del missier, f., mäntylä, t. y bruine de bruin, w. (2010). executive fuctions in decision making...
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Executive functions in decision making: An individual
differences approach
Fabio Del MissierUniversity of Trieste, Italy
Timo Ma ntylaUniversity of Umea, Sweden
Wa ndi Bruine de BruinCarnegie Mellon University, Pittsburgh, PA, USA
This individual differences study examined the relationships between threeexecutive functions (updating, shifting, and inhibition), measured as latentvariables, and performance on two cognitively demanding subtests of theAdult Decision Making Competence battery: Applying Decision Rules andConsistency in Risk Perception. Structural equation modelling showed that
executive functions contribute differentially to performance in these two tasks,with Applying Decision Rules being mainly related to inhibition andConsistency in Risk Perception mainly associated to shifting. The resultssuggest that the successful application of decision rules requires the capacity toselectively focus attention and inhibit irrelevant (or no more relevant) stimuli.They also suggest that consistency in risk perception depends on the ability toshift between judgement contexts.
Keywords: Cognitive control, Decision making, Decision-making competence,Executive functions, Individual differences.
Correspondence should be address to Fabio Del Missier, Department of Psychology,
University of Trieste, Via S.Anastasio, 12, I-34134, Trieste (TS), Italy. E-mail: [email protected]
The authors thank Mim` Visentini, Giovanna Mioni, and Rino Rumiati for their support
and help in data collection. We also thank two anonymous Thinking & Reasoningreviewers and
Edward T. Cokely for their detailed and insightful comments on a previous version of this
paper. Fabio Del Missier thanks Consorzio Universitario di Pordenone for financial and
logistic support. The research was also supported by a grant of the University of Trieste (FRA
grant, Passolunghi & Del Missier).
THINKING & REASONING, 2010, 16 (2), 6997
2010 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business
http://www.psypress.com/tar DOI: 10.1080/13546781003630117
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Decision making has traditionally been conceived as a complex interplay of
high-level process, involving option generation, evaluation of risks and
consequences, and choice of a course of action in line with personal
preferences (e.g., Baron, 2008; Hastie & Dawes, 2001). According to this
view, decision making may require a high degree of cognitive control
(Tranel, Anderson, & Benton, 1994). Consistent with this idea, a close link
between frontal/executive functions and decision-making processes has been
suggested by patient studies (e.g., Eslinger & Damasio, 1985; Manes et al.,
2002), brain-imaging research (e.g., Clark, Cools, & Robbins, 2004; De
Martino, Kumaran, Seymour, & Dolan, 2006), and behavioural experiments
(e.g., Hinson, Jameson, & Whitney, 2003; Shiv & Fedorikhin, 1999). For
example, some experimental studies have provided support for the idea that
decision procedures sensitive to long-term consequences of options entailworking memory resources and control processes (Hinson et al., 2002, 2003;
Shiv & Fedorikhin, 1999, 2002). However, these studies did not identify the
specific executive processes involved in decision procedures.
According to dual-process theories, decision making is supported by
heuristic and analytic processes (e.g., Epstein & Pacini, 1999; Evans, 2003,
2007; Evans & Over, 1996; Goel, 1995; Kahneman, 2003; Kahneman &
Frederick, 2005; Peters, Hess, Va stfja ll, & Auman, 2007; Reyna, 2004;
Sloman, 1996). Although dual process theories differ in many respects (for a
review, see Evans, 2008), they generally assume that heuristic decision makingdepends on learned associations and intuitive heuristics, while analytic
decision making is guided by rules and principles. Heuristic decision making
would rely on fast automatic processes, whereas analytic decisions would
entail slower control processes and working memory. Despite its intuitive
appeal and propulsive role, the research conducted within the dual-process
framework has not yet provided detailed insights into the nature of the control
processes involved in different kinds of decision-making tasks: Dual process
theories nicely describe what the two systems do but it is not clear how the
systems actually operate (De Neys & Glumicic, 2008, p. 1250; see also Evans,2007; Gigerenzer & Regier, 1996; Keren & Schul, 2009; Osman, 2004).
One of the reasons underlying our poor knowledge of the nature of
control processes in decision making is the scarce attention devoted to
individual differences and measurement instruments (cf. Lopes, 1987; Parker
& Fischhoff, 2005). Traditionally, decision-making processes, such as the
selection of decision rules to choose between options (Bro der, 2003; Larrick,
Nisbett, & Morgan, 1993; Payne, Bettman, & Johnson, 1993) and the
evaluation of risks associated with options (Mandel, 2005), have been
studied in isolation, mainly focusing on systematically understanding
deviations from normative standards (e.g., Kahneman, Slovic, & Tversky,
1982). As a result, relatively little is known about how individual decision-
making skills are related to each other, to cognitive abilities and to
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real-world outcomes (cf. Bruine de Bruin, Parker, & Fischhoff, 2007).
Moreover, past research made only sporadic attempts to develop and
validate measures of individual differences in decision-making competence,
which are crucial to investigating the connections between cognitive
processes and decision behaviour.
Stanovich and West (1998, 2000, 2008) conducted an important stream of
individual differences studies involving reasoning and decision making.
They found moderate correlations between performance in some tasks (e.g.,
resistance to overconfidence and hindsight bias) and measures of cognitive
ability. Following this line of research, Bruine de Bruin et al. (2007; see also
Parker & Fischhoff, 2005) developed and validated a battery of decision
tasks aiming to measure individual differences in decision-making compe-
tence. The decision-making tasks, including Applying Decision Rules andConsistency in Risk Perception, were selected from the judgement and
decision-making literature, representing skills relevant to normative theories
of decision making. Using a diverse sample and a variety of performance
criteria, the Adult Decision Making Competence (A-DMC) battery was
found to have appropriate reliability and validity. The availability of this
validated A-DMC measure of individual differences now makes it possible
to examine in a more reliable way the connection between cognitive skills
and decision-making tasks.
Studies on control processes in decision making could also have beenlimited by methodological problems that, until recently, plagued research on
executive functions. The expression executive functioning has seen a
variety of interpretations, and the construct validity of most neuropsycho-
logical tests of executive functioning, such as the Wisconsin Card Sorting
Test (WCST) is not well established (Miyake et al., 2000; Royall et al., 2002;
Salthouse, 2005). Furthermore, commonly used individual-differences
measures of executive functioning, including the WCST, suffer from low
reliability and, perhaps as a result, show very low intercorrelations
(Denckla, 1996; Duncan, 1986; Miyake et al., 2000; Rabbitt, 1997;Salthouse, 2005).
Individual-differences studies of executive control have recently adopted
a methodological approach that has reduced these methodological
problems. Instead of using complex frontal tests, such as the WCST,
these recent studies have examined executive functioning by means of latent
variable analyses on simpler control tasks (Friedman et al., 2006; Ma ntyla ,
Carelli, & Forman, 2007; Ma ntyla , Kliegel, & Ro nnlund, in press; Miyake
et al., 2000; Salthouse, Atkinson, & Berish, 2003; see also Salthouse, 2005).
Their main strategy has been to use multivariate analyses for examining
individual differences in more specific control functions. Specifically, the
structure of executive functioning has typically been examined at the level of
latent variables (i.e., identifying what is statistically shared among the
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multiple exemplar task scores for each executive function), rather than at the
level of manifest variables (i.e., analysing individual task scores). By
statistically extracting what is common among tasks selected to tap a
putative executive function, the resulting latent variable is a purer and
probably more reliable measure of the theoretical construct, which can be
related to a target manifest variable (e.g., performance in a decision task).
Recent research has adopted a latent variable approach for examining the
role of executive functions in a several domains of cognition, including
complex frontal lobe tasks (Miyake et al., 2000), fluid intelligence
(Friedman et al., 2006), attentional difficulties (Friedman et al., 2007), and
duration judgements (Carelli, Forman, & Ma ntyla , 2008).
Following these recent lines of work, the present study adopted an
individual differences approach to investigate cognitive control processesthat are assumed to play a role in decision making. In particular we
examined, through a latent-variable approach, the contribution of distinct
executive functions to performance on two cognitively demanding subtests
taken from a validated decision-making battery. We focused on three
control functions: updating working memory representations, shifting
between tasks and information sets, and inhibiting responses and stimuli
(hereafter referred to as updating, shifting, and inhibition, respectively).
These functions have frequently been postulated in the literature (e.g.,
Baddeley, 1996; Miyake et al., 2000; Nigg, 2000; Rabbitt, 1997; Smith &Jonides, 1999) and they have been reliably identified as important elements
of executive control (e.g., Friedman et al., 2006, 2007, 2008; Garon, Bryson
& Smith, 2008; Miyake et al., 2000; Shimamura, 2000). However, Miyake
et al. (2000) conceived of these executive functions as a non-exhaustive
conceptualisation of control processes, at a relatively low level of analysis,
which proved to be appropriate for reaching a better understanding of the
relationship between control processes and complex cognitive tasks. As a
result, we are not claiming that these are the only executive functions
relevant to decision-making competence or that these functions areprimitives of cognitive control.
The updating function is thought to be involved in the active revision and
monitoring of working memory representations. It is usually assessed by
tests that require performing a revision of working memory content by
replacing older, no longer relevant information, with newer information (see
the Materials section for a detailed description of tests of executive
functions). The shifting function is assumed to play a role when the
individual has to switch between tasks or mental sets, and it is measured by
tests in which participants perform repeated shifts from one task (or mental
set) to another. The inhibition function is needed to actively suppress
responses or thoughts or, in general, to keep the individuals attention
focused on goal-relevant information in the face of interference
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(cf. Friedman et al., 2008). Tests of voluntary inhibition require stopping
prepotent responses and resisting interfering stimuli or thoughts.
We selected as target decision tasks the Applying Decision Rules (ADR)
and Consistency in Risk Perception (CRP) subtests from the A-DMC
battery (see the Materials section for a detailed description). ADR and CRP
measure significant aspects of decision-making competence: respectively, the
ability to correctly apply choice strategies when selecting from a set of
alternatives and the capacity to consistently judge the probability of the
occurrence of various risky events. ADR and CRP were selected for two
concurrent reasons. First, from the theoretical viewpoint, these two tasks
can be distinguished according to their postulated control requirements,
allowing for specific predictions about executive involvement to be
formulated. Second, previous research showed that, in addition to havinga good reliability and validity, ADR and CRP are the most cognitively
demanding and analytical A-DMC tasks. That is, they show the strongest
correlations with measures of fluid and crystallised intelligence (Bruine de
Bruin et al., 2007), which partly depend on efficiency in executive control
(e.g., Friedman et al., 2006; Salthouse et al., 2003).
Indeed, a precursor of ADR was found to be related with a generic
composite measure of executive functioning (Giancola, Martin, Tarter,
Pelham, & Moss, 1996). Furthermore, both ADR and CRP are also better
handled by participants who self-report relying more on rational decision-making styles (cf. Bruine de Bruin et al., 2007). Moreover, ADR and CRP
display the highest correlations with the other A-DMC subtasks, and show
the highest loadings on the one-factor solution of A-DMC, suggesting that
they may reflect core analytical A-DMC skills (Bruine de Bruin et al.,
2007). This idea is supported by a recent re-analysis of Bruine de Bruin
et al.s original data, which identified ADR and CRP as cognitive (vs
experiential) decision tasks (Bruine de Bruin, Parker, & Fischhoff, 2009).
To summarise, the study described in the present paper aims to understand
which control processes are more involved in two different decision-makingtasks capturing significant aspects of analytical decision competence (ADR
and CRP). Its broader goal is to promote an approach capable of establishing
a closer link between decision making and research in executive functioning,
which could allow a further specification of theories of individual differences
in decision making (e.g., Stanovich & West, 2000, 2008).
AN INDIVIDUAL DIFFERENCES STUDY ON COGNITIVECONTROL IN DECISION MAKING
We conducted a multi-indicator individual differences study, with the aim of
testing our hypotheses on the relationships between executive functions and
decision-making performance via structural equation modelling. Starting
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from previous research and from a cognitive task analysis, we hypothesised
that either inhibition or updating could play a prevailing and substantial
role in ADR, while we expected that shifting could play such a role in CRP.
The capacity to effectively apply decision rules is essential in multi-
attribute choice contexts (Bettman, Luce, & Payne, 1998; Payne & Bettman,
2004). While it is usually assumed that the application of decision strategies
depends on cognitive control and working memory (e.g., Payne et al., 1993),
to the best of our knowledge no specific research on this topic has been
published (possibly because past studies focused instead on strategy
selection: e.g., Bro der, 2003; Larrick et al., 1993; Payne et al., 1993). The
ADR task in the A-DMC battery specifically evaluates the ability to apply
decision rules of varying complexity (lexicographic, satisficing, equal
weights, etc.). Participants are presented with different multi-attributedecision problems involving choices between DVD players with different
features, and they are asked to select one or more options according to a
different decision rule (see Appendix A for one example). The application of
decision rules (see Bettman et al., 1998) usually requires selectively focusing
on goal-relevant information while carrying out an ordered stream of
operations and inhibiting irrelevant (or no more relevant) information.
Thus, inhibition may play a major role in ADR (cf. Friedman et al., 2008).
For example, the lexicographic decision rule involves first comparing
options on the attribute that is deemed the most important, then (if no clearwinner emerges) comparing the best options on the second most important
attribute (but ignoring or inhibiting unsatisfying options and already-
considered attributes), and so on. On the other hand, some ADR problems
require the execution of mental operations (comparisons and computations)
and the temporary maintenance of intermediate results, such as a reduced
choice set from which a preferred option will be selected. Thus updating
could be also involved, at least in the more complex problems composing
this task. Shifting is expected to play a less-significant role in ADR, because
this task requires the application of different rules or combination of rules(lexicographic, satisficing, etc.) to different problems (and thus there is no
need to reinstate previously encountered problems or rules).
The ability to follow basic principles of probability theory when judging
probabilities of different events is generally deemed to be an important
prerequisite to decision under uncertainty. The CRP task in the A-DMC
battery requires a participant to specify the probability of various events
that could happen to him/her in different time frames (see Appendix A for
one example). A series of judgements has to be provided in sequence, while
event type and time frame change. Some of these judgements are logically
related. Performance is then assessed by evaluating the congruency of the
participants judgements with basic probability principles. To summarise,
CRP essentially requires that participants maintain coherence in a series of
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conceptually related probabilistic judgements while switching between
different event descriptions and time frames. Past research showed that
the shifting executive function is involved in mentally switching from one
information set to another (Rende, Ramsberg, & Miyake, 2000). Thus
individuals with greater shifting ability should be better able to shift back
and forth between related probability judgements, facilitating comparisons
between different probability judgements, and increasing the likelihood that
they will recognise ones that are related. As a result, they should be better
able to give consistent judgements to probabilistic judgements that ask
about related events varying only in time frame or descriptive detail.
Evidence in line with this hypothesis also comes from Ma ntyla et al. (in
press), who examined metamemory judgements in relation to executive
functions, finding a relationship between set shifting (but not updating andinhibition) and metacognitive judgements on memory problems. Therefore
we hypothesised that the shifting executive function is significantly related to
consistency in risk perception. On the other hand, inhibition and updating
should play a minor role in this task, because single CRP judgements do not
appear to require a great deal of selective processing or integration/
maintenance, and an external memory of the previous responses is always
available on the questionnaire.
Analytic approach
We used structural equation modelling to test our hypotheses about the
relationships between executive functions and decision-making perfor-
mance. Following previous work, data analysis was carried out in two stages
(cf. Kline, 1998, Miyake et al., 2000): (1) the identification of a measurement
model of executive functioning (stage 1), and (2) the estimation of structural
models of decision tasks based on the measurement model. The second stage
can be reached only if the first one allows the identification of a valid
measurement model (e.g., Hair, Black, Babin, & Anderson, 2009).In the first stage we identified a measurement model that aims to capture
the structure of executive functions, starting from a candidate three-
component model that, as noted earlier, has been repeatedly supported (e.g.,
Friedman et al., 2006, 2008; Miyake et al., 2000). In other words, we tested a
hypothesis on the structure of executive functions. To this aim, we used two
tasks for each of the three functions (see Figure 1, and the Method section
for a detailed description of variables included in the model). According to
this model, the three executive functions are distinct but relatedconstructs.
Through structural equation modelling, we compared the fit of the
candidate measurement model with two reference models, which departed
from the idea that inhibition, shifting, and updating are distinct but related
constructs. The first reference was a one-component model (assuming unity
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of executive functions) and the second reference was a three-component
unrelated model (assuming independence of executive functions). The
endorsed measurement model was then kept fixed in the next stage of data
analysis (cf. Hair et al., 2009; Kline, 1998).
In the second stage of analysis we focused on the contribution ofexecutive functions to performance in different decision tasks (structural
models) while keeping the measurement model fixed. This means that, for
each decision task (ADR and CRP), we tested structural models that share
the measurement model (i.e., the structure of executive functions), but differ
in a principled way in the posited relationships between each executive
function and performance in the target decision task (which is always a
manifest dependent variable). This approach, similar to the one followed by
Miyake et al. (2000), is summarised in Figure 1 (assuming the measurement
model that we actually used). In the second stage we also started from acandidate model for each decision task, which embodied a selective
relationship between executive functions and the target decision task. In
particular, the candidate model was specified by combining the measure-
ment model identified in the first stage with a structural model substantiat-
ing our selective hypothesis on the relationships between executive functions
and the target decision task (see the previous section). Thus we tested the
shifting hypothesis for CRP and assessed two alternative hypotheses for
ADR (inhibition and updating). The candidate model was always compared
with two reference structural models: a no-path model (assuming complete
independence between executive functions and decision performance), and a
full-path model (assuming that each executive function significantly
contributes to decision performance). In order to be convincingly supported,
Figure 1.Abstract structure of the models tested in our study. Ellipses represent latent variables,
while rectangles represent manifest variables. According to the measurement model (solid
arrows), the three executive functions (shifting, updating, and inhibition) are clearly separable
but correlated. Dashed arrows represent potential relationships between executive functions
and decision tasks, which may or may not be included in specific structural models (depending
on the hypotheses).
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the candidate model should show a significantly better fit than the no-path
model and should not have a worse fit than the full-path model.
METHOD
Participants
A total of 116 undergraduates of the Trieste and Padua universities took
part in the study. The sample comprised 90 females and 26 males (mean
age 23.45, SD 5.04). Participants were awarded course credits for their
collaboration.
Procedure
A series of individual differences measures were collected in two sessions: (1)
A-DMC session, (2) executive functioning session. The tasks were presented
in separate sessions in order to avoid fatigue effects, and each participant
completed the two sessions within 1020 days. In the A-DMC session small
groups of participants completed the A-DMC tasks (including ADR and
CRP), in the same order as in the original questionnaire. Participants
completed the executive functioning session individually, in the psycholo-
gical laboratories of the universities of Trieste and Padua. We selected twotests that are thought to tap each of the three target executive functions:
plusminus and numberletter (shifting), Stroop and stop-signal (inhibi-
tion), letter-memory and n-back (updating; see also Figure 1). These tests,
which are frequently used to measure the three executive functions (see next
section), were administered in the following fixed order: plusminus (trial 1),
letter-memory, Stroop, numberletter, stop-signal, n-back, plusminus
(trial 2).1 After each test participants received a short break. A longer
pause was allowed between Stroop and numberletter tasks. The entire
executive functioning session lasted approximately 1 hour and 15 minutes(pauses included). Ethical and privacy protection standards were followed
throughout data collection and analysis.
Materials
Decision-making tasks
The A-DMC is a set of seven decision tasks that has been recently
validated and proposed as an instrument to measure individual differences
in decision-making competence (Bruine de Bruin et al., 2007). A-DMC
1The plusminus test was composed of two separate trials whose results were averaged.
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originates from a decision-making battery tailored for adolescents (Y-DMC:
Parker & Fischhoff, 2005), and it comprises a series of tasks that appear to
tap different aspects of decision competence. Performance was found to be
weakly correlated across the different A-DMC tasks (significant correlations
ranging from .15 to .43, mean of significant correlations .16). Bruine de
Bruin et al., (2007) reported that the overall A-DMC index (average of
standardised measures of performance in the different tasks) and each
individual subtask showed good to acceptable reliability and nomological
validity (with the exception of Path Independence). The original A-DMC
questionnaire was initially translated into Italian and underwent a first pilot
test.2 The accuracy of the translation was checked with the back-translation
method, applied by a professional translator. A small number of minor
discrepancies were detected, which were resolved after a joint discussionwith the translator. After a second pilot test the final version of the Italian
A-DMC was employed in the present study.3 We will now describe the ADR
and the CRP subtests, which are of interest here.
Applying Decision Rules (ADR). This A-DMC task evaluates partici-
pants ability to apply decision rules of varying complexity. Participants are
presented with 10 different multi-attribute decision problems involving
choices between DVD players with different features (such as picture
quality). For each problem, participants are asked to select one or moreoptions according to a different decision rule (lexicographic, satisficing,
equal weights, etc.), from a table presenting numeric ratings of features.
Scores represent the percent of responses across items that reflect
normatively correct answers that would have been obtained from an
errorless application of the prescribed decision rules.
Consistency in Risk Perception (CRP). This A-DMC task is devised to
assess participants capacity to follow the rules of probability theory when
providing probability judgements for risky events. Ten events are described,and participants are asked to judge the probability that each event could
2In the Italian version of the A-DMC dollars were replaced by euros. In order to maintain
the original figures, we avoided converting nominal values. However, euro values appeared to
be completely reasonable to participants of our pilot test. Minor word changes were applied
whenever the original wording made reference to cultural/social aspects that could be
unfamiliar or appear strange to Italian participants (e.g., Halloween was replaced by Carnevale
in a sunk cost problem). As can be seen by the descriptive statistics reported in Table 1, the
results of our study generally agree with those reported by previous studies. Moreover, our
A-DMC results show a good agreement with the results of a pilot test of a Swedish version ofthe A-DMC carried out on a sample of undergraduates (Marklund, 2008).
3The Italian version of the A-DMC used in our study is available from the first author of
this paper. A Swedish version is available from the second author of this paper.
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happen in 1 years time (e.g., What is the probability that you will get into a
car accident while driving during the next year?). Then the same events are
presented again, but this time participants are asked to evaluate their
probability of occurrence in 5 years time (e.g., What is the probability that
you will get into a car accident while driving during the next 5 years?).
Judgements of probability are expressed by ticking a graduated ruler that
displays a scale from 0% (no chance) to 100% (certain). Consistency in risk
perception is then evaluated by assessing the congruency of the participants
judgements with three principles: (i) the judged probabilities of the same
event in different time frames should be consistent (e.g., the probability of
getting in a car accident could not be greater in 1 years time than in 5 years
time), (ii) the judged probability of a subset event cannot exceed that of its
superset event (e.g., the probability of dying in a terrorist attack during thenext year cannot be greater than the probability of dying from any
causecrime, illness, accident, and so onduring the next year), and (iii)
the judged probabilities of complementary events should add up to 100%
(e.g. probability ofmoving your permanent address to another state some
time during the next year and probability of keeping your permanent
address in the same state during the next year). Performance is evaluated
by measuring the proportion of consistency checks (on a total of 20)
successfully passed by participants probabilistic judgements.
Executive functioning tasks
Plusminus. This paper-and-pencil task is commonly used to evaluate
the capacity to resist task interference when shifting between tasks (Jersild,
1927; Miyake et al., 2000; Spector & Biederman, 1976). Participants are
initially asked to add three to each of a series of numbers. Subsequently they
are asked to subtract three from each of another series of numbers. The final
task requires alternatively summing and subtracting three from each of a
third series of numbers. In our version of the task (as in Miyake et al., 2000),each series was composed of 30 two-digit numbers between 10 and 99
(randomly generated without replacement). Participants have to keep in
memory their current goal because no external cues are provided to remind
them. Performance (a shift cost measure) is measured by taking the
difference between the RT needed to complete the third (alternating) series
and the mean RTs of the first two series. We asked participants to execute
the three tasks twice, using different series of numbers, in order to obtain a
more reliable assessment and allow the computation of a reliability measure.
Thus our performance measure was the mean shift cost of these two trials
(participants accuracy was over 98%). Before the first administration of
each of the three tasks (sum, subtract, sum/subtract), a short training series
was presented. Participants were asked to work both quickly and accurately,
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and RTs needed to complete each series of numbers were recorded using a
stopwatch.
Numberletter. This computerised task is usually employed as an
indicator of the shifting capacity (Miyake et al., 2000; Rogers & Monsell,
1995). We used a version of the task that closely follows the one adopted by
Miyake et al. (2000). In the first block of trials a series of numberletter
pairs (e.g., 5B) is presented in the upper quadrants of the screen.
Participants are asked to press a first key when the number in the pair is
odd (3, 5, 7, 9) and a second key when the number is even (2, 4, 6, 8). In the
second block of trials another series of numberletter pairs is presented in
the lower quadrants of the screen. This time participants are instructed to
press a third key when the letter in the pair is a vowel (A, E, I, U) and afourth key when the letter is a consonant (G, K, M, R). In the final block of
trials the numberletter pairs are presented in clockwise order in the four
quadrants of the screen, and participants respond to the number when the
numberletter pair appears in the upper quadrants and to the letter when
the numberletter pair is presented in the lower quadrants. Thus, in half of
the trials participants shift between the two response sets previously
practised. In each trial the next numberletter pair was presented 150 ms
after the preceding response. Participants were given detailed written
instructions that fully explained each phase of task. They were asked torespond both quickly and accurately. Then 32 trials (plus 10 trials of
practice) were presented for each of the first two blocks of trials, and 128
trials were presented for the third block of trials (plus 12 trials of practice).
Performance was measured by taking the difference between the mean RT of
the shift trials of the third block of trials and the average RT of the first two
blocks of trials. RTs were computed on correct responses (whose percentage
was higher than 95% in each block of trials).
Letter-memory. This task is commonly used to measure the capacity toactively update working memory contents (Miyake et al., 2000; Morris &
Jones, 1990). In each trial a series of letters is presented, one by one, in the
centre of the computer screen (2000 ms per letter). Participants have to
rehearse the last three letters presented. When the presentation ends they
have to report the last three letters of the series. The length of the series of
letters varied randomly in each trial (5, 7, 9, or 11). After two practice trials
participants underwent 12 test trials. Performance was measured by taking
the proportion of final letters correctly recalled.
n-back. The n-back task is frequently used to measure individuals
capacity to update and actively manipulate working memory contents (cf.
Owen, McMillan, Laird, & Bullmore, 2005). We employed a version of this
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task that has been proven to be sensitive to individual differences in
updating (e.g., Ma ntyla , Karlsson, & Marklund, 2009). Participants were
presented with a series of high-frequency words (between three and seven
letters). Each word was presented for 2000 ms, centred on the computer
screen. Participants were required to press a key whenever the word
presented on the screen was the same as the one presented three serial
positions earlier in the series (3-back). This happened for 24 of the 96 stimuli
presented (25%). The stimuli also included 25% of foils (i.e., words repeated
at intervals of 2, 4, and 5). Participants underwent a practice session before
the test trials, and performance was measured as the proportion of correct
responses.
Stop-signal. This task has been often used to measure the capacity toinhibit a learned response. In each trial a stimulus letter (X or O) was
presented in the centre of the screen, and participants were required to
identify each by pressing a different specific key. They were also instructed
to withhold the response when they heard a beep (i.e., a stop signal)
immediately after the presentation of the stimulus letter. A fixation point
appeared on the screen 1000 ms before the stimulus presentation, and the
stop signal was delivered between 400 and 600 ms after the target (see also
Logan, 1994; Salthouse et al., 2003). We asked participants to be both fast
and accurate. After 20 training trials participants underwent 72 test trials.This number of trials was sufficient to obtain a reliable assessment of
individual differences in our previous studies (e.g., Ma ntyla et al., 2007,
2009) and allowed the task duration to be kept short. Task performance was
examined in terms of the proportion of correct responses in stop trials (cf.
Miyake et al., 2000).
Stroop. This task (Stroop, 1935) is commonly employed to assess
individual differences in inhibitory capacity. We used a version of the task
requiring manual responses (see also Ma ntyla et al., 2009). A series of 96word triples was presented on the computer screen. The central word of the
triple (stimulus word) was printed in colour (blue, green, yellow, red) at the
centre of the screen. In half of the trials the colour of the printed word was
congruent with the stimulus word (e.g., the word red was printed in red),
while in the other half it was incongruent (e.g., the word red was printed
in blue). The two adjacent words also referred to colour names (blue, green,
yellow, red) but were always printed in black. Participants were asked to
identify thecolour in which the central word was printed by pressing one of
two keys to respond. The first key was on the right side of the computer
keyboard and marked with a right arrow, while the second, on the left side
of the keyboard, was marked with a left arrow. Participants were instructed
to press the right arrow to indicate that the colour of the central word
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corresponded to the word presented in the right side of the screen, while
pressing the left arrow meant that the colour of the central word was
designated by the black word presented in the left side of the screen. We
asked participants to be both fast and accurate, and they underwent a
short series of training trials before starting the test. The difference between
mean RTs in incongruent and congruent trials was used as the dependent
variable.
RESULTS
As previously explained, in the first stage of data analysis we identified a
measurement model, which specifies the structure of the executive functions.
Then, in the second stage, the endorsed measurement model was kept fixedand we evaluated the models for the two decision tasks. All the models were
tested through the SEPATH program of the STATISTICA package
(version 8). We estimated the models through the maximum likelihood
technique, starting from the correlation matrix. Following previous
structural equation modelling studies, we evaluated the models through
multiple fit indices: the w2 statistic, Akaikes Information Criterion (AIC),
the standardised root mean-squared residual (SRMR), Bentlers Compara-
tive Fit Index (CFI), and the Adjusted Population Gamma Index (APGI).
Finally, we took into account standardised residuals and used the w2
difference test to compare the fit of nested models.4
4The w2 statistic is a common measure of badness of fit of a candidate model (compared to a
saturated model), and a small value of w2 corresponds to a small difference between the
correlation matrix generated by the candidate model and the observed matrix. A model with
acceptable fit is associated with a non-significant w2 at the conventional alpha level (i.e., p 4
.05), but more stringent alpha levels are preferred (i.e., p 4 .10 or greater). The SRMR index
also takes into account the difference between observed and predicted correlations, and valueslower than .08 indicate a good fit. AIC is a modified w2 statistic that takes into account also the
complexity of the model. Simpler models, with more degrees of freedom, are preferred and
associated with lower AIC values (which indicate better fit). CFI measures the fit of a candidate
model (compared to a baseline null model), and higher values of this index indicate better fit
(good fit when CFI 4 .90). APGI is an adjusted estimate of the population AGFI (Jo reskog &
So rbom, 1984) that would be obtained if we could analyse the population correlation matrix
(Steiger, 1989). Good fit is indicated by values above .95. The w2 difference test is used to
appraise the difference of fit between two nested models. The difference between the w2 statistics
of the two models (fuller vs nested) is evaluated in relation to the difference between their
degrees of freedom. If the difference w2 is significant (at the .05 alpha level), then the fuller model
has a significantly better fit than the nested one. AIC in STATISTICA 8 is computed with aformula that takes into account the maximum likelihood discrepancy function for a model
(Fml), the degrees of freedom for the model (v) and the sample size ( n): AICFml (2v/n 1).
This formula makes the AIC more stable across differing sample sizes.
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Data transformations and descriptive statistics
Descriptive statistics of untransformed variables are presented in Table 1.
After an arcsin transformation, commonly used for proportion correct
measures (see, e.g., Miyake et al., 2000), the executive functioning measures
(letter-memory, n-back, stop-signal) achieved normality. The distributions
of untransformed RT-based executive functioning measures (plusminus,
numberletter) did not differ from normality after the substitution of three
outlier values with the nearest non-outlier values (two cases for plusminus
and one case for numberletter, respectively). The decision-making
measures were arcsin transformed to achieve the normality of their
distributions. Reliability of measures was computed with the split-half
(oddeven) correlation adjusted by the Spearman-Brown prophecy formula(RT measures) or with Cronbachs alpha (proportion correct measures). It
was generally in line with previous studies (e.g., Bruine de Bruin et al., 2007;
Miyake et al., 2000).
As can be seen in Table 1 and Appendix B, descriptive statistics and zero-
order correlations for the executive functioning measures agree with
previous studies (e.g., Friedman et al., 2006, 2008; Miyake et al., 2000).
TABLE 1Descriptive statistics of untransformed executive tests and decision-making measures
Task N Mean Min Max SD Skew. Kurtosis Reliability
Executive functioning
Plusminus (s)a,b 116 18.46 75.25 59.59 13.28 1.06 1.10 .60
Numberletter (ms)a,b 116 649 122 1515 272 0.78 0.55 .84
Letter-memoryc 116 0.85 0.50 1.00 0.11 70.93 0.69 .40
n-backc 116 0.85 0.72 0.95 0.05 70.43 70.12 .73
Stop-signalc 116 0.64 0.00 1.00 0.26 70.71 70.13 .84
Stroop (ms)a 116 185 34 347 65 0.17 70.11 .78
Decision makingApplying Decision
Rulesc116 0.64 0.10 1.00 0.21 70.62 0.11 .70
Cons. in Risk
Perceptionc,d113 0.74 0.35 1.00 0.14 70.47 70.02 .73
aThese variables were reversed (higher scores indicate better performance), but we report the
descriptive statistics before their reversal in order to increase readability.bThree plusminus outlier values and one numberletter outlier value were substituted by the
nearest non-outlier values.cThese variables were arcsin-transformed, but we report the descriptive statistics before the
transformation in order to increase their readability.dThe number of valid cases for CRP did not reach 116 because some items of the subtest were
not completed by three participants. The entire subtest score for those participants was
discarded.
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Correlations are low and positive with some sign of specificity. Descriptive
statistics and the zero-order correlation for the A-DMC tasks are generally
in line with previous research (Bruine de Bruin et al., 2007).
Structure of executive functions: Measurement model
We started from a candidate three-component measurement model, in which
the three executive functions are clearly distinct but correlated. As already
explained, this model has been supported by previous studies. The candidate
model was compared with two reference models: (a) a unitary model
(assuming the unity of executive functions), and (b) a three-component
independence model (in which the three executive functions are indepen-
dent). For estimation, we fixed the correlations between executive functions atone in the unitary model and at zero in the independence model (see Miyake
et al., 2000). The results are summarised in Table 2.
The three-component correlated model showed a good fit on all the indices.
The fit of this model was fully acceptable: the w2 statistic was not significant at
the .20 level and the standardised root mean-squared residual index (SRMR)
was low. Bentlers Comparative Fit Index (CFI) and the Adjusted Population
Gamma Index (APGI) reached their respective thresholds. The three-
component independent model was unacceptable according to various
measures of fit (p5 .01, SRMR greater than .08, CFI much lower than.90). Moreover, the w2 difference test showed that the three-component
correlated model had a significantly better fit than the three-component
independent model (p5 .01). Finally, the one-component model achieved an
inferior evaluation on the majority of indices (only AIC and APGI are at the
same level), although if it was not inferior according to the w2 difference test.
To summarise, the candidate measurement model was supported by measures
of fit and by the comparison with two reference models.
TABLE 2
Fit indices for the measurement model (N116)
Model df w2 p SRMR AIC CFI APGI
Three-component correlated 6 8.51 .203 0.056 0.33 0.91 0.98
Three-component correlated (revised) 7 8.52 .289 0.055 0.32 0.95 0.99
Three-component independent 12 27.62 .006 0.112 0.40 0.46 0.93
One-component 9 14.43 .108 0.070 0.33 0.81 0.97
The endorsed model is indicated in bold. SRMR: standardised root mean-squared residual
(good fit if5.08); AIC: Akaikes Information Criterion (lower values indicate a better fit); CFI:Comparative Fit Index (good fit when 4.90); APGI: Adjusted Population Gamma Index (good
fit when 4.95). Non-significant w2 statistics (i.e., p 4 .05 or, better, p 4 .10) indicates
acceptable fit.
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The structural coefficients of executive functioning measures were all
significant (p5 .05), as well as the updating-inhibition interfactor correla-
tion. The shifting-updating correlation was instead marginally significant
(p .08). However, the correlation between shifting and inhibition was not
significant and very close to zero (7.04, p .89). This suggested that the
shifting-inhibition free parameter was not needed in the model. Thus, we re-
estimated the model fixing that parameter at zero. This simpler version of
the correlated model, with one additional degree of freedom, achieved an
equivalent level of fit than the original three-component correlated model
according to all the indices (see Table 2). The estimated coefficients of this
final model are presented in Figure 2, together with their standard errors. In
conclusion, we considered this revised three-component correlated model as
the best measurement model of executive functions for our data, andemployed it in the second stage of analysis.
Executive functions in decision-making tasks
In the second stage of data analysis, we tested our hypotheses about control
processes entailed in successful decision performance in Applying Decision
Rules and Consistency in Risk Perception through structural equation
models. In each of these models the measurement model was the three-
component correlated model identified in the first stage (i.e., all the
Figure 2.Three-component measurement model of the executive functioning data. Numbers on
arrows are standardised coefficients (all significant, at least at the p5 .05 level), those next to
the smaller arrows on the left are residual variances, and those on curved double-headed arrowsare inter-factor correlations. Standard errors are in parentheses, after the corresponding
coefficients. The updating-inhibition correlation is significant (p5 .05), while the updating-
shifting correlation is marginally significant (p .06).
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coefficients and terms of the model were fixed at the estimates reported in
Figure 2).
Applying Decision Rules (ADR)
The application of decision rules is assumed to involve working memory
functions. In particular, we hypothesised that a major role could be played
by the inhibition executive function, needed to ensure goal-oriented
processing through the inhibition of irrelevant (or no longer relevant)information. Alternatively, the mental operations required by this A-DMC
task could require the support of the updating executive function. The
results of structural equation modelling on Applying Decision Rules are
presented in Table 3.5
All the single-factor models (updating, inhibition, and shifting) achieved
a better fit than the no-path model (w2 difference tests: inhibition p5 .0001;
updating p5 .0001; shifting p5 .05). According to the w2 difference test,
however, the full-path model was significantly better than the shifting model
(p5
.001). The same test did not show a significantly better fit of the full-path model versus the inhibition model (p .29) or updating model
(p .35). In single-factor models, inhibition was the strongest predictor of
decision performance, as shown by standardised coefficients (see Table 3).
Moreover, only the inhibition coefficient was marginally significant (p .07)
in the full-path model, in which all the predictors (shifting, updating, and
inhibition) are considered.
Thus, although shifting and updating do appear to be related to ADR
performance in single-factor models, their influence is no more significant
when all the predictors are included in the model. Considering the whole
TABLE 3
Fit indices for Applying Decision Rules (N116)
Model df w2 p SRMR AIC
updating
coefficient
inhibition
coefficient
shifting
coefficient
Full-path 24 12.47 0.97 0.055 0.18 .07 (.31) .44^ (.31) .17 (.21)
Updating 26 14.55 0.96 0.059 0.16 .46*** (.09)
Inhibition 26 14.95 0.96 0.063 0.16 .54*** (.11)
Shifting 26 28.39 0.34 0.099 0.28 .32* (.14)
No-path 27 33.42 0.18 0.114 0.31
The endorsed model is indicated in bold. Estimated coefficients are followed by respective
standard errors (in parentheses). Significance levels of one-tailed tests are as follows: ^p5 .10;
*p5 .05; **p5 .01; ***p5 .001.
5CFI and APGI were not used in the following analyses because they did not contribute to
the discrimination of the best model.
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pattern of results, inhibition seems to have a prevailing role in the successful
application of decision rules, while shifting seems to be much less involved in
this task.
Consistency in Risk Perception (CRP)
This decision task requires maintaining consistency in probabilistic
judgements while switching between event descriptions and time frames.We hypothesised that participants with a higher shifting capacity should be
able to express more consistent judgements than participants less able in
shifting. The results of structural equation modelling are presented in Table 4.
The candidate model (shifting) was clearly better than the no-path model,
according to all the fit indices (including the w2 difference test: p5 .01).
Moreover, according to the w2 difference test, the shifting model showed a
better fit than the updating or inhibition models, which did not expose
significant improvements versus the no-path model (updating: p .18;
inhibition: p .55). The full-path model did not attain a significantly betterfit than the shifting model (non-significant w2 difference test: p .72).
However, the full-path model had a better fit than the updating or the
inhibition models (p5 .05 in both cases). Finally, only the shifting
coefficient was significant in the full-path model, in which all the predictors
were included. These results show that shifting plays an important role in
the expression of consistent risk judgements, while inhibition and updating
do not.
GENERAL DISCUSSIONIn this paper we have presented an individual differences study that
investigated the relationship between executive functions and two
TABLE 4
Fit indices for Consistency in Risk Perception (N113)
Model df w2 p SRMR AIC
updating
coefficient
inhibition
coefficient
shifting
coefficient
Full Path 24 16.39 0.87 0.065 0.22 7.27 (.33) .26 (.33) .53* (.22)
Updating 26 22.53 0.66 0.081 0.24 .15^ (.11)
Inhibition 26 23.99 0.58 0.087 0.25 .08 (.13)
Shifting 26 17.06 0.91 0.066 0.19 .38** (.13)
No-path 27 24.35 0.61 0.089 0.23
Note: The endorsed model is indicated in bold. Estimated coefficients are followed by respective
standard errors (in parentheses). Significance levels of one-tailed tests are as follows: p5 .10 ^;
p5 .05 *; p5 .01 **; p5 .001***.
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decision-making tasks (ADR and CRP), building on recent advances in the
measurement of decision-making skills and in the assessment of executive
functions. Our main aim was to provide new insights into the nature of
control processes relevant to decision-making competence, measured by two
cognitively demanding subtests of the A-DMC battery. Here we first trace
the general implication of the findings for the research on control processes
in decision making. Then we will illustrate how our results contribute to the
explanation of performance and errors in the specific decision tasks we
considered, and outline potential applied implications. Finally, the main
limitations of the present study will be discussed in relation to future
research directions.
In accordance with our expectations, significant relationships between
executive functions and the two target decision-making tasks were observed.However, going beyond previous work, the present study was able to show
more specific associations between executive functions and two cognitively
demanding decision-making tasks, which were selected as valid indicators of
analytic decision making. These results suggest that there is specificity in the
control requirements of different decision-making tasks. In particular,
shifting is mainly involved in the capacity to provide consistent judgements
on risky events, while inhibition appears to play a significant role in the
accurate implementation of decision rules. Thus our study indicates which
control processes are most operative in successful performance on twodifferent decision tasks, suggesting that some decision errors can be partially
traced back to the ineffectiveness of different types of control process. In
other words, our results qualify existing theoretical accounts of the
relationship between cognitive abilities and decision making by identifying
different sources of cognitive control limitations that mainly affect different
decision tasks (cf. Stanovich & West, 2000, 2008).
The shifting executive function was found to be related to the capacity to
express consistent judgements of risky events (CRP). This finding agrees
with the results of recent studies that showed a relation between shifting-related performance and metacognitive judgements requiring mental
flexibility (e.g., Ma ntyla et al., in press; Souchay & Isingrini, 2004).
Individuals with greater shifting ability, being more able to switch from one
context to another, may be more sensible to the need to harmonise
probabilistic judgements related to different time frames and situations, and
they may also be more able in accomplishing this task. Thus participants
with better shifting skills may have the appropriate mindset and cognitive
resources for deploying more consistent risk assessment strategies, involving
more systematic evaluation procedures. Inhibition was instead significantly
associated with the accurate application of decision rules. This result can be
explained in terms of the functional support of inhibition to goal-directed
processing. In most goal-directed tasks, inhibition plays an important role in
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keeping task-relevant information active by suppressing interfering or
irrelevant information (Carretti, Cornoldi, De Beni, & Romano , 2005;
Friedman et al., 2008). Thus inhibition may support the goal-directed and
selective information processing required by the successful application of
multi-attribute choice procedures.
The results of the present study suggest interesting applied implica-
tions. First, if different executive functions are mainly required for the
successful accomplishment of some decision-making task, training these
functions may improve some aspects of decision-making performance.
Thus it could be useful to examine the effects of training and
rehabilitation of executive functions (e.g. Dahlin, Stigsdotter Neely,
Larsson, Ba ckman, & Nyberg, 2008; Olesen, Westerberg, & Klingberg,
2003) on decision making. Beneficial side-effects could be especiallyvaluable in vulnerable segments of the population, such as older adults
and individuals with executive/frontal problems. Another practical
implication of our findings is that some decision tasks can be challenging
for individuals with limited executive control capacity. A variety of
decisions about health, finance, and everyday living require the
application of rather complex choice strategies and the expression of
consistent probabilistic judgements (e.g., Finucane & Lees, 2005;
Finucane, Mertz, Slovic, & Schmidt, 2005; Finucane et al., 2002; Parker
& Fishhoff, 2005). To facilitate the decision making, these tasks shouldbe presented in a format that simplifies information processing and
minimises demands on specific executive functions. Decision makers can
be helped through an appropriate design of information display (e.g.,
Bettman, 1975; Bettman, Payne, & Staelin, 1986) or through the use of
decision aids that can mechanise part of the task (e.g., Edwards &
Fasolo, 2001). However, information design and decision-aiding measures
should not be aimed only at a general reduction of cognitive load, but
should also be directed at counteracting specific executive difficulties.
Limitations and future work
Four limitations of the present study need to be acknowledged and
discussed. The first limitation is the correlational nature of the research,
which might allow alternative interpretation of our findings (i.e., the
relationships we highlighted might be spurious). Although alternative
interpretations of correlational researches are possible, we think that the
selectivity of the relationships observed in our study strongly speaks against
the existence of spurious associations stemming from the influence a
common cognitive factor. Thus there are reasons to think that our findings
reflect genuine relationships between executive functions and decision
performance. Future research might examine whether individual differences
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in executive functioning really provide a predictive contribution to
decision-making performance that goes beyond the contribution of
individual differences in more general cognitive measures (such as fluid
intelligence and numeracy; e.g., Bruine de Bruin et al., 2009; Cokely &
Kelley, 2009; Mata, Schooler, & Rieskamp, 2007). This line of research
might offer very interesting insights both for decision-making scholars and
for researchers interested in executive functioning and intelligence.
Preliminary findings obtained in our lab seem to support the idea that
individual differences in executive functioning can provide a specific
predictive contribution, at least for some decision tasks (Del Missier,
Ma ntyla & Bruine de Bruin, 2009).
A second limitation of the research is represented by the use of a
relatively small sample of undergraduate participants, which might boundthe external validity of our findings. However, this potential concern is
attenuated by the observation that our descriptive A-DMC results
generally agree with findings obtained in previous studies with diverse
populations (Bruine de Bruin et al., 2007, 2009; Parker & Fischhoff,
2005). If anything, we expect that the relationships we identified in a
rather homogeneous sample of young educated participants can emerge
more strongly in heterogeneous samples, where individual differences in
executive functioning are certainly more pronounced. From this point of
view, the use of a sample of undergraduates assured a particularlystringent test of our hypotheses. In any case we think that further
research on more heterogeneous samples will have the merit of increasing
the external validity of the present findings.
A related limitation concerns the stability of structural equation models,
possibly due to the relatively small sample size and the adoption of two
indicators for each latent construct. Even though we selected executive
functioning tasks that previous studies considered as appropriate indicators
of the respective latent constructs (see the Materials section), increasing the
number of executive measures in future studies should help to examine thegenerality of our findings.
A final limitation of the present study concerns our set of measures.
Even though we focused on three executive functions that have been
frequently postulated and investigated in the literature (e.g., Friedman
et al., 2006, 2007, 2008; Garon et al., 2008; Ma ntyla et al., 2007, 2009, in
press; Miyake et al., 2000; Nigg, 2000; Rabbitt, 1997; Shimamura, 2000;
Smith & Jonides, 1999), we do not imply that these are the sole functions
relevant to decision-making competence. Moreover, studies adopting a
lower-level decomposition of executive functioning can possibly be carried
out with success. Additionally, given that the conceptualisation of
executive functioning constructs is still being debated, some of these
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functions can be conceived in different ways (for inhibition see, e.g.,
Friedman et al., 2008; Friedman & Miyake, 2004; Nigg, 2000). Taking all
these aspects into consideration, we deem the present work as a first step
in the exploration of the relationship between executive functioning and
decision making. Further steps could investigate different aspects of
cognitive control or the adoption of different potential theoretical
decompositions/conceptions of executive functions. As noted earlier, the
two target A-DMC tasks used in the present study have been selected for
theoretical and methodological reasons, and they measure two significant
aspects of analytical decision-making competence. However, other
judgement and decision-making tasks need to be considered by future
research and this will hopefully advance our understanding of the role of
control processes in decision making.To conclude, the present study tried to relate executive functions and
decision making, two important research areas in the realm of higher-
order cognition. While these two areas are usually considered to be
tightly connected, the relationships between executive control and
decision-making processes are rarely articulated in sufficient detail and
they are usually not supported by specific empirical evidence. We hope
that the present study helps to bridge this gap and stimulates further
research in the field.
Manuscript received 30 June 2009Revised manuscript received 22 December 2009
First published online 23 March 2010
REFERENCES
Baddeley, A. (1996). Exploring the central executive. Quarterly Journal of Experimental
Psychology A, 49, 528.
Baron, J. (2008). Thinking and deciding (4th ed.). New York: Cambridge University Press.
Bettman, J. R. (1975). Issues in designing consumer information environments. Journal ofConsumer Research, 2, 169177.
Bettman, J. R., Luce, M. F., & Payne, J. W. (1998), Constructive consumer choice processes.
Journal of Consumer Research, 25, 187217.
Bettman, J. R., Payne, J. W., & Staelin, R. (1986), Cognitive considerations in designing
effective labels for presenting risk information.Journal of Marketing and Public Policy, 5, 1
28.
Bro der, A. (2003). Decision making with the adaptive tool box: Influence of environmental
structure, intelligence, and working memory load. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 29, 611625.
Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2007). Individual differences in adult
decision making competence. Journal of Personality and Social Psychology, 92, 938956.Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2009). Adult age differences in decision-
making competence. Manuscript submitted for publication.
EXECUTIVE FUNCTIONS IN DECISION MAKING 91
Downl
oad
ed
By:[
Del
Mi
ssi
er,
Fabi
o]
At:13
:2718
May2010
-
8/13/2019 Del Missier, F., Mntyl, T. y Bruine de Bruin, W. (2010). Executive fuctions in decision making an individual differences approach.
24/29
Carelli, M. G., Forman, H., & Ma ntyla , T. (2008). Time monitoring and executive functioning
in children and adults. Child Neuropsychology, 14, 372386.
Carretti, B., Cornoldi, C., De Beni, R., & Romano , M. (2005). Updating in working memory: A
comparison of poor and good comprehenders. Journal of Experimental Child Psychology,91, 4566.
Clark, L., Cools, R., & Robbins, T. W. (2004). The neuropsychology of ventral prefrontal
cortex: Decision-making and reversal learning. Brain and Cognition, 55, 4153.
Cokely, E. T., & Kelley, C. M. (2009). Cognitive abilities and superior decision making under
risk: A protocol analysis and process model evaluation. Judgement and Decision Making, 4,
2033.
Dahlin, E., Stigsdotter Neely, A., Larsson, L., Ba ckman, L., & Nyberg, L. (2008). Transfer of
learning after updating training mediated by the striatum. Science, 320, 15101512.
De Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J., (2006). Frames, biases, and
rational decision-making in the human brain. Science, 313, 684687.
De Neys, W., & Glumicic, T. (2008). Conflict monitoring in dual process theories of thinking.Cognition, 106, 12481299.
Del Missier, F., Ma ntyla , T., & Bruine de Bruin, W. (2009). Executive control and decision
competence in young adults. Paper presented at the 22nd SPUDM conference, Rovereto,
Italy.
Denckla, M. (1996). A theory and model of executive function: A neuropsychological
perspective. In G. Lyon & N. Krasnegor (Eds.), Attention, memory and executive function
(pp. 263278). Baltimore, MD: Paul Brookes.
Duncan, J. (1986). Disorganization of behavior after frontal lobe damage. Cognitive
Neuropsychology, 3, 271290.
Edwards, W., & Fasolo, B. (2001). Decision technology.Annual Review of Psychology, 52, 581
606.Epstein, S., & Pacini, R. (1999). Some basic issues regarding dual-process theories from the
perspective of cognitive-experiential theory. In S. Chaiken & Y. Trope (Eds.), Dual-process
theories in social psychology (pp. 462482). New York: Guilford Press.
Eslinger, P. J., & Damasio, A. R. (1985). Severe impairment of higher cognition after bilateral
ablation: Patient EVR. Neurology, 35, 17311741.
Evans, J. St. B. T. (2003). In two minds: Dual process accounts of reasoning. Trends in
Cognitive Sciences, 7, 454459.
Evans, J. St. B. T. (2007). Hypothetical thinking: Dual processes in reasoning and judgement.
Hove, UK: Psychology Press.
Evans, J. St. B. T. (2008). Dual-processing accounts of reasoning, judgement and social
cognition. Annual Review of Psychology, 59, 255278.
Evans, J. St. B. T., & Over, D. E. (1996). Rationality and reasoning. Hove, UK: Psychology
Press.
Finucane, M. L., & Lees, N. B. (2005). Decision-making competence of older adults: Models and
methods. Report for the National Research Council Workshop on Decision Making by
Older Adults, Washington, DC.
Finucane, M. L., Mertz, C. K., Slovic, P. E, & Schmidt, E. S. (2005). Task complexity and older
adults decision-making competence. Psychology & Aging, 20, 7184.
Finucane, M. L., Slovic, P., Hibbard, J. H., Peters, E., Mertz, D. K., & McGregor, D. G.
(2002). Aging and decision-making competence: An analysis of comprehension and
consistency skills in older versus younger adults. Journal of Behavioural Decision Making,
15, 141164.
Friedman, N. P., Haberstick, B. C., Willcutt, E. G., Miyake, A., Young, S. E., Corley, R. P.,
et al. (2007). Greater attention problems during childhood predict poorer executive
functioning in late adolescence. Psychological Science, 18, 893900.
92 DEL MISSIER, MANTYLA, BRUINE DE BRUIN
Downl
oad
ed
By:[
Del
Mi
ssi
er,
Fabi
o]
At:13
:2718
May2010
-
8/13/2019 Del Missier, F., Mntyl, T. y Bruine de Bruin, W. (2010). Executive fuctions in decision making an individual differences approach.
25/29
Friedman, N. P., & Miyake, A. (2004). The relations among inhibition and interference control
functions: A latent-variable analysis. Journal of Experimental Psychology: General, 133,
101135.
Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., DeFries, J. C., & Hewitt, J. K.(2006). Not all executive functions are related to intelligence. Psychological Science, 17, 172
179.
Friedman, N. P., Miyake, A., Young, S. E., Defries, J. C., Corley, R. P., & Hewitt, J. K. (2008).
Individual differences in executive functions are almost entirely genetic in origin. Journal of
Experimental Psychology: General, 137, 201225.
Garon, N., Bryson, S. E., & Smith, I. M. (2008). Executive functioning in preschoolers: A
review using an integrative framework. Psychological Bulletin, 134, 3160.
Giancola, P. R., Martin, C. S., Tarter, R. E., Pelham, W. E., & Moss, H. B. (1996). Executive
cognitive functioning and aggressive behavior in preadolescent boys at high risk for
substance abuse/dependence. Journal of Studies on Alcohol, 57, 352359.
Gigerenzer, G., & Regier, T. (1996). How do we tell an association from a rule? Comment onSloman (1996). Psychological Bulletin, 119, 2326.
Goel, V. (1995). Sketches of thought. Cambridge, MA: MIT Press.
Hair, J. F. Jr., Black, W. C., Babin, B., & Anderson, R. (2009). Multivariate data analysis(7th
ed.). Upper Saddle River, NJ: Prentice Hall.
Hastie, R., & Dawes, M. D. (2001). Rational choice in an uncertain world. Thousand Oaks, CA:
Sage.
Hinson, J. M., Jameson, T. L., & Whitney, P. (2002). Somatic markers, working memory, and
decision making, Cognitive, Affective, & Behavioral Neuroscience, 2, 341353.
Hinson, J. M., Jameson, T. L., & Whitney, P. (2003). Impulsive decision making and working
memory. Journal of Experimental Psychology: Learning, Memory and Cognition, 29, 298
306.Jersild, A. T. (1927). Mental set and shift. Archives of Psychology, Whole No. 89.
Jo reskog, K. G., & So rbom, D. (1984). Lisrel VI. Analysis of linear structural relationships by
maximum likelihood, instrumental variables, and least squares methods. Mooresville, IN:
Scientific Software.
Kahneman, D. (2003). A perspective on judgement and choice: Mapping bounded rationality.
American Psychologist, 58, 697720.
Kahneman, D., & Frederick, S. (2005). A model of heuristic judgement. In K. Holyoak & R. G.
Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 267294).
Cambridge, UK: Cambridge University Press.
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgement under uncertainty: Heuristics and
biases.New York: Cambridge University Press.
Keren, G., & Schul, Y. (2009). Two is not always better than one: A critical evaluation of two-
system theories. Perspectives on Psychological Science, 4, 533550.
Kline, R. B. (1998).Principles and practice of structural equation modelling. New York: Guilford
Press.
Larrick, R. P., Nisbett, R. E., & Morgan, J. N. (1993). Who uses the costbenefit rules of
choice? Implications for the normative status of microeconomic theory. Organizational
Behavior and Human Decision Processes, 56, 331347.
Logan, G. D. (1994). On the ability to inhibit thought and action: A users guide to the stop
signal paradigm. In D. Dagenbach & T. H. Carr (Eds.), Inhibitory processes in attention,
memory, and language (pp. 189239). San Diego, CA: Academic Press.
Lopes, L. L. (1987). Between hope and fear: the psychology of risk. Advances in Experimental
Social Psychology, 20, 255295.
Mandel, D. R. (2005). Are risk assessments of a terrorist attack coherent? Journal of
Experimental Psychology: Applied, 11, 277288.
EXECUTIVE FUNCTIONS IN DECISION MAKING 93
Downl
oad
ed
By:[
Del
Mi
ssi
er,
Fabi
o]
At:13
:2718
May2010
-
8/13/2019 Del Missier, F., Mntyl, T. y Bruine de Bruin, W. (2010). Executive fuctions in decision making an individual differences approach.
26/29
Manes, F., Sahakian, B., Clark, L., Rogers, R., Antoun, N., Aitken, M., et al. (2002).
Decision-making processes following damage to the prefrontal cortex. Brain, 125, 624
639.
Ma ntyla , T., Carelli, M. G., & Forman, H. (2007). Time monitoring and executive functioningin children and adults. Journal of Experimental Child Psychology, 96, 119.
Ma ntyla , T., Karlsson, M., & Marklund, M. (2009). Executive control functions in simulated
driving. Applied Neuropsychology, 16, 1118.
Ma ntyla , T., Kliegel, M, & Ro nnlund, M. (in press). Components of executive functioning in
metamemory.Applied Neuropsychology.
Marklund, M. (2008).An evaluation of the Swedish Adult Decision-Making Competence battery.
Technical report, University of Umea , Sweden.
Mata, R., Schooler, L. J., & Rieskamp, J. (2007). The aging decision maker: Cognitive aging
and the adaptive selection of decision strategies. Psychology and Aging, 22, 796810.
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D.
(2000). The unity and diversity of executive functions and their contributions to complexfrontal lobe tasks: A latent variable analysis. Cognitive Psychology, 41, 49100.
Morris, N., & Jones, D. M. (1990). Memory updating in working memory: The role of the
central executive. British Journal of Psychology, 81, 111121.
Nigg, J. T. (2000). On inhibition/disinhibition in developmental psychopathology: Views from
cognitive and personality psychology and a working inhibition taxonomy. Psychological
Bulletin, 126, 220246.
Olesen, P., Westerberg, H., & Klingberg, T. (2004) Increased prefrontal and parietal brain
activity after training of working memory. Nature Neuroscience, 7, 7579.
Osman, M. (2004). An evaluation of dual-process theories of reasoning.Psychonomic Bulletin &
Review, 11, 9881010.
Owen, A. M., McMillan, K. M., Laird, A. R., & Bullmore, E. (2005). N-Back working memoryparadigm: A meta-analysis of normative functional neuroimaging studies. Human Brain
Mapping, 25, 4659.
Parker, A. M., & Fischhoff, B. (2005). Decision-making competence: External validation
through an individual-differences approach. Journal of Behavioral Decision Making, 18, 1
27.
Payne, J. W., & Bettman, J. R. (2004). Walking with the scarecrow: The information-processing
approach to decision research. In D. J. Koehler & N. Harvey (Eds.), Blackwell handbook of
judgement and decision making(pp. 110132). Oxford, UK: Blackwell.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. New York:
Cambridge University Press.
Peters, E., Hess, T. M., Va stfja ll, D., & Auman, C. (2007). Adult age differences in dual
information processes. Perspectives on Psychological Science, 2, 123.
Rabbitt, P. (1997).Methodology of frontal and executive function. Hove, UK: Psychology Press.
Rende, B., Ramsberger, G., & Miyake, A. (2000). Commonalities and differences in the
working memory components underlying letter and category fluency tasks: A dual-task
investigation.Neuropsychology, 16, 309321.
Reyna, V. F. (2004). How people make decisions that involve risk. A dual-processes approach.
Current Directions in Psychological Science, 13, 6066.
Rogers, R. D., & Monsell, S. (1995). Costs of a predictable switch between simple cognitive
tasks. Journal of Experimental Psychology: General, 124, 207231.
Royall, D. R., Lauterbach, E. C., Cummings, J. L., Reeve, A., Rummans, T. A., Kaufer, D. I.,
et al. (2002). Executive control function: A review of its promise and challenges for clinical
research. Journal of Neuropsychiatry and Clinical Neuroscience, 14, 377405.
Salthouse, T. A. (2005). Relations between cognitive abilities and measures of executive
functioning. Neuropsychology, 19, 532545.
94 DEL MISSIER, MANTYLA, BRUINE DE BRUIN
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oad
ed
By:[
Del
Mi
ssi
er,
Fabi
o]
At:13
:2718
May2010
-
8/13/2019 Del Missier, F., Mntyl, T. y Bruine de Bruin, W. (2010). Executive fuctions in decision making an individual differences approach.