Hiscock, & Foreman, 2004) often experience pronounced declines in attentional processing capacity. Alternatively, relating variability and practice-related plasticity may help at identifying high-benefit persons already in the initial practice stages. These persons might illustrate the maximum potential of plastic alterations of the cognitive system in response to experience (e.g., Lövden et al., 2010) and the maximum potential of improvements (Baltes & Kliegl, 1992; Brehmer, Li, Müller, von Oertzen, & Lindenberger, 2007). RT Variability in Aging Research Research has long documented that people often exhibit considerable variability in RT performance. For example, in a n-back working-memory task, West, Murphy, Armilio, Craik, and Stuss (2002) reported that average variability from one trial to the next amounted to an average of 4.6 standard deviations (SDs) for younger adults and 7.5 SDs for older adults (see also Li, Lindenberger, & Sikstrom, 2001; Rabbitt, 2000; Rabbitt et al., 2001; Salthouse, 1993). Such findings of an age-related increase in variability were applied to investigate (1) age-related decrements and (2) the utility of variability information in a number of aging research traditions. One of these traditions relates variability measures with measures of mean cognitive performance within and across task situations (e.g., Coyle, 2003; Ode, Robinson, & Hanson, 2011; Schmiedek, Oberauer, Wilhelm, Süß, & Wittmann, 2007) with cross-sectional (Li, Huxhold, & Schmiedek, 2004; Spieler, Balota, & Faust, 1996; Williams, Hultsch, Strauss, Hunter, & Tannock, 2005) and longitudinal (Lövden, Li, Shing, & Lindenberger, 2007) perspectives. For example, higher within-individual RT variability in perceptual speed precedes and predicts greater cognitive decline for a group of individuals aged 70–102 years over 13 years (Lövden et al., 2007). In fact, sizable correlations between initial perceptual-speed variability and declines in performance levels on tasks assessing perceptual speed and ideational fluency (i.e., category fluency) were observed. In the present study, we extend these previous research traditions and apply measures of variability in relation with changes as a result of practice. In particular, we focus on practice changes in complex task situations because variability typically increases with increasing age (but see Hale, Myerson, Smith, & Poon, 1988; Myerson, Robertson, & Hale, 2007) in


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such situations, for example, situations of the Stroop-task type (West, 1999) or several working-memory tasks (Salthouse, 1993; West et al., 2002). This relation between variability and age was explained with less efficient executive control processing in old age (Garrett, MacDonald, & Craik, 2012; West, 1996) and, in particular, the integrity of the brain structure as well as neural activation supporting these functions (e.g., Stuss, Murphy, Binns, & Alexander, 2003; West et al., 2002). Executive functions are defined as a set of general-purpose control mechanisms, often linked to the prefrontal cortex of the brain, that modulate the operation of various cognitive subprocesses and thereby regulate the dynamics of human cognition (Baddeley, 1986; Miyake & Friedman, 2012; Miyake, Friedman, Emerson, Witzki, & Howerter, 2000). These functions are important to study because they are a core component of self-control or self-regulation ability, which has been shown to have significant implications for everyday activities (Mischel et al., 2011; Moffitt et al., 2011). Further, performance declines with age in a variety of situations demanding high levels of executive control and this decline co-occurs with age-related increases in performance variability (e.g., van Lersel, Kessels, Bloem, Verbeek, & Rikkert, 2008; West et al., 2002). Dual-Task Performance and Practice Effects Dual-task situations represent a type of situation in which executive functions play an important role. In these situations, two-choice RT tasks are performed within the same trial (e.g., Pashler, 1994; Schubert, 1999, 2008; Welford, 1952). The overall latency of these two tasks is much longer when they are performed in dual-task conditions relative to when they are performed in single-task conditions. This increase in latency is indicative for dual-task processing costs and was associated with (1) interference between processes within the combined component tasks (De Jong, 1993; Pashler, 1994) and (2) the involvement of time-consuming executive processes dealing with the control of this intertask interference (De Jong, 1995; Logan & Gordon, 2001; Sigman & Dehaene, 2006; Szameitat, Lepsien, von Cramon, Sterr, & Schubert, 2006). Applying Brinley plot analyses, dual-task processing costs are robustly increased in older age, even after controlling for general slowing and baseline processing latencies in single-task processing (Verhaeghen, 2011; Verhaeghen, Steitz, Sliwinski, & Cerella, 2003; see also Glass et al., 2000). Such findings are consistent with the assumption of larger effects of executive functioning on dual-task processing in older age. Despite this general age effect on dual tasks and executive functioning, a number of studies have shown that dual-task performance can be



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greatly improved with several practice sessions in younger as well as older adults (e.g., Allen, Ruthruff, Elicker, & Lien, 2009; Bherer et al., 2005, 2006, 2008; Kramer, Larish, & Strayer, 1995; Strobach, Frensch, Müller, & Schubert, 2012a, 2012b). Although mean performance data demonstrated relatively similar practice benefits on dual-task performance in both age groups (Allen et al., 2009; Baron & Matilla, 1989; Bherer et al., 2006, 2008; Strobach et al., 2012a, 2012b; see, however, Maquestiaux, Hartley, & Bertsch, 2004), large interindividual differences in the improvement of dual-task performance with practice within these age groups have often been found (Hartley, Maquestiaux, & Silverman Butts, 2011; Maquestiaux, Laguë-Beauvais, Ruthruff, & Bherer, 2008; Maquestiaux, Laguë-Beauvais, Ruthruff, Hartley, & Bherer, 2010; Schumacher et al., 2001). In addition to optimization of component task processing (e.g., Ruthruff, Van Selst, Johnston, & Remington, 2006), optimized dual-task performance is explained with practice-related changes in executive control processing (Hirst, Spelke, Reaves, Caharack, & Neisser, 1980; Liepelt, Strobach, Frensch, & Schubert, 2011; Sigman & Dehaene, 2006). One previous attempt to assess practice-related changes in individual RT variability (Morse, 1993; West et al., 2002) has provided initial insights into processing fluctuations with cognitive plasticity in demanding dual tasks (Bherer et al., 2006). In Bherer et al.’s (2006) study, older and younger adults practiced a simple tone choice-RT task and a simple letter choice-RT task under single- and dual-task conditions for five consecutive sessions. The variability results can be summarized as follow: First, RT variability was larger when two tasks were coordinated in dual-task condition relative to when each task was performed in single-task condition. Second, RT variability was larger for older adults than for younger adults. Thus, in combination with other studies (e.g., Shammi, Bosman, & Stuss, 1998; West, 1999), larger variability with increasing demands may be viewed as involving executive control processes much more with advancing age. Third, variability was sensitive to the influence of task experience (i.e., variability decreased with practice), but age differences remained uninfluenced. The Present Study: Relating Dual-Task Variability and Cognitive Plasticity Our study takes Bherer et al.’s (2006) study on variability during dual-task practice several steps further. It adds to the aging literature by relating dualtask practice research with elaborated analyses of performance variability (e.g., Lövden et al., 2007; MacDonald et al., 2003) as well as cognitive plasticity (e.g., Lövden et al., 2010) in different age groups for the first


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time. In fact, we analyzed the relationship between variability during practice and the benefit of practice to investigate the utility of variability in predicting later improvements in single- and dual-task conditions, representing conditions with different loads on executive functioning. Our major goal is to investigate if single- and dual-task RT variability at early practice (i.e., first practice sessions) provide reliable predictions of later improvements with practice (i.e., a practice benefit) in single-task and dual-task performance in younger and older adults over and above the predictive utility of individual performance means. With analogies from longitudinal studies (e.g., MacDonald et al., 2003), our hypothesis is that variability is associated with cognitive plasticity with practice. Because of the close relation between variability and executive control functions (e.g., Garrett et al., 2012; Salthouse, 1993), we primarily expect an association between dual-task variability and dual-task performance. This relation is realized by the complexity of dual-task situations with simultaneous presentation and execution of two tasks; particularly at low levels of practice, such highly complex tasks require a strong involvement of control in the form of executive functions. Because both younger and older adults improve dual-task performance as a result of practice (e.g., Bherer et al., 2006, 2008; Kramer et al., 1995; Strobach et al., 2012b), such improvements should be associated with improved executive functioning in these age groups. Because variability is a proxy for the level of executive functioning, we expect to find these relations (i.e., dual-task variability associated with plasticity), even when controlling for age. Further, the expectation of an association between dual-task variability and plasticity in dual-task performance should primarily hold for a more demanding component task in dual tasks, since such tasks increasingly require executive functioning (Meyer & Kieras, 1997) and optimizations in this functioning are associated with the practice-related optimization of dual-task performance in younger and older adults (e.g., Kamienkowski, Pashler, Dehaene, & Sigman, 2011; Kramer et al., 1995; Strobach, Frensch, Soutschek, & Schubert, 2012c). To manipulate the level of task demands, we applied a dual-task practice situation (e.g., Schumacher et al., 2001; Hazeltine, Teague, & Ivry, 2002) including component tasks with different degrees of compatibility relations between stimuli and responses: a spatially highly compatible task where stimulus and response codes show a highly “natural” dimensional overlap (i.e., low demand; Ruthruff et al., 2006; Strobach, Frensch, & Schubert, 2008) and a task with an arbitrary stimulus-response (S-R) mapping where there is no prelearned relationship between stimulus and response information (i.e., high demand). In the present situation, low- and high-demand tasks were realized in a visualmanual task (i.e., visual task) and auditory-verbal task (i.e., auditory task), respectively.



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In addition to focusing on dual-task variability, we do not exclude the potential of single-task variability to predict a later practice-related benefit in dual tasks. Such an assumption is plausible given that changes in component task processing represent one source to explain practice-related optimization in dual tasks (Maquestiaux et al., 2008; Ruthruff, Johnston, & Van Selst, 2001; Strobach, Liepelt, Pashler, Frensch, & Schubert, 2013; Van Selst, Ruthruff, & Johnston, 1999) in addition to improved executive functioning. However, we rather assume the predictive utility of single-task variability in younger adults because of the higher level of automatization in single-task processing in this age group in contrast to older adults (Maquestiaux et al., 2010; Ruthruff et al., 2006) at the end of practice. More generally, the association between variability and practice effects could be illustrated by significant correlation values indicating that variability levels are related to high or low plasticity level.

METHODS Participants Forty-one participants were included into the present study. Twenty older adults (M = 64.5 years, SD = 4.1, range: 57–71; 12 women and 8 men) were recruited from senior university courses at Ludwig Maximilian University (Munich, Germany) as well as from the larger Munich and Berlin communities.1 The 21 younger adults (M = 24.1 years, SD = 3.4, range: 19–32; 9 women and 12 men) were recruited from courses at Ludwig Maximilian University (Munich) and the Humboldt University (Berlin).2 All the participants were paid 8 euros per session plus performance-based pecuniary bonuses for their participation. All participants were screened for normal or corrected-to-normal vision and hearing via self-report; no participants with hearing aids or eye surgery were included. Older adults had no history of neurological diseases, diabetes, or coronary diseases. None of them took any medication that might have affected cognition. The Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975) indicated no impaired cognitive abilities among the older participants (M = 29.7, SD = 0.7, range: 28–30). A handedness test (Oldfield, 1971) indicated that all participants were right-handed.

1 The 2 The

data of 10 of these participants were used in Strobach et al. (2012a). data of 10 of these younger participants were used in Strobach et al. (2012b).

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Apparatus and Stimuli Stimuli were presented on a 17-inch color monitor that was connected to a Pentium 1 PC. Experiments were carried out using ERTS software (Experimental Runtime System; Beringer, 2000). A task using visual stimulus and a task using an auditory stimulus were performed. For the visual task, the stimulus was a circle appearing in one of three possible locations on the screen (left, middle, or right). Participants responded manually, indicating the location of the circle with the corresponding index, middle, or ring finger of the right hand. The circles were white, horizontally arranged, and they were presented on a black background. Each circle subtended approximately 2.5 cm, which corresponds to a 2.38◦ visual angle, from a viewing distance of 60 cm. Three horizontal white lines served as placeholders at the possible left, middle, and right locations of the screen. The distance between the circles was 1 cm, which corresponds to approximately 0.95◦ of visual angle. Responses were recorded with a response board connected to the computer. For the auditory task, participants verbally responded to one of three possible sine-wave tones played over headphones with a sound level of 75 dB. They responded by saying “eins” (German for “one”) to the low-frequency tone (350 Hz), “zwei” (German for “two”) to the middle-frequency tone (900 Hz), or “drei” (German for “three”) to the high-frequency tone (1650 Hz). Verbal responses were recorded with a Sony microphone connected to a voice key. Procedure and Design The procedure and design are basically similar to that of Schumacher et al. (2001; see also Strobach et al., 2012a, 2012b, 2012c). A single-task trial started with three white lines serving as placeholders, signaling the beginning of a trial for 500 ms. After this period had elapsed, an additional circle appeared in the visual task and remained visible until the participant responded or until a maximum of 2000 ms had elapsed. A tone lasting for 40 ms was played in the auditory task. In dual-task trials, a circle and a tone were presented simultaneously. RTs were given as feedback after each trial for 1500 ms, followed by a blank screen for 700 ms (in dualtask trials: only the faster RT). When participants committed an error or 2000 ms had elapsed, the RT feedback was replaced by the German word for error (“Fehler”) for the same amount of time. There were two basic types of blocks: single-task blocks (for the visual task or for the auditory task) and mixed blocks. During the “visual” singletask blocks, participants performed 45 trials of the visual task. During the



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“auditory” single-task blocks, they performed 45 trials of the auditory task. During the mixed blocks, participants performed 48 trials that consisted of a random intermixing of 15 single-task trials on the visual task, 15 singletask trials on the auditory task, and 18 dual-task trials. The random intermixing required participants to switch between processing different single- and dual-task trials. Participants were instructed to respond to both stimuli as quickly and accurately as possible in all blocks and to give both tasks equal priority. In an initial familiarization session, participants were presented with 6 “visual” and 6 “auditory” single-task blocks in alternating order, with half of the participants performing an initial “visual” single-task block and the other half an initial “auditory” single-task block. The subsequent 7 sessions (labeled from 1 to 7) proceeded as follows: Participants started with 2 single-task blocks (1 block for each type of task) and then performed 14 blocks (except during Session 1 in which they performed 12 blocks) consisting of 4 single-task blocks (2 blocks for each type of task) and 10 mixed blocks (8 in Session 1). Except for the initial 2 blocks, single-task blocks were alternated and separated by 2 mixed blocks. All sessions (including the familiarization session) were completed on 8 successive days. RESULTS Prior to statistical RT analyses, we excluded all trials in which responses were incorrect or omitted (i.e., not performed in a response window of 2000 ms after stimulus onset) and these trials were rated as error trials (Table 1). This strategy excludes trials with processing inconsistent with the given task instruction.3 The initial familiarization session was devoted to helping participants get familiar with the material and was thus not included in our analyses. Mean Practice Data in Older and Younger Adults Similar to earlier studies with versions of this dual-task situation (e.g., Hazeltine et al., 2002; Tombu & Jolicoeur, 2004; Strobach et al., 2012a, 3 In the present analyses, we did not exclude posterror trials because the mix of single-task and dualtask trials in mixed blocks makes the definition of posterror trials very critical. Potential definitions of posterror trials could be the exclusion of (1) only single-task trials, (2) only dual-task trials, (3) singletask and dual-task trials, or (4) only the corresponding component tasks (in dual tasks) after erroneous single and/or dual tasks. Since the definition of posterror trials is thus critical, we refrained from their exclusion.

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Table 1. Mean error rates in single-task trials of single-task blocks and dual-task trials for the visual task and auditory task in older and younger adults across Sessions 1–7 Older adults


Task

Younger adults

Session Single-task trials Dual-task trials Single-task trials Dual-task trials

Visual task

1 2 3 4 5 6 7

3.5 3.4 3.9 2.6 2.2 3.8 2.9

8.1 6.4 5.3 4.9 5.3 4.7 4.3

1.7 2.1 2.9 3.0 3.4 4.1 3.9

3.4 2.2 2.0 1.8 1.9 2.0 2.0

Auditory task

1 2 3 4 5 6 7

6.1 6.2 4.8 3.7 5.4 3.4 4.2

16.1 9.6 8.4 7.4 6.1 5.7 4.7

3.6 4.0 2.9 3.3 5.2 4.7 4.1

6.6 5.5 4.1 4.5 4.9 5.4 4.2

2012b; Strobach, Liepelt, Schubert, & Kiesel, 2012d), we assessed dualtask performance (i.e., dual-task RTs and errors) in contrast to single-task performance of single-task blocks (i.e., single-task RTs and errors). This contrast provides the use of a strong and reliable criterion of the dualtask performance level (Hazeltine et al., 2002; Tombu & Jolicoeur, 2004). Furthermore, this combination of trials represents the relationship between the less-related cognitive processing (those underlying pure single-task trials and those underlying dual-task trials) and therefore the most informative ones for investigating the relation between performance variability during practice and the benefit of practice. In contrast, we excluded mixed single-task trials from the present analyses because the primary reason to include this trial type in the present protocol was to ensure equal preparation for both component tasks in dual-task trials (Schumacher et al., 2001; Strobach et al., 2012c). Further, processing associated with mixed singletask trials is less specified. For instance, on these trials, participants will also be partially prepared for a task type that did not occur. The omission of an expected stimulus and task to occur may have thrown off or surprised subjects, causing them to perform more poorly in contrast to in single-task block trials (De Jong, 1995; Tombu & Jolicoeur, 2004). On the other hand, the occurrence of two tasks in the mixed-task context increases the load on working-memory capacity and, thus, the efficiency of stimulus-response transmission on mixed single tasks.

Predicting Optimized Dual-Task Performance (A) Visual task

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1000 800 600 400

1200 RTs [ms]: Dual tasks


RTs [ms]: Single tasks

1200

1000 800 600 400 200 1 2 3 4 5 6 7 - Sessions Younger adults

1 2 3 4 5 6 7 - Sessions Older adults

1 2 3 4 5 6 7 - Sessions Younger adults

1 2 3 4 5 6 7 - Sessions Older adults

Figure 1. Group-mean reaction times (RTs, represented by open symbols) and individual RTs (represented by filled symbols) in milliseconds (ms) in single-task blocks and dual-task trials for (left panels) the visual task and (right panels) the auditory task across Sessions 1–7 for older and younger adults. Both group-mean and individual RTs decrease with practice, with a stronger trend in dual-task data.

We illustrated single-task and dual-task mean RT data in Figure 1 (left side: visual task; right side: auditory task).4 For the analysis of the practice benefit, we analyze performance in single- and dual-task trials and their change across sessions and age. As analyzed in three-way mixed-measure analyses of variance (ANOVAs) including trial type (single tasks vs. dual tasks), group (older adults vs. younger adults), and session (Session 1 to Session 7), general age differences across sessions were evident in both tasks, Fs(1, 39) > 55.77, ps < .001, partial η2 s > .59, with higher RTs in older than in younger adults. A general RT decrease across sessions (i.e., practice effect) was evident in the visual task, F(6, 234) = 90.49, p < .001, partial η2 = .70, and auditory task, F(6, 234) = 207.64, p < .001, partial η2 = .84. These decreases did not differ between both groups in 4 RT distributions have been visually inspected and showed unimodal and normal distributions in each component task as well as task condition.


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both tasks, visual task: F(6, 234) = 2.08, p > .06, partial η2 = .05, auditory task: F(6, 234) < 1, and were, for the visual task, similar under singleand dual-task conditions in both groups, F(6, 234) < 1. These conditions changed, however, differently in younger and older adults with practice in the auditory task, F(6, 234) = 4.57, p < .001, partial η2 = .11; older adults increasingly benefited from practice under dual-task conditions, whereas there was no such increased benefit in single tasks. Generally, dual-task RTs increased single-task RTs in the visual task, F(1, 39) = 79.24, p < .001, partial η2 = .67, and in the auditory task, F(1, 39) = 118.21, p < .001, partial η2 = .75. This increase was larger among older than younger adults in these tasks, F(1, 39) = 22.80, p < .001, partial η2 = .37, and F(1, 39) = 10.27, p < .01, partial η2 = .21, respectively. The error data are illustrated in Table 1 and analyzed in parallel to the RT data in three-way mixed-measures ANOVAs including trial type (single tasks vs. dual tasks), group (older adults vs. younger adults), and session (Session 1 to Session 7). As resulting in a Session × Trial Type interaction on the visual task’s error data, dual-task error rates decreased in contrast to error rates in single tasks, F(6, 234) = 7.70, p < .001, partial η2 = .17. Further, older adults showed (1) higher error rates, particularly in dual tasks, in contrast to their single-task data and single- and dualtask data in younger adults (i.e., Trial Type × Group interaction), F(1, 39) = 11.00, p < .002, partial η2 = .22, as well as (2) an exclusive practice effect (i.e., no practice effect in younger adults) across single and dual tasks from Session 1 to Session 7 (i.e., Session × Group interaction), F(6, 234) = 2.33, p < .05, partial η2 s = .06. In the analysis on the error data in the auditory task, we found decreased error rates (1) during practice (i.e., main effect of Session), F(6, 234) = 12.59, p < .001, partial η2 = .24, as well as (2) in single tasks, in contrast to dual tasks, F(1, 39) = 26.32, p < .001, partial η2 = .40. The effect of Session was modulated (1) by group, F(6, 234) = 9.07, p < .001, partial η2 = .19, illustrating an exclusive practice effect on error data in older adults, and (2) by trial type, F(6, 234) = 11.43, p < .001, partial η2 = .23, indicating a particular effect on dual-task performance. The interaction of trial type and group, F(1, 39) = 7.37, p < .01, partial η2 = .16, was further modulated by session, F(6, 234) = 2.96, p < .01, partial η2 = .07: particularly, the dual-task performance in older adults was improved (i.e., error rates decreased) as a result of practice. Task Order in Dual Tasks Although both tasks were given equal priority in dual tasks, data in Figure 1 generally illustrates different RTs on the visual and auditory tasks. In fact, auditory-task RTs are generally slower than visual-task RTs; these



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RT differences may result from the different compatibility of stimulusresponse information in the visual and auditory tasks. For the case of dual tasks, this RT relation resulted in executions of vocal responses after manual response executions (see also Hazeltine et al., 2002; Tombu & Jolicoeur, 2004; Strobach et al., 2012a, 2012b). In detail, we found first manual and second vocal responses in 95.8% of the dual-task trials that was consistent across age groups, F(1, 39) < 1, sessions, F(6, 234) < 1, as well as their interaction (i.e., the interaction of Group × Session), F(6, 234) < 1, in a mixed-measures ANOVA. On an individual level, all participants showed a “first manual–second vocal” rate of more than 87.8%. Variability in Older and Younger Adults’ Practice Data Intraindividual standard deviations (iSDs) and intraindividual coefficients of variation (CoVs; SD/mean) represent prominent representatives to investigate variability (Lövdén et al., 2007; Ram, Conroy, Pincus, Hyde, & Molloy, 2012). Whereas the former measure type represents the absolute amount of variability of each individual participant (aggregated then on a group level), the latter controls for general processing speed. That is, to generate CoVs, the iSD of each participant and condition (i.e., singletask vs. dual task, Sessions 1 to 7) was divided by its corresponding mean. As a consequence, iSDs and CoVs complement one another in representing the amount of within-person variability. High values in iSDs and CoVs represent a high amount of variability and variability controlled for processing speed. Correlations between RTs and iSDs as well as between RTs and CoVs within all sessions, age groups, and single tasks/dual tasks are listed in Table 2 and Table 3, respectively. Note that most of the correlations between iSDs and means were positively significant, whereas there were only rare significant cases when relating CoVs and mean RTs. The practice effects on iSDs from Session 1 to Session 7, shown in Figure 2 for the visual task (left panels) and the auditory task (right panels), were analyzed using mixed-measures ANOVAs with session (from Session 1 to Session 7) and type of task (single tasks vs. dual tasks) as within-subjects factors and age group (older vs. younger adults) as a between-subjects factor. Summing up visual and auditory task findings, iSDs decreased from older to younger adults, visual task: F(1, 39) = 23.24, p < .001, partial η2 = .38, auditory task: F(1, 39) = 34.98, p < .001, partial η2 = .48; from Session 1 to Session 7, visual task: F(6, 234) = 39.49, p < .001, partial η2 = .50, auditory task: F(6, 234) = 52.91, p < .001, partial η2 = .58; and from dual tasks to single-tasks, visual task: F(1, 39) = 79.25, p < .001, partial η2 = .67, auditory task: F(1,

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Table 2. Correlations between RTs and iSDs in single-task trials of singletask blocks and dual-task trials for the visual task and auditory task in older and younger adults across Sessions 1–7 Older adults


Task

Younger adults


Visual task

1 2 3 4 5 6 7

.45∗ .76∗ .41 .56∗ .40 .48∗ .53∗

.83∗ .80∗ .74∗ .73∗ .78∗ .73∗ .46∗

.64∗ .64∗ .68∗ .63∗ .48∗ .66∗ .76∗

.64∗ .71∗ .69∗ .35 .55∗ .72∗ .43

Auditory task

1 2 3 4 5 6 7

.86∗ .81∗ .89∗ .87∗ .81∗ .82∗ .81∗

.85∗ .83∗ .76∗ .89∗ .80∗ .80∗ .80∗

.85∗ .69∗ .74∗ .55∗ .54∗ .30 .33

.72∗ .66∗ .66∗ .62∗ .49∗ .52∗ .42

Note. All cells show (significantly or nonsignificantly) positive correlations. Significant correlations are indicated by an asterisk (∗ ).

Table 3. Correlation betweens RTs and CoVs in single-task trials of singletask blocks and dual-task trials for the visual task and auditory task in older and younger adults across Sessions 1–7 Older adults Task

Younger adults


Visual task

1 2 3 4 5 6 7

.11 .52∗ .17 .36 .11 .39 .12

.27 .35 .24 .23 .17 .24 .05

.50∗ .53∗ .58∗ .51∗ .36 .53∗ .64∗

.04 .47∗ .52∗ .09 .44∗ .60∗ .28

Auditory task

1 2 3 4 5 6 7

.61∗ .40 .65∗ .67∗ .34 .39 .35

.41 .34 .30 .48∗ .37 .41 .25

.66∗ .23 .39 .19 .11 .07 .21

.37 .16 .22 .22 .02 .09 .14

Note. All cells show (significantly or nonsignificantly) positive correlations. Significant correlations are indicated by an asterisk (∗ ).

Predicting Optimized Dual-Task Performance (A) Visual task

71 (B) Auditory task

iSDs [ms]: Single tasks

400

iSDs [ms]: Dual tasks


500

400

300 200 100

300 200 100 0 1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

- Sessions Younger adults

- Sessions Older adults



Figure 2. Group-mean individual standard deviation (iSD, represented by open symbols) and individual iSD (represented by filled symbols) in milliseconds (ms) in single-task blocks and dual-task trials for (left panels) the visual task and (right panels) the auditory task across Sessions 1–7 for older and younger adults. Both group-mean and individual RTs decrease with practice, with a stronger trend in dual-task data.

39) = 31.14, p < .001, partial η2 = .44. The practice-related reduction of iSDs was increased in older compared with younger adults, visual task: F(6, 234) = 2.52, p < .05, partial η2 = .06, auditory task: F(6, 234) = 2.43, p < .05, partial η2 = .06; and in dual tasks than in single tasks, visual task: F(12, 234) = 16.81, p < .001, partial η2 = .30, auditory task: F(12, 234) = 4.10, p < .001, partial η2 = .10. This reduction (i.e., practice effect) of the difference between dual tasks and single tasks was similar for both age groups in both component tasks, Fs(12, 234) < 1.58, ps > .15, partial η2 s < .04. We analyzed the effects of practice on CoV, identically to this iSD analysis (Figure 3). This analysis demonstrated that (1) CoVs decreased from Session 1 to Session 7, visual task: F(6, 234) = 17.47, p < .001, partial η2 = .31, auditory task: F(6, 234) = 6.11, p < .001, partial η2 = .14; (2) from older to younger adults exclusively in the auditory task, F(1,

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T. Strobach et al. (A) Visual task

(B) Auditory task

CoVs [ms]: Single tasks

80

CoVs [ms]: Dual tasks


100

80

60 40 20

60 40 20 0 1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7





Figure 3. Group-mean individual coefficient of variation (iSD, represented by open symbols) and individual coefficient of variation (represented by filled symbols) in milliseconds (ms) in single-task blocks and dual-task trials for (left panels) the visual task and (right panels) the auditory task across Sessions 1–7 for older and younger adults. Both group-mean and individual RTs decrease with practice, with a stronger trend in dual-task data.

39) = 9.51, p < .01, partial η2 = .20 (and not in the visual task: F(1, 39) = 1.06, p > .31, partial η2 = .03); and (3) from dual tasks to single tasks exclusively in the visual task, F(1, 39) = 32.09, p < .001, partial η2 = .45 (and not in the auditory task: F(1, 39) < 1). The practice-related reduction of CoV was increased in dual tasks than in single tasks in the visual task, F(6, 234) = 5.76, p < .001, partial η2 = .13, but not in the auditory task: F(1, 39) < 1. In both component tasks, the practice-related CoV reduction was (1) similar across younger and older adults, visual task: F(6, 234) < 1, auditory task: F(6, 234) = 1.47, ps > .19, partial η2 s = .04; and (2) the reduction of the difference between dual tasks and single tasks was similar for both age groups, Fs(6, 234) < 1. Thus, there were less consistent main effects and interactions with CoVs across the two component tasks (after controlling for mean data) than in the iSD analyses (e.g., the main effect of age group and interaction of Trial Type × Session).


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Using Performance During Practice to Predict Practice Benefit To predict the benefit of practice on dual-task performance, we firstly computed the RT difference between dual-task trials in Session 1 and Session 7; thus, this dual-task benefit score resulted from dual-task RTs in Session 1 minus dual-task RTs in Session 7. Secondly, an equivalent score was generated with the single-task data: the single-task benefit score resulted from single-task RTs in Session 1 minus single-task RTs in Session 7. Third, as illustrated in Figure 4, these benefit scores were simultaneously applied as dependent variables in sets of multivariate variance models and the independent variables (i.e., in this case covariates) age group, individual mean RTs, and variability (i.e., iSDs [Table 2] or CoVs (Table 3]) separately for each practice session (Sessions 1 to 6; note that we performed no analyses with mean and variability data of Session 7, since these data do not allow predicting a practice benefit before the end of practice), each task (i.e., visual task or auditory task), and each trial type (mean and variability data of single tasks or dual tasks); age group was dummy coded with 0 = younger adults and 1 = older adults. For example, the Session 1 version of the visual single-task means and iSDs included the covariates age group (0 vs. 1), mean single-task RTs (Session 1), and single-task iSDs (Session 1) as well as the dependent variables single-task benefit score and dual-task benefit score (all visual-task data). This way of analyzing the data has the advantage to combine analyses of single- and dual-task practice benefits as well as data in younger and older adults. Note that in the applied multivariate variance models, each covariate is tested as if it were fitted last in a sequential analysis after adjusting for the alternative covariate (e.g., variability [iSDs or CoVs] was fitted after adjusting for mean RTs and age group); this analysis type allows for separate conclusions of the effects of age group, mean RTs, and variability. The critical question is whether the single-task or dual-task iSDs/CoVs can significantly contribute to predict the benefit scores in addition to the predictions of age group and RT means (i.e., after adjusting for these covariates). Since iSDs and mean RTs typically show a fairly strong relationship (e.g., Faust, Balota, Spieler, & Ferraro, 1999), the possibility of unique predictive utility of iSDs over and above mean RTs in contrast to CoVs is potentially decreased; this assumption is consistent with the inspection of Table 2 and Table 3, with more significant correlations in the former table (with iSDs) than in the latter table (with CoVs), respectively. This would be one reason why CoVs are more sensitive and less redundant in uniquely predicting practice-related changes in RTs in comparison with iSDs. As a result, we assume an increased association of CoV measures of variability with benefit scores as compared with iSD measures. With perspective on different contributions to the prediction of single-task benefit scores

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T. Strobach et al. A Age group

Session mean RT

+/–*b?

Session iSDs

+/–*b?


Single-task benefit score

Dual-task benefit score

B Age group

Session iSDs

Session mean RT

+/–*b?

+/–*b?



C Age group

Session mean RT

+/–*b?


Session iSDs

+/–*b?


Figure 4. Overview of the performed multivariate variance models including the independent variables (i.e., covariates) age group, mean reaction times (RTs), and RT variability and the dependent variables single-task benefit score and dual-task benefit score. The magnitude and direction (i.e., positive or negative) of the relation between independent and dependent variables is illustrated with beta (+/-∗ b?). The present example illustrates this model in the context of individual standard deviations (iSDs). For mean RTs and iSDs, the values of each session are applied. (A) Assessment of the beta values when predicting single-task and dual-task benefit scores due to age group (note: this assessment includes a previous adjustment of the variables mean RT and iSDs). (B) Assessment of the beta values when predicting single-task and dual-task benefit scores due to Session mean RT (note: this assessment includes a previous adjustment of the variables age group and iSDs). (C) Assessment of the beta values when predicting single-task and dual-task benefit scores due to Session iSDs (note: this assessment includes a previous adjustment of the variables age group and mean RTs).



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and dual-task benefit scores, we speculate that the increased association of the former variability measure with benefit scores is particularly evident under dual-task conditions because of the increased contribution of executive functioning in dual tasks and the close relation of this functioning with variability over and above mean performance (e.g., Garrett et al., 2012). The following sections sequentially focus on the unique utility of iSDs/CoVs, mean RTs, and age group when predicting the single-task and dual-task benefit scores. Table 4 and Table 5 include the beta values for predictions of age group, mean RTs, and iSDs/CoVs, respectively (significant relations are accompanied by an asterisk [∗ ] and are framed by a gray background). As illustrated, significant predictions demonstrate positive relations between the benefit score and mean/variability data, indicating that if a mean RT value increases by 1 ms, the benefit score increases (by beta in ms). Similarly, if a variability value increases by 1 ms, the predicted benefit score increases. For the cases of significant predictive power of age group, and since we coded younger adults as the reference category (i.e., coded 0; older adults are coded 1), positive values indicate that the benefit score was increased for older adults (again by beta in ms). iSDs/CoVs As illustrated in Table 4 and 5, respectively, iSDs as well as CoVs in dual tasks can predict later plasticity in dual-task situations to some extent (i.e., there is a significant relation between variability and the dual-task benefit score under specific conditions), whereas data of the visual and the auditory task also include condition with no such predictive value of variability. The similarities across both age groups and both tasks are the following: there are no significant predictions of iSDs and CoVs in Session 1, but these predictions occur in Sessions 2 and 3. However, the result patterns differ after Session 3: Whereas auditory-task variability is sensitive to contribute to the prediction of later dual-task plasticity (i.e., the dual-task benefit score), there is no such contribution of visual-task variability. Interestingly, variability values (i.e., iSDs and CoVs) in single tasks can also contribute to predicting this dual-task plasticity in latter practice sessions. However, this finding is exclusive for the auditory-task data. Consistent in all analyses, neither iSDs nor CoVs contribute to the prediction of single-task benefit scores. This is consistent in the visual and auditory tasks as well as predictions due to single- or dual-task data. In sum, we specified the impact of variability to the prediction of later plasticity in situations including executive function processing (i.e., dual tasks). These predictions are evident when applying dual-task variability data (and in some cases for variability data of single tasks). Importantly, these findings are independent of age and are apparent in younger and

Age Mean RTs iSDs Age Mean RTs iSDs Age Mean RTs iSDs Age Mean RTs iSDs Age Mean RTs iSDs Age Mean RTs iSDs

Session 1

7.09 .24∗ .13 1.80 .18 .05 1.86 .20 −.04 −5.13 .11 .14 −6.03 .08 .28 −6.89 .10 −.01

ST benefit 1.90 .33 .02 −4.96 .24 .04 4.91 .45 −.48 3.40 .39 .08 −7.77 .27 −.01 −3.35 .36 −.27

DT benefit

ST benefit 81.01∗ .38∗ .04 72.84∗ .16 .30 57.82 −.11 .63 40.64 .01 .19 48.27 −.08 .62 33.88 .07 −.03

DT benefit 90.89∗ .52∗ .32 80.01∗ .30 .57∗ 58.17 .16 .75∗ 45.84 .34 −.02 48.54 .44 .01 35.23 .55 −.53

−1.46 .02 .12 −1.28 .00 .14 −1.46 −.02 .12 −4.39 .03 .07 −2.68 .01 .18 −8.41 .01 .10

−20.52 .21 −.07 −12.04 −.11 .66 −53.00 −.69 1.44∗ −64.76 −.27 .38 −45.51 −.53 1.46∗ −70.78 .63∗ 1.45∗

DT benefit

Single-task data

ST benefit

Dual-task data

74.80∗ .04 .39 59.37∗ .01 .37 57.24 −.22 .48 37.62 −.20 .35 36.76 −.06 .40 29.98 −.24 .32

117.35∗ .39∗ .48 55.61 −.01 1.13∗ 37.73 −.26 1.72∗ −.18 −.32 1.64∗ −26.71 −.30 1.28∗ −79.50 .59∗ 1.59∗

DT benefit

Dual-task data ST benefit

Auditory task

Note. The dependent variables were single-task benefit score (ST benefit) and dual-task benefit score (DT benefit), representing the difference between single-task RTs in Session 1 minus single-task RTs in Session 7 as well as dual-task RT in Session 1 minus dual-task RTs in Session 7, respectively (gray-shaded cells indicate significant unique predictive utility of independent variables). ∗ p < .05.

Session 6

Session 5

Session 4

Session 3

Session 2

Predictor

Session

Single-task data

Visual task

Dependent variable: Single-task and Dual-task learning gain (single-task/dual-task RT difference, Session 1 to Session 7)

Table 4. Beta values of multivariate variance models (see text) including the independent variables (i.e., covariates) age group (age), single-task data, and dual-task data (i.e., mean reaction times [RTs] and individual standard deviation [iSDs])


76 T. Strobach et al.

Age Mean RTs CoVs Age Mean RTs CoVs Age Mean RTs CoVs Age Mean RTs CoVs Age Mean RTs CoVs Age Mean RTs CoVs

Session 1

7.34 .27∗ .36 1.36 .19∗ .20 1.69 .19 −.09 −5.64 .13 .43 −7.05 .12 .39 −6.93 .09 −.03

ST benefit 2.97 .34 −.10 −7.50 .22 .36 6.84 .36 −1.50 6.62 .44 −.14 −7.41 .27 −.11 −3.10 .30 −.74

DT benefit

ST benefit 81.49∗ .37∗ .60 73.49∗ .24∗ 1.84 63.01∗ .04 4.72 41.84 .04 1.80 49.17 .08 3.08 34.59 .05 .72

DT benefit 91.30∗ .65∗ 1.12 87.39∗ .50∗ 2.77∗ 64.73 .40∗ 2.68∗ 45.31 .42∗ −.28 48.44 .23 −.13 34.23 .43 −1.94

−1.22 .06 .56 .34 .05 .60 .65 .05 .53 −3.89 .05 .23 −2.05 .05 .59 −8.50 .03 .43

−18.62 .10 2.05 −6.00 .04 5.60 −39.50 .43∗ 13.40∗ −62.89 −.24 4.47 −43.02 −.16 7.42∗ −69.18 −.26 6.81∗

DT benefit

Single-task data

ST benefit

Dual-task data

73.24∗ .13 3.57 59.65∗ .10 3.45 53.35 .01 2.42 36.70 −.01 3.20 35.70 .03 2.17 29.63 −.02 2.10

115.44∗ .51∗ 4.20 55.54 .27∗ 9.15∗ 31.19 .12 12.16∗ −1.65 .07 9.41∗ −30.11 .01 6.96∗ −80.29 −.22 8.74∗

DT benefit

Dual-task data ST benefit

Auditory task

Note. The dependent variables were single-task benefit score (ST benefit) and dual-task benefit score (DT benefit) representing the difference between single-task RTs in Session 1 minus single-task RTs in Session 7 as well as dual-task RT in Session 1 minus dual-task RTs in Session 7, respectively (gray-shaded cells indicate significant unique predictive utility of independent variables). ∗ p < .05.

Session 6

Session 5

Session 4

Session 3

Session 2

Predictor

Session

Single-task data

Visual task

Dependent variable: Single-task and Dual-task learning gain (single-task/ dual-task RT difference Session 1 to Session 7)

Table 5. Beta values of multivariate variance models (see text) including the independent variables (i.e., covariates) age group (age), single-task data, and dual-task data (i.e., mean reaction times [RTs] and coefficients of variation [CoVs])


Predicting Optimized Dual-Task Performance 77


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older adults (note that the contribution of all covariates is assessed after adjusting for all other included covariates; i.e., a significant contribution of the variability measures is demonstrated after controlling for impacts of age group and mean RT). This independence also shows that, under certain conditions, there is an exclusive contribution of iSDs/CoVs to predict the benefit scores (e.g., dual-task benefit score: auditory task/dual-task data/Session 4). That is, although iSDs/CoVs obey a significant contribution to the prediction, there is no contribution of either mean RTs or age group. In cases of a significant contribution of variability measures, there is a positive relation between these measures and the benefit scores. In other words, the increase of variability is related to an increased practice benefit, i.e., to an increased reduction of dual-task RTs during practice. We will come back to the direction of this relation in Discussion. Mean RTs There are some cases of contribution of single-task and dual-task mean RTs to the prediction of single-task and dual-task benefit scores. These contributions are primarily evident in Session 1 and under some conditions in following sessions. However, these contributions are not existent in sessions rather at the end of practice. Exclusively when predicting the auditory dual-task benefit score, there is evidence for a contribution of mean RTs in Session 7 and single-task RTs predict the dual-task benefit score, indicating predictions across different trial types. Similar to the contribution of iSDs/CoVs, there is an exclusive contribution of mean RTs to predict benefit scores (and no contribution of age group and iSDs/CoVs) under certain conditions (e.g., single-task benefit score: visual-task/singletask data/Session 1). In cases of significant contribution of mean RTs, there is a positive relation between these measures and the benefit scores. That is, the increase of these RTs is related to an increased practice benefit, i.e., with an increased practice-related reduction of single-task and dual-task RTs. Age Group In some early practice sessions, the age group covariate contributes to a significant prediction of single- and dual-task benefit scores. In these cases, there is a positive relation between age and the benefit scores. This positive relation means that with increasing age (from the young adult category to the category of older adults), there is an increase in practice benefit, i.e., an increased reduction of single-task and dual-task RTs during practice in older in contrast to younger adults. This is consistent with the observation of an increased dual-task practice effect in the older adults’ auditory task (i.e., mean RTs) that was, however, not evident in this single-task condition. Such contribution is evident when using dual-task and single-task data as covariates.


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DISCUSSION Does RT variability during practice of a single and a dual task provide unique utility to predict the later benefit of practice on single- and dualtask performance? What effect has age on this utility? The present study is among the first to investigate these questions. Our data specified a unique predictive utility of RT variability in form of iSDs and CoVs when analyzing dual-task data. The predictive utility is obvious rather in earlier practice sessions when investigating visual-task data and in early and late practice sessions when investigating auditory-task data. Age group is independent from variability and mean RTs in very early practice sessions, which is similar for mean RTs and their independence from age group and iSDs/CoVs when predicting practice benefit in single and dual tasks. Mean Dual-Task Performance and Its Variability We thoroughly analyzed and presented performance variability data in a dual-task practice situation (for preliminary findings, see Bherer et al., 2006) using the variability measures of iSDs and CoVs (Figures 1 to 3): iSDs demonstrated age group differences, which was also the case for auditory-task CoVs (this difference in CoVs demonstrates that differences in variability levels remain even after controlling for baseline performance); variability decreased during practice as well as from dual tasks to single tasks. The iSD and CoV dual-task processing costs (i.e., the difference between dual tasks and single tasks) were susceptible to practice effects mainly to a similar degree across age groups. Consistent with a number of prior studies (e.g., Schumacher et al., 2001; Strobach et al., 2012a, 2012b), mean RT data in younger and older adults showed optimized dual-task performance with practice in the present situation. Alternative to the effects on iSDs and CoVs, mean auditory-task RTs showed differential age- and practice-related effects, with a dualtask-specific benefit in older adults. Thus, older adults demonstrated an increased dual-task benefit specifically in the auditory task (see also Bherer et al., 2005; Göthe, Oberauer, & Kliegl, 2007; Kramer et al., 1995). Dual-Task Variability and Practice-Related Plasticity in Dual Tasks The present study extended prior research (e.g., Bherer et al., 2006) and demonstrates that variability and performance fluctuations contribute to the prediction of plastic alterations in response to experience and practice (Lövdén et al., 2010). In fact, the application of measures of variability has proven to uniquely contribute to the prediction of later practice-related changes in dual-task performance under particular conditions. For both


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component tasks (i.e., visual task, auditory task), both types of applied variability measures (i.e., iSDs, CoVs) in dual tasks were able to contribute to the prediction of changes in dual tasks. This contribution was rather evident in early practice sessions when applying visual dual-task variability on its dual-task benefit score and in early to late ones when applying auditory dual-task variability on the dual-task benefit score of this task. Inconsistent with our assumption, there was a similar number and type of cases in which the iSDs and CoVs contributed to this score, demonstrating that iSDs are similarly sensitive to predict later practice effects despite their potential redundancy with mean RTs (Faust et al., 1999). Further, our data provide no reliable prediction of practice effects due to Session 1 variability data. Potentially, unpracticed and inefficient executive functioning at this very early practice level may explain this finding. The present data demonstrated that dual-task variability measures have the potential to predict later practice-related changes in dual tasks, whereas there is no evidence for the predictions of such changes in single tasks (i.e., single-task benefit score). In this way, these data specify relations between performance variability and executive functioning (e.g., Salthouse, 1993; Schmiedek et al., 2007) as well as longitudinal development of cognitive processing (e.g., Lövdén et al., 2007; MacDonald et al., 2003); in these latter studies, variability was able to predict developmental changes across several years in older adults. Importantly, the present study extends this previous knowledge to the case of practice-related changes in an executive function situation of the dual-task type. Note that the relation between variability and later changes in dual-task performance with practice is independent of the age of participants (since we controlled our variability predictions for the age group of participants), which is consistent with our prediction. This relation is evident in younger and older adults and adds to the aging literature: Variability data have unique utility to predict later practice benefit across different age groups. In other words, these data are effective in providing information that may serve at identifying those persons who might benefit most and those who might benefit less from dual-task cognitive interventions (Strobach et al., 2012a). Particularly in older adults, dual-task variability is critical in identifying persons who potentially compensate for age-related declines with practice (West, 1996). However, what about the direction of this relation (i.e., range of variability and practice benefit)? There is typically a positive correlation of (increased) variability and (higher) benefit of practice (see beta values, Tables 4 and 5). Such correlation is at odds with the typical finding that higher variability predicts impaired task performance at low levels of practice (e.g., Li, Brehmer, Shing, Werkle-Bergner, & Lindenberger, 2006). We assume that this relation is potentially inverted in practice



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and dual-task situations. Some support for this assumption comes from a functional perspective that suggests that higher variability potentially reflects a flexible scheduling of two simultaneous tasks. Such flexible scheduling provides optimal conditions to perform two tasks with no interference between them and to optimize dual-task performance because interfering processing stages in one task can be flexibly scheduled in a way avoiding any temporal overlap with interfering processing stages in another, concurrent task (Anderson, Taatgen, & Byrne, 2005). According to this assumption, flexible task scheduling in the beginning of practice (i.e., Sessions 2 and 3) is rather associated with optimization of dual-task processing in the visual task, whereas this optimization is rather related to flexible scheduling of auditory-task processing during early and late practice sessions. Such inconsistencies between the two tasks are potentially explained by the observation that auditory-task RTs are longer relative to visual-task RTs (see also Schumacher et al., 2001; Strobach et al., 2012a, 2012b), and a detailed analyses of the task characteristics reveals that the auditory task is the more demanding task (Strobach et al., 2008, 2013). As a result, the auditory task is the latter executed task in the present dual-task situation (see analysis on task order in dual tasks in Results) and this latter task is, in general, increasingly affected by concurrent component tasks (e.g., Pashler, 1994) and processes of executive functioning (e.g., Maquestiaux et al., 2004; Meyer & Kieras, 1997). On the other hand, optimal task scheduling potentially has an increased practice benefit on this latter task. In this way, the auditory task’s variability is particularly related to the mean dual-task performance and its later practice-related change as described above. Single-Task Variability and Practice-Related Plasticity in Dual Tasks Equivalent to the relation of dual-task variability and dual-task benefit scores, there was some evidence for such a relation and unique predictive utility when we apply variability data of auditory single tasks on these dual-task benefit scores (note there was no relation between variability data of the visual single task and its dual-task benefit scores). That is, auditory single-task variability has unique utility to predict the benefit of practice on improving dual-task performance. This utility exists even after we adjust for age group and, thus, holds for both younger and older adults. The evidence of a relation of auditory single-task variability and the dual-task benefit score has two general implications. First, evidence for the relation of dual-task variability and dual-task practice benefit is potentially not exclusively explained by the fact that all types of data (the covariates individual mean dual-task RT and variability as well as the


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dependent variable dual-task benefit score) are merely data of the identical type of situation, i.e., the dual-task situation. Second, processes in single tasks overlap with processes important for dual-task processing and its improvement. This latter finding is consistent with a large body of dual-task practice literature that attributes improvement in dual-task processing to changes within the component tasks that constitute a dual task (i.e., Ahissar, Laiwand, & Hochstein, 2001; Kamienkowski et al., 2011; Maquestiaux et al., 2008, 2010; Ruthruff et al., 2006). In fact, dual-task processing is improved because of the automatization, shortening, and/or improvement of processing stages in these component tasks. In detail, there is evidence for improved perception and the stimulusresponse mapping stages in the auditory task, whereas the visual task analyses exclusively provided evidence for shorting at the latter stage (Strobach et al., 2013). Therefore, there is a reduced number of practicerelated foci of performance improvement in this task, which might explain the reduced contribution of visual single-task variability to plasticity under dual-task conditions. The present data add to the existing literature that (increased) variability in auditory single tasks is beneficial for the improvement of dual-task performance. Similar to this explanation of (increased) variability in dual tasks and its contribution to an (increased) benefit of practice (i.e., reduction of dual-task RTs), it could be that a flexible single-task processing of the auditory task optimally prepares for performing two tasks with no interference between them and to optimize dual-task performance. This is because interfering processing stages in one task can be flexibly scheduled in a way avoiding any temporal overlap with interfering processing stages in another, concurrent task (Anderson et al., 2005). The present study is one of the first in the aging literature that emphasizes the relation of practice-related changes in component-task processing and practice effects in dual-task performance in younger and older adults. In sum, our findings characterize and specify the predictive potentials of RT variability in the present dual-task practice situation for both younger and older adults. One possible future avenue of this research could be the identification of common factors of different aspects of dualtask RT variability and their relation to practice-related changes. For a set of choice-reaction tasks in single-task situations, Schmiedek et al. (2007) have shown that for each parameter of an ex-Gaussian distribution (e.g., the mean and standard deviation of the Gaussian part, and an exponential portion parameter), a common factor could be identified. There are correlations of these factors being positive but far from unity. This means that each factor captured relatively independent individual differences that shared a substantial part of their variance across different tasks.


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It is of interest to transfer this logic to the context of dual tasks and their practice-related performance changes.


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