Laura Broeker, Jovita Brüning, Yana Fandakova, Neda Khosravani,
Andrea Kiesel, Veit Kubik, Sebastian Kübler, Dietrich Manzey, Irina
Monno, Markus Raab, Torsten Schubert
Individual differences fill the uncharted
intersections between cognitive structure,
flexibility, and plasticity in multitasking
Open Access via institutional repository of Technische Universität Berlin
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Broeker, L., Brüning, J., Fandakova, Y., Khosravani, N., Kiesel, A., Kubik, V., Kübler, S., Manzey, D., Monno, I.,
Raab, M., & Schubert, T. (2022). Individual differences fill the uncharted intersections between cognitive
structure, flexibility, and plasticity in multitasking. In Psychological Review. American Psychological
Association (APA). https://doi.org/10.1037/rev0000376.
©American Psychological Association, 2022. This paper is not the copy of record and may not exactly replicate
the authoritative document published in the Psychological Review. The final article is available, upon
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1
Theoretical Note: Individual differences fill the uncharted
intersections between cognitive structure, flexibility and plasticity
in multitasking
Laura Broeker1,2, Jovita Brüning3, Yana Fandakova4, Neda Khosravani4, Andrea Kiesel5, Veit
Kubik6, Sebastian Kübler7, Dietrich Manzey2, Irina Monno5, Markus Raab1,8 & Torsten Schubert7
1 German Sport University Cologne, Institute of Psychology, Am Sportpark Müngersdorf 6, 50933 Köln, Germany
2Technical University Dortmund, Institute of Sport and Sport Science, Otto-Hahn-Str. 3, 44227 Dortmund
3 Technical University Berlin, Department of Psychology and Ergonomics, Marchstr. 12, 10587 Berlin, Germany
4 Max Planck Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin,
Germany
5 Albert-Ludwigs-Universität Freiburg, Department of Psychology, Engelbergerstraße 41, 79085 Freiburg, Germany
6 University Bielefeld, Department of Psychology and Sports Science, PO Box 10 01 31, 33501 Bielefeld, Germany
7 Martin-Luther-Universität Halle-Wittenberg, Faculty of Philosophy I, Department Psychology, 06099 Halle, Germany
8 London South Bank University, School of Applied Sciences, 103 Borough Road, London SE1 0AA, UK
Corresponding author: Laura Broeker, l.broeker@dshs-koeln.de
2
Individual differences fill the uncharted intersections between cognitive
structure, flexibility and plasticity in multitasking
Abstract
It has been recently suggested that research on human multitasking is best organized according to
three research perspectives, which differ in their focus on cognitive structure, flexibility and plasticity.
Even though it is argued that the perspectives should be seen as complementary, there has not been
a formal approach describing or explaining the intersections between the three perspectives. With this
theoretical note, we would like to show that the explicit consideration of individual differences is one
possible way to elaborate in more detail on how and why the perspectives complement each other.
We will define structure, flexibility and plasticity, describe what constitutes individual differences, will
outline selected empirical examples and raise possible future research questions helping to develop
the research field.
Keywords: individual differences; multitasking; cognitive structure; flexibility; plasticity
3
Little more than a decade ago, a study by Watson and Strayer (2010) brought forward the concept of
supertaskers, because 5 of the 200 participants tested had the remarkable ability to perform a driving
task and an operation span task without any costs. This outcome, which accentuates the potential
impact of individual differences on multitasking, caused a stir in dual-task research by challenging
previous assumptions about the cognitive architecture and the robust empirical evidence about limited
cognitive resources and imperfect time-sharing (Kahneman, 1973; Tombu & Jolicœur, 2003; Wickens,
2002). Adhering to the common assumption that multiple tasks cannot be performed without interference
or costs, dual-task research had primarily focused on discovering and explaining the mechanisms
underlying limitations in information processing (Meyer & Kieras, 1997; Wickens, 1980). As a result,
researchers have been very successful in developing distinguished paradigms and establishing
differentiated theories on dual-task and task-switching costs valid across multiple studies on human
participants (Koch et al., 2018). It has been suggested that this work is best organized according to
three research perspectives, which differ in their focus on cognitive structure, flexibility, and plasticity
(for the full review see Koch et al., 2018). In their review, Koch et al. mention that there might be some
relations between the three perspectives and that they should be seen as complementary rather than
competitive in the sense that they refer “either to the current status of the cognitive system (structure,
flexibility) or its dynamic change (plasticity)” (p. 558). However, the review remains short on explicitly
describing or explaining the intersections between the three perspectives. The overall goal of this
theoretical note is to show that the explicit consideration of individual differences is one possible way to
elaborate in more detail on how and why the perspectives complement each other, that is, why the
consideration of individual differences in one perspective can enhance the understanding of the other
perspective. Whereas most established work on multitasking has been derived from group means, we
posit that too little emphasis has been put on variability between and within participants (see also the
requirement for nomothetic instead of Aristotelian view; e.g., Hommel & Colzato, 2017). To remedy this
state, we will first define structure, flexibility and plasticity and describe what constitutes individual
differences in these three perspectives. We will then outline selected empirical results on the
intersections, without claiming to be exhaustive, and raise possible future research questions and
directions.
Structure, Flexibility and Plasticity
4
Cognitive structure refers to invariable properties and limitations of the cognitive architecture,
which operate as hardware and make up restrictions for multitasking performance. Inter-individual
differences in hardware can concern capacity, which would be differences in attentional resources or
working memory capacity (Oberauer, 2019; Szameitat et al., 2016; for opposing views see Hommel,
2020; Meyer & Kieras, 1997). Inter-individual differences in cognitive structure also relate to skills in
executive mechanisms (Miyake et al., 2000; Pashler, 1994; but see Sigman & Dehaene, 2006) and/or
to skills in basic processes involved in, for instance, perception, decision and motor responding during
task processing. With a focus on multitasking, variance in structure creates the basis for different
manifestations of multitasking performance and related costs. In that respect, dual-task costs can be
considered as resulting from insufficient attentional resources and/or the (in)ability to inhibit interfering
tasks or stimuli acting together with basic processes prone to interference from other tasks (Garner et
al., 2021).
Flexibility refers to the organization of cognitive processes; it manifests in different strategies when
dealing with multiple tasks and reflects the degree of adaptability of the cognitive system to face new
and unexpected/changing conditions in the environment. Referring to a computer metaphor, flexible
process organization would represent the software running on the cognitive structure as the hardware
components, both contributing to the emergence of dual-task costs.
Differences can thus be identified between individuals, for instance when people pursue
different strategies in response order or serial versus overlapping processing (e.g., Brüning et al., 2020,
2021; Lehle et al., 2009; Meyer et al., 1995; Reissland & Manzey, 2016) as well as within individuals,
for instance when being instructed to coordinate response order according to different experimental
conditions (e.g., Kübler et al., 2018; Lague-Beauvais et al., 2015).
Plasticity refers to long-term potential for change in performance due to experience (e.g.,
training or other long-lasting cognitive challenges) or ageing.
As outlined earlier, the three perspectives are not competitive and can be dependent. For
instance, flexibility can be viewed as the result or manifestation of differences in hardware. Further,
flexibility can be viewed as a determinant governing how control mechanisms are reinforced and
eventually influencing manifestations of plasticity in the long run. In the following we will show why and
how individual differences can inform the dependencies between the three perspectives.
Filling the intersections between cognitive structure – flexibility – plasticity
5
We propose that we will be better able to understand inter-individual differences in multitasking if they
were not only considered from one, but from the intersection of two or all three perspectives. For
instance, variability in flexibility could depend on variability in structure if certain resources determine
whether and to which extent cognitive processes can be flexibly organized or they are vulnerable to
interference from other processes. Thus, differences in hardware like working memory capacity would
explain why individuals differ in their efficiency to organize their processes (i.e. their software), for
instance, in the speed of task-set reconfiguration (Draheim et al., 2016; Pettigrew & Martin, 2016; but
see Liefooghe et al., 2008), in their ability of scheduling responses (e.g., Kübler et al., 2019) or in
shielding vs. shifting (e.g., Zwosta et al., 2013). Likewise, variability in plasticity could emerge from
variability in structure. The extent to which the cognitive architecture is malleable would thus determine
how much improvement occurs with multitasking training (Garner & Dux, 2015). Eventually, differences
in plasticity can depend on flexibility considering that multitasking strategies change throughout
development with typically less flexible strategies at older age (e.g., Bherer et al., 2005). Further,
strategies may change with the level of experience with a task (Schubert & Strobach, 2018; Stoet &
Snyder, 2003), although it is unclear whether more experience allows for more flexible strategies or
promotes trusted and established strategies. In the other direction, dynamic experience-dependent
change itself might depend on the individual’s ability or motivation to adapt to different situations
(“trainability”, e.g., Strobach et al., 2015). We will exemplify these proposals in further detail below.
Reviewing the current state-of-the-art research has revealed an imbalance in the quantity of empirical
evidence on the different intersections. While there is ample evidence for a bidirectional
interdependency between structure and flexibility, less is known about the intersection of structure and
plasticity and even less for the intersection of flexibility and plasticity. Hence, we will first have a focus
on the intersection of structure and flexibility.
Structure-Flexibility
Hints to important links of structure and flexibility have been provided in several multitasking studies, for
example in task-switching and psychological refractory period (PRP) research. As we will outline in
further detail below, most of these studies highlight the link between certain control mechanisms of the
hardware (e.g., task-set reconfiguration, response selection) and flexibility, yet others highlight the link
between constraints of the cognitive hardware like WMC and flexibility.
Experiments demonstrate that differences in flexibility, which amongst others are represented
by an individual’s decision to repeat or switch task sets, are also attributable to structural differences.
6
For instance, across multiple experiments, task switches were induced by increasing the waiting time
for the repetition stimuli for each subsequent repetition trial (Mittelstädt et al., 2018, 2019; Monno et al.,
2021). When correlating switch costs and switch rates across all experiments, the following result pattern
emerged: participants with small switch costs switch tasks more often than participants with larger switch
costs, especially with shorter time for task selection (see also Arrington & Logan, 2004). If smaller switch
costs reflect higher skills in executive functioning, then this result would indicate that differences in
process organization were directly impacted by differences in structure. Furthermore, there is evidence
that despite basic capacities such as the human ability of processing multiple tasks in parallel, yet
different processing strategies differing in their degree of flexibility can emerge. Brüning et al. (2021)
identified individual preferences for serial versus overlapping (parallel) process organization in a, what
they refer to as, task-switching-with-preview paradigm. More specifically, participants had to classify a
set of digits regarding their parity (odd vs. even), and a set of letters regarding their kind of sound
(consonant vs. vowel), while receiving a preview about upcoming stimuli. Whereas some participants
made use of the opportunity of parallel processing (“overlapping processing”) others apparently
separated tasks as much as possible (“serial processing”). These two modes of task processing, thus,
can be seen as flexible approaches to deal with a structural limitation. Furthermore, Brüning and Manzey
(2018) could show that individuals differ in the degree to which they are able to adapt their preferred
mode of processing to the level of risk for interference. The authors compared the modes of task
processing participants preferred under conditions of low risk for interference (i.e., involving digit and
letter stimuli) and under high risk for interference (i.e., involving two sets of letter stimuli). The modes of
processing were identified in each condition of interference by a comparison of mixing and switch costs
in reaction times (RTs). Mixing costs are defined as the difference in RTs between repetition and single-
task trials and switch costs reflect the difference in RTs between switch and repetition trials. The authors
hypothesized that switch trials comprise the processing time for the task stimulus plus additional time
for a task-set reconfiguration and/or task-inertia. If participants processed serially, RTs in switch trials
should reflect some switch costs as they shift from one task-set to the other just in the switch trial. In
contrast, if switch RTs in the preview group were faster than the typical RTs in single-task trials (along
with no increase of mixing costs in repetition and pre-switch trials), this was to “be taken as evidence
that at least some processing of the previewed switch stimulus must have taken place before the switch
and, thus, reduced the switch costs to a considerable degree” (p. 98, see also Brüning et al., 2021 for
an in-depth description of the classification criteria). All participants who prefer serial processing in
7
conditions of low risk for interference used this mode also in conditions of high risk for interference.
However, most of the participants preferring an overlapping processing mode when the risk for task
interference was low shifted to a more serial processing mode in the condition where risk for task
interference was high.
Just as some individuals are able to lower switch costs in task-switching paradigms, several
individuals have been shown to eliminate the PRP effect in dual-task paradigms (Maquestiaux et al.,
2008, 2018; Ruthruff, Van Selst, et al., 2006; Schumacher et al., 2001). It seems that executive functions
contribute to the flexible regulation of task-order in dual tasks (Kübler et al., 2019; Schubert, 2008;
Steinhauser et al., 2021). This eventually leads to a reduction of dual-task interference or even a
bypassing of the bottleneck - regardless of the stage at which it is located (Ruthruff, Hazeltine, et al.,
2006; Schubert, 2008). Individual differences in the PRP effect therefore cannot exclusively be attributed
to the extent to which a bottleneck does or does not exist, but the flexibility of efficiently using control
mechanisms in a top-down manner, which means efficiently deploying the software (Lague-Beauvais et
al., 2013; Maquestiaux et al., 2008). In this regard, Kübler et al. (2018) showed that participants who
relied on their own task-order choice decisions showed better performance (i.e., lower dual-task costs)
compared to participants who adjusted their task order to an external, mandatory order criterion. While
this indicates that the requirement to adhere to externally- compared to internally-determined processing
demands can lead to increasing dual-task costs, the mechanism of task-order regulation by itself
requires sufficient WMC. This was shown by Kübler et al. (2021) who showed that the difference in dual-
task costs between situations of changing vs. non-changing task orders disappears under conditions of
increased working memory load during task-order regulation.
Proper investigation of flexibility (and its relation to structure or plasticity) calls for the use of
appropriate experimental paradigms, which release experimental control and allow for more freedom in
task choice and in processing order between situations. This is also evident in situations with more
complex multitasking scenarios (e.g., SynWin paradigm, Elsmore & McBride, 1994; multitimer paradigm,
Frick et al., 2021; counter task, Mäntylä, 2013) demanding from participants to process and switch
between several tasks within a limited time frame (Logie et al., 2011; Oswald et al., 2007). With the aim
of circumventing bottlenecks, participants vary in their multitasking strategies and show pronounced
inter-individual differences in the temporal monitoring and coordination of multiple tasks (e.g., Kubik et
al., 2020; Todorov et al., 2018).
8
Irrespective of considering task-switching or dual-task paradigms, it seems important to discuss
that whether or not flexibility makes a major contribution to task choice may depend on the measure of
performance. Many studies, like the studies by Kübler et al. (2018) as well as Brüning and Manzey
(2018, 2021) mentioned here, infer differences in reaction times to differences in flexibility among
individuals. Reaction times are a typical measure of shifting and it has been suggested that “it is indeed
easy to consider cognitive flexibility and shifting as one and the same because if we decompose any
flexible behaviour, we will find shifting to be an important component of it” (Ionescu, 2012, p.193).
Reaction times and/or costs might however not always be a sufficient indicator of flexibility, because
flexibility can be understood as both a specific ability as well as a property of different cognitive
processes. Considering several mechanisms in the study of cognitive flexibility in multitasking, which
would include multiple measures and thus performance variables, would help to further understand
individual differences in flexibility and should be implemented in the future (for further details on
deductive vs. inductive measures of flexibility see Ionescu, 2012).
As outlined earlier, while these results represent relations between control mechanisms of the
hardware and flexibility there is, interestingly, little evidence so far on the relationship between basic
capacity constraints of the cognitive hardware and flexibility. Preliminary results suggest that working
memory capacity (WMC) sets boundaries for the degree of adaptability to different tasks. For example,
Kübler et al. (2021) showed that flexible task scheduling in dual-task situations relies heavily on WMC.
Likewise, Brüning and Manzey (2018) found that individual preferences for overlapping versus serial
processing are associated with differences in WMC. Specifically, individuals with higher WMC more
often engaged in an overlapping processing mode and were more flexible to adapt to contexts for
instance with higher risk of crosstalk (i.e., contexts with (content-based) code overlap between tasks;
Koch, 2009). In the more complex multitasking paradigms, WMC also explained a substantial amount
of inter-individual differences in multitasking performance (Hambrick et al., 2010; Redick et al., 2016;
Todorov et al., 2018). Furthermore, there is evidence that beyond WMC and executive functions, visuo-
spatial processing ability can be an important structural determinant for multitasking scenarios involving
higher temporal demands of monitoring, coordinating, and choosing when to execute the individual tasks
in time as compared to more experimentally controlled paradigms (Frick et al., 2021; Mäntylä, 2013).
For example, Mäntylä et al. (2013; see also Kubik et al., 2020; Todorov et al., 2018) showed that spatial
ability (as measured by mental rotation performance) was an independent predictor of multitasking
performance beyond WMC. Furthermore, in situations demanding high temporal monitoring, gender-
9
related differences in multitasking mainly reflected differences in spatial ability (Mäntylä, 2013; Mäntylä
et al., 2017): men’s better multitasking performance was mediated by individual differences in spatial
ability, but not in executive functions. These results support the spatiotemporal hypothesis (cf. Mäntylä,
2013) which proposes that multitasking involves the representation of temporal deadlines in spatial
terms and thus that everyday multitasking can be alleviated by representing multiple tasks or deadlines
in spatial terms.
Future research is required to determine how individual differences in software, other abilities
within the cognitive architecture (e.g., processing speed, fluid intelligence, decision-making ability) as
well as non-cognitive characteristics (e.g., preferences, personality dimensions) may account for
different performance measures of multitasking (Broeker et al., 2018). A first step to establish more
concrete relations between hardware and flexibility, and to increase data availability in this regard, could
be to integrate more standardized measures of executive functioning into multitasking studies by default.
This would also partly increase comparability between studies. However, it is to be avoided to artificially
inflate designs to not violate utility and reasonableness.
In addition to Kübler et al. (2021), Brüning et al. (2020) postulate that response strategies cannot
be exclusively explained by soft- or hardware, or context. They argue that people tend to prefer and
rigidly follow an approach that is consistently characterised by either frequent switches (requiring high
degrees of flexibility for frequent reconfiguration) or blocked responses (requiring lower degrees of
flexibility due to higher separation of task sets). A concept that might explain why people tend to rigidly
prefer either approach is the Metacontrol State model by Hommel (2015; see also Mekern et al., 2019).
According to this model, individuals have a default mode to deal with multitasking requirements in a
more flexible or in a more persistent manner. The preferences for response scheduling might therefore
represent behavioural correlates of the coherent metacontrol default value. There is already some
evidence showing that, for example, convergent thinking (Fischer & Hommel, 2012) or negative mood
(Zwosta et al., 2013), which are both supposed to determine the parameters of cognitive control, can
result in less crosstalk. However, the model needs further empirical support and to understand if
metacontrol states explain individual differences in multitasking it has to be further clarified to which
extent control states are considered trait biases vs. adaptive state biases (see Mekern et al., 2019).
There is also scarce empirical support that other structural differences such as psychological
characteristics (e.g., impulsivity, sustained attention) affect processing or response organization (Fröber
& Dreisbach, 2016; Katidioti & Taatgen, 2014).
10
Beyond, we also find evidence for individual differences in flexibility affecting cognitive structure.
For instance, some types of dual tasks allow task integration which eventually helps to reduce or even
circumvent structural limitations (e.g., Broeker et al., 2020; de Oliveira et al., 2017). Participants who
practiced a tracking task and an auditory response task, reduced costs in both tasks once the auditory
task was tempo-spatially coupled to the tracking task (Broeker et al., 2021): Whenever the sounds of
the auditory task did not occur in random intervals along the tracking path, but shortly before tracking
turns, all participants improved tracking accuracy and reaction times. One possible explanation for this
result is that the response organization changed the representation from “performing two separate tasks”
to “performing one integrated task” (flexible strategy), thereby outsmarting structural constraints.
However, as there was large inter-individual variance in the improvement on both and not only one task,
not all participants seem to be equally able to adopt flexible strategies. Therefore, it remains unclear
whether such strategies are subject to the particular skill levels of participants and whether individuals
per se differ in flexibility, or whether strategies are subject to training and everyone can acquire flexible
strategies sooner or later (plasticity perspective, see below).
Taken together, an individual difference perspective on the flexibility-structure intersection may
improve our understanding of why individual differences in strategies (e.g., switching vs. blocking) or
decisions (e.g., to switch or repeat) occur, and by which invariable properties or control mechanisms
they are influenced. Besides, this perspective might inform our understanding of how individuals are
able to efficiently deal with structural prerequisites to reduce or even circumvent cognitive limitations.
Overall, the work done so far mostly focused on the link between individual differences in flexibility and
control mechanisms. It provides accumulating evidence for individual differences in the use of
multitasking strategies, which are more or less flexible. However, more research needs to be conducted
on the relation between flexibility and the basic constraints of the hardware as well as on the question
how individual differences in flexibility might affect cognitive structures. Such a new focus could also
contribute to rethinking classic theoretical approaches and related discourses, for instance, whether
bottlenecks are structural or strategic in nature (e.g., Han & Marois, 2013; Ruthruff et al., 2009).
Structure-Plasticity and Flexibility-Plasticity
Several lines of research indicate that individual differences in hardware (e.g., in WMC)
potentially contribute to differences in plasticity. First, evidence from age-comparative studies can speak
to the structure-plasticity intersection as children and older adult groups typically show larger switch and
mixing costs, and lower WMC relative to younger adults. Accordingly, studies have demonstrated
11
pronounced age differences in training benefits with some studies showing that children and older adults
benefit more from task-switching training than younger adults, while others demonstrated greater
training benefits in younger adults (Cepeda et al., 2001; Karbach & Kray, 2009). Yet other studies
demonstrated equivalent improvement in dual tasks across age groups when using the so-called
“testing-the-limits”-approach (Bherer et al., 2006; Kliegl & Baltes, 1991). This approach highlights the
need to consider different aspects of performance in order to avoid the overestimation of individual
differences in unpractised or non-optimized testing conditions. These performance aspects include a
baseline level of cognitive performance, the baseline reserve in optimized conditions (i.e., “current
maximum potential of cognitive performance”, p. 263) and eventually the developmental reserve, or the
maximum latent potential of an individual after training (Bherer et al., 2006). Taken together, these
findings indicate that differences in structural limitations due to age may affect training benefits (Bherer
et al., 2005; Lussier et al., 2015; Sabah et al., 2019) and that a more individualised approach is
necessary to understand the impact of structure on plasticity in multitasking. Second, one study that
directly examined individual differences in a lifespan sample (after controlling for age) demonstrated that
training benefits were higher for individuals who showed higher switch costs and lower working memory
at pre-test (Karbach et al., 2017). These results are in line with the compensation (vs. magnification)
hypothesis (e.g., Lövdén et al., 2012), suggesting that individuals starting out with lower structural
prerequisites benefit more from training. Other training studies have also provided evidence consistent
with magnification effects such that individuals with higher structural prerequisites benefit more from
training (e.g., Foster et al., 2017; Strobach et al., 2012; Strobach & Huestegge, 2017). While evidence
with respect to the compensation vs. magnification of individual differences with training is mixed (Laube
et al., 2020; Traut et al., 2021), these studies strengthen our claim that inter-individual differences in
structure affect plasticity and point to the need to investigate this interaction in future work. Second, to
the degree to which neural structures (i.e., brain structure and function measured in-vivo with MRI) are
considered to relate to cognitive structures, one study demonstrated that the volume of the left
dorsolateral prefrontal cortex predicted an individual’s response to dual-task training in healthy adults
(Verghese et al., 2016). In another study, a group undergoing dual n-back training (as compared to
single n-back training) showed improved performance accompanied by increased functional connectivity
of the ventral default mode network in the right inferior frontal gyrus, which correlated with improvements
in working memory performance (Salminen et al., 2016, 2020). To date, these correlational results do
not allow to ascertain the extent to which hardware limitations (e.g., WMC) are causing multitasking
12
improvements or may reflect a common underlying component. Here, training studies may provide
valuable insight in the future by examining whether and how training-related improvements in WMC
leads to better multitasking performance when compared to a direct training of multitasking.
Incorporating neurophysiological measures in such designs may further help disentangle direct effects
of structural/hardware limitations from common underlying components.
Considering the potential contributions of individual differences in plasticity on structure, there
is evidence that participants experiencing different degrees of practice exhibit different levels of cost
reduction (Schumacher et al., 2001; Van Selst et al., 1999). For instance, Schumacher and colleagues
showed that after relatively modest amounts of practice, some participants achieved virtually perfect
time sharing in dual-tasks. These results suggest that variability in plasticity contributes to variability in
the minimization of the rigidity of cognitive structures.
To date, only a handful of studies have examined the potential link between flexibility and
plasticity. For example, one training study with children compared a group that practiced various tasks
(including task switching and other executive functioning tasks) to a group of children who additionally
received metacognitive scaffolding on detecting relevant features and using effective strategies to
perform the tasks (Pozuelos et al., 2019). The results indicated greater training benefits in the
metacognitive scaffolding group, providing indirect evidence for a potential link between flexibility and
plasticity such that individual differences in response strategies may contribute to individuals’ potential
to benefit from multitasking training (cf. Fandakova et al., 2012).
Future research should also critically address the potential interaction between all three
perspectives, in particular with regard to the variability of capacities/control mechanisms (i.e., hardware)
and flexible strategies across the lifespan. One question includes whether children that adopt flexible
processing strategies early also develop cognitive structures that allow more parallel processing later.
Alternatively, can flexible processing strategies alleviate age-related declines in structure in later
adulthood? For instance, individuals grown up bilingually from birth and who switch back and forth
between languages often, have been measured to actually have better executive functions in
adolescence and young adulthood (Bialystok, 2015; Gold et al., 2013; but see Lowe et al., 2021). If
executive functions are part of the structure, then this would indicate that training flexibility could lead to
changes in the structure. Such a new focus could also be extended to possible transfer effects. If a
flexibility training and the coherent individual responsiveness can fundamentally change structure, can
we expect transfer effects to other tasks? A series of studies (Schubert et al., 2017; Strobach et al.,
13
2015) showed that task coordination in dual-task situations is subject to training-related changes, which
are even transferable to other new task situations and this ability to training and transfer is preserved
even to older subjects though to lesser degree in the latter compared to younger subjects.
Taken together, an individual difference perspective on the structure-plasticity intersection can
be especially informative with respect to understanding how different types of training may be more or
less beneficial across individuals depending on their structural limitations. In addition, examining
individual differences in the plasticity-flexibility intersection can be informative with respect to
understanding why some people show considerable improvements after training in practiced and in
novel transfer tasks. It is possible that individual differences in training gains may be related to individual
differences in strategies (e.g., switching vs. blocking) or decisions (e.g., switch or repeat) during training.
Integrating individual differences in strategies with WMC in future training studies applying the “testing-
the-limits”-approach is promising for uncovering how structure and flexibility interact to facilitate or
restrict the potential for lasting change in multitasking ability.
Overall, the plasticity perspective involves the greatest potential for the development of the field,
because it may solve open questions in the structure-flexibility intersection. For instance, structural
differences might develop through inefficient multitasking strategies that individuals had followed over a
long time even though they had not benefited from them. To the best of our knowledge, no training
studies have examined the extent to which strategies can be changed, leading to long-term structural
benefits (i.e., higher working memory capacity).
Conclusion and outlook
Little more than a decade ago, Watson and Strayer (2010) claimed that an individual-differences
perspective would “significantly improve our theoretical understanding of attention and performance in
both traditional laboratory settings and more applied contexts” (p.484). Surprisingly few studies have
followed this claim the past 10 years, although most studies show that individuals react differently to
tasks and demands, and that individual differences in e.g. age, processing mode or dual-task costs
beyond group averages deserve attention. Still, mostly group means are reported and even though
standard deviations are reported, too, not much value is attached to them. As studies by Brüning and
Manzey (2020, 2021), as well as many others show, particular data patterns pointing to individual
differences, or even extraordinary multitasking abilities, do not only become apparent in very large
samples but with comparably smaller samples. Classic multitasking paradigms including a sufficient
number of trials are theoretically suited to detect and further examine variance without violating statistical
14
power, thus following the general trend towards big data sets might not always be required (but see
Hedge et al., 2018 for counter arguments; see LeBel et al., 2017, for alternative considerations regarding
sample sizes).
We recommend the specific comparison of strongly controlled paradigms against modified versions in
which some control is systematically relinquished (e.g., in only one specific task characteristic) to make
it more likely to find (relevant) individual differences. As we have tried to convey with this theoretical
note, differentiating cognitive structure, flexibility, and plasticity by means of individual differences shows
how previously established work of the field can be better linked and also how it can be further
developed. We might even conclude that an individual difference focus can nicely put together all three
perspectives by asking questions like “Is the relation between flexibility and plasticity mediated by
structural limitations?”. We ask for research that investigates different degrees of flexibility and variance
in structure more systematically, reflecting human’s tendency to circumvent cognitive bottlenecks and
to maximize the performance score across the lifespan, including more longitudinal designs (for an
exception see Yang et al., 2019). Therefore, we ask for research designs that do not oversimplify the
complexity of human cognition and for multivariate data analyses and multilevel models which allow the
portioning of inter- and intra-individual variability (e.g., Meiran, 2000; Wasylyshyn et al., 2011). One
promising line of future research is to employ more complex multitasking paradigms that involve more
than two tasks and allow participants to freely choose the order and number of chosen tasks within a
limited time frame, with the aim to examine inter-individual differences of multitasking performance in
relation to cognitive structures, age (Frick et al., 2021), as well as to process organization. Another
possibility is to establish multicentre studies that use the same paradigms and individual differences to
increase data reliability and progress the field further. Ultimately, one challenge would be to not only
understand what structural or flexible aspects characterize a supertasker, but to achieve optimal person-
task fit. Research could for instance match task and processing mode, allow response organization
according to cognitive constraints or train individuals to achieve optimal multitasking performance
depending on their preferred strategies. This might include individual adjustment of instructions, stimuli,
number of trials or feedback and incentives. Eventually, more results on individual differences can help
to re-evaluate the established theoretical frameworks on multitasking interference to create a more
complete and diverse understanding of multitasking functioning in humans.
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