Latent profiles of executive functioning in neurologically healthy young adults: the role of individual differences in hemispheric asymmetry

In response to the growing need to understand individual differences in executive functioning (EF) among non-neurologic populations, this study examined two competing theoretical models of EF among healthy, neurologically-intact individuals: the prefrontal convexity model and the hemispheric asymmetry model. A total of 315 neurologically healthy individuals (M = 20.8 years; 50% female) completed two phases of the study. In the first phase (i.e., Model Identification), latent profile analysis was applied to variables measuring the abilities to form, switch, and maintain mental sets under conditions designed to tax the left or right hemisphere (i.e., a modified switching task). In the second phase (i.e., Model Validation), latent clusters from the first phase were compared on a separate EF task (i.e., Attention Network Test; ANT). The Model Identification phase yielded a three-profile solution consistent with the hemispheric asymmetry model. Profile 1 (N=203) was characterized by average EF performances. Profile 2 (N=43) revealed a set maintenance weakness under nonverbal conditions. Profile 3 (N=38) demonstrated a global weakness in cognitive flexibility and a specific weakness on tasks administered under verbal conditions. The Model Validation phase confirmed group/cluster differences (F(4,554) = 5.938, p<.001). Individual differences in EF follow a hemispheric asymmetry model of EF, with approximately 15% of neurologically healthy individuals exhibiting weaknesses in set maintenance and nonverbal processing, and 13% exhibiting weaknesses in set formation/switching and verbal processing.

LATENT PROFILES OF EXECUTIVE FUNCTIONING IN NEUROLOGICALLY HEALTHY YOUNG ADULTS: THE ROLE OF INDIVIDUAL DIFFERENCES IN HEMISPHERIC ASYMMETRY by Holly Kristen Rau A thesis submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Master of Science Department of Psychology The University of Utah August 2013 Copyright © Holly Kristen Rau 2013 All Rights Reserved The University of Utah Graduate School STATEMENT OF THESIS APPROVAL The thesis of Holly Kristen Rau has been approved by the following supervisory committee members: Paula Williams , Chair 6/2/2009 Date Approved Yana Suchy , Member 6/2/2009 Date Approved Jonathan Butner , Member 6/2/2009 Date Approved and by Carol Sansone , Chair of the Department of Psychology and by Donna M. White, Interim Dean of The Graduate School. ABSTRACT In response to the growing need to understand individual differences in executive functioning (EF) among non-neurologic populations, this study examined two competing theoretical models of EF among healthy, neurologically-intact individuals: the prefrontal convexity model and the hemispheric asymmetry model. A total of 315 neurologically healthy individuals (M = 20.8 years; 50% female) completed two phases of the study. In the first phase (i.e., Model Identification), latent profile analysis was applied to variables measuring the abilities to form, switch, and maintain mental sets under conditions designed to tax the left or right hemisphere (i.e., a modified switching task). In the second phase (i.e., Model Validation), latent clusters from the first phase were compared on a separate EF task (i.e., Attention Network Test; ANT). The Model Identification phase yielded a three-profile solution consistent with the hemispheric asymmetry model. Profile 1 (N=203) was characterized by average EF performances. Profile 2 (N=43) revealed a set maintenance weakness under nonverbal conditions. Profile 3 (N=38) demonstrated a global weakness in cognitive flexibility and a specific weakness on tasks administered under verbal conditions. The Model Validation phase confirmed group/cluster differences (F(4,554) = 5.938, p<.001). Individual differences in EF follow a hemispheric asymmetry model of EF, with approximately 15% of neurologically healthy individuals exhibiting weaknesses in set maintenance and nonverbal processing, and 13% exhibiting weaknesses in set formation/switching and verbal processing. TABLE OF CONTENTS ABSTRACT………………………………………………………………………. iii LIST OF TABLES..………………………………………………………...…….. vi LIST OF FIGURES..……………………………………………………………… vii EXECUTIVE FUNCTIONING IN HEALTHY YOUNG ADULTS…………….. 1 MODELS OF EXECUTIVE FUNCTIONING……………………………………. 4 The Prefrontal Convexity Model……………………………………….………. 4 The Hemispheric Asymmetry Model…………………………………………... 7 CURRENT STUDY………………………………………………………….…….. 10 Model Identification Phase…………………………………………………….. 10 Model Validation Phase………………………………………………………… 11 METHODS…………………………………………………………………………. 13 Participants………………………………………………..…………………… 13 Model Identification Measures……………………………..…………………. 13 Model Validation Measures………………………………..………………….. 19 Procedure…………………………………………………..…………………... 21 DATA ANALYSIS…………………………………..……………………………. 27 Modified Switching Task………..……………………………………………... 27 Latent Profile Analysis…………………………..……………………………... 29 MODEL IDENTIFICATION RESULTS………..…………………………………. 34 Profiles of Executive Functioning……………..………………………………… 34 Model Identification Discussion…………………..…………………………….. 36 v MODEL VALIDATION RESULTS……..………………………………………….. 38 Model Validation Discussion………………..…………………………………… 38 GENERAL DISCUSSION………………..…………………………………………. 41 Profiles of Executive Functioning………………………..………………………. 41 Interpretation……………………………………………..………………………. 46 Limitations………………………………………………..………………………. 46 Future Directions…………………………………………………………….…… 47 REFERENCES……………………………………………………………………….. 49 LIST OF TABLES 1. Goodness of fit statistics and class frequencies for latent profile models………..… 32 2. Maximum likelihood estimates (MLE) and 95% confidence intervals (CI) of mean profiles…………………………………………………………........ 33 LIST OF FIGURES 1. Sample spatial locations and trial types for the nonverbal task…………………… 23 2. Sample sequence of trials showing switch, form, and comparison trials…………… 24 3. Sample sequence of trials showing maintain trials…………………………………. 25 4. Task conditions for the Attention Network Task, showing: (a) the four cue conditions, (b) the three flanker/target conditions, and (c) the temporal sequence of a single trial………………………………………………………. 26 5. Means by cognitive marker for the three-profile solution. Error bars indicate the 95% confidence interval around the mean. VT = verbal trials; CT = comparison trials; NVT = nonverbal trials……………....……………… 37 6. Interaction graph for the Attention Network Task. Latent profile classification is represented by three lines: Class 1 (solid line, circles), Class 2 (dotted line, squares), and Class 3 (dashed line, triangles)...................................................... 40 EXECUTIVE FUNCTIONING IN HEALTHY YOUNG ADULTS Executive functioning (EF) is an umbrella term that refers to a set of cognitive and behavioral-control abilities that allow for goal-directed, purposeful behavior in everyday life (Cummings & Miller, 2007; Gazzaley, D'Esposito, Miller, & Cummings, 2007; Suchy, 2009). Although EF was originally almost exclusively studied by clinical neuropsychologists working with brain-injured populations, in recent years there is increasing recognition that individual differences in EF play a key role in daily functioning even among nonbrain-injured individuals. Specifically, discrete patterns of strengths and weaknesses in EF have been implicated in criminal offending (Brower & Price, 2001; Eastvold, Suchy, & Strassberg, 2011; Morgan & Lilienfeld, 2000; Suchy & Kosson, 2006; Suchy, Whittaker, Strassberg, & Eastvold, 2009), substance abuse (Ersche, Clark, London, Robbins, & Sahakian, 2006; Giancola & Moss, 1998; Giancola & Tarter, 1999), medical compliance (Hinkin et al., 2002; Stilley, Bender, Dunbar-Jacob, Sereika, & Ryan, 2010), and psychiatric disorders (Burdick, Robinson, Malhotra, & Szeszko, 2008; Palmer & Heaton, 2000; Reichenberg et al., 2009; Robinson et al., 2006). Additionally, individual differences in EF have been implicated in personality patterns (Nigg et al., 2002; Suchy, Williams, Kraybill, Franchow, & Butner, 2010; Williams, Suchy, & Kraybill, 2010; P. G. Williams, Y. Suchy, & H. K. Rau, 2009). Consequently, researchers from a variety of psychological disciplines are increasingly turning to clinical neuropsychology for guidance on how to best conceptualize EF, as well as which 2 components of EF are most likely to generate clinically and theoretically meaningful profiles of EF strengths and weaknesses. Evidence from neurologically-impaired populations supports the conceptualization of EF as a nonunitary construct, as different profiles of executive dysfunction emerge among brain-injured individuals. These profiles of dysfunction are typically thought to be linked to general organization of the prefrontal cortex (PFC) and other cortical regions richly connected to the PFC (Miller & Cohen, 2001; Stuss & Alexander, 2007; Stuss et al., 2002), with the assumption that compromised functioning in specific aspects of EF is related to dysfunction in specific cortical-subcortical networks involving the frontal lobes (Duffy, Campbell, Salloway, & Malloy, 2001; Hanna-Pladdy, 2007; Stuss et al., 2002). Importantly, some specificity of EF components within the PFC is also supported by functional imaging research conducted with healthy individuals, suggesting that unique EF profiles may also present as normal individual differences. However, some inconsistencies in research exist. Factor analytic examinations of EF have yielded equivocal results, with some studies reporting a two-factor model (Adrover-Roig, Sese, Barcelo, & Palmer, 2012; Bamdad, Ryan, & Warden, 2003; Doiseau & Isingrini, 2005; Goldman, Axelrod, Heaton, & Chelune, 1996; Hull, Martin, Beier, Lane, & Hamilton, 2008; Piguet et al., 2005; Savla et al., 2012; Willner, Bailey, Parry, & Dymond, 2010) and others supporting a three-factor solution (Boone, Ponton, Gorsuch, Gonzalez, & Miller, 1998; Burgess, Alderman, Evans, Emslie, & Wilson, 1998; Busch, McBride, Curtiss, & Vanderploeg, 2005; Kucera-Thompson, 2003; Miyake et al., 2000; Nagahama et al., 2003; Ross, 1995; Stout, Ready, Grace, Malloy, & Paulsen, 2003; Vaughan & Giovanello, 2010; Willner et al., 2010). Importantly, most of these studies 3 examined either older or neurologically impaired adult populations, rather than neurologically healthy adult individuals (e.g., Miyake et al., 2000; Ross, 1995), leaving questions unanswered about the structure of EF in the absence of neurological insult. Confusion also stems from the notion that EF abilities can be, at least in theory, fractionated into progressively more discrete processes (Suchy, 2009), yet it is likely that subsets of such processes are highly associated, either due to their neuroanatomic proximity or due to their joint emergence during neural development (V. A. Anderson, Anderson, Northam, Jacobs, & Catroppa, 2001; Stuss, 1992). In response to the growing need to understand individual differences in EF among nonneurologic populations, this study examines two competing theoretical models of EF among healthy, neurologically-intact individuals. Based on past research linking neuroanatomic regions to patterns of EF symptomatology, at least two models of EF profiles can be identified to guide this investigation. These are: (1) the prefrontal convexity model, which assumes three profiles of EF weakness, and (2) hemispheric asymmetry model, which assumes two profiles of EF weakness. MODELS OF EXECUTIVE FUNCTIONING The Prefrontal Convexity Model Duffy and Campbell (1994) have organized constellations of executive dysfunction symptoms into three clinical syndromes: (a) dysexecutive/disorganized, (b) disinhibited/impulsive, and (c) apathetic/hypokinetic. These three syndromes are associated with damage to one of three main PFC convexities (Karnath & Kammer, 2003) subserving EF: (a) dorsolateral PFC, (b) orbitofrontal PFC, and (c) medial PFC, respectively. Empirical support for these syndromes is provided by imaging and lesion studies, described below. Dorsolateral Convexity: Dysexecutive/Disorganized Syndrome The dorsolateral PFC (dlPFC) receives projections from the visual, auditory, and somatosensory cortices, allowing for top-down coordination of task-relevant cognitions and behaviors (Shimamura, 2000). Therefore, the dlPFC appears to be particularly important for establishing and executing mental set (Mega & Cummings, 2001). Imaging studies with neurologically-intact individuals have shown that increased dlPFC activation is associated with conceptualizing task demands (Baker et al., 1996; Blumenfeld & Ranganath, 2006; Kroger et al., 2002) and generating responses that are consistent with identified task demands (Frith, Friston, Liddle, & Frackowiak, 1991; Garavan, Ross, Murphy, Roche, & Stein, 2002; Nathaniel-James & Frith, 2002; Pochon et al., 2001). 5 Similarly, patients with dlPFC damage tend to demonstrate impaired reasoning and problem-solving skills (Colvin, Dunbar, & Grafman, 2001; Eslinger, Biddle, Pennington, & Page, 1999; Lombardi et al., 1999; Milner, 1963) resulting in impaired performance on tasks such as the Wisconsin Card Sorting Test (Demakis, 2003; Milner, 1963; Stuss et al., 2000). Lastly, individuals with certain neuropsychiatric syndromes that are known to exhibit deficits in reasoning (e.g., cognitive slippage seen among schizophrenic patients; Gooding, Tallent, & Hegyi, 2001) also exhibit dlPFC dysfunction (Callicott et al., 2000; Manoach et al., 2000; Weinberger, Berman, & Zec, 1986). Orbitofrontal Convexity: Disinhibited/Impulsive Syndrome The orbitofrontal PFC's (ofPFC) rich connections to the paralimbic cortex implicate this brain region in monitoring and managing the release of limbic drives within changing contexts or contingencies (Kringelbach, 2005; Price, 1999). More specifically, the ofPFC quickly integrates environmental stimuli, refreshes awareness of behavioral consequences, and sustains behaviors consistent with top-down goals (Beer, Heerey, Keltner, Scabini, & Knight, 2003; Beer, John, Scabini, & Knight, 2006). In other words, when task or situational demands change, the ofPFC is involved with rapidly updating and switching mental set, thereby ensuring that behaviors are congruent with current contingencies. Imaging studies support this notion; increased ofPFC activity is observed when neurologically-healthy individuals evaluate the relevance of environmental stimuli (O'Doherty, Kringelbach, Rolls, Hornak, & Andrews, 2001), detect discrepancies between current and task-appropriate behaviors (Berthoz, Armony, Blair, & Dolan, 2002), and alter behavior to accommodate changing task demands (Kim & Ragozzino, 2005). Similarly, patients with damage to the ofPFC tend to disregard 6 relevant environmental or social cues and are instead driven by basic desires, exhibiting impaired behavioral monitoring (Beer et al., 2006; Mah, Arnold, & Grafman, 2004) and failures to integrate environmental feedback (Hornak et al., 2004). These deficits in turn result in impulsive releases of inappropriate responses. Lastly, certain psychiatric populations that are known to exhibit poor responsiveness to social and environmental cues combined with excessive reliance on basic drives and desires (e.g., high reward sensitivity and low punishment sensitivity seen among adults with psychopathy; Blair, Morton, Leonard, & Blair, 2006) also exhibit weaknesses in ofPFC (N. E. Anderson & Kiehl, 2012; Blair, Newman, et al., 2006; Gorenstein, 1982; Sequin, 2004). Medial Convexity: Apathetic/Hypokinetic Syndrome The medial PFC (mPFC) is located between the cingulum, which controls wakefulness and arousal (Parent, 1990), and the supplementary motor area (SMA), which controls volition and motivation (Goldberg, 1985). Therefore, healthy functioning of the mPFC network allows for adequate levels of arousal and motivation required for maintaining mental set. In neurologically-intact individuals, mPFC activation corresponds to both subjective and physiological arousal (Phan et al., 2003) as well as mental computations involved with motivating and directing attentional resources (Botvinick, Braver, Barch, Carter, & Cohen, 2001; Kouneiher, Charron, & Koechlin, 2009; Rushworth, Walton, Kennerley, & Bannerman, 2004). Disruption of the mPFC is associated with decreased motivation and impaired motor planning (Cunnington et al., 1996; Gerloff, Corwell, Chen, Hallett, & Cohen, 1997), with patterns of dysmotivation ranging from apathy to akinetic mutism (Duffy & Campbell, 1994). Consequently, performances on tasks requiring speed, internally-generated behaviors, or internally- 7 maintained attentional set are likely to be impaired (Grinband et al., 2011; Laplane, Talairach, Meininger, Bancaud, & Orgogozo, 1977; Picton, Stuss, Shallice, Alexander, & Gillingham, 2006; Stuss & Alexander, 2007; Stuss et al., 1998). Lastly, certain neurodevelopmental disorders that are marked by low arousal and the inability to sustain attention and maintain mental set (e.g., individuals with Inattentive Attention Deficit Hyperactivity Disorder; Brown & McMullen, 2006) exhibit dysfunctional processing within the mPFC (Bush et al., 1999; Ernst et al., 2003; Tamm, Menon, Ringel, & Reiss, 2004). The Hemispheric Asymmetry Model Although profiles of EF weaknesses have traditionally been conceptualized according to PFC convexities, additional evidence suggests that these profiles may also be hemispherically lateralized. In particular, attention and arousal systems appear to be right-hemisphere dominant, whereas problem-solving abilities tend to be left-hemisphere dominant. Empirical support for these ‘hemispheric syndromes' is described below. Left Hemisphere: Cognitive Inflexibility Syndrome Evidence suggests that the left cerebral hemisphere contributes to executive functions such as problem-solving (Grossman et al., 1998; Gundel & Wilson, 1992; Martin, 1999; S. D. Newman, Carpenter, Varma, & Just, 2003) and updating cognitive or behavioral sets (Collette et al., 2005; Dreher & Grafman, 2003; Meyer et al., 1998; Moll, de Oliveira-Souza, Moll, Bramati, & Andreiuolo, 2002; Rezai et al., 1993; Stuss et al., 2002; Sylvester et al., 2003). In other words, this model suggests that the ability to flexibly update and switch mental set is inherently linked with the ability to establish a 8 new mental set. These particular cognitive abilities fall under the domain of cognitive flexibility and suggest that a weakness in cognitive flexibility may be associated with a left hemisphere weakness. Consistent with this notion, patients with left hemisphere lesions demonstrate a wide range of behavioral difficulties, including impaired problem solving, cognitive inflexibility, and poor conflict resolution (Grafman, Jonas, & Salazar, 1990; Keele & Rafal, 2000; Mecklinger, von Cramon, Springer, & Matthes-von Cramon, 1999; Rogers et al., 1998; Troyer, Moscovitch, Winocur, Alexander, & Stuss, 1998). Additionally, certain neuropsychiatric populations characterized by inflexibility (e.g., impaired abstraction and environmental adaptation seen in patients with schizophrenia (Rund & Borg, 1999) also exhibit pronounced left hemisphere abnormalities (Crow, 1997; Flor-Henry, 2003; Gur et al., 1985). Right Hemisphere: Set-loss Syndrome In neurologically healthy adults, the right cerebral hemisphere appears to be involved in sustaining adequate alertness/arousal (Fernandez-Duque & Posner, 2001; Pardo, Fox, & Raichle, 1991; Sturm et al., 1999; Sturm & Willmes, 2001) and attention (Beeman & Bowden, 2000; Collette et al., 2005; Deutsch, Papanicolaou, Bourbon, & Eisenberg, 1987; Pardo et al., 1991), that is, cognitive abilities that fall under the domain of set maintenance. Patients suffering from brain lesions or disorders primarily affecting the right hemisphere often demonstrate impaired set maintenance abilities, including greater set-loss errors (Devinsky, Morrell, & Vogt, 1995; Goldberg, 1985; Rueckert & Grafman, 1998; Stuss et al., 1983; Vendrell et al., 1995) or slower performances (Alexander, Stuss, Shallice, Picton, & Gillingham, 2005; Pujol et al., 2001; Rueckert & Grafman, 1998; A. J. Wilkins, Shallice, & McCarthy, 1987) on tasks measuring 9 attentional vigilance. In addition, certain neurodevelopmental disorders characterized by decreased arousal and failure to maintain set (e.g., individuals with Inattentive Attention Deficit Hyperactivity Disorder; Brown & McMullen, 2006) are associated with right hemisphere dysfunction/weakness (Stefanatos & Wasserstein, 2001). CURRENT STUDY The purpose of the present study was to test which of the two models described above is better at meaningfully capturing individual differences in EF among neurologically healthy individuals. To that end, we recruited 315 young healthy volunteers for a single assessment session that consisted of two phases: The first phase (i.e., Model Identification phase), designed to test the two competing models of EF, and the second phase (i.e., Model Validation phase), designed to test the validity of the identified models. Model Identification Phase To allow simultaneous testing of the two competing models of EF, we tested the participants' abilities to form, switch, and maintain mental sets in the context of task stimuli that are preferentially processed primarily by the left versus the right cerebral hemispheres (Suchy & Kosson, 2006). Because the primary interest of this study was to capture the distribution of individual differences (rather than capture principle components of EF), we employed modern cluster-analytic techniques to determine whether the participants' performance would generate classes that would be consistent with either of the two theoretical models. We tested two competing hypotheses: (1) If the prefrontal convexity model best captured the individual differences in EF in this population, then we expected that the data would yield four clusters, one each 11 (2) characterized by: (a) average performance across all task variables (i.e., no EF weaknesses), (b) relative weakness in set formation (i.e., reflecting the dysexecutive/disorganized syndrome) regardless of the hemispheric demands of the stimuli, (c) relative weakness in set switching (i.e., reflecting the disinhibited/impulsive syndrome) regardless of the hemispheric demands of the stimuli, and (d) relative weakness in set maintenance (i.e., reflecting the apathetic/hypokinetic syndrome) regardless of the hemispheric demands of the stimuli. (3) If the hemispheric asymmetry model best captured the individual differences in EF in this population, then we expected that the data would yield three clusters, one each characterized by: (a) average performance across all task variables (i.e., no EF weaknesses), (b) relative weakness in set formation and set switching (i.e., reflecting the cognitive inflexibility syndrome), especially on trials with greater demands for left hemisphere resources, or (c) relative weakness in set maintenance (i.e., reflecting the set-loss syndrome), especially on trials with greater demand for right hemisphere resources. Model Validation Phase To allow validation of the clusters yielded by the first phase of the study, all participants completed a previously validated test (the Attentional Network Test; ANT; Fan, McCandliss, Sommer, Raz, & Posner, 2002) of three attentional processes: (a) the ability to maintain an alert state so as to rapidly take in information, facilitating rapid formulation of mental sets (referred to in the ANT literature as ‘alerting'); (b) the ability to flexibly shift attention in response to sensory cues (referred to in the ANT literature as ‘orienting'); and (c) the ability to maintain a mental set despite distracting or conflicting stimuli (referred to in the ANT literature as ‘executive attention'). We hypothesized that: 12 (1) if the prefrontal convexity model was supported, weaknesses in alerting, orienting, and executive attention would be uniquely present in clusters characterized by weaknesses in forming, switching, and maintaining mental sets, respectively, or (2) if the hemispheric asymmetry model was supported, then weaknesses in orienting (which has been shown to be predominantly a left-hemisphere process) would be present in the cluster characterized by weaknesses in set formation and set switching; and weaknesses in executive attention (which has been shown to be predominantly a right-hemisphere process) would be present in the cluster characterized by weaknesses in set maintenance. We did not expect differences in alerting, as this process has been shown to be bilaterally distributed). METHODS Participants A total of 315 college students (50.5% female) received course credit for their participation. Mean age was 20.8, SD = 2.7, and mean education was 13.2, SD = 1.1. Because EF performances were partially indexed via response latencies, individuals older than 30 were excluded in order to reduce the effect of age on processing speed (Schretlen et al., 2000). The racial/ethnic distribution of the sample was 88.6% White, 4.4% Asian, 0.7% African American, 0.3% Pacific Islander, and 5.4% other; of these individuals, 7.3% identified their ethnicity as Latino/Latina. Exclusion criteria included the following characteristics assessed by self-report: (a) English as a second language, (b) left-handedness, (c) age less than 18 or more than 30 years, and (d) physical or sensory impairment that would preclude test performance (e.g., paralysis in the dominant hand). Individuals who met these exclusion criteria were given the opportunity to complete alternative procedures as specified according to the University's Internal Review Board (IRB). Model Identification Measures We used a modified switching task that we successfully used in the past to examine EF profiles in the context of differential hemispheric processing demands (Suchy & Kosson, 2006). The task was administered via computer. Within this task, two classification procedures were administered: a verbal task (VT), containing stimuli 14 designed to rely primarily on left-hemisphere processing, and a nonverbal task (NVT), containing stimuli designed to rely primarily on right-hemisphere processing. Tasks were designed to contain: (a) high executive-demand (HED) trials designed to assess the abilities to form, switch, and maintain mental sets, and (b) comparison trials (CTs) designed to examine relative strengths and weaknesses on trials reflecting left versus right hemispheric processing demands (i.e., VT, NVT) under low executive-demand conditions. CTs also served as a baseline of comparison for calculating costs (i.e., speed, accuracy) associated with HED trials. All instructions, cues, stimuli, and feedback were presented via computer screen. Verbal Task (VT) The VT trials were designed to tax left-hemisphere resources, based on empirical support for left hemisphere involvement in letter recognition and discrimination tasks (Cohen, 1972; Farah, Gazzaniga, Holtzman, & Kosslyn, 1985; Gootjes, Raij, Salmelin, & Hari, 1999; A. Wilkins & Stewart, 1974). The VT presented letters, one at a time. On each trial, participants were asked to respond ‘yes' (an index finger press on the ‘1' key of a keyboard number pad) or ‘no' (a middle finger press on the ‘2' key of the number pad) regarding one of the following classifications: (a) Is this letter capitalized? or (b) Is this letter a vowel?). Some letters belonged to both categories (e.g., ‘U'), or neither category (e.g., ‘t'), and as such were designated as ‘congruent;' other letters belonged to only one category (e.g., ‘T' or ‘u') and as such were designated as ‘incongruent.' Participants classified letters according to cues presented on the computer screen (i.e., ‘CAP?' or ‘vowel?'), and received feedback regarding the speed and accuracy of their performance. Specifically, responses slower than 1200 ms were followed by the words 15 ‘Too slow,' and incorrect responses were followed by error feedback (the word ‘Wrong,' accompanied by a cue regarding the currently correct classification principle). Nonverbal Task (NVT) The NVT trials were designed to tax the right cerebral hemisphere, based on studies indicating increased right hemisphere activation during tasks involving judgments of spatial coordinates (Jager & Postma, 2003; Kosslyn et al., 1989). The NVT presented the same stimuli used for the VT. However, during the NVT trials, participants were asked to classify the stimuli according to their spatial location on the computer screen. On each trial, participants were asked to respond either ‘yes' (an index finger press) or ‘no' (a middle finger press) regarding one of the following classifications: (a) Is the figure located in the lower left half of the screen? (see Figure 1a), or (b) Is the figure located in the lower right half of the screen? (see Figure 1d). Once again, stimuli could be either congruent or incongruent (see Figure 1). Participants were instructed to classify the stimuli according to the location that was indicated to them via both visual and verbal cues (‘Here?' presented in either the lower left or the lower right portion of the screen). As was the case in the VT, feedback regarding speed and accuracy was provided. High Executive-demand (HED) Trials Both the VT and the NVT were designed to instantiate three types of executive demands: forming, switching, and maintaining mental set. Increases in executive demands were accomplished by: (a) presenting cues indicating the classification principle to be used in the subsequent block of trials, based on the classic switching task paradigm (Allport, Styles, & Hsieh, 1994; Jersild, 1927), and (b) arranging the sequence in which 16 the trials occurred, based loosely on the Wisconsin Card Sorting Test (Heaton, Chelune, Talley, Kay, & Curtiss, 1993) and on principles employed in various continuous performance tasks (e.g., Connors, 2000). First, to manipulate set switching and set forming demands, approximately every eight trials a cue was presented signaling which classification principle (e.g., cap/vowel vs. left/right location) should be observed next. This principle was valid until the next cue appeared. Each new cue could be either different from the previous cue, indicating a change in classification principle, or the same as the previous cue, indicating that the classification principle should remain unchanged. When a cue indicated a change, participants needed to switch to the new principle on the immediately following trial; these cues were referred to as ‘switch cues,' in which the participant is required to modify their objective. When a cue did not indicate a change, participants simply needed to ‘reconsider' (Gopher, Armony, & Greenshpan, 2000) their current response set, ascertaining that their set and the cue matched and that no switching was required; these cues were referred to as ‘form cues,' in which the participant is required only to reconsider their objective. Trials immediately following switch and form cues are known to be associated with increased processing demands, reflected in longer response latencies (Allport et al., 1994; Gopher et al., 2000; Jersild, 1927). Because of the top-down requirements of responding to cues under these conditions, the additional processing demand is believed to represent an index of executive control (Mecklinger et al., 1999; Rogers et al., 1998). This notion is corroborated by experimental studies conducted with normal participants (Gopher et al., 2000; Lorist et al., 2000; Monsell, Yeung, & Azuma, 2000) and with 17 individuals known to demonstrate weaknesses in executive abilities (Cepeda, Kramer, & Gonzalez de Sather, 2001; Kramer, Hahn, & Gopher, 1999; Kray, Li, & Lindenberger, 2002; Kray & Lindenberger, 2000; Salthouse, Fristoe, McGuthry, & Hambrick, 1998). In this study, trials immediately following switch and form cues were referred to as ‘switch trials' and ‘form trials,' respectively. All switch and form trials were incongruent, as were all trials immediately preceding switch or form cues. See Figure 2 for a sample trial sequence. Second, to increase set-maintenance demands, trial sequences were manipulated such that, some of the time, a set of approximately eight congruent trials was followed by an incongruent trial. As a reminder, each incongruent trial has two different potentially correct responses (one for each classification principle), whereas each congruent trial has only one potentially correct response (regardless of the current classification principle). This difference in the number of possible responses has important implications for set maintenance demands. In particular, when performing a series of incongruent trials, the need to select from among two potential responses forces the participant to constantly refresh the classification principle in working memory, thereby maintaining arousal and vigilance. In contrast, when performing a series of only congruent trials, nothing about the stimuli reminds participants to refresh their mental set regarding the classification principle (because the response is the same regardless), which increases the potential for participants to become inattentive and forget which classification principle they are supposed to be responding to. Thus, to perform the task correctly, participants need to self-cue to maintain mental set and to avoid allowing the congruent nature of these trials to lull them into attentiveness. 18 In this paradigm, the trials of interest (referred to as ‘Maintenance trials') are the first incongruent trials immediately following a series of congruent trials (see Figure 3 for a sample trial sequence). We have previously demonstrated that the Maintenance trials in this task are associated with increased number of errors among participants with mixed features of inattentiveness and impulsivity (Suchy, Gold, Biechler, & Osmon, 2003), as well as among psychopathic offenders (Suchy & Kosson, 2006), who are known to have difficulties with self-monitoring and self-cueing (Brazil et al., 2009; J. P. Newman, Patterson, & Kosson, 1987; J. P. Newman, Schmitt, & Voss, 1997). Comparison Trials (CTs) The CTs (i.e., trials that placed fewer demands on executive systems) were identical to switch, form, and maintain trials in every regard except the need to switch, form, or maintain mental set. They consisted of incongruent trials that occurred between cues, excluding switch, form, and maintenance trials. CTs were similar to maintenance trials in that they were incongruent and were not preceded by a cue, but they also were not preceded by a series of congruent trials, and as such did not require the same demands for self-cued set-maintenance. See Figure 1 for examples of CTs. Task Parameters Each task consisted of 302 trials (141 congruent and 161 incongruent) and only the incongruent trials were used in the analyses. There were eight each of form, switch, and maintenance, and 137 comparison trials. The order of manipulations (i.e., VT vs. NVT) was randomized for each participant. Visual stimuli boxes were 2.5 inches tall by 2.5 inches wide and contained verbal stimuli (1.75 inches tall) overlying a neutral 19 background (i.e., mixture of grey shades and hues). Each stimulus box remained on the screen until a participant responded. Response-stimulus interval was 20 ms. Cues and feedback were presented on the screen for 750 ms, followed by a 20 ms interval. Model Validation Measures In order to determine whether performance patterns generalized beyond a single measure, participants were administered The Attention Network Task (ANT; Fan et al., 2002; Fan, Wu, Fossella, & Posner, 2001). The ANT, a combination of a cued reaction time (Posner, Snyder, & Davidson, 1980) and flanker task (Eriksen & Eriksen, 1974) was designed to measure the efficiency of three attentional networks: alerting, orienting, and executive attention. Prior to calculating the efficiency variables, median reaction times (RTs) were calculated for each participant across eight variables calculated from two types of trial-based stimuli: cue type (no cue vs. center cue vs. double cue vs. spatial cue) and flanker type (congruent vs. incongruent). Figure 4 provides a visual schematic of various task conditions. Next, we generated arithmetical means of the median reaction time (RT) values so as to create the alerting, orienting, and executive attention scores, described below. Alerting This network refers to attentional readiness, or the ability to sustain an alert state for the purpose of preparing a reaction if necessary. The alerting network is thought to be associated with activation of the right frontal and parietal brain regions based on the cortical distribution of the brain's norepinephrine system (Coull, Frith, Frackowiak, & Grasby, 1996), which affects alertness and arousal (Beane & Marrocco, 2004). In 20 addition, certain clinical populations characterized by a right hemisphere weakness (i.e., ADHD; Stefanatos & Wasserstein, 2001) have been found to perform more poorly on the alerting trials compared to healthy controls (Johnson et al., 2008). The alerting variable (i.e., the ability to distribute attention across two potential target locations) was calculated by subtracting the mean RT of the double-cue conditions from the mean RT of the no-cue conditions. Orienting This network refers to environmental attention, or the ability to rapidly and flexibly shift the focus of attention in response to changing task demands. The orienting network is thought to reflect underlying attentional processes involved with switching mental set, as the ability to respond to unexpected targets is essential for both conceptualizing task demands and modifying cognitive and behavioral set as indicated. Whereas broad distribution of attention (i.e., alerting network) appears to rely on the right hemisphere, results from neuroimaging studies suggest that the ability to respond to rapidly changing events or cues is left lateralized (Coull, Frith, Buchel, & Nobre, 2000; Fan, McCandliss, Fossella, Flombaum, & Posner, 2005). The orienting variable (i.e., how well attention is directed to task-relevant cues) was calculated by subtracting the mean RT of the spatial-cue conditions from the mean RT of the center-cue conditions. Executive Attention The executive attention network refers to higher-order EF abilities involved with response selection, or the ability to quickly and accurately select the correct response among competing, incongruent stimuli. Executive attention is thought to rely on 21 prefrontal areas, based on research examining other cognitive tasks involving response selection, such as the Stroop task (Bush, Luu, & Posner, 2000; MacDonald, Cohen, Stenger, & Carter, 2000), and evidence suggests predominantly right hemisphere involvement (see Vendrell et al., 1995 for a review). Individuals with more effective response selection abilities exhibit less interference in performance; therefore, better executive attention performances are indicated by a relatively smaller increase in response latencies on incongruent flanker trials. The executive attention variable was calculated by subtracting the mean RT of all congruent flanking conditions from the mean of all incongruent flanking conditions (i.e., index of conflict monitoring). ANT Task Parameters Each task consisted of 288 trials, with the four cue conditions (i.e., no, center, double, spatial) consisting of 72 trials each and presented at random. Cues were presented on the screen for 100 ms, followed by a brief 400 ms delay interval. Each target arrow was displayed until the participant responded or 1700 ms had elapsed. Response-stimulus interval varied randomly from 400-1600 ms. Procedure Eligible participants underwent IRB-approved informed consent procedures. The VT and NVT were counterbalanced and were preceded by 12 practice trials. Participants were given the option to repeat practice trials if they felt they did not fully understand how to perform the task. Participants responded by pressing designated keys on a computer keyboard number pad using their index and middle fingers. The ANT task was completed according to published guidelines (see Fan et al., 2000). All task stimuli were 22 presented on a Gateway desktop computer with a 14 inch computer screen. Response latency and number of errors were recorded. 23 24 Figure 2. Sample sequence of trials showing switch, form, and comparison trials. CAP? Vowel? Vowel? Vooweell?? T time Comparison Trials Comparison Switch Cue Trials Form Trial Switch Trial Form Cue 25 Figure 3. Sample sequence of trials showing maintain trials. CAP? U u time String of Congruent Trials Maintain Trial 26 Figure 4. Task conditions for the Attention Network Task, showing: (a) the four cue conditions, (b) the three flanker/target conditions, and (c) the temporal sequence of a single trial. (a) + No-Cue Congruent Flankers (b) (b) Incongruent Flankers Neutral Flankers Center-Cue Double-Cue Spatial-Cue * * + * + * * +     - -  - - - -  - - fixation  cue (100ms)  fixation (400ms)  target / response (<1700ms) DATA ANALYSIS Modified Switching Task Form, Switch, and Comparison Scores We first computed the median response latency and total errors for each participant separately for form, switch, and comparison trials. Next, we generated arithmetical median response latencies and total error values so as to create the following scores: (1) verbal task, high executive demand (VT-HED), consisting of the VT form and switch scores, (2) nonverbal task, high executive demand (NVT-HED), consisting of the NVT form and switch scores, (3) verbal task, low executive demand (VT-CT), consisting of all the VT comparison trial scores, and (4) nonverbal task, low executive demand (NVT-CT), consisting of all the NVT comparison trial scores. In order to calculate the form and switch cost variables, we subtracted the mean of the CT score from the mean of each of the HED scores for VT and NVT. Thus, the experimental design produced eight ‘cost' variables resulting from three types of conditions and measurement: trial type (switch vs. form), task type (verbal vs. nonverbal), and outcome type (response latency vs. errors). In addition, four CT variables were created from two types of conditions and measurement: task type (verbal vs. nonverbal) and outcome type (response latency vs. errors). Although task conditions were counterbalanced, an order effect was identified such that participants who began with the NVT condition performed significantly more poorly on the NVT trials relative to 28 participants who began with the VT condition. To minimize this effect, task order was controlled for in all analyses. Finally, because both speed and accuracy are indices of forming and switching mental set, composite scores were created by extracting a principal component from an analysis into which response latencies and errors were entered. A total of six final composite scores were used in the final analysis, based on two types of conditions: trial type (comparison, form, switch) and task type (verbal vs. nonverbal). Set Maintenance Scores Set maintenance scores were computed using only accuracy data, in line with our prior use of this task (Suchy & Kosson, 2006). Total number of errors made on the pre-set- loss trials (i.e., string of eight consecutive congruent trials preceding the single set-loss trial) were divided by the total number of preset-loss trials (i.e., 36 per condition), producing a percentage of preset-loss errors. The same approach was taken for set-loss trials (i.e., the first incongruent trial following the string of congruent trials, for a total of eight per condition). This resulted in four variables derived from two within subjects factors: trial type (preset-loss trial vs. set-loss trial) and task type (Verbal vs. Nonverbal). As with the comparison, form, and switch variables, task sequence was controlled to minimize order effects. Lastly, the trial type variables (preset-loss trial vs. set-loss trial) were collapsed into composite scores via principal components analysis, producing two final set maintenance variables based on task type (Verbal vs. Nonverbal). 29 Latent Profile Analysis The first aim of the study was to identify profiles of executive functioning. Therefore, latent profile analysis (LPA) in Mplus (Version 5; Muthen & Muthen, 2009) was used as a classification procedure to group participants on the basis of patterns in their neurocognitive markers. LPA is a maximum likelihood procedure that uses a latent mixture model and that identifies probable groupings within the data (Lanza, Flaherty, & Collins, 2003). Similar to other clustering techniques, LPA is a classification procedure designed to identify various groupings within a larger data set. LPA has the advantage of being model dependent and has the ability to test alternative models (e.g., variances and covariances differing across the groups, versus forcing variances and covariances to be equal) that would otherwise be model assumptions according to other clustering methods. LPA has become an increasingly promising method in the typology literature (for an in depth discussion of latent class analysis, see Lanza et al., 2003) as it can provide statistical indicators that assist in identifying the proper number of profile solutions. One such indicator is the Bayesian information criterion (BIC) that, when minimized, indicates the best fit compared to other possible solutions (Nagin, 1999). For small samples (n < 500), the BIC has been shown to be superior for determining model fit compared to other indicators (Nylund, Asparouhov, & Muthen, 2007) although adjustments for sample size (ssBIC) may also be appropriate (Sclove, 1987). Because studies have shown improved class selection with both the unadjusted BIC (Bauer, 2007; Nylund et al., 2007) and the sample size adjusted BIC (Lubke & Neale, 2006; Tofighi & Enders, 2007), both fit statistics were examined when determining optimal model fit. Although the BIC and ssBIC both provide a statistical criterion regarding the ideal 30 number of solutions, the derived profiles are still considered probabilistic and do not reflect absolute group membership. Model identification can also be informed by various likelihood ratio tests (LRT), which are used to test relative model fit by testing the null hypothesis that competing models demonstrate comparable fit (Vuong, 1989). Within latent variable models, the Vuong-Lo-Mendell-Rubin test (Lo, Mendell, & Rubin, 2001) is an accepted methodology for testing the equivalence of two associated probability density functions (Henson, Reise, & Kim, 2007). Simulation studies have indicated that the VLMR test favors selection of more components when used with small samples, resulting in increased Type I error rates; this suggests the need for an adjusted test (aVLMR) with samples less than 300 (Lo et al., 2001). Because both LRT approaches involve relative strengths, including increased power with VLMR and decreased Type I error rates with aVLMR, both statistics were used when determining optimal model fit. The LPA was performed on the eight EF variables included in the study. Only the means for each variable were allowed to vary across clusters. Missing data (i.e., due to technical problems; n=31) were handled during the analysis with full information maximum likelihood, in which it is assumed that the data were missing at random. Because no extreme values were detected, no outliers were removed in order to maintain representativeness of normal profiles of EF in healthy adults. Therefore, the total sample size consisted of data from 284 participants. The fit statistics suggested that the model with three LPs had the best fit, producing minimum BIC and ssBIC values, and having the best relative model fit as determined by VLMR and aVLMR statistics (see Table 1). Table 2 shows the parameter 31 estimates for the three selected LPs. These parameters represent each latent profile's prevalence, the specific mean profiles, and the 95% confidence intervals considered in the LPA model. Significantly different (p<.05) performances between and within classes were determined via confidence intervals (CIs); performance means that were mutually exclusive across any two 95% CIs were considered statistically different. These profiles are described in detail below, identifying one large class characterized by generally average performance and two smaller classes characterized by distinct EF weaknesses. 32 33 MODEL IDENTIFICATION RESULTS Profiles of Executive Functioning In the three-profile solution, the first class was the more favorable profile (Latent Profile 1: LP1) and included the majority of the participants in the sample (203 participants; 71.5% of the sample). See Figure 5 for means of profiles for all markers of executive functioning. Participants belonging to LP1 were generally positive in their profile of EF abilities and were characterized by consistent performances across CT and switching markers. Within LP1, participants performed significantly better on set-formation trials under VT conditions relative to NVT conditions. The opposite pattern was observed on set-maintenance trials, with significantly better performances observed under NVT conditions compared to VT conditions. Because these differences are to be expected and do not reflect any EF weaknesses per se, participants belonging to LP1 were classified as having normal EF abilities. The other two classes were noticeably smaller and demonstrated weaknesses relative to LP1. The second class (LP2) consisted of 43 participants (15.1% of the sample). Participants in Profile 2 exhibited balanced performance across forming and switching trials (i.e., no significant difference between VT and NVT trials). Relative to these preserved performances, however, participants in LP2 exhibited a relative weakness across all comparison trials, with significantly poorer performances on NVT comparison trials compared to VT comparison trials. LP2 participants also performed 35 poorly on set-maintenance trials, but only under NVT conditions. Poor performance across comparison trials indicated that LP2 participants were both slower and less accurate on tasks involving low-executive demand, suggesting weaknesses in attention and arousal. In addition, difficulties with set-maintenance under NVT conditions only, combined with significant difficulties across comparison trials (especially under NVT conditions), suggests that maintaining cognitive set was particularly difficult for LP2 participants when the task also required right hemisphere activation. Because this combination of performances is consistent with weaknesses in mPFC and right hemisphere, LP2 was classified as having a right/superomedial EF weakness. The third class (LP3) was the smallest (38 participants; 13.4% of the sample) and demonstrated a near-inverted profile compared to LP2. Compared to LP1, participants in LP3 exhibited a relative weakness across all CT and switching trials (i.e., regardless of VT versus NVT conditions). When required to form or maintain mental set, LP3 participants performed poorly but only under VT conditions (i.e., performances were preserved under NVT conditions). Stated another way, Profile 3 was characterized by a global weakness (i.e., regardless of VT/NVT conditions) on tasks requiring cognitive flexibility (i.e., switching) and organized behavior (i.e., CT), combined with a specific weakness on tasks administered under VT conditions alone. Because this pattern of performances overlaps strongly with both the dysexecutive/disorganized syndrome characterizing dlPFC weakness as well as the cognitive inflexibility syndrome characterizing left hemisphere weakness, LP3 was classified as having a left/dorsolateral EF weakness. 36 Model Identification Discussion Results from the Model Identification Study indicated that that a 3-class model provided the best fit, resulting in one large class characterized by generally average executive functioning (EF) performances (LP1) and two smaller classes characterized by distinct EF weaknesses (LP2, LP3). Participants in LP2 exhibited a global weakness in attention and arousal combined with a relative set-maintenance weakness, especially on trials with greater demand for right hemisphere resources. Based on this combination of performances, LP2 participants were classified as having a right/superomedial EF weakness. Participants in LP3 exhibited a global weakness on tasks requiring cognitive flexibility and organized behavior, combined with a specific weakness on trials posing a greater demand on left hemisphere resources. Therefore, LP3 participants were classified as having a left/dorsolateral EF weakness. Together, these results provide support for the hemispheric asymmetry model, suggesting that set-maintenance abilities may be right hemisphere lateralized, whereas cognitive-flexibility abilities may be left hemisphere lateralized. 37 MODEL VALIDATION RESULTS Repeated-measures analysis of variance was conducted to detect significant within-subjects effects between latent profiles (LPs) on alerting, orienting, and executive attention performances on the ANT. There was a significant interaction effect between ANT performance and LP classification (F(4,554) = 5.938, p<.001), indicating that LP classification had different effects on ANT performances. Looking at the interaction graph in Figure 6, these effects reflect that classification into LP1 was associated with balanced performances across the three attentional networks, whereas classification into LP2 or LP3 resulted in distinct patterns of relative EF weaknesses. Consistent with hypotheses, LP2 participants demonstrated a relative weakness on orienting trials and LP3 participants demonstrated a relative weakness on executive attention trials. Contrary to predicted results, LP2 was also characterized by relatively poorer performances on alerting trials. Model Validation Discussion Results from the Model Validation Study are generally consistent with the predicted results. Specifically, LP1 participants exhibited balanced performances across the three ANT measures, consistent with the lack of apparent EF weaknesses characterizing LP1 in the Model Identification Study. Participants classified into the remaining LPs demonstrated contrasting performance patterns, consistent with 39 hemispherically lateralized EF weaknesses. In particular, LP2 participants exhibited a relative weakness on executive attention, consistent with a right/superomedial weakness, whereas LP3 participants exhibited a relative weakness on orienting, consistent with a left/dorsolateral weakness. Interestingly, the pattern predicted for alerting was not supported and indicated that a weakness on this attentional network was associated with LP3 (i.e., left/dorsolateral weakness) rather than LP2 (i.e., right/superomedial weakness). This discrepancy is interpreted in greater detail in the General Discussion section. 40 Figure 6. Interaction graph for the Attention Network Test. Latent profile classification is represented by three lines: Class 1 (solid line, circles), Class 2 (dotted line, squares), and Class 3 (dashed line, triangles). GENERAL DISCUSSION Profiles of executive strengths and weaknesses can be used to describe and predict certain patterns of behaviors (P. Williams, Y. Suchy, & H. Rau, 2009). The present study examined eight markers of neurocognitive function in order to identify profiles of executive functioning (EF) and provide support for neuroanatomical models of EF. Study design involved administering an experimental cognitive task that allowed assessment of switching, forming, and maintaining mental set under conditions believed to activate the left and right cerebral hemispheres. Within a sample of neurologically intact adults, three profiles of executive functioning emerged. These profiles suggest that most individuals exhibit patterns of average EF, with slightly better ability to conceptualize task demands (i.e., form mental set) under left-hemisphere-conditions and slightly better ability to sustain task-focused cognitions (i.e., maintain mental set) under right hemisphere conditions. Those with an EF weakness appeared to have contrasting profiles suggesting difficulties with either cognitive organization and flexibility or self-monitoring, arousal, and attentional control. Profiles of Executive Functioning This study approached EF from a multidimensional perspective, using latent profiles to characterize specific patterns of cognitive imbalances that may contribute to maladaptive executive functioning. In essence, Profile 1 depicts what we would expect 42 to see with hemispherically-balanced executive functioning, whereas Profiles 2 and 3 appear to depict manifestations of right or left hemispheric weaknesses as described below. No EF Weaknesses Of the three profiles, only participants belonging to LP1 demonstrated good performances (i.e., no detected weaknesses) across tasks and hemispheric conditions. Interestingly, LP1 participants also performed significantly better on tasks thought to be hemispherically-mediated, especially when those tasks were administered under hemispherically-activating conditions; that is, set-forming performances were better under VT conditions and set-maintaining performances were better under NVT conditions. This finding alone holds interest for three reasons. First, relatively better performances under conditions thought to preferentially activate separate cerebral hemispheres provides support for models emphasizing EF laterality, with reasoning and switching abilities being relegated to the left hemisphere and attention and self-monitoring abilities being relegated to the right hemisphere. Second, the left hemisphere-form and right hemisphere-maintain pattern suggests that balanced performance on other tasks (i.e., both low-executive/CT and high-executive/switching) requires a left-lateralized strength in reasoning and a right-lateralized strength in monitoring. Third, the notion that reasoning and monitoring may be asymmetrically lateralized provides support for opponent processor models of executive functioning (Osmon, 1996) and suggests that different EFs may function dynamically in the effort of balanced performance across various tasks. For example, LP1 participants performed equally well across the three 43 attentional network measures included in the Model Validation Study, suggesting balanced executive functioning abilities. As described below, the performance patterns observed in LP2 and LP3 support many of the notions suggested by LP1. Right/Superomedial Weakness Results from the Model Identification Study indicated that participants classified according to LP2 demonstrated significant set-maintenance difficulties, but only under nonverbal conditions. On CT trials, LP2 demonstrated a global weakness, consistent with research linking a right hemisphere weakness to difficulty maintaining an active attentional state. In addition, this group of participants performed significantly more poorly on verbal CT trials compared to nonverbal CT trials, suggesting that difficulties with alertness and arousal may become exacerbated under conditions thought to preferentially tax the right hemisphere. Because this particular pattern of performances is consistent with those characterizing the set-loss syndrome (i.e., hemispheric asymmetry model) and the apathetic/hypokinetic syndrome (i.e., prefrontal convexity model), this class was referred to as having a right/superomedial weakness. With respect to the Model Validation Study, a relative weakness on executive attention trials demonstrated by LP2 participants was consistent with a right/superomedial weakness. However, the lack of a predicted relative weakness on the ANT alerting variable was unexpected and can be interpreted in several ways. Keeping in mind that that alerting reflects a within-subject difference-score, comparable performances across double-cue and no-cue trials could reflect: (a) adequate ability to maintain attentional readiness, regardless of whether a cue preceded the flanker trial (i.e., 44 no alerting weakness); (b) globally-suppressed ability to maintain attentional readiness, regardless of whether a cue preceded the flanker trial (i.e., pervasive alerting weakness); or (c) poor innate arousal combined with an inability to benefit from visuospatial cues (i.e., arousal-visuospatial weakness). As a reminder, in the Model Identification Study LP2 was characterized by poor performance across low-executive demand conditions (i.e., comparison trials), consistent with the pervasive alerting weakness and arousal-visuospatial weakness interpretations described above. Importantly, however, on trials requiring consistent behavioral responses (i.e., comparison trials) or internally sustained mental representations (i.e., maintain trials), LP2 participants performed substantially more poorly under conditions thought to preferentially involve the right hemisphere (i.e., visuospatial classification). This distinct pattern of performances lends additional support to the arousal-visuospatial weakness interpretation. In sum, the results of the Model Validation Study appear to support those obtained in the Model Identification Study and further suggest that individuals characterized as having a right/superomedial weakness exhibit difficulty with alertness and arousal, especially under conditions that increase right hemisphere demands. Left/Dorsolateral Weakness The Model Identification Study revealed that LP3 produced a near-opposite profile relative to LP2; that is, a distinct weakness for all tasks performed under left-hemisphere- conditions (i.e., regardless of difficulty) combined with a specific weakness for CT and switching trials. Consistent with research implicating the left hemisphere in cognitive and behavioral set shifting, this finding suggests that a pervasive left 45 hemisphere weakness (i.e., such as that characterizing LP3) may contribute to poor performance on tasks that require cognitive flexibility. Because this particular pattern of performances is consistent with those characterizing the cognitive inflexibility syndrome (i.e., hemispheric asymmetry model) and the dysexecutive/disorganized syndrome (i.e., prefrontal convexity model), this class was referred to as having a left/dorsolateral weakness. In the Model Validation Study, LP3 participants exhibited a relative weakness on orienting, consistent with a left/dorsolateral weakness. Contrary to predicted results, however, LP3 participants also exhibited an alerting weakness; based on research linking alerting performances to increased right-hemisphere activation, poorer performances by LP3 participants (i.e., conceptualized as having a left-hemisphere weakness) was unexpected. However, considering that the alerting variable reflects a difference-score between no-cue and double-cue trials, one possibility is that relatively slower performances on no-cue trials could reflect cognitive inflexibility (i.e., difficulty shifting attention) rather than low levels of innate arousal, whereas relatively faster performances on double-cue trials could merely reflect compensation by the right-hemisphere (i.e., in response to a visuospatial cue). Alternatively, based on studies demonstrating increased left hemisphere activation in response to both auditory and visual temporal cues (see Coull 1998), one possibility is that the double-cue trials may have facilitated performance by providing information about timing. That is, increased left-hemisphere stimulation prior to trial exposure may have helped to initiate the process of shifting attention toward the upcoming target. Although this theory would need to be tested, this implies that certain conditions may help compensate for underlying weakness in cognitive flexibility. 46 Overall, the results of the Model Validation Study appear to support those obtained in the Model Identification Study and further suggest that individuals characterized as having a left/dorsolateral weakness exhibit difficulty with cognitive flexibility, especially under conditions that increase left hemisphere demands. Interpretation The results obtained in both the Model Identification and Model Verification studies provide support for the hemispheric asymmetry model. This indicates that hemispherically-mediated patterns of executive dysfunction observed in clinical populations may also exist within neurologically healthy populations. More broadly, the profiles of EF identified in this study demonstrate the utility of approaching EF from a multidimensional perspective. Rather than considering EF to be a dimensional construct ranging from good to poor or adaptive to maladaptive, this approach allowed for variability to occur across dimensions. Our results revealed that although most young adults demonstrate average EF abilities balanced across hemispheric domains, the unevenness characterizing individuals with EF-weaknesses may be partly attributable to hemispheric weaknesses. Limitations The three-profile solution obtained in the Model Identification Study must be interpreted with caution for several reasons. First, the fit statistics led us to stop at the three-profile solution, indicating that the vast majority of individuals in this sample of healthy adults exhibited average and consistent performances across EF markers. This larger profile likely reflects our particular sample, i.e., young college students who may 47 have better than average abilities to think abstractly, shift attention, and perform at a faster pace. Therefore, what we have interpreted as normal may in fact be above normal for the average adult, and the results of this study may not be generalizable to adults over the age of 30, those without a college background, or those with physical or mental health problems. Second, it is unclear what additional factors may be contributing to the profiles of EF-weakness characterizing the two smaller classes. A history of neuropsychiatric impairment could, for example, be responsible for the small number of individuals characterized as having either left or right hemisphere weakness. However, it is also possible that a small percentage of the population experiences similar profiles of weaknesses. Third, despite evidence linking switching/maintenance and verbal/spatial abilities to opposite hemispheres, the extent to which the two hemispheres interact during task performance is unclear. Although the left and right hemisphere likely play a dominant role in certain cognitive functions, both hemispheres are likely to involved to some degree with discrete EFs, thus making it difficult to conclude that set maintenance is governed primarily by the right hemisphere and set switching is governed primarily by the left hemisphere. Future Directions Future research examining profiles of EF should investigate similar markers in a more demographically diverse population. In addition, the implications of profiles of EF-weaknesses identified in this study should be examined with respect to the neurobiological underpinnings associated with psychological and neuropsychiatric impairment. Certain clinical populations are known to have executive deficits in either switching or maintaining mental set, and it is possible that these difficulties are associated 48 with gross hemispheric weaknesses. For example, behaviors characteristic of depression (e.g., impaired cognitive flexibility, poor initiation and motivation, ruminative thought patterns) may reflect hemispheric weaknesses associated with neuropsychological impairments, such as difficulty inhibiting prior mental set (Davis & Nolen-Hoeksema, 2000; Whitmer & Banich, 2007). Establishing these associations in clinical populations could potentially improve our understanding of the cognitive underpinnings of certain disorders and inform ways to improve clinical assessments, interventions, and treatments. REFERENCES Adrover-Roig, D., Sese, A., Barcelo, F., & Palmer, A. (2012). A latent variable approach to executive control in healthy ageing. Brain Cogn, 78(3), 284-299. Alexander, M. P., Stuss, D. T., Shallice, T., Picton, T. W., & Gillingham, S. (2005). Impaired concentration due to frontal lobe damage from two distinct lesion sites. Neurology, 65(4), 572-579. Allport, D. A., Styles, E. A., & Hsieh, S. (1994). Shifting intentional set: Exploring the dynamic control of tasks. Attention and performance: Conscious and nonconscious information processing (Vol. 15, pp. 421-452). Cambridge, MA: The MIT Press. Anderson, N. E., & Kiehl, K. A. (2012). The psychopath magnetized: Insights from brain imaging. Trends Cogn Sci, 16(1), 52-60. Anderson, V. A., Anderson, P., Northam, E., Jacobs, R., & Catroppa, C. (2001). Development of executive functions through late childhood and adolescence in an Australian sample. Dev Neuropsychol, 20(1), 385-406. Baker, S. C., Rogers, R. D., Owen, A. M., Frith, C. D., Dolan, R. J., Frackowiak, R. S., et al. (1996). Neural systems engaged by planning: A PET study of the Tower of London task. Neuropsychologia, 34(6), 515-526. Bamdad, M. J., Ryan, L. M., & Warden, D. L. (2003). Functional assessment of executive abilities following traumatic brain injury. Brain Inj, 17(12), 1011-1020. Bauer, D. J. (2007). Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research, 42(4), 757-786. Beeman, M. J., & Bowden, E. M. (2000). The right hemisphere maintains solution-related activation for yet-to-be-solved problems. Mem Cognit, 28(7), 1231-1241. Beer, J. S., Heerey, E. A., Keltner, D., Scabini, D., & Knight, R. T. (2003). The regulatory function of self-conscious emotion: Insights from patients with orbitofrontal damage. J Pers Soc Psychol, 85(4), 594-604. 50 Beer, J. S., John, O. P., Scabini, D., & Knight, R. T. (2006). Orbitofrontal cortex and social behavior: Integrating self-monitoring and emotion-cognition interactions. J Cogn Neurosci, 18(6), 871-879. Berthoz, S., Armony, J. L., Blair, R. J., & Dolan, R. J. (2002). An fMRI study of intentional and unintentional (embarrassing) violations of social norms. Brain, 125(Pt 8), 1696-1708. Blair, K. S., Morton, J., Leonard, A., & Blair, R. J. (2006). Impaired decision-making on the basis of both reward and punishment information in individuals with psychopathy. Personality and Invidual Differences, 41(1), 155-165. Blair, K. S., Newman, C., Mitchell, D. G., Richell, R. A., Leonard, A., Morton, J., et al. (2006). Differentiating among prefrontal substrates in psychopathy: Neuropsychological test findings. Neuropsychology, 20(2), 153-165. Blumenfeld, R. S., & Ranganath, C. (2006). Dorsolateral prefrontal cortex promotes long-term memory formation through its role in working memory organization. J Neurosci, 26(3), 916-925. Boone, K. B., Ponton, M. O., Gorsuch, R. L., Gonzalez, J. J., & Miller, B. L. (1998). Factor analysis of four measures of prefrontal lobe functioning. Arch Clin Neuropsychol, 13(7), 585-595. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychol Rev, 108(3), 624-652. Brazil, I. A., de Bruijn, E. R., Bulten, B. H., von Borries, A. K., van Lankveld, J. J., Buitelaar, J. K., et al. (2009). Early and late components of error monitoring in violent offenders with psychopathy. Biol Psychiatry, 65(2), 137-143. Brower, M. C., & Price, B. H. (2001). Neuropsychiatry of frontal lobe dysfunction in violent and criminal behaviour: A critical review. J Neurol Neurosurg Psychiatry, 71(6), 720-726. Brown, T. E., & McMullen, W. J. (2006). Attention deficit disorders and sleep/arousal disturbances. Annals of the New York Academy of Sciences, 931(1), 271-286. Burdick, K. E., Robinson, D. G., Malhotra, A. K., & Szeszko, P. R. (2008). Neurocognitive profile analysis in obsessive-compulsive disorder. J Int Neuropsychol Soc, 14(4), 640-645. Burgess, P. W., Alderman, N., Evans, J., Emslie, H., & Wilson, B. A. (1998). The ecological validity of tests of executive function. J Int Neuropsychol Soc, 4(6), 547-558. 51 Busch, R. M., McBride, A., Curtiss, G., & Vanderploeg, R. D. (2005). The components of executive functioning in traumatic brain injury. J Clin Exp Neuropsychol, 27(8), 1022-1032. Bush, G., Frazier, J. A., Rauch, S. L., Seidman, L. J., Whalen, P. J., Jenike, M. A., et al. (1999). Anterior cingulate cortex dysfunction in attention-deficit/hyperactivity disorder revealed by fMRI and the Counting Stroop. Biol Psychiatry, 45(12), 1542-1552. Callicott, J. H., Bertolino, A., Mattay, V. S., Langheim, F. J., Duyn, J., Coppola, R., et al. (2000). Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cereb Cortex, 10(11), 1078-1092. Cepeda, N. J., Kramer, A. F., & Gonzalez de Sather, J. C. (2001). Changes in executive control across the life span: Examination of task-switching performance. Dev Psychol, 37(5), 715-730. Cohen, G. (1972). Hemispheric differences in a letter classification task. Perception & Psychophysics, 11(2), 139-142. Collette, F., Van der Linden, M., Laureys, S., Delfiore, G., Degueldre, C., Luxen, A., et al. (2005). Exploring the unity and diversity of the neural substrates of executive functioning. Hum Brain Mapp, 25(4), 409-423. Colvin, M. K., Dunbar, K., & Grafman, J. (2001). The effects of frontal lobe lesions on goal achievement in the water jug task. J Cogn Neurosci, 13(8), 1129-1147. Connors, C. K. (2000). Continuous Performance Test II. Toronto: Multi-Health System. Crow, T. J. (1997). Schizophrenia as failure of hemispheric dominance for language. Trends Neurosci, 20(8), 339-343. Cummings, J. L., & Miller, B. L. (2007). Conceptual and clinical aspects of the frontal lobes. In B. L. Miller & J. L. Cummings (Eds.), The human frontal lobes: Functions and disorders. (2nd ed., pp. 12-21). New York, NY, US: Guilford Press. Cunnington, R., Iansek, R., Thickbroom, G. W., Laing, B. A., Mastaglia, F. L., Bradshaw, J. L., et al. (1996). Effects of magnetic stimulation over supplementary motor area on movement in Parkinson's disease. Brain, 119 ( Pt 3), 815-822. Davis, R. N., & Nolen-Hoeksema, S. (2000). Cognitive inflexibility among ruminators and nonruminators. Psychological Medicine, 24, 699-711. 52 Demakis, G. J. (2003). A meta-analytic review of the sensitivity of the Wisconsin Card Sorting Test to frontal and lateralized frontal brain damage. Neuropsychology, 17(2), 255-264. Deutsch, G., Papanicolaou, A. C., Bourbon, W. T., & Eisenberg, H. M. (1987). Cerebral blood flow evidence of right frontal activation in attention demanding tasks. Int J Neurosci, 36(1-2), 23-28. Devinsky, O., Morrell, M. J., & Vogt, B. A. (1995). Contributions of anterior cingulate cortex to behaviour. Brain, 118 ( Pt 1), 279-306. Doiseau, F., & Isingrini, M. (2005). Updating information in verbal working memory and executive functioning. Psychol Rep, 96(1), 67-76. Dreher, J. C., & Grafman, J. (2003). Dissociating the roles of the rostral anterior cingulate and the lateral prefrontal cortices in performing two tasks simultaneously or successively. Cereb Cortex, 13(4), 329-339. Duffy, J. D., & Campbell, J. J., 3rd. (1994). The regional prefrontal syndromes: A theoretical and clinical overview. J Neuropsychiatry Clin Neurosci, 6(4), 379- 387. Duffy, J. D., Campbell, J. J., 3rd, Salloway, S. P., & Malloy, P. F. (2001). Regional prefrontal syndromes: A theoretical and clinical overview. In S. P. Salloway, P. F. Malloy & J. D. Duffy (Eds.), The frontal lobes and neuropsychiatric illness (pp. 113-123). Arlington, VA: American Psychiatric Publishin. Eastvold, A., Suchy, Y., & Strassberg, D. (2011). Executive function profiles of pedophilic and nonpedophilic child molesters. J Int Neuropsychol Soc, 17(2), 295-307. Ernst, M., Kimes, A. S., London, E. D., Matochik, J. A., Eldreth, D., Tata, S., et al. (2003). Neural substrates of decision making in adults with attention deficit hyperactivity disorder. Am J Psychiatry, 160(6), 1061-1070. Ersche, K. D., Clark, L., London, M., Robbins, T. W., & Sahakian, B. J. (2006). Profile of executive and memory function associated with amphetamine and opiate dependence. Neuropsychopharmacology, 31(5), 1036-1047. Eslinger, P. J., Biddle, K., Pennington, B., & Page, R. B. (1999). Cognitive and behavioral development up to 4 years after earlyr right frontal lobe lesion. Developmental Neuropsychology, 15(2), 157-191. Fan, J., McCandliss, B. D., Sommer, T., Raz, A., & Posner, M. I. (2002). Testing the efficiency and independence of attentional networks. J Cogn Neurosci, 14(3), 340-347. 53 Farah, M. J., Gazzaniga, M. S., Holtzman, J. D., & Kosslyn, S. M. (1985). A left hemisphere basis for visual mental imagery? Neuropsychologia, 23(1), 115-118. Fernandez-Duque, D., & Posner, M. I. (2001). Brain imaging of attentional networks in normal and pathological states. J Clin Exp Neuropsychol, 23(1), 74-93. Flor-Henry, P. (2003). Lateralized temporal-limbic dysfunction and psychopathology. 1976. Epilepsy Behav, 4(5), 578-590. Frith, C. D., Friston, K., Liddle, P. F., & Frackowiak, R. S. (1991). Willed action and the prefrontal cortex in man: A study with PET. Proc Biol Sci, 244(1311), 241-246. Garavan, H., Ross, T. J., Murphy, K., Roche, R. A., & Stein, E. A. (2002). Dissociable executive functions in the dynamic control of behavior: Inhibition, error detection, and correction. Neuroimage, 17(4), 1820-1829. Gazzaley, A., D'Esposito, M., Miller, B. L., & Cummings, J. L. (2007). Unifying prefrontal cortex function: Executive control, neural networks, and top-down modulation. In B. L. Miller & J. L. Cummings (Eds.), The human frontal lobes: Functions and disorders (2nd ed., pp. 187-206). New York, NY, US: Guilford Press. Gerloff, C., Corwell, B., Chen, R., Hallett, M., & Cohen, L. G. (1997). Stimulation over the human supplementary motor area interferes with the organization of future elements in complex motor sequences. Brain, 120 ( Pt 9), 1587-1602. Giancola, P. R., & Moss, H. B. (1998). Executive cognitive functioning in alcohol use disorders. Recent Dev Alcohol, 14, 227-251. Giancola, P. R., & Tarter, R. E. (1999). Executive cognitive functioning and risk for substance abuse. Psychological Science, 10(3), 203-205. Goldberg, G. (1985). Supplementary motor area structure and function: Review and hypotheses. The Behavioral and Brain Sciences, 8, 567-616. Goldman, R. S., Axelrod, B. N., Heaton, R. K., & Chelune, G. J. (1996). Latent structure of the WCST with the standardization samples. Assessment, 3(1), 73. Gooding, D. C., Tallent, K. A., & Hegyi, J. V. (2001). Cognitive slippage in schizotypic individuals. J Nerv Ment Dis, 189(11), 750-756. Gootjes, L., Raij, T., Salmelin, R., & Hari, R. (1999). Left-hemisphere dominance for processing of vowels: A whole-scalp neuromagnetic study. Neuroreport, 10(14), 2987-2991. 54 Gopher, D., Armony, L., & Greenshpan, Y. (2000). Switching tasks and attention policies. J Exp Psychol Gen, 129(3), 308-339. Gorenstein, E. E. (1982). Frontal lobe functions in psychopaths. J Abnorm Psychol, 91(5), 368-379. Grafman, J., Jonas, B., & Salazar, A. (1990). Wisconsin Card Sorting Test performance based on location and size of neuroanatomical lesion in Vietnam veterans with penetrating head injury. Percept Mot Skills, 71(3 Pt 2), 1120-1122. Grinband, J., Savitskaya, J., Wager, T. D., Teichert, T., Ferrera, V. P., & Hirsch, J. (2011). The dorsal medial frontal cortex is sensitive to time on task, not response conflict or error likelihood. Neuroimage, 57(2), 303-311. Grossman, M., Payer, F., Onishi, K., D'Esposito, M., Morrison, D., Sadek, A., et al. (1998). Language comprehension and regional cerebral defects in frontotemporal degeneration and Alzheimer's disease. Neurology, 50(1), 157-163. Gundel, A., & Wilson, G. F. (1992). Topographical changes in the ongoing EEG related to the difficulty of mental tasks. Brain Topogr, 5(1), 17-25. Gur, R. E., Gur, R. C., Skolnick, B. E., Caroff, S., Obrist, W. D., Resnick, S., et al. (1985). Brain function in psychiatric disorders. III. Regional cerebral blood flow in unmedicated schizophrenics. Arch Gen Psychiatry, 42(4), 329-334. Hanna-Pladdy, B. (2007). Dysexecutive syndromes in neurologic disease. J Neurol Phys Ther, 31(3), 119-127. Heaton, R. K., Chelune, G. J., Talley, J. L., Kay, G. G., & Curtiss, G. (1993). Wisconsin Card Sorting Test manual: Revised and expanded: Psychological Assessment Resources, Inc. Henson, J. M., Reise, S. P., & Kim, K. H. (2007). Detecting mixtures from structural model differences using latent variable mixture modeling: A comparison of relative model fit statistics. Structural Equation Modeling, 14(2), 202-226. Hinkin, C. H., Castellon, S. A., Durvasula, R. S., Hardy, D. J., Lam, M. N., Mason, K. I., et al. (2002). Medication adherence among HIV+ adults: Effects of cognitive dysfunction and regimen complexity. Neurology, 59, 1944-1950. Hornak, J., O'Doherty, J., Bramham, J., Rolls, E. T., Morris, R. G., Bullock, P. R., et al. (2004). Reward-related reversal learning after surgical excisions in orbito-frontal or dorsolateral prefrontal cortex in humans. J Cogn Neurosci, 16(3), 463-478. 55 Hull, R., Martin, R. C., Beier, M. E., Lane, D., & Hamilton, A. C. (2008). Executive function in older adults: A structural equation modeling approach. Neuropsychology, 22(4), 508-522. Jager, G., & Postma, A. (2003). On the hemispheric specialization for categorical and coordinate spatial relations: A review of the current evidence. Neuropsychologia, 41(4), 504-515. Jersild, A. T. (1927). Mental set and shift. Archives of Psychology, 89, 5-82. Karnath, H.-O., & Kammer, T. (2003). Manifestationen von Frontalhirnschadigungen. In H.-O. Karnath & P. Thier (Eds.), Neuropsychologie (pp. 515-528). Berlin: Springer. Keele, S. W., & Rafal, R. (2000). Deficits of task-set in patients with left prefrontal cortex lesions. In S. Monsell & J. S. Driver (Eds.), Control of cognitive processes: Attention and performance (Vol. XVIII, pp. 627-651): MIT Press. Kim, J., & Ragozzino, M. E. (2005). The involvement of the orbitofrontal cortex in learning under changing task contingencies. Neurobiol Learn Mem, 83(2), 125- 133. Kosslyn, S. M., Koenig, O., Barrett, A., Cave, C. B., Tang, J., & Gabrieli, J. D. (1989). Evidence for two types of spatial representations: Hemispheric specialization for categorical and coordinate relations. J Exp Psychol Hum Percept Perform, 15(4), 723-735. Kouneiher, F., Charron, S., & Koechlin, E. (2009). Motivation and cognitive control in the human prefrontal cortex. Nat Neurosci, 12(7), 939-945. Kramer, A. F., Hahn, S., & Gopher, D. (1999). Task coordination and aging: Explorations of executive control processes in the task switching paradigm. Acta Psychol (Amst), 101(2-3), 339-378. Kray, J., Li, K. Z., & Lindenberger, U. (2002). Age-related changes in task-switching components: The role of task uncertainty. Brain Cogn, 49(3), 363-381. Kray, J., & Lindenberger, U. (2000). Adult age differences in task switching. Psychol Aging, 15(1), 126-147. Kringelbach, M. L. (2005). The human orbitofrontal cortex: Linking reward to hedonic experience. Nat Rev Neurosci, 6(9), 691-702. Kroger, J. K., Sabb, F. W., Fales, C. L., Bookheimer, S. Y., Cohen, M. S., & Holyoak, K. J. (2002). Recruitment of anterior dorsolateral prefrontal cortex in human 56 reasoning: A parametric study of relational complexity. Cereb Cortex, 12(5), 477- 485. Kucera-Thompson, J. (2003). Executive processes and performance on the Booklet Category Test. Dissertation Abstracts International: Section B: The Sciences and Engineering, 63(8-B), 3924. Lanza, S., Flaherty, B. P., & Collins, L. M. (2003). Latent class and latent transition analysis (Vol. 2). New York: Wiley. Laplane, D., Talairach, J., Meininger, V., Bancaud, J., & Orgogozo, J. M. (1977). Clinical consequences of corticectomies involving the supplementary motor area in man. J Neurol Sci, 34(3), 301-314. Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767-778. Lombardi, W. J., Andreason, P. J., Sirocco, K. Y., Rio, D. E., Gross, R. E., Umhau, J. C., et al. (1999). Wisconsin Card Sorting Test performance following head injury: Dorsolateral fronto-striatal circuit activity predicts perseveration. J Clin Exp Neuropsychol, 21(1), 2-16. Lorist, M. M., Klein, M., Nieuwenhuis, S., De Jong, R., Mulder, G., & Meijman, T. F. (2000). Mental fatigue and task control: Planning and preparation. Psychophysiology, 37(5), 614-625. Lubke, G., & Neale, M. C. (2006). Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood? Multivariate Behavioral Research, 41, 499-532. Mah, L., Arnold, M. C., & Grafman, J. (2004). Impairment of social perception associated with lesions of the prefrontal cortex. Am J Psychiatry, 161(7), 1247- 1255. Manoach, D. S., Gollub, R. L., Benson, E. S., Searl, M. M., Goff, D. C., Halpern, E., et al. (2000). Schizophrenic subjects show aberrant fMRI activation of dorsolateral prefrontal cortex and basal ganglia during working memory performance. Biol Psychiatry, 48(2), 99-109. Martin, A. (1999). Automatic activation of the medial temporal lobe during encoding: Lateralized influences of meaning and novelty. Hippocampus, 9(1), 62-70. Mecklinger, A. D., von Cramon, D. Y., Springer, A., & Matthes-von Cramon, G. (1999). Executive control functions in task switching: Evidence from brain injured patients. J Clin Exp Neuropsychol, 21(5), 606-619. 57 Mega, M. S., & Cummings, J. L. (2001). Frontal subcortical circuits: Anatomy and function. In S. P. Salloway, P. F. Malloy & J. D. Duffy (Eds.), The frontal lobes and neuropsychiatric illness (pp. 15-32). Washington D.C.: American Psychiatric Publishing, Inc. Meyer, D. E., Evans, J. E., Lauber, E. J., Gmeindl, L., Rubinstein, J., Junck, L., et al. (1998). The role of dorsolateral prefrontal cortex for executive cognitive processes in task switching. Journal of Cognitive Neuroscience, Supplement S, 106. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annu Rev Neurosci, 24, 167-202. Milner, B. (1963). Effects of different brain lesions on card sorting: The role of the frontal lobes. Archives of Neurology, 9, 90-100. 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 complex ‘Frontal Lobe' tasks: A latent variable analysis. Cogn Psychol, 41(1), 49-100. Moll, J., de Oliveira-Souza, R., Moll, F. T., Bramati, I. E., & Andreiuolo, P. A. (2002). The cerebral correlates of set-shifting: An fMRI study of the trail making test. Arq Neuropsiquiatr, 60(4), 900-905. Monsell, S., Yeung, N., & Azuma, R. (2000). Reconfiguration of task-set: Is it easier to switch to the weaker task? Psychol Res, 63(3-4), 250-264. Morgan, A. B., & Lilienfeld, S. O. (2000). A meta-analytic review of the relation between antisocial behavior and neuropsychological measures of executive function. Clin Psychol Rev, 20(1), 113-136. Muthen, L. K., & Muthen, B. O. (2009). Mplus User's Guide (5 ed.). Los Angeles. Nagahama, Y., Okina, T., Suzuki, N., Matsuzaki, S., Yamauchi, H., Nabatame, H., et al. (2003). Factor structure of a modified version of the wisconsin card sorting test: An analysis of executive deficit in Alzheimer's disease and mild cognitive impairment. Dement Geriatr Cogn Disord, 16(2), 103-112. Nagin, D. S. (1999). Analyzing developmental trajectories: A semi-parametric group-based approach. Psychological Methods, 4, 139-157. Nathaniel-James, D. A., & Frith, C. D. (2002). The role of the dorsolateral prefrontal cortex: Evidence from the effects of contextual constraint in a sentence completion task. Neuroimage, 16(4), 1094-1102. 58 Newman, J. P., Patterson, C. M., & Kosson, D. S. (1987). Response perseveration in psychopaths. J Abnorm Psychol, 96(2), 145-148. Newman, J. P., Schmitt, W. A., & Voss, W. D. (1997). The impact of motivationally neutral cues on psychopathic individuals: Assessing the generality of the response modulation hypothesis. J Abnorm Psychol, 106(4), 563-575. Newman, S. D., Carpenter, P. A., Varma, S., & Just, M. A. (2003). Frontal and parietal participation in problem solving in the Tower of London: fMRI and computational modeling of planning and high-level perception. Neuropsychologia, 41(12), 1668-1682. Nigg, J. T., John, O. P., Blaskey, L. G., Huang-Pollock, C. L., Willcutt, E. G., Hinshaw, S. P., et al. (2002). Big five dimensions and ADHD symptoms: Links between personality traits and clinical symptoms. J Pers Soc Psychol, 83(2), 451-469. Nylund, K. L., Asparouhov, T., & Muthen, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14, 535-569. O'Doherty, J., Kringelbach, M. L., Rolls, E. T., Hornak, J., & Andrews, C. (2001). Abstract reward and punishment representations in the human orbitofrontal cortex. Nat Neurosci, 4(1), 95-102. Osmon, D. C. (1996). Understanding symptoms of medial frontal lobe disorder: A clinical case study. Journal of Clinical Psychology in Medical Settings, 3(1), 23- 39. Palmer, B. W., & Heaton, R. K. (2000). Executive dysfunction in schizophrenia. In T. Sharma & P. D. Harvey (Eds.), Cognition in schizophrenia: Impairments, importance and treatment strategies (pp. 52-72). New York, NY: Oxford University Press. Pardo, J. V., Fox, P. T., & Raichle, M. E. (1991). Localization of a human system for sustained attention by positron emission tomography. Nature, 349(6304), 61-64. Parent, A. (1990). Extrinsic connections of the basal ganglia. Trends Neurosci, 13(7), 254-258. Phan, K. L., Taylor, S. F., Welsh, R. C., Decker, L. R., Noll, D. C., Nichols, T. E., et al. (2003). Activation of the medial prefrontal cortex and extended amygdala by individual ratings of emotional arousal: A fMRI study. Biol Psychiatry, 53(3), 211-215. 59 Picton, T. W., Stuss, D. T., Shallice, T., Alexander, M. P., & Gillingham, S. (2006). Keeping time: Effects of focal frontal lesions. Neuropsychologia, 44(7), 1195- 1209. Piguet, O., Grayson, D. A., Tate, R. L., Bennett, H. P., Lye, T. C., Creasey, H., et al. (2005). A model of executive functions in very old community dwellers: Evidence from The Sydney Older Persons Study. Cortex, 41(1), 27-37. Pochon, J. B., Levy, R., Poline, J. B., Crozier, S., Lehericy, S., Pillon, B., et al. (2001). The role of dorsolateral prefrontal cortex in the preparation of forthcoming actions: An fMRI study. Cereb Cortex, 11(3), 260-266. Price, J. L. (1999). Prefrontal cortical networks related to visceral function and mood. Ann N Y Acad Sci, 877, 383-396. Pujol, J., Vendrell, P., Deus, J., Junque, C., Bello, J., Marti-Vilalta, J. L., et al. (2001). The effect of medial frontal and posterior parietal demyelinating lesions on stroop interference. Neuroimage, 13(1), 68-75. Reichenberg, A., Harvey, P. D., Bowie, C. R., Mojtabai, R., Rabinowitz, J., Heaton, R. K., et al. (2009). Neuropsychological function and dysfunction in schizophrenia and psychotic affective disorders. Schizophr Bull, 35(5), 1022-1029. Rezai, K., Andreasen, N. C., Alliger, R., Cohen, G., Swayze, V., 2nd, & O'Leary, D. S. (1993). The neuropsychology of the prefrontal cortex. Arch Neurol, 50(6), 636- 642. Robinson, L. J., Thompson, J. M., Gallagher, P., Goswami, U., Young, A. H., Ferrier, I. N., et al. (2006). A meta-analysis of cognitive deficits in euthymic patients with bipolar disorder. J Affect Disord, 93(1-3), 105-115. Rogers, R. D., Sahakian, B. J., Hodges, J. R., Polkey, C. E., Kennard, C., & Robbins, T. W. (1998). Dissociating executive mechanisms of task control following frontal lobe damage and Parkinson's disease. Brain, 121 ( Pt 5), 815-842. Ross, T. P. (1995). Theoretical models of prefrontal function: An empirical examination of three models of executive functioning using confirmatory factor analysis. Dissertation Abstracts International: Section B: The Sciences and Engineering, 56(2-B), 1146. Rueckert, L., & Grafman, J. (1998). Sustained attention deficits in patients with lesions of posterior cortex. Neuropsychologia, 36(7), 653-660. Rund, B. R., & Borg, N. E. (1999). Cognitive deficits and cognitive training in schizophrenic patients: A review. Acta Psychiatr Scand, 100(2), 85-95. 60 Rushworth, M. F., Walton, M. E., Kennerley, S. W., & Bannerman, D. M. (2004). Action sets and decisions in the medial frontal cortex. Trends Cogn Sci, 8(9), 410-417. Salthouse, T. A., Fristoe, N., McGuthry, K. E., & Hambrick, D. Z. (1998). Relation of task switching to speed, age, and fluid intelligence. Psychol Aging, 13(3), 445- 461. Savla, G. N., Twamley, E. W., Delis, D. C., Roesch, S. C., Jeste, D. V., & Palmer, B. W. (2012). Dimensions of executive functioning in schizophrenia and their relationship with processing speed. Schizophr Bull, 38(4), 760-768. Schretlen, D., Pearlson, G. D., Anthony, J. C., Aylward, E. H., Augustine, A. M., Davis, A., et al. (2000). Elucidating the contributions of processing speed, executive ability, and frontal lobe volume to normal age-related differences in fluid intelligence. J Int Neuropsychol Soc, 6(1), 52-61. Sclove, L. S. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333-343. Sequin, J. R. (2004). Neurocognitive elements of antisocial behavior: Relevance of an orbitofronal cortex account. Brain and Cognition, 55(1), 185-197. Shimamura, A. P. (2000). The role of the prefrontal cortex in dynamic filtering. Psychobiology, 28(2), 207-218. Stefanatos, G. A., & Wasserstein, J. (2001). Attention deficit/hyperactivity disorder as a right hemisphere syndrome. Selective literature review and detailed neuropsychological case studies. Ann N Y Acad Sci, 931, 172-195. Stilley, C. S., Bender, C. M., Dunbar-Jacob, J., Sereika, S., & Ryan, C. S. (2010). The impact of cognitive function on medication management: Three studies. Health Psychology, 29(1), 50-55. Stout, J. C., Ready, R. E., Grace, J., Malloy, P. F., & Paulsen, J. S. (2003). Factor analysis of the frontal systems behavior scale (FrSBe). Assessment, 10(1), 79-85. Sturm, W., de Simone, A., Krause, B. J., Specht, K., Hesselmann, V., Radermacher, I., et al. (1999). Functional anatomy of intrinsic alertness: Evidence for a fronto-parietal- thalamic-brainstem network in the right hemisphere. Neuropsychologia, 37(7), 797-805. Sturm, W., & Willmes, K. (2001). On the functional neuroanatomy of intrinsic and phasic alertness. Neuroimage, 14(1 Pt 2), S76-84. Stuss, D. T. (1992). Biological and psychological development of executive functions. Brain Cogn, 20(1), 8-23. 61 Stuss, D. T., & Alexander, M. P. (2007). Is there a dysexecutive syndrome? Philos Trans R Soc Lond B Biol Sci, 362(1481), 901-915. Stuss, D. T., Alexander, M. P., Floden, D., Binns, M. A., Levine, B., McIntosh, A. R., et al. (2002). Fractionation and localization of distinct frontal lobe processes: Evidence from focal lesions in humans. In D. T. Stuss & R. T. Knight (Eds.), Principles of frontal lobe function (pp. 392-407). London: Oxford University Press. Stuss, D. T., Alexander, M. P., Hamer, L., Palumbo, C., Dempster, R., Binns, M., et al. (1998). The effects of focal anterior and posterior brain lesions on verbal fluency. J Int Neuropsychol Soc, 4(3), 265-278. Stuss, D. T., Benson, D. F., Kaplan, E. F., Weir, W. S., Naeser, M. A., Lieberman, I., et al. (1983). The involvement of orbitofrontal cerebrum in cognitive tasks. Neuropsychologia, 21(3), 235-248. Stuss, D. T., Levine, B., Alexander, M. P., Hong, J., Palumbo, C., Hamer, L., et al. (2000). Wisconsin Card Sorting Test performance in patients with focal frontal and posterior brain damage: Effects of lesion location and test structure on separable cognitive processes. Neuropsychologia, 38(4), 388-402. Suchy, Y. (2009). Executive functioning: Overview, assessment, and research issues for non-neuropsychologists. Ann Behav Med, 37(2), 106-116. Suchy, Y., Gold, A., Biechler, R., & Osmon, D. C. (2003). Set maintenance and switching differences in college students with inattentive and impulsive features. Archives of Clinical Neuropsychology, 18(765-766). Suchy, Y., & Kosson, D. S. (2006). Forming, switching, and maintaining mental sets among psychopathic offenders during verbal and nonverbal tasks: Another look at the left-hemisphere activation hypothesis. J Int Neuropsychol Soc, 12(4), 538- 548. Suchy, Y., Whittaker, J. W., Strassberg, D. S., & Eastvold, A. (2009). Neurocognitive differences between pedophilic and nonpedophilic child molesters. J Int Neuropsychol Soc, 15(2), 248-257. Suchy, Y., Williams, P. G., Kraybill, M. L., Franchow, E., & Butner, J. (2010). Instrumental activities of daily living among community-dwelling older adults: Personality associations with self-report, performance, and awareness of functional difficulties. J Gerontol B Psychol Sci Soc Sci, 65(5), 542-550. 62 Sylvester, C. Y., Wager, T. D., Lacey, S. C., Hernandez, L., Nichols, T. E., Smith, E. E., et al. (2003). Switching attention and resolving interference: fMRI measures of executive functions. Neuropsychologia, 41(3), 357-370. Tamm, L., Menon, V., Ringel, J., & Reiss, A. L. (2004). Event-related FMRI evidence of frontotemporal involvement in aberrant response inhibition and task switching in attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry, 43(11), 1430-1440. Tofighi, D., & Enders, C. K. (2007). Identifying the correct number of classes in growth mixture models. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 317-341). Greewich, CT: Information Age. Troyer, A. K., Moscovitch, M., Winocur, G., Alexander, M. P., & Stuss, D. (1998). Clustering and switching on verbal fluency: The effects of focal frontal- and temporal-lobe lesions. Neuropsychologia, 36(6), 499-504. Vaughan, L., & Giovanello, K. (2010). Executive function in daily life: Age-related influences of executive processes on instrumental activities of daily living. Psychol Aging, 25(2), 343-355. Vendrell, P., Junque, C., Pujol, J., Jurado, M. A., Molet, J., & Grafman, J. (1995). The role of prefrontal regions in the Stroop task. Neuropsychologia, 33(3), 341-352. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-tested hypotheses. Econometrics, 57, 307-333. Weinberger, D. R., Berman, K. F., & Zec, R. F. (1986). Physiologic dysfunction of dorsolateral prefrontal cortex in schizophrenia. I. Regional cerebral blood flow evidence. Arch Gen Psychiatry, 43(2), 114-124. Whitmer, A. J., & Banich, M. T. (2007). Inhibition versus switching deficits in different forms of rumination. Psychol Sci, 18(6), 546-553. Wilkins, A., & Stewart, A. (1974). The time course of lateral asymmetries in visual perception of letters. J Exp Psychol, 102(5), 905-908. Wilkins, A. J., Shallice, T., & McCarthy, R. (1987). Frontal lesions and sustained attention. Neuropsychologia, 25(2), 359-365. Williams, P., Suchy, Y., & Rau, H. (2009). Individual differences in executive functioning: Implications for stress regulation. Annals of Behavioral Medicine, in press. 63 Williams, P. G., Suchy, Y., & Kraybill, M. L. (2010). Five-factor model personality traits and executive functioning in older adults. Journal of Research in Personality, 44, 485-491. Williams, P. G., Suchy, Y., & Rau, H. K. (2009). Individual differences in executive functioning: Implications for stress regulation. Ann Behav Med, 37(2), 126-140. Willner, P., Bailey, R., Parry, R., & Dymond, S. (2010). Evaluation of executive functioning in people with intellectual disabilities. J Intellect Disabil Res, 54(4), 366-379.