A multiperspective approach to the conceptualization

JOURNAL OF CLINICAL AND EXPERIMENTAL NEUROPSYCHOLOGY 2011, 33 (4), 456–470

A multiperspective approach to the conceptualization of executive functions Sonia Packwood1 , Helen M. Hodgetts1,2 , and Sébastien Tremblay1 1

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2

School of Psychology, Université Laval, Quebec, QC, Canada School of Psychology, Cardiff University, Cardiff, UK

The concept of executive function (EF) is deemed unclear and difficult to operationalize. We use a multiperspective approach to quantify and reduce the current proliferation of EFs. A literature review of 60 studies identified 68 subcomponents of EF: Through objective statistical techniques, these terms were reduced to 18 by removing semantic overlap (using latent semantic analysis) and psychometric overlap (using hierarchical cluster analysis). However, still such a large number of functions lacks parsimony. We therefore revisit the concept of EF and suggest that the many proposed subcomponents are not functions per se but rather a number of task-specific behaviors. Keywords: Executive function; Latent semantic analysis; Hierarchical cluster analysis; Neuropsychological assessment; Cognitive processes.

Executive functions (EFs) are generally recognized as cognitive control mechanisms that direct and coordinate human behavior in an adaptive way when no preestablished schema of action is available (e.g., Lezak, 1995; Shallice, 1988). Despite the general acceptance that such high-level functions play a role in cognition, there is no consensus as to what these functions are, how they might be organized, or which specific test should be used in the assessment of each one. Abundant research, together with the wide use of EF concepts and executive tests in clinical neuropsychology, has contributed to an extensive list of EFs including such functions as goal formation, planning, set shifting, verbal fluency, and inhibition. Indeed the lack of a formal definition of EFs may have led to some overlap and redundancy in the number of terms used, but even attempts to gain a more coherent structure through factorial analysis have failed to find any consistency across studies in the type or number of functions involved (e.g., Fisk & Sharp, 2004; Huizinga, Dolan, & van der Molen, 2006; Miyake et al., 2000).

The diversity of taxonomies and general absence of consensus have led to a proliferation of EFs. Since science is guided by the principle of parsimony—whereby only the minimum of elementary causes is used to explain a phenomenon—it would seem reasonable to seek greater unity within the field (Banich, 2009; Uttal, 2001). The current paper uses a multiperspective approach to first quantify the extent of the proliferation and then to estimate the extent to which EF subcomponents overlap both conceptually and psychometrically. This meta-analysis will provide an objective portrayal of the current state of affairs with regard to EF and aims towards a clearer understanding of the concept. Some of the earliest models to incorporate the idea of a higher order management system proposed a unitary mechanism responsible for all processes involving attentional control (Baddeley & Hitch, 1974; Grafman, 1989; Norman & Shallice, 1986; Pribram, 1960). However, this view of a single executive entity—often referred to as the homunculus—has been criticized for lacking

Helen M. Hodgetts is an honorary research fellow at School of Psychology, Cardiff University. This research was supported by an operating grant to Sébastien Tremblay and a graduate scholarship to Sonia Packwood from the Natural Sciences and Engineering Research Council of Canada (NSERC). Part of this work was presented at the International Congress of Psychology, Berlin, Germany (July, 2008). We would like to thank Daniel Lafond and Jean-François Gagnon for their significant help and suggestions with regard to the ideas proposed in this paper. We would also like to thank Cindy Chamberland for comments on an earlier draft and to Marie-Josée Côte for assistance with the analysis. Address correspondence to Sonia Packwood or Sébastien Tremblay, École de Psychologie, Université Laval, Québec, G1V 0A6, Canada (E-mail: [email protected] or [email protected]). © 2011 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business http://www.psypress.com/jcen DOI: 10.1080/13803395.2010.533157

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CONCEPTUALIZATION OF EXECUTIVE FUNCTIONS

specificity, leading subsequent research efforts to focus on decomposing the proposed “black box” into more informative subcomponents (see Baddeley, 1996; Shallice, 2002). Much of this work has relied upon patients with frontal lobe damage, for whom the higher level control abilities associated with EF—broadly the coordination, execution, and regulation of behaviors—are noticeably impaired. That patients deficient in these everyday functions tend to have damage to the same area indicates a common mechanism, but the pattern of performance deficits between patients and across tasks is not uniform and instead seems to support the existence of multiple dissociable executive skills. Despite copious research, the complex nature of neuropsychological impairment means that it is difficult to determine precisely how many fundamentally distinct executive abilities there are (see Andrés, 2003). Different models have suggested that the functions of the frontal lobe can be divided into three (Fuster, 1980), four (Baddeley, 1996; Luria, 1973), or five subcomponents (Shallice & Burgess, 1991; Stuss & Benson, 1986), and more recent factor analyses have produced similarly inconsistent results with models proposing up to six EFs (e.g., Fisk & Sharp, 2004; Floyd, Bergeron, & Hamilton, 2004; Huizinga et al., 2006). Disagreement about the taxonomies of EF extends beyond just the number, to also the critical roles proposed for each; although there are some commonalities, it seems that there is no one subcomponent that is shared by all models (see Fournier-Vicente, Larigauderie, & Gaonac’h, 2008; Hull, Martin, Beier, Lane, & Hamilton, 2008; Miyake et al., 2000). Jurado and Rosselli (2007) reported a review of 11 papers published between 1974 and 2004 that identifies more than 30 executive subcomponents. In sum, the current fractionation of the central executive is unclear and perhaps of little more help than the original black box itself (see Banich, 2009; Logan, 2003). As well as inconsistency regarding the core structure of the central executive, the numerous terms used to describe often seemingly similar functions further obfuscate the concept of EF. In a large number of studies over the last two decades or so, factorial analysis has been a privileged tool in the attempt to gain a more coherent structure of EF; however, due to different researchers’ opinions on the processes underlying performance on different tasks, the results of such studies may even compound the problem of proliferation by providing labels for factors that vary from one author to another (Séguin & Zelazo, 2005). For example, it is difficult to see how the factor of “visual processing” in one factor analysis (Floyd et al., 2004) could be considered conceptually distinct from that of “visuospatial storage-and-processing coordination” in another (Fournier-Vicente et al., 2008). Until researchers become more uniform in their terminology, it will be difficult to compare between studies and to identify core, separable underlying functions. The problem of proliferation is further exacerbated by the variety of tasks available to measure different facets of EF. With no formal definitions, clinicians tend to use their own labels to express the functions that a neuropsychological task measures (Royall et al., 2002); furthermore, with the development of various new tasks

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there follows an increasing number of terms to describe the potential processes tapped by each. Generally, any test sensitive to frontal lobe damage is deemed to assess “executive” processes, and some of those more commonly used include the Tower of Hanoi (TOH) or Tower of London (TOL), verbal fluency, Stroop, and the Wisconsin Card Sorting task (WCST). A problem of task impurity means that each task may assess a number of different executive subcomponents and/or non-EF cognitive processes as well; as such, it can be very difficult to know how impairment on a particular task should be interpreted. For example, verbal fluency can be described as a measure of fluency capacities (Baldo, Shimamura, Delis, Kramer, & Kaplan, 2001), memory retrieval (Rosen & Engle, 1997), set shifting (Troyer, Moscovitch, & Winocur, 1997), or inhibition (Brosnan et al., 2002). Similarly, the Stroop task is mostly considered a measure of inhibitory function (Miyake et al., 2000) but is also used to evaluate working memory (Kane & Engle, 2003), cognitive flexibility (Zalonis et al., 2009), impulse control (Peterson et al., 1999), selective attention (Melcher & Gruber, 2006), concentration (Van Diest, Stegen, Van de Woestijne, Schippers, & Van den Bergh, 2000), and so on. Undoubtedly the range of tasks available, as well as the lack of specificity regarding what each task does and does not measure, has contributed to the multiplicity of terms. The ambiguity surrounding the concept of EF is a problem for clinical diagnosis such that the more taxonomies we have, the less clear the executive profile is for each given disorder. For example, while theories suggest that EFs are at the heart of the difficulties associated with attention-deficit/hyperactivity disorder (ADHD), five recent studies show inconsistent executive profiles especially with regard to inhibition, verbal fluency, and planning deficits (e.g., Boonstra, Oosterlaan, Sergeant, & Buitelaar, 2005; Geurts, Verté, Oosterlaan, Roeyers, & Sergeant, 2005; Marzocchi et al., 2008; Pasini, Paloscia, Alessandrelli, Porfirio, & Curatolo, 2007; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Given the plethora of executive tasks, it is perhaps not surprising that a patient’s performance can differ between various tasks that purport to measure the same thing and also between patients who have similar diagnoses (Andrés, 2003). Greater unity in the field of EFs will be critical to understanding the underlying basis of neuropsychological impairment. Neuropsychologists should be able to discriminate between each EF (deficits of planning, inhibition, fluency, etc) in order to provide accurate diagnosis and treatment. A major consequence of the wide variety of subcomponents of EF is that the profile of neuropathology is hard to determine with certainty, and so diagnoses and treatment may lack specificity and uniformity between clinicians. The current paper aims to quantify the extent of the proliferation that we are faced with and to seek some coherence within the literature with regard to definitions and the tasks used to measure each EF. Our multiperspective approach uses three phases: a targeted review of the literature, latent semantic analysis (LSA), and hierarchical cluster analysis (HCA). The literature review,

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for which we select a large number of well-cited articles, will reveal an estimate of the number of tasks and the number of different terms used in the field. LSA is a novel application of an approach borrowed from social sciences and the study of language: Given that there may be overlap and redundancy with regard to some terms obtained in the review, it provides a first filter to guard against an overestimation by combining semantically similar items. HCA will then be used to reduce the number of EFs still further: Those individual subcomponents that are measured using the same types of tasks will be grouped together, since these are likely to involve the same mechanisms and thus represent the same underlying function. HCA is a well-established method of data exploration used in various fields such as social sciences, archeology, and biology to cluster together common themes, but, to our knowledge, it has never before been applied to the concept of EF. Our novel three-phase methodology will provide an objective estimate of the multiplicity of terms and employs specific techniques to identify commonalities between studies and thus reduce the number of terms used.

GENERAL METHOD Literature review The purpose of this systematic review was to synthesize prior research regarding the number of EFs in current use, as well as the executive tasks frequently used in their assessment. To this end, we performed a search on the Web of Science database, one of the largest available databases, for published papers from 1970 up until 2007 with the following terms: “executive function,” “executive functioning,” or “frontal function.” This returned 1,443 articles, but in order to ensure high quality we selected 60 highly cited articles for this targeted review. Studies were identified that focused on the assessment of at least one EF with specific executive tasks, and which had been frequently cited given the time since publication (range of citations was between 10 and 1,235). Of course older papers had a greater opportunity for more citations to accumulate, but we considered a minimum of 10 citations to be an appropriate threshold so that recently published articles were not biased against. The 60 papers selected involved a broad range of studies, including those using children, adults, and elderly people, and could be considered representative of the literature on how EFs are generally conceptualized and measured. Each different EF mentioned in these articles was recorded, as well as the task(s) used to measure them (see Table 1).

Latent semantic analysis LSA was used as a first method to estimate the strength of the semantic link between the different definitions of EFs identified in the review. LSA is a mathematical/statistical technique for extracting and representing words and passages similar in meaning by analyzing large bodies

of text (Landauer & Dumais, 1997; Landauer, Foltz, & Laham, 1998). It takes into account the redundancy between definitions associated with each subcomponent and enables the grouping of those that are similarly defined. The computer program uses singular value decomposition, a general form of factor analysis, to condense a very large matrix of word-by-context data according to 300–500 dimensions. These dimensions represent how often a word occurs within a document (defined at the sentence level, the paragraph level, or in larger sections of texts), and each word, sentence, or text becomes a weighted vector. LSA takes into account the tracking of words that are semantically similar, but may not be related morphologically—for example, the word mouse has a higher LSA score when compared to cat than when compared to either dog or house. Those items that are strongly connected are grouped together to avoid an overestimation of the proliferation, thus leaving only those terms that are considered conceptually distinct. The similarity between the resulting vectors for words and contexts, as measured by the cosine of their contained angle, has shown to closely mimic human judgments of meaning similarity. For example, after practicing with about 2,000 pages of English text, the program scored as well as the average test-takers of the synonym portion of the Educational Testing Service’s Test of English as a Foreign Language (TOEFL; Landauer & Dumais, 1997), and after training on an introductory psychology textbook it achieved a passing score on a multiple-choice exam (Landauer et al., 1998). In order to be entered into the program, each term needed a definition. Given that a number of terms were not specifically defined by the authors, the missing definitions were replaced by those offered by the online Webster dictionary (Parker, 2009), recognized as one of the widest dictionaries of modern language usage. This corresponds to the equivalent of 500 encyclopedias and was thus chosen for its impressive bank of available terminologies. Examples of definitions provided by the dictionary include perseveration as the inability to switch; creativity as constructing a novelty without constituent components; abstraction as the process of formulating general concepts by abstracting common properties of instances; and problem-solving as a learning situation involving more than one alternative from which a selection is made. A matrix comparison was used to compare the similarity of multiple definitions within a particular LSA space, where the LSA space is defined as “a semantic space representing a mathematical representation of a large body of text” (Landauer et al., 1998). This space contained the text from three college level psychology textbooks with each paragraph used as a document, totaling 13,902 documents and 30,119 unique terms. Each definition was compared to all other definitions. The LSA system computed a similarity score between –1 and 1 for each submitted definition compared to all submitted definitions. Identical passages in meaning were given cosines of 1, unrelated ones, 0, and those of opposite meaning, –1. Definitions of subcomponents of EF that were strongly semantically connected (i.e., cosine ≥ .5) were grouped together.

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TABLE 1 References of the literature review Authors

Year

Authors

Year

Ahola, K., Vilkki, J., & Se rvo, A.

1996

1999

Anderson, P.

2002

Kempton, S., Vance, A., Maruff, P., Luk, E., Costin, J., & Pantelis, C.

Anderson, V.

1998

Kerr, A., & Zelazo, P. D.

2004

Anderson, V., Anderson, P., Northam, E., Jacobs, R., & Catroppa, C.

2001

Klenberg, L., Korkman, M., & Lahti-Nuuttila, P.

2001 2004

Austin, M. P., Mitchell, P., Wilhelm, K., Parker, G., Hickie, I., Brodaty, H., et al.

1999

Klimkeit, E. I., Mattingley, J. B., Sheppard, D. M., Farrow, M., & Bradshaw, J. L. Lafleche, G., & Albert, M. S.

1995

Baddeley, A., DellaSala, S., Papagno, C., & Spinnler, H.

1997

Lehto, J.

1996

Barnett, R., Maruff, P., Vance, A., Luk, E. S. L., Costin, J., Wood, C., & Pantelis, C.

2001

Lehto, J. E., Juujärvi, P., Kooistra, L., & Pulkkinen, L.

2003

Belleville, S., Rouleau, N., & Van der Linden, M.

2006

1991

Brocki, K. C., & Bohlin, G.

2004

Levin, H. S., Culhane, K. A., Hartmann, J., Evankovich, K., Mattson, A. J., Harward, H., et al.

Brosnan, M., Demetre, J., Hamill, S., Robson, K., Shepherd, H., & Cody, G.

2002

Lopez, B. R., Lincoln, A. J., Ozonoff, S., & Lai, Z.

2005

Bryan, J. & Luszcz, M. A.

2000

Lovejoy, D. W., Ball, J. D., Keats, M., Stutts, M. L., Spain, E. H., Janda, L., & Janusz, J.

1999

Burgess, P. W., Alderman, N., Evans, J., Emslie, H., & Wilson, B. A.

1998

McPherson, S., Fairbanks, L., Tiken, S., Cummings, J. L., & Back-Madruga, C.

2002

Burgess, P.W., & Shallice, T.

1996 2005

Mattson, S. N., Goodman, A. M., Caine, C., Delis, D. C., & Riley, E. P.

1999

Busch, R. M., McBride, A., Curtiss, G., & Vanderploeg, R. D.

1999

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D.

2000

Channon, S., & Green, P. S. S. Coppin, A. K., Shumway-Cook, A., Saczynski, J. S., Patel, K. V., Ble, A., Ferrucci, L., & Guralnik, J. M.

2006

Miyake, A., Friedman, N. P., Rettinger, D. A., Shah, P., & Hegarty, P.

2001

De Luca, C. R., Wood, S. J., Anderson, V., Buchanan, J. A., Proffitt, T. M., Mahony, K., & Pantelis, C.

2003

Moritz, S., Birkner, C., Kloss, M., Jahn, H., Hand, I., Haasen, C., & Krausz, M.

2002

Denney, D. R., Sworowski, L. A., & Lynch, S. G.

2005

Doyle, A. E., Wilens, T. E., Kwon, A., Seidman, L. J., Faraone, S. V., Fried, R., et al.

2005

Duncan, J., Johnson, R., Swales, M., & Freer, C.

1997

Perret, E.

1974

Espy, K. A., Kaufmann, P. M., McDiarmid, M. D., & Glisky, M. L.

1999

Purcell, R., Maruff, P., Kyrios, M., & Pantelis, C.

1998

Fisher, N., & Happé, F.

2005

1998

Foong, J., Rozewicz, L., Quaghebeur, G., Davie, C. A., Kartsounis, L. D., Thompson, A. J., et al.

1997

Robbins, T. W., James, M., Owen, A. M., Sahakian, B. J., Lawrence, A. D., McInnes, L., & Rabbitt, P. M. A. Rowe, A. D., Bullock, P. R., Polkey, C. E., & Morris, R. G.

2001

Fossati, P., Amar, G., Raoux, N., Ergis, A. M., & Allilaire, J. F.

1999

Salthouse, T. A., Atkinson, T. M., & Berish, D. E.

2003

Fucetola, R., Seidman, L. J., Kremen, W. S., Faraone, S. V., Goldstein, J. M., & Tsuang, M. T.

2000

Scheres, A., Oosterlaan, J., Geurts, H., Morein-Zamir, S., Meiran, N., Schut, H., et al.

2004

Garavan, H., Ross, T. J., Li, S. J., & Stein, E. A.

2000

Murphy, K. R., Barkley, R. A., & Bush, T.

2001

Nigg, J. T., Stavro, G., Ettenhofer, M., Hambrick, D. Z., Miller, T., & Henderson, J. M.

2005

Ozonoff, S., & Jensen, J.

1999

Schoechlin, C., & Engel, R. R.

2005

Sergeant, J. A., Geurts, H., & Oosterlaan, J.

2002

Shallice, T.

1982

Geurts, H. M., Verté, S., Oosterlaan, J., Roeyers, H., Hartman, C. A., Mulder, E. J., et al.

2004

Greene, J. D. W., Hodges, J. R., & Baddeley, A. D.

1995

Shallice, T., & Burgess, P.

1991

Hughes, C., Leboyer, M., & Bouvard, M.

1997

Welsh, M. C., Pennington, B. F., & Groisser, D. B.

1991

Hutton, S. B., Puri, B. K., Duncan, L. J., Robbins, T. W., Barnes, T. R. E., & Joyce, E. M.

1998

Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & Pennington, B. F.

2005

See the Appendix for the full references.

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Hierarchical cluster analysis HCA is a statistical procedure that tries to identify clusters of relatively homogeneous variables based on selected characteristics, in this case identifying the subcomponents of EF according to the neuropsychological tasks with which they are measured. The analysis was performed with SPSS Version 16.0 using an agglomerative algorithm that starts with each variable in a separate cluster and combines them until there is finally only one (Finch, 2005). A binary measure of pattern distance was used on the dichotomous data, whereby every variable is compared with every other in order to determine distance based on response pattern similarity. Those with the smallest cluster-to-cluster distance at each stage were combined. Items were clustered according to Ward’s method because it minimizes the variance between clusters at each step ensuring that they are as distinct as possible (see Ward, 1963). HCA is useful to establish a pattern regarding which tasks are used more often than others when measuring particular EFs and to determine the level of agreement between authors. We postulate that two subcomponents consistently measured with the same tasks would not be functionally distinct and would therefore meet in the same cluster of the analysis. These two subcomponents would require similar cognitive mechanisms and thus represent the same underlying function. In other words, by establishing the similarity between the different EFs in terms of the tests with which they are measured, the HCA helps to estimate the proximity between each EF, clustering together those that are closest and thus reducing the overall number of individual functions. It tries to determine a set of tasks that would allow us to distinguish one cluster of EFs from another and helps to find a recurring pattern of tasks that would be used mostly to measure a particular group of subcomponents.

RESULTS In our review of 60 of the most frequently cited studies, 68 different terms for EFs were identified as well as 98 tasks used to assess them. We extracted only the precise terms used by authors, and in some cases it seemed that a number of different labels were being used for what might essentially be a single EF—for example, inhibition, interference control, mental control, and control of response all appear to be very similar. LSA is a way to objectively quantify this overlap, reducing the number of terms to only those that are conceptually distinct. The result of the LSA suggests the presence of 50 EFs rather than 68. Eighteen terms were thus included with one of the 50 remaining EFs because they were strongly semantically connected (cosine ≥ .5). For example, the terms set shifting, selective attention, attention shifting, attentional control, and cue-directed attention were reduced simply to set shifting. In each case, the most common term was used, and the others were absorbed by it. Figure 1 represents the five most common terms in rectangles: Planning was assessed in 48% of the studies,

working memory in 42%, set shifting in 32%, inhibition in 42%, and fluency in 27%. Those taxonomies subsumed by these umbrella terms are connected by solid lines (cosine ≥ .5). Items linked by dotted lines are less strongly associated (cosine .3–.5), and the 12 subcomponents represented by numbered circles are those that the LSA did not link semantically to any other term (cosine < .3). This final grouping constitutes a better estimate of the proliferation of EFs because it takes into account associations between their definitions. This analysis allows us not only to determine the EFs that should be grouped together but also to estimate the proximity between each of them. Table 2 provides a summary of the results regarding which 50 components were retained, and which 18 were sufficiently semantically related to be subsumed by a more common term. An overlap remains, however, especially with regard to the tasks used to measure EFs. If the same subcomponents are consistently measured by the same sets of tasks, then we might assume that these are not separate functions after all, but rather individual labels provided by various researchers for the same EF. HCA was employed to reduce the number of EFs further, by progressively grouping together subcomponents according to similarity in the tasks with which they are measured. Unlike factor analysis that looks at individual differences in task performance, this novel approach is based upon the similarities/differences in authors’ opinions regarding what is thought to be measured by a particular task. It assesses whether a given task is used more frequently than another to measure a specific function, and so a pattern emerges regarding which tasks are associated with particular EFs. Thus if Task A is most frequently used to measure Function X but is also used to measure Functions Y and Z, we might infer that these functions must also be associated—for example, according to the literature review the Tower of Hanoi is mainly used to measure planning, but can also be used to measure organization and problem solving; therefore all three functions are associated and are combined in the same cluster. Not all the data could be entered into the cluster analysis because 18 of the 50 EFs were idiosyncratic and listed only once; that is, they were not measured by a test common to any other EF and so could not be assigned to a cluster. Had these 18 EFs been included, the number of separate clusters would have been artificially increased. Table 2 indicates which EFs were eliminated at this stage (e.g., intentionality, creativity, complex integration, selfregulation), leaving just 32 EFs to be entered into the HCA. Figure 2 shows the full dendrogram from the analysis. Items are combined at each level until the clusters are increasingly coarse-grained, and there is finally only one. If we look just at the first level of clustering, which is the most conservative, the 32 EFs have been reduced to 18. Of course, we could look at later stages of the analysis to reduce the number of terms still further, but we prefer to remain cautious rather than to suggest combining items at too high a level. The tasks used to measure the subcomponents of each of these clusters are shown in Table 3. From an initial set of 68 EFs we are left

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central executive

activation and monitoring goals

1

concentration

execute a plan

develop a plan goal setting

carry out a sequence of actions

controlling of actions

goal management divided attention

planning

sequencing

motor planning

2

efficiency to retrieve words from memory

executive motor skills

executive memory

STM capacity

working memory temporal coding organization

complex integration

resist to distraction

perseveration

3

information processing

set shifting

self-monitoring

6

selective attention

5

4

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self-generative behavior self-regulation response suppression

cognitive flexibility

inhibition

sustained attention/vigilance

shift of attention

7

attentionnal control

response generation

8 use of strategies

impulsivity control of response

interference control

response modulation

9 cue-directed attention

10

fluency

programming

conceptualization

mental control 12

verbal efficiency

word generation

11

concept formation

spontaneous verbal formation

abstraction

1. Workload 2. Creativity 3. Theoryofmind 4. Visual search 5. Time sharing 6. Intentionality

7. Generation of strategy 8. Proneness to interference 9. Discoveringchangesinrules 10. Affective decision making 11. Verification of hypothesis 12. Initiation

attentionnal set formation

maintain set reasoning

flexibility of thinking

problem solving

Figure 1. Strength of the semantic link between definitions of executive functions (EFs) according to the latent semantic analysis (LSA). EFs represented in rectangles are the most frequently postulated in the literature review. EFs are connected by a full line (cosine of .5 or more), connected by a dotted line (cosine between .3 and .5), or are unconnected (cosine less than .3). Note: The 12 items in the legend correspond to the 12 items associated with the 12 circles. To view a color version of this figure, please see the online issue of the Journal.

with 18; this is quite a considerable reduction, although such a large number of functions is still lacking in parsimony.

DISCUSSION The concept of EF has been criticized for its lack of clarity and profusion of terms (e.g., Andrés, 2003; Jurado & Rosselli, 2007; Miyake et al. 2000), and in the current article we aimed to both quantify and reduce the proposed number of executive subcomponents. A multiperspective methodology was used that incorporated three filters: a targeted review of the literature incorporating highly cited EF articles, LSA, and HCA. The literature review

revealed 68 different terms and a set of 98 executive tasks. The sheer number of tasks and labels to describe EFs clearly shows the inconsistency in the literature and illustrates the need for a more coherent approach. The number of terms was reduced to 50 with LSA and to 18 following the HCA (or 36 if we consider the idiosyncratic terms that were eliminated from this analysis), but even after three filters this still seems too large a number to suggest that the problem has been adequately resolved. There are too many abilities, definitions, and tasks to provide any meaningful taxonomy; continuing in this manner will not be helpful in clarifying the notion of EF, and as such we suggest that the concept is revisited. The results of the literature review demonstrate the extent of the problem with regard to EF; since clinicians

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TABLE 2 Results of the LSA: EFs retained and EFs eliminated after the analysis EFs retained after the LSA

EFs eliminated after the LSA (and terms subsumed under)

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.a 23. 24. 25.a 26. 27. 28.a 29.a 30. 31. 32.a 33.a 34.a 35. 36. 37. 38.a 39.a 40.a 41.a 42. 43.a 44.a 45.a 46.a 47.a 48.a 49.a 50.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.

planning inhibition working memory (WM) set shifting fluency cognitive flexibility impulsivity sustained attention goal setting perseveration organization concept formation initiation problem solving generation of strategy executive memory resist to distraction sequencing reasoning maintain set information processing divided attention use of strategies conceptualization short-term memory visual search central executive proneness to interference motor planning develop a plan execute a plan verification of hypothesis discovering changes in categorizing rules word generation self-generative behavior controlling of actions verbal efficiency intentionality programming flexibility of thinking creativity concentration complex integration activation and monitoring goals time sharing decision making workload theory of mind self-regulation abstraction

goal management (planning) interference control (inhibition) control of response (inhibition) mental control (inhibition) efficiency to retrieve words from memory (WM) temporal coding (WM) selective attention (set shifting) shift of attention (set shifting) attentional control (set shifting) cue-directed attention (set shifting) attentional set formation (maintain set) response modulation (fluency) response generation (fluency) response suppression (fluency) executive motor skills (motor planning) self-monitoring (self generative behavior) spontaneous verbal formation (verbal efficiency) carry out a sequence of actions (concentration)

Note. LSA = latent semantic analysis. EF = executive function. A total of 50 EFs were retained after the LSA, and 18 EFs were eliminated. a The executive functions that were excluded from the hierarchical cluster analysis (HCA).

often use their own terminologies based on what they consider a task to measure, semantically overlapping and superfluous labels are increasingly created. LSA and HCA are two novel approaches in the area of EF—one based on semantics and the other on psychometrics— that have proven useful in our attempt to gain more

coherence amongst the multiplicity of terms. LSA and HCA combine those subcomponents that do not share the same label but that are defined and/or measured in the same way across different authors. LSA provides an innovative method to reduce the number of EFs, allowing us to quantify the overlap at the abstract level by offering

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Rescaled Distance Cluster Combine 5 10 15

20

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Executive Functions Working Memory Concentration Strategy Generation Conceptualization Maintain Set Executive Memory Goal Setting Central Executive Impulsivity Initiation Inhibition Visual Search Sustained Attention Resist Distraction Problem Solving Controlling Actions Develop Plan Execute Plan g Planning Organization Use Strategies Self Monitoring Set Shifting Cognitive Flexibility Concept Formation Abstraction Fluency Verbal Efficiency Sequencing Information Processing Perseveration Reasoning Figure 2. Dendrogram of the cluster analysis using Ward’s method. From the left side of the figure, the first nodes represent the 18 clusters of the hierarchical cluster analysis (HCA) reduced from the initial set of 32 executive functions (EFs).

a grouping of EFs based on definitions. Although intuitively we may consider that two descriptions in the literature are equivalent and that one can be subsumed by the other, this method allows us to make that decision objectively. We identified which of any corresponding terms was the most frequently used by authors and now make the recommendation that these most common terminologies become more uniform in the field of EF (see Table 2 for the most frequent terms highlighted in our review and those more idiosyncratic terms that could be subsumed). A greater transparency in the labeling of subcomponents will better facilitate comparison between studies in this conceptually complex area. HCA reduced the number of functions still further by grouping together those that were strongly associated in terms of the tasks used. If two subcomponents are consistently measured by the same types of task, we might infer that different labels have simply evolved for the same EF; after all, it would seem reasonable to assume that those measured by the same tasks rely upon the same mechanisms and therefore tap a common

underlying function. Based on semantic and psychometric overlap we have reduced the number of terms to 18, or 36 if we also consider those idiosyncratic terms that were excluded from the HCA. However, to regard each of these as separate functions would be unparsimonious given their number and also the lack of any clear operationalization: Those terms deemed separate by the LSA and HCA are not necessarily completely independent of one another, just associated to a lesser degree than those terms that the analyses combined together. Since the 18 (or 36) EFs derived from the analyses cannot be said to each represent a clear functional subcomponent, it would be difficult to incorporate these into a meaningful or parsimonious theory of executive functioning. We therefore consider another viewpoint whereby the different subcomponents identified do not reflect separate functions per se, but rather task-specific manifestations of behaviors. Such an explanation allows for the many variations in executive task performance highlighted in our review, but without the need to suggest that these are each functionally distinct components.

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PACKWOOD, HODGETTS, TREMBLAY TABLE 3 EFs and executive tasks included in each of the 18 clusters according to the HCA

1.1 1.2

2.1 2.2 2.3 3.1 4.1 4.2 5.1

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6.1

7.1 7.2 7.3

8.1 9.1 9.2 10.1 10.2 10.3 11.1 12.1 12.2 12.3

13.1

14.1 15.1 16.1 16.2 17.1 17.2 18.1 18.2

Working memory/efficiency to retrieve words from memory/temporal coding Concentration/carry out a sequence of actions Stroop, SOPT, verbal and nonverbal fluency tasks, WCST, TOL, TMT, Delis sorting test, spatial span, digit span, CVLT, cognitive estimates test, auditory attention and response set, Porteus mazes, delayed alternation, visual search test, A not B task, self-ordered searching task. Generation of strategy Conceptualization Maintain set/attentional set formation Verbal and nonverbal fluency tasks, WCST, SOPT, similarities subtest, visual discrimination task. Executive memory Verbal and non verbal fluency tasks, CVLT, MCST. Goal setting Central executive TOL, spatial learning task, verbal and nonverbal fluency tasks, cognitive estimates test. Impulsivity Verbal and nonverbal fluency tasks, Stroop, TOL, WCST, selective reaching task, spatial puzzle, statue. Initiation Verbal and nonverbal fluency tasks, COWAT, Delis sorting test, cognitive effort test, Hayling, cognitive estimates test, spatial puzzle, list learning task. Inhibition/interference control /control of response/mental control Visual search Sustained attention or vigilance Matching familiar figures, selective reaching task, CPT, verbal and nonverbal fluency tasks, Stroop, WCST, TOL, SOPT, TMT, six element test, go/no go task, digit span, Hayling, arithmetic subtest, cognitive estimates test, statue, change task, A not B task, group-embedded figures test. Resist to distraction Stroop, digit span, arithmetic subtest. Problem solving Controlling of actions WCST, TOL, TOH, 20 questions, delayed alternation. Develop a plan Execute a plan Planning/goal management TOL, TOH, WCST, TMT, cognitive effort test, selective reaching task, CFR, Porteus mazes. Organization TOL, COWAT, TOH, SOPT, six element test, CVLT, CFR, Porteus mazes. Use of strategies Self-generative behavior/self-monitoring Set shifting/selective attention/shift of attention/attentional control/cue-directed attention Verbal and nonverbal fluency tasks, WCST, COWAT, TMT, self-ordered searching task, Stroop, digit span, selective reaching task, auditory attention and response set, MCST, CNT, ID/ED, visual search test. Cognitive flexibility Verbal and nonverbal fluency tasks, Stroop, WCST, COWAT, TMT, Delis sorting test, digit span, CNT, ID/ED, change task, group-embedded figures test. Concept formation WCST, COWAT, CVLT, similarities subtest, California word context, 20 questions. Abstraction WCST, COWAT, Delis sorting test, 20 questions, MCST. Fluency/response suppression/response modulation/response generation/control of response Verbal efficiency/spontaneous verbal formation Stroop, COWAT, WCST, TMT, object usage test, go/no go task, Hayling. Sequencing Information processing COWAT, SOPT, TMT, spatial span. Perseveration Reasoning WCST, TMT, verbal and nonverbal fluency tasks, COWAT, list learning task, California word context.

Note. Executive tasks in italics are most frequently postulated to measure the EFs that are within that cluster. EF = executive function. HCA = hierarchical cluster analysis. SOPT = Self-Ordered Pointing Task; WCST = Wisconsin Card Sorting Test; TOL = Tower of London; TMT = Trail Making Test; CVLT = California Verbal Learning Test; MCST = Modified Card Sorting Test; COWAT = Controlled Oral Word Association Test; CPT = Continuous Performance Test; TOH = Tower of Hanoi; CNT = Contingency Naming Task; ID/ED = Intradimensional/Extradimensional; CFR = Complex Figure of Rey.

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Although EFs are generally considered the managers of all decision-making processes, some models in cognitive psychology suggest that fundamental processes like memory and attention instead govern the choice of which behavior to adopt in a given situation (see GoldmanRakic, 1987; Kimberg & Farah, 1993). If complex decision making is controlled by just a few key functions, then the numerous EFs that clinicians refer to might simply be different behaviors that result from the interaction between these fundamental processes when performing different tasks. Similar tasks or situations would generate similar behaviors because the decision-making process that one goes through in each case would be almost the same; for example, tasks that require the inhibition of a usual behavior are grouped together in the HCA, but inhibition is not necessarily a function, it could simply be the manifestation of the interaction between key processes that are required to make a decision (see MacLeod, Dodd, Sheard, Wilson, & Bibi, 2003). Evidence from neuroimaging suggests that such key functions (e.g., memory and attention) could be located in the cortex and that although specific areas might be responsible for a specific type of information processing (e.g., either visual or auditory), it is the interaction of multiple parts of the cortex that are necessary to make a complex decision or to solve a novel problem (see Smith & Jonides, 1999). Undoubtedly if we consider the numerous subcomponents as examples of executive behaviors rather than as executive functions, then this would represent a more parsimonious approach. Neuroimaging studies indicate that the activity of many neurons in the prefrontal cortex—the centre of executive functioning—is task dependent (Asaad, Rainer, & Miller, 2000); however, although a particular task may activate a new set of neurons, one should not make the erroneous assumption that this is then tapping into a new function. In basic research rather than clinical studies, the way to operationalize concepts is quite different: Two different tasks or paradigms might activate different sets of neurons but can still be considered to elicit the same cognitive function. For example, the attentional blink and the psychological refractory period (PRP) are two different paradigms, but both are assumed to perform similar cognitive operations (Jolicoeur, Dell’Acqua, & Crebolder, 1998). That is, it is clear from other areas of psychology that a number of similar but distinct paradigms can converge upon the same fundamental cognitive function. Research efforts to identify and characterize various isolated functions only serve to further obfuscate the concept of EF; rather, EF could be defined as a system responsible for the acquisition of task context and the implementation of rules used to guide behavior, regardless of the specific behaviors or response required by a given task (e.g., inhibition of a prepotent response, set shifting). Such a definition would shift our operationalization of EF closer to the concept of the g factor of intelligence, for which the same kind of debate has occurred (see Duncan, 2010; Stiers, Mennes, & Sunaert, 2010). As Duncan and Owen (2000) state in their review, although there must be an increase in the local specialization of a function as the scale of analysis approaches

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the single neuron, many of the same specific regions of the frontal lobe are activated by multiple kinds of cognitive demands. Like the g factor, the concept of EF has increased in interest due to its predictive success for many real-world activities, yet by concentrating our work on isolated operations such as response inhibition, we may have lost sight of the key reason why the concept of EF was originally developed: to illustrate how humans address adaptive behavior. Such adaptive behavior usually implies complex sequential programming of goal-directed behaviors, and thus it seems illogical to reduce this holistic concept to a multitude of isolated functions. Indeed, it is unlikely that deficits of complex behaviors can be captured by isolating operations (e.g., response inhibition), when many different kinds of problem-solving situations require complex and multicomponent behavior (Duncan, 2010). Carpenter, Just, and Reichle (2000) also subscribe to this view, proposing that cognitive processes arise from networks across multiple cortical sites with interactive and overlapping functions; as such, each possible interaction could generate a different pattern of actions or behaviors depending on the task in hand, the specific situation, or even the cognitive skills that a person has developed within a particular culture: “the variety and generativity of human cognition, like the variation observed in other complex adaptive systems, arises from the combinatorics of simpler elements” (Carpenter et al., 2000, p. 197). Thus the interaction of a number of factors can give rise to a multitude of possible behaviors, and this explanation may go some way towards understanding why we can observe so many executive deficits yet the existence of such a large number of separate, specialized executive functions seems implausible (see Uttal, 2001). A good example of a more parsimonious approach is the model of Shallice and Burgess (1996): Instead of describing executive functioning with a multitude of subcomponents, the authors describe three general stages that one must go through when facing a new or complex situation (construction of temporary new schema; implementation of temporary new schema; assessment and verification). The output of the model is a behavior that emerges from these three general principles. If we consider all the EFs identified in the literature review as being examples of different outputs—or as behaviors deriving from a few basic underlying functions—then this model would seem to be in keeping with our viewpoint of a more parsimonious representation. One further model to provide an explanation as to how executive control can operate as a unitary function without resorting to a multitude of separate EFs is the cascade model, based on functional imaging studies (see Koechlin & Summerfield, 2007). According to this model, executive functioning does not comprise individual subcomponents but rather chosen actions are dependent upon different contexts; thus, there are as many possible actions as there are contexts in which an individual can be placed (or, to relate to our current argument, there may be as many possible executive behaviors as there are different executive tasks to perform). The anterior portion of the

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prefrontal cortex is thought to support the selection of actions in multitasking contexts, whereby the individual must keep in mind relevant information for a future task whilst currently engaging in another. This component is referred to in the model as branching control and may account for a number of proposed EFs across different contexts that require the maintenance and constant updating of information whilst performing multiple tasks (e.g., planning, inhibition, working memory, cognitive flexibility).

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Limitations and future directions The novel approaches of LSA and HCA offer a useful perspective within the complex field of EF, but are not of course without their limitations. The LSA is constrained by the specific semantic space available—in the case of psychology, the only semantic space available was that of college-level text books. Ideally, an LSA space relating more specifically to EF could be useful, but given that the definitions used were fairly simple, we believe that this was adequate for our analysis to give an initial idea of the semantic overlap. The output does show some surprises, however; for example, some EFs that one may consider to be highly related were not combined in the analysis. This is because the semantic link between two EFs strongly depends on the specific definition used, and so it is possible that some associations may have been missed, thus slightly underestimating the number of terms that could otherwise have been combined. We acknowledge that this is a limitation of this analysis; however, we felt that using this approach based on objective definitions was better than adopting more lenient criteria whereby more items could have been combined on the basis of author opinion. A further point is that we used a high cutoff point in order to ensure that the EFs grouped together after the LSA were exclusively those that were strongly semantically linked (cosine ≥ .5). Thus, EFs like attentional control and interference control that were reasonably well associated (cosine = .26) were not grouped together in the analysis. Our conservative criteria for objectively combining items would explain why some seemingly similar EFs are not related in this analysis, which could be considered an initial first step at reducing the number of terms. The HCA allowed us to determine the extent of agreement between authors regarding which EF a specific task is thought to measure. For some tasks there appears to be a high level of agreement, such as the Continuous Performance Test (CPT), which features in only one cluster (i.e., inhibition, visual search, and sustained attention) and so is closely tied to the measurement of one specific function. On the other hand, the WCST appears in two thirds of clusters in the HCA (12 of 18), thus illustrating problems we have in the current EF literature: (a) the lack of agreement between authors concerning what EF is measured by a task, and/or (b) the use of this task to assess many EFs at a time. Clearly, these problems make any performance deficit on such a task difficult to interpret, and the HCA highlights which tasks may be most problematic in this regard.

One might argue that some of the tasks included in the HCA were not originally developed or validated as “executive measures”—for example, tasks like Stroop and Trail B were first recognized as sensitive measures to differentiate between patients with or without brain injury (Davids, Goldenberg, & Laufer, 1957; Houston, 1969). However, they are now commonly used in the field by researchers and neuropsychologists alike (see Miyake et al., 2000), and patients’ performance on these tasks is used to make clinical diagnoses regarding their capacity for executive functioning and independent living. Although these tasks might not have been developed directly from the current executive semantic space, they are certainly widely used to refer to this concept. Since our paper is a representation of the measures and terms currently used in the literature, these tasks are still deemed relevant in our present conceptualization of EF. The 60 papers used in the literature review were frequently cited so give a good indication of the current state of play in the cognitive and neuropsychological literature with regard to the conceptualization and measurement of EFs. As a future avenue of research it would perhaps be interesting to compare this review to one that includes articles from another research area such as neuroimaging, as this would allow us to compare the extent to which the operationalization of EFs is comparable across different domains. In the current paper our contribution is threefold: We have quantified the extent of the problem with regard to EF; we demonstrate the use of novel methods to objectively reduce the number of terms based on semantic and psychometric overlap; and we have suggested a need to revisit the concept of EF. Although researchers may agree that the concept of EF is difficult to operationalize, this paper is critical in highlighting just how difficult this problem has become. If researchers continue in the same manner—creating new executive tasks and using new labels for functions that these tasks may measure—it will eventually become impossible to make any meaningful comparisons between studies and to understand one clinician’s diagnosis relative to another’s. One of the main goals of this study was to stress the theoretical and psychometric inconsistency in this field. It is important for both cognitive psychology and clinical practice that we make a concerted effort now to move towards greater coherence and uniformity regarding these constructs. We recommend that more standardized rather than idiosyncratic terms be established within the literature, perhaps adopting those most frequent terms highlighted by the LSA. Furthermore, this paper emphasizes the need to revise our view of EFs so that content is not confounded by form; that various types of behaviors/deficits exhibited through the performance measures of different executive tasks are just that—the expression of different executive behaviors—and not necessarily a reflection of multiple separate executive “functions.”

Original manuscript received 9 November 2009 Revised manuscript accepted 10 September 2010 First published online 24 January 2011

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