Dissociating strategy-dependent and independent components in task preparation

Neuropsychologia ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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Dissociating strategy-dependent and independent components in task preparation Wouter De Baene n, Marcel Brass Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, Ghent B-9000, Belgium

art ic l e i nf o

Keywords: Task preparation Strategy Functional dissociation Contextual effects

a b s t r a c t A central aspect of cognitive control is the capacity to anticipatorily prepare for specific task requirements prior to carrying out a task. To study the changes caused by task preparation, the cued task-switching paradigm has generally been used. While research on anticipatory control has long focused on general processing differences between switch and repeat trials, more recent research suggests that contextual variations strongly modulate such differences. In the current paper, we argue that anticipatory task set preparation strongly depends on contextual variables leading to different strategies to prepare for an upcoming task. We provide behavioral as well as neuroscientific evidence for this claim. Furthermore, we show that some preparatory processes are sensitive to strategic modulations whereas other preparatory processes are not. Based on this, we propose a functional dissociation within the fronto-parietal network involved in task preparation. & 2014 Elsevier Ltd. All rights reserved.

1. Introduction In daily life, we permanently need to adapt our behavior to new task situations, requiring cognitive control. Cognitive control processes refer to the ability to flexibly adapt one’s thoughts and actions in the pursuit of internal goals. A central aspect of cognitive control is the capacity to anticipatorily prepare for specific task requirements prior to carrying out the task. A large body of behavioral research has used the cued task-switching paradigm (Meiran, 1996) to study the preparatory changes that enable fluent task implementation (for reviews, see Kiesel et al., 2010; Vandierendonck, Liefooghe, & Verbruggen, 2010). In the cuing procedure, a task cue indicates which task needs to be executed on each trial. By changing the time between the cue and the target, the degree of anticipatory preparation can be manipulated. Consequently, this procedure allows dissociating different preparation-related components from execution-related components in task switching. Independent of the specifics of the tasks that are used, a common observation in behavioral task-switching studies is that people are generally slower and less accurate at switching than at repeating tasks but these switch costs are reduced when participants are able to prepare the next task (Hoffmann, Kiesel, & Sebald, 2003; Kiesel & Hoffmann, 2004; Koch, 2001; Meiran, 1996; Meiran, Chorev, & Sapir, 2000; Monsell, Sumner, & Waters, 2003; Rogers & Monsell, 1995). n Correspondence to: Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, Ghent, B-9000, Belgium. Tel.: þ32 9 2646404; fax: þ32 9 2646496. E-mail address: [email protected] (W. De Baene).

Traditionally, two opposing theoretical models on the source of the task switch cost and the preparatory reductions of these costs have been proposed (for a review, see Vandierendonck et al., 2010). According to the “reconfiguration” account, switch trials as compared to repetition trials require additional reconfiguration processes (e.g. Rogers & Monsell, 1995; Rubinstein, Meyer, & Evans, 2001). According to this view, the switch cost reflects the time needed to reconfigure a task set (Monsell & Mizon, 2006; Rogers & Monsell, 1995): In task switch trials, the appropriate task is not yet active, necessitating reconfiguration. By contrast, reconfiguration will normally not be needed in task repeat trials, since the task set from the previous trial is still active. According to the “interference” account, by contrast, the switch cost reflects the time needed to resolve the interference from the previous task set (e.g. Allport, Styles, & Hsieh, 1994; Allport & Wylie, 1999; Wylie & Allport, 2000). This account assumes that the activation of the previous relevant task set persists. In case of a switch trial, this persisting passively decaying activation of the previous task set interferes with the new task set, which is not the case in repeat trials. Many models of task switching assume that task set preparation can be differentiated into a number of different processes operating on different task set components (e.g. Mayr & Kliegl, 2000, 2003; Monsell, 2005; Nicholson, Karayanidis, Poboka, Heathcote, & Michie, 2005; Rogers & Monsell, 1995; Rubinstein et al., 2001). A first critical process in task preparation is related to the retrieval of the task goal (e.g., Fagot, 1994; Mayr & Kliegl, 2000; Rogers & Monsell, 1995; Rubinstein et al., 2001). In this stage, the task set representations are maintained and updated by activating the relevant task set and inhibiting the irrelevant task set as

http://dx.doi.org/10.1016/j.neuropsychologia.2014.04.015 0028-3932/& 2014 Elsevier Ltd. All rights reserved.

Please cite this article as: De Baene, W., & Brass, M. Dissociating strategy-dependent and independent components in task preparation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.04.015i

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needed (e.g. Karayanidis et al., 2009; Kiesel et al., 2010; Nicholson, Karayanidis, Davies, & Michie, 2006). This active maintenance of task set representations is assumed to bias responding according to the currently relevant task (e.g. Braver & Cohen, 2000; Gilbert & Shallice, 2002; Miller & Cohen, 2001). This is tightly linked to a second constituent process of task preparation, i.e. the activation of the relevant task rule (e.g., Jamadar, Hughes, Fulham, Michie, & Karayanidis, 2010a; Jamadar, Michie, & Karayanidis, 2010b; Rubinstein et al., 2001). Note that the task rule might refer to different things, depending on the paradigm used: in classical task-switching paradigms (e.g. Allport et al., 1994; Rogers & Monsell, 1995), different task rules (or categorization rules) involve two or more different stimulus-response (S-R) mappings that are defined, for instance, on different stimulus dimensions of a single item (such as magnitude vs. parity or color vs. motion). In these paradigms, activating a task rule involves setting the attentional focus on the relevant stimulus dimension (i.e. attentional control). In other (S-R reversal) paradigms, different task rules involve opposite S-R mappings. Consequently, activating a task rule solely involves activating the relevant S-R mapping (i.e. intentional control). We will come back to this distinction between attentional and intentional control below (for a discussion of this issue, see Ruge, Jamadar, Zimmermann, & Karayanidis, 2013). In sum, task preparation requires the specification of two types of information: one needs to specify “what to do next” by setting the task goal and “how to do it” by activating the relevant task rule (e.g. De Baene, Albers, & Brass, 2012; Rubinstein et al., 2001).

1.1. ERP markers of task preparation While task preparation has been investigated in the behavioral literature for almost two decades, only in the last 15 years it has also been investigated with neuroscientific methods such as EEG and fMRI. EEG research on task switching has been particularly successful in linking specific ERP components to task-set preparation, demonstrating larger amplitudes in switch compared to repeat trials (e.g. Jost, Mayr, & Rö sler, 2008; Karayanidis, Provost, Brown, Paton, & Heathcote, 2011; Lavric, Mizon, & Monsell, 2008). These studies have identified various, temporally distinct, cue-locked EEG markers of task preparation that reflect the different preparatory processes (see Fig. 1; see Karayanidis et al., 2010 for a review): Task goal activation or task set

Fig. 1. Overview of the dissociation between a preparatory process that is affected by the adopted strategy (left) and a preparatory process that is not affected by the preparatory strategy (right). Task goal activation is assumed to be reflected by an early parietal and frontal positivity and is reflected by activation in the lateral prefrontal cortex and the inferior parietal lobule. This process is strategy-dependent and can be modulated by manipulation of the context. Task rule activation, by contrast, is strategy-independent and is assumed to be reflected by a late parietal positivity and a late frontal negativity and by activation in the pre-SMA and the superior parietal lobule.

updating is thought to be reflected by an early parietal positivity (e.g. Eppinger, Kray, Mecklinger, & John, 2007; Jamadar et al., 2010a; Jost et al., 2008; Kray, Eppinger, & Mecklinger, 2005; Manzi, Nessler, Czernochowski, & Friedman, 2011; West, 2004) that is present as early as 200 ms after cue onset. This early positivity has been associated with activity in the lateral prefrontal cortex (Jamadar et al., 2010a, see below). A second, but less consistently observed component linked to task set updating is an early frontal positivity emerging around 150–200 ms after cue onset (e.g. Astle, Jackson, & Swainson, 2008; Lavric et al., 2008; Rushworth, Hadland, Paus, & Sipila, 2002). This component has been particularly associated with inhibition of the alternative task set (Wylie, Murray, Javitt, & Foxe, 2009). Important to note is that this cue-dependent frontal positivity should not be confused with the target-dependent P2 that has been related to stimulus-dependent processes such as the retrieval of stimulus-response mappings (e.g. Allport et al., 1994; Kieffaber & Hetrick, 2005; Wylie & Allport, 2000). The second preparatory process, namely task rule activation, by contrast, has been linked to a late parietal positivity (e.g. Barceló, ́ ̃ ez, & Nyhus, 2008; Jamadar et al., 2010a; Jost et al., 2008; Perian Karayanidis et al., 2011; Lavric et al., 2008; Nicholson, Karayanidis, Bumak, Poboka, and Michie 2006) and has been observed between about 400 and 1000 ms after cue onset. This parietal positivity has been consistently found but inconsistently labeled: sometimes it is referred to as a cue-locked P3 (e.g. Gajewski & Falkenstein, 2011), sometimes as an increased P3b (e.g. Barceló, Muñoz-Céspedes, Pozo, & Rubia, 2000; Goffaux, Phillips, Sinai, & Pushkar, 2006; Kieffaber & Hetrick, 2005) and sometimes as a parietal switch positivity (e.g. Astle, Jackson, & Swainson, 2006; Karayanidis, Coltheart, Michie, & Murphy, 2003; Swainson, Jackson, & Jackson, 2006). This component, further denoted as the late parietal positivity, has been associated with activity in the posterior parietal cortex (Jamadar et al., 2010a, see below) and tends to reach its maximum over left-lateralized parietal scalp locations (Astle et al., 2006; Lavric et al., 2008). Finally, some studies have also identified a late frontal negative component, especially when using a common average reference (e.g. Astle et al., 2008; Hsieh & Chen, 2006; Lavric et al., 2008; Mueller, Swainson, & Jackson, 2007). This frontal negativity occurs between about 500 and 1000 ms after cue onset and tends to be slightly right lateralized (Astle et al., 2006; Lavric et al., 2008). Lavric et al. (2008) observed a high interdependence between the late parietal positivity and the late frontal negativity, suggesting that they may reflect the two poles of the underlying dipolar generators and thus reflect the same underlying processes of anticipatory task preparation (though see Astle et al., 2008 and Mueller et al., 2007, for an alternative view). Indeed, the late frontal negativity has been interpreted, amongst others, as reflecting rule mapping or retrieval (e.g. Hsieh & Chen, 2006; Travers & West, 2008), as is the case for the late parietal positivity. Several studies have described this late frontal negativity as a contingent negative variation (CNV) or CNV-like component (e.g. Astle et al., 2008; Gajewski et al., 2010; Goffaux et al., 2006; Goffaux, Phillips, Sinai, & Pushkar, 2008; Lorist et al., 2000). The frontal CNV has been interpreted as reflecting a reassignment of resources, preparatory attention, motivation or response readiness (Falkenstein, Hohnsbein, Hoormann, & Kleinsorge, 2003; Grent & Woldorff, 2007; Tecce, 1972; van Boxtel & Brunia, 1994; Walter, Cooper, Aldridge, McCallum, & Winter, 1964). Lavric et al. (2008), however, showed that the late frontal negativity (with its frontal-polar topography) and the CNV (with its frontal-central or posteriorcentral distribution) are distinct components. 1.2. Task preparation and fMRI Besides an extended amount of ERP research, also numerous fMRI studies have been performed using variants of the task-switching

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paradigm. This research has identified a fronto-parietal network underlying cognitive flexibility, including lateral and medial prefrontal, premotor, and anterior and posterior parietal regions (e.g. Brass & von Cramon, 2002; Dove, Pollmann, Schubert, Wiggins, & von Cramon, 2000; Dreher, Koechlin, Ali, & Grafman, 2002; Yeung, Nystrom, Aronson, & Cohen, 2006). Several fMRI-studies have reported increased BOLD-activation in this fronto-parietal cognitive control network for switch compared to repeat trials (e.g. Braver, Reynolds, & Donaldson, 2003; Dove et al., 2000; Kimberg, Aguirre, & D’Esposito, 2000; Rushworth et al., 2002; Rushworth, Paus, & Sipila, 2001; Smith, Taylor, Brammer, & Rubia, 2004; Sohn, Ursu, Anderson, Stenger, & Carter, 2000). Furthermore, this network was also found to be activated during advanced preparation of task sets (Brass & von Cramon, 2002; De Baene & Brass, 2011; De Baene, Kühn, & Brass, 2012; Luks, Simpson, Feiwell, & Miller, 2002; MacDonald, Cohen, Stenger, & Carter, 2000; Shi, Zhou, Müller, & Schubert, 2010; Sohn et al., 2000). Based on the involvement of multiple brain areas in task preparation (see Ruge et al., 2013 for a review), several fMRI studies have suggested similar dissociations between preparatory processes as described earlier for behavioral and ERP studies. Below, we will try to delineate the tentative role of these areas in task goal activation and task rule activation during task preparation (see Fig. 1). We will focus here on the lateral prefrontal cortex, medial prefrontal cortex, inferior parietal lobule and superior parietal lobule. Note, however, that our aim here is neither to give a full functional specification of these brain regions nor claim that the list discussed below is complete. Neurophysiological research in monkeys (Asaad, Rainer, & Miller, 2000; Johnston, Levin, Koval, and Everling, 2007; Wallis & Miller, 2003; Wallis, Anderson, & Miller, 2001) as well as human fMRI studies (e.g. Brass & von Cramon, 2002; Crone, Wendelken, Donohue, and Bunge, 2006; Sakai & Passingham, 2003) have provided clear evidence for the representation of task rules in lPFC. Given the task rule representation in lPFC, this area has classically been thought to be involved in the maintenance, retrieval and implementation of task goals and in performance adjustments by engaging regulatory processes to overcome interference and resolve competition from the previously implemented task set (e.g. Bunge, Kahn, Wallis, Miller, & Wagner, 2003; De Baene et al., 2012; Hyafil, Summerfield, & Koechlin, 2009; MacDonald et al., 2000; Miller, Erickson, & Desimone, 1996; Sakai, Rowe, & Passingham, 2002; Sohn et al., 2000). As such, lPFC is assumed to play a key role in the task goal activation or task updating phase and is thought to guide the processing elsewhere in the brain (e.g. Miller & Cohen, 2001), for instance by biasing task-appropriate S-R transformation processes in posterior regions such as parietal cortex (e.g. Brass, Ullsperger, Knoesche, von Cramon, & Phillips, 2005; Ruge & Braver, 2007). Furthermore, it has been argued that a region in the posterior lPFC, namely the inferior frontal junction (IFJ) plays a crucial role in task preparation. The IFJ is thought to be involved in updating of general task representations (Brass et al., 2005; Brass & von Cramon, 2004a; Derrfuss, Brass, Neumann, & von Cramon, 2005; Derrfuss, Brass, & von Cramon, 2004; Roth, Serences, & Courtney, 2006). These task representations are general in the sense that they comprise information represented in the lPFC (task goal information) and information represented in the IPL/aIPS (S-R mapping information). Information from these two areas is integrated in the IFJ (De Baene et al., 2012; Stelzel, Basten, & Fiebach, 2011). A second area involved in this task goal activation process is the inferior parietal lobule (IPL), together with the anterior intraparietal sulcus (aIPS). These areas are classically thought to be important for sensory-motor integration (Andersen & Buneo, 2002). It is believed that information stemming from the sensory, cognitive and motor domain are bound here (Gottlieb, 2007; Pouget, Deneve, & Duhamel, 2002). In task preparation, they have been attributed a similar role as the lPFC (Brass & von Cramon,

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2004a, 2004b; Bunge et al., 2003; Hartstra, Kühn, Verguts, & Brass, 2011). However, contrary to the lPFC where task goals are represented, the IPL and aIPS are assumed to house representations of cue-associated response contingencies (Bunge et al., 2003) or stimulus-response (S-R) mappings (De Baene et al., 2012; Hartstra et al., 2011). The superior parietal lobule (SPL), by contrast, seems to be involved in the second preparatory process, i.e. in task rule activation. The SPL has previously been shown to be involved in switching the attentional focus (Posner & Petersen, 1990), with higher activation when the attention is shifted relative to when it is held (e.g. on a spatial location, feature, object or sensory modality; for a review see Yantis, 2008). Chiu and Yantis (2009), however, showed that the SPL is similarly involved in the control of shifting spatial attention and in switching between categorization rules. They reasoned that switching between categorization rules requires active selection of one rule from among competitors in the same way as shifting visuospatial attention requires the visual system to actively select one sensory representation from among competitors. This interpretation is in line with other findings suggesting that the superior parietal cortex is involved in manipulation of categorization rules or S-R mappings (Andersen, 1987; Andersen & Buneo, 2002; Braver et al., 2003; Brass & von Cramon, 2004a; Bunge, Hazeltine, Scanlon, Rosen, & Gabrieli, 2002; Fassbender, Foxe, & Garavan, 2006; Hester, D’Esposito, Cole, & Garavan, 2007; Ruge et al., 2005). Note that the SPL and the IPL are thought to have a differential role in task preparation: the IPL is mainly involved in representing and maintaining S-R mappings whereas the SPL is assumed to be involved in the manipulation of these representations (Cf. Jamadar et al., 2010a). Finally, also the medial prefrontal cortex (more specifically the pre-SMA and the dACC) has been assumed to be involved in task preparation (e.g. Hyafil et al., 2009; Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004). The medial PFC detects changes in task set evidenced as conflict between the previous and the new task set. It resolves the conflict and configures the cognitive system for the upcoming task. Generally, these different tasks are ascribed to two parts of the medial PFC: The ACC is thought to have a monitoring function and detects the conflict (Ridderinkhof et al., 2004). The pre-SMA, by contrast, resolves the conflict by suppressing active but inappropriate actions from a previous task set and boosts the selection of appropriate actions as demanded by the new task set (Hikosaka & Isoda, 2010). As such, the pre-SMA is suggested to play a role in the task rule activation part of task preparation (Crone et al., 2006; Slagter et al., 2006). This fits the observation that the pre-SMA becomes more important when subjects are changing to another task that entails distinct ways of selecting actions (Rushworth, Buckley, Behrens, Walton, & Bannerman, 2007) by instantiating the correct S-R mapping for the new task (Chiu & Yantis, 2009).

2. Is task preparation strategy-dependent? Both the reconfiguration and interference account argue that there are qualitative differences between switch and repeat trials. By doing so, they do not give much room for contextual or strategic influences on these differences. However, recent research suggests that the difference between switch and repeat trials might not necessarily reflect a qualitative difference of the cognitive processes that are involved but rather a quantitative difference that depends on contextual variables. From this perspective, the size and presence of this difference is dependent on the preparatory strategy engaged by the participant.

Please cite this article as: De Baene, W., & Brass, M. Dissociating strategy-dependent and independent components in task preparation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.04.015i

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For a long time, strategies have been considered a matter of explicit, deliberate, conscious and controlled processes. However, dependent on the situation, people might also rely more or less on automatic processes that integrate information unconsciously (see Glö ckner, 2008, 2009 for a discussion of these different modes). Implicit knowledge about, for instance, the frequency (e.g. Sedlmeier & Betsch, 2002) or the value of events (e.g., Betsch, Plessner, Schwieren, & Gütig, 2001) systematically informs subsequent behavior. In the current context, the term “strategy” entails both passive, implicit processes modulating task preparation as well as deliberate, conscious processes affecting task preparation. Furthermore, the term describes adjustments that result from the integration of information across sequences of trials. In this sense, the term refers to higher-order control processes that go beyond the single trial level. In a certain context, the recommended strategy could be to keep the previous task set active. This would necessitate overcoming the activation of the previous task set in switch trials in order to implement the now relevant task set. These additional control demands lead to large differences between switch and repeat trials. In other contexts, people might adopt different preparation strategies. For instance, they might move to an intermediate “neutral” control state between the two task sets (Monsell & Mizon, 2006). This implies that preparatory processes are needed on task repeat trials (to go back to the previous task set) as well as on switch trials (to go to the alternative task set), which will result in smaller switch costs. An alternative strategy is to guess which task will come up next and prepare the alternative task on a subset of trials. When the guess is wrong (i.e. on task repeat trials), people need to prepare the previous task again, leading to extra preparation on task repeat trials. When the guess was correct, no additional preparation is needed on task switch trials. This strategy will also result in smaller differences between switch and repeat trials. In the following section, we will discuss several findings from behavioral studies supporting the idea that differences between switch and repeat trials are dependent on the preparatory strategy induced by different contexts. We will then turn to the neuroscientific evidence in favor of this view and discuss relevant ERP and fMRI results. Finally, we will discuss how the neural underpinnings of the task preparation processes might be modulated by different strategies. 2.1. Behavioral evidence A first indication of context-dependent preparatory strategies comes from the observation of mixing costs. Mixing costs reflect the difference in reaction times and accuracy between single-task blocks in which only one task set is used for all trials in a block, and mixed-task blocks in which people must alternate between two or more tasks (e.g. Meiran et al., 2000). Since repeat trials in mixed-task blocks and repeat trials in single-task blocks only differ in the context they are presented in, the poorer performance in mixed-task blocks relative to single-task blocks suggests some sort of strategic difference on these different types of repeat trials. This is in line with the general assumption that people adopt different strategies depending on whether the experimental conditions are blocked or random (Strayer & Kramer, 1994). Some proposed that this mixing cost is due to a higher mental load in the mixed compared to the single-task blocks (e.g, Los, 1996). In the mixedtask blocks, participants need to hold two tasks in mind, whereas only one task at a time needs to be active in memory in the singletask blocks. Rubin and Meiran (2005), however, found no evidence for working memory demands contributing to the mixing costs. Their findings suggested that interference between the tasks was the main contributor to mixing cost. They argued that, because of

this task competition, participants will engage in a task decision (or goal setting) process on each trial (both switch and repeat trials) within the mixed-task blocks but not in pure single-task repeat trials. One might also argue that differences between mixed-task blocks and single-task blocks merely reflect differences in cue encoding, since the cue is completely redundant in the latter condition whereas it is essential in the former condition to be able to successfully prepare the upcoming task. This seems, however, unlikely, given the results of Poljac, Koch, and Bekkering (2009). They reported mixing costs on trials that were not cued. Consequently, mixing costs cannot solely be the result of cue encoding differences between mixed-task blocks and single-task blocks. A second line of evidence suggesting that task preparation is strategically modulated comes from studies that manipulated the preparation time in a between-subject and a within-subject design (e.g. Altmann, 2004a, 2004b; Koch, 2001; see Kiesel et al., 2010). Longer preparation times led to faster reaction times in switch and repeat trials but did not affect the size of the switch cost when preparation time was varied between subjects. When preparation time was manipulated within subjects, however, a preparatory reduction of the switch cost was observed. This suggests that, depending on the context, participants applied different preparation strategies for long and short preparation times. Assuming that keeping a task set in a preparatory state is costly, people might use a strategy in which they start preparing the task set somewhat later when confronted only with a long preparation time to avoid maintaining a state of high preparedness for durations longer than necessary. When preparation times are manipulated within subjects, the presence of short preparation times might force people to start preparing the task set as early as possible. Poljac, de Haan, and van Galen (2006) explained the observed task performance differences in a within-subjects design and a between-subjects design by assuming a higher strength of the task representation for a short preparation time in the former compared to the latter design. They argued that the variability of the preparation time in the withinsubjects design could help the system to recognize the functionality of advance preparation (for a similar reasoning, see Yeung & Monsell, 2003). Additional support for the idea of strategic shifts in task preparation depending on the use of a within- or betweensubjects manipulation of preparation time comes from Verbruggen, Liefooghe, Vandierendonck, and Demanet (2007) who argued that a (within-subjects) mixture of short and long preparation times leads to better preparation in general. A third line of evidence is based on studies manipulating the characteristics of the task sequences. Firstly, in several studies manipulating the predictability of task sequences (e.g. by providing a cue indicating the probability of a task repeat on that trial; see Dreisbach, Haider, & Kluwe, 2002), performance in unpredictable sequences was slower (Dreher et al., 2002; Gotler, Meiran, and Tzelgov (2003); Heuer, Schmidtke, & Kleinsorge, 2001; Koch, 2001, 2005; Ruthruff, Remington, & Johnston, 2001; Sohn & Anderson, 2001; Sohn & Carlson, 2000) or less accurate (Barton, Kuzin, Polli, & Manoach, 2006; Tornay & Milan, 2001) than in predictable sequences. This advantage was, however, not switchspecific, resulting in no change or a slight increase in switch costs with predictable compared to unpredictable task sequences. These findings suggest that, when possible (i.e., with predictable task sequences), people use the sequence information to prepare the upcoming task, for instance by adapting their task set readiness for optimal task performance (Barton et al., 2006; Dreisbach et al., 2002). A second important characteristic of task sequences is the task switch probability. Echoing several previous studies (e.g. Dreisbach & Haider, 2006; Meiran et al., 2000; Monsell & Mizon, 2006), Bonnin, Gaonac’h, and Bouquet (2011) investigated whether switch costs

Please cite this article as: De Baene, W., & Brass, M. Dissociating strategy-dependent and independent components in task preparation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.04.015i

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varied according to the task switch probability within a block of trials and found that performance was affected by task switch probability: the switch cost was reduced by the increase in switch probability and performance on both the switch and repeat trials was modified depending on the switch probability. Duthoo, De Baene, Wühr, and Notebaert (2012) also examined the effect of task switch probability context on the switch cost. In this study, however, participants were asked to predict the upcoming task. Their results showed a decrease of the switch cost with increasing switch probability. Additionally, their results suggested that this attenuation of the switch cost with increased switch probability was caused by a diminished expectancy for a task to repeat. Indeed, the switch cost was strongly reduced when participants expected a task switch, which suggests a crucial role for task expectancies in the adjustments of cognitive control processes. Note that in this study, asking participants to make their expectations explicit might have affected task preparatory processes, since explicit expectations feed back into task processing (Umbach, Schwager, Frensch, & Gaschler, 2013). Obviously, task predictability strongly depends on switch probability. Studies in which task sequence predictability is manipulated might even be considered a special subset of cases in which the prior probabilities of trial properties (i.e. transition status: switch or repeat) are varied to study the effects of expectation on task preparation (e.g. Barton et al., 2006; Dreisbach et al., 2002). All these studies show that expectations of whether the task set will remain relevant or not can dynamically modulate the preparatory activation of the different task sets, both in switch and repeat trials. These findings suggest that knowledge about what is likely to happen next may form a basis for strategic shifts underlying task preparation efficiency: different task expectations might lead to differences in task set preparation processes depending on predictability and switch probability.

2.2. Neuroscientific evidence One major advantage of using neuroscientific methods to investigate contextual influences on task preparation is that one can directly test whether such influences lead to qualitative or quantitative differences in the brain correlates of task preparation. Accordingly, several ERP and fMRI studies provide support for preparatory strategy-dependent differences between switch and repeat trials.

2.2.1. Mixing costs vs. switch costs Strong evidence that task preparation is dependent on a context-specific strategy comes from ERP and fMRI studies comparing repeat trials in mixed-task blocks with repeat trials in single-task blocks. Whereas repeat trials in single-task blocks show no cue-locked early parietal positivity during the preparation interval, repeat trials (as well as switch trials) in mixed-task blocks do show this early positivity. Some have interpreted this as showing that the cue in the single-task blocks is less deeply processed than the cue in the mixed-task blocks (West, 2004). Jamadar et al. (2010a), however, showed that in mixed-task blocks, this early parietal positivity was only evoked by informative switch or repeat cues but not by non-informative cues (cues that only signaled the timing of an upcoming stimulus without specifying whether it would require switching or repeating), suggesting some type of preparation in repeat trials in mixedtask blocks that is not present in single-task blocks (e.g. Jost et al., 2008; Kray et al., 2005; Manzi et al., 2011; Nessler, Friedman, & Johnson, 2012; Wylie et al., 2009). As discussed before, it was argued that this early cue-locked parietal positivity indexes a task

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goal activation process. This interpretation was further supported by the fact that Jamadar et al. (2010a) found a correlation between the amplitude difference of the posterior positivity for informatively vs. non-informatively cued trials and the activity difference between these two conditions in the dorsolateral prefrontal cortex, an area that has been linked to task goal activation. Also fMRI studies have provided support for the assumption that there is some type of preparation in repeat trials in mixedtask blocks that is not present in single-task blocks. They showed higher BOLD activation for repeat trials (as well as switch trials) in the mixed-task blocks relative to repeat trials in the single-task blocks in several areas, including lateral prefrontal cortex and superior parietal lobule (Braver et al., 2003; Crone et al., 2006). Again, one might argue that this could merely reflect a difference in cue processing between single-task and mixed-task blocks. De Baene and Brass (2011), however, have shown that these frontoparietal areas, which display preparatory activity in task-switching paradigms, are engaged in task preparation but not in cue processing. In sum, these neurophysiological results clearly suggest that people adopt different preparatory strategies for identical trials dependent on the context in which they are presented.

2.2.2. Switch probability effects Additional evidence for strategically modulated task preparation was recently provided by an ERP study (Nessler et al., 2012) on the effects of task switch probability on task preparation. The evidence provided by Nessler et al. (2012) for context-dependent, strategically modulated task preparation is fourfold. Firstly, as discussed in the previous part, there was an early parietal component for repeat trials in mixed-task blocks that was not present for repeat trials in single-task blocks. Secondly, although this early parietal effect was found regardless of the switch probability context, the duration of this parietal positivity was increased for the high compared to the low switch probability condition, probably reflecting increased task set updating for mixed repeat trials with higher switch probability. Both these results suggest different preparatory strategies for identical trials dependent on the context (mixed-task or single-task; high or low switch probability) in which they are presented. Thirdly, when Nessler et al. (2012) compared switch trials with repeat trials in mixed-task blocks, they found a larger early parietal positivity for switch relative to repeat trials in the low switch probability condition but not in the high switch probability context, probably reflecting increased task set updating for switch trials with lower switch probability. One alternative view on these findings is that this parietal positivity merely reflects the detection of rare events, as has been shown extensively in the auditory and visual perceptual domain (e.g. Johnson, 1984). Although this alternative oddball-related explanation might hold for the results of the parietal positivity, it does not hold for the observed early frontal switch-repeat positivity with a longer duration in the low relative to the high switch probability context. Nessler et al. (2012) interpreted this as a reflection of top-down control in the form of shifting attention to the new task that was higher in the low compared to the high switch probability condition. These results clearly suggest that some preparatory processes (such as task updating) can be modulated by context-dependent strategies. However, this study also provided evidence that not all preparatory processes are modulated by strategy: Nessler et al. (2012) also found a late parietal switch-repeat positivity for which no difference was observed between the two switch probability conditions. This was interpreted as an indication that task-set configuration was not affected by the switch probability context, although different strategies were used in the different switch

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probability contexts, as indicated by the pattern of results for the other components. Along this line, De Baene and Brass (2013) also reported a dissociation of different preparatory processes based on their sensitivity to contextual modulation. In this fMRI study, they examined the effect of switch probability on preparatory switchspecific activity by manipulating the switch probability between blocks within the same subjects. In the low switch probability (30% switch trials) condition, they found switch-related preparatory activation in the lPFC, pre-SMA, IPL, superior parietal lobule (SPL) and middle temporal gyrus (MTG). In the high switch probability (50% switch trials) blocks, however, only part of these areas (more particularly the (pre-) SMA, extending into the dorsal ACC and the SPL) showed higher activation in switch compared to repeat trials. The activation levels in the other areas were very similar for switch and repeat trials. The different pattern of results in several preparation-related areas depending on the switch probability context suggests that some preparatory processes are more susceptible to the contextual modulation than others. The activation patterns in areas sensitive to the switch probability context (i.c. the lPFC, IPL and MTG; see Figure 2 of De Baene & Brass, 2013) can be interpreted in the following way: when the likelihood of switching is low, participants keep the previous task in an activated state. Therefore, in switch trials, they have to inhibit the activation of the previous, now irrelevant task set and activate the other, now relevant task set. These additional task updating or task goal activation demands lead to higher activation in switch compared to repeat trials in these context sensitive areas. When the likelihood of switching and repeating a task is identical, participants will move to an intermediate “neutral” control state between the two task sets (Monsell & Mizon, 2006). Consequently, no additional task updating is necessary in switch compared to repeat trials, leading to similar activation levels in switch and repeat trials in these context sensitive areas. Furthermore, activating a new task set in switch trials in the high switch probability condition will be easier compared to the low switch probability condition since there is no need to inhibit the previous task set. Additionally, participants will need to activate the relevant task in the repeat condition, leading to higher activation in the task updating areas in this condition compared to the repeat trials in the low switch probability blocks. Note that the alternative account, in which participants are thought to adopt a “guessing” strategy (Monsell & Mizon, 2006), was not supported by the activation pattern in the context-sensitive areas in this study. If people would indeed guess which task will come up, a wrong guess should lead to slower reaction times and extra preparation in order to activate the correct task compared to a correct guess. The result pattern was, however, in the opposite direction, with lower preparatory activity associated with slower reaction times.

3. Towards a functional dissociation Despite the huge methodological differences between the ERP study of Nessler et al. (2012) and our fMRI study (De Baene & Brass, 2013), both studies support the idea that anticipatory task set configuration comprises components that are sensitive to contextual manipulations and components that are insensitive to contextual manipulations. Such a subdivision is compatible with the more general observation that task set preparation is a multicomponent process, as discussed before (e.g. Jamadar et al., 2010b; Karayanidis, et al., 2009; Mansfield, Karayanidis, & Cohen, 2012; Nicholson et al., 2006; Swainson et al., 2006). In the next section, we will discuss how the distinction between strategy-dependent and strategy-independent cognitive control processes in task

preparation maps onto the functional and neuroanatomical distinction of different components being involved in anticipatory control. Both components of task preparation (i.e. task goal activation and task rule activation) will always be involved, regardless of the preparatory strategy. However, we argue that these components will be differently modulated by the context as induced by manipulating the switch probability. Retrieval of the task goal or task set updating (which might include overcoming the activation of the previous task set and activating the currently relevant task set) will depend on the preparatory strategy used: One strategy might involve keeping the previous task in an activated state (low switch probability), whereas another strategy might involve equally activated states for the two task sets (high switch probability). This will lead to context-dependent levels of effort needed to retrieve the task goal: in the high switch probability context, switch and repeat trials will need a similar level of effort to retrieve the task goal whereas this will be much harder for switch trials compared to repeat trials in the low switch probability context. The neuroscientific findings support this interpretation: The ERP components thought to reflect task goal activation or updating, namely the early parietal positivity and the early frontal positivity, have indeed been shown to be sensitive to the switch probability context (Nessler et al., 2012). Furthermore, the areas presumably involved in task goal activation or updating (more specifically lPFC and IPL) have also been found to be sensitive to the switch probability context (De Baene & Brass, 2013). In contrast, task rule activation will not depend on the preparatory strategy and will always be more effortful in switch compared to repeat trials. Regardless of the context, switch trials will need a shift of the attentional focus to the newly relevant task information whereas this is not the case for repeat trials. Again, this interpretation is backed up by the neuroscientific findings: In the study by Nessler et al. (2012), the late parietal positivity, assumed to reflect task rule activation, was not affected by the switch probability context. Furthermore, in our fMRI study (De Baene & Brass, 2013), the areas presumably involved in task rule activation (more specifically pre-SMA and SPL) have been found not to be sensitive to the switch probability context. A dissociation between areas involved in task goal retrieval (i.c. lPFC and IPL) and task rule activation (i.c. pre-SMA and SPL) has consistently been put forward in recently fMRI studies on task preparation. Crone et al. (2006) suggested a similar dissociation: On the one hand, lPFC and IPS were suggested to be involved in rule representation or the retrieval, maintenance and implementation of relevant rules. Medial PFC and SPL, on the other hand, were suggested to be involved in processes involved in rule switching. The dissociation found here was, however, not so strict: lPFC also played a role in rule switching whereas SPL also contributed to rule representation. This might be due to the fact that Crone et al. (2006) did not disentangle task preparation and task execution. Hyafil et al. (2009) also reported evidence that the medial PFC (i.c. ACC) and the lPFC (i.c. caudal dlPFC) respond in a dissociable manner during task switching. As in our study (De Baene & Brass, 2013), medial PFC was activated on every switch trial regardless of the context whereas lPFC responded to switch trials only when competition needed to be resolved from the previously implemented task set. According to Hyafil et al. (2009), the medial PFC organizes priorities between competing task sets by readying the cognitive system for the upcoming task. The lateral PFC, by contrast, is needed to overcome interference and resolve competition from the previously implemented task set. Also neurophysiological results support the dissociation between medial and lateral PFC. Johnston et al. (2007) recorded

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the activity of single neurons in lPFC and ACC in monkeys during a task switching experiment. Whereas lPFC neurons showed constant task selectivity during task preparation across trials, ACC neurons had a higher level of task selectivity immediately following a task switch. This suggests that lPFC is involved in maintenance of task rules whereas ACC neurons are selectively engaged to enhance cognitive control in response to an increase in task difficulty (e.g. when the task switches). Finally, Ruge, Müller, and Braver (2010) also showed a similar dissociation between medial PFC and SPL on the one hand and lPFC and IPS on the other hand. They assumed, however, two different preparatory modes: one related to attentional control involving the medial PFC (i.c. pre-SMA) and SPL and one related to intentional control involving the lPFC (i.c. the middle frontal gyrus) and the anterior IPS. The involvement of these different modes was suggested to be dependent on task conditions (Ruge et al., 2013). Note that the two studies reported here that show effects of switch probability (De Baene & Brass, 2013; Nessler et al., 2012) targeted attentional control, which involved setting the attentional focus on the relevant stimulus. It remains unclear whether a similar effect of context manipulation could be shown when intentional control is targeted in which adjustment of the action set (or activating the relevant S–R mapping without shifting the attentional focus) is involved. In sum, based on our results and on the existing literature, we argue that activation in areas involved in task goal activation or task updating depend on the adopted preparatory strategy whereas activation in areas involved in task rule activation are not modulated by preparatory strategies (see Fig. 1).

4. Concluding remarks and open questions In this article, we have argued that differences between switch and repeat trials are modulated by the preparatory strategy used. The crucial insight from the evidence we have discussed is the dissociation between preparatory processes that are strategydependent and preparatory processes that do not depend on a strategy. This implies that not all processes involved in task preparation are under strict strategic control: some preparatory processes (such as the task rule activation process) might be more automatically triggered and less context-dependent than previously thought. Manipulating contextual variables (as was done by Nessler et al., 2012 and by De Baene & Brass, 2013) might be the ideal way to try to dissociate these more automatic preparatory processes from more controlled preparatory processes. In the current paper, we have mainly focused on one specific contextual factor (namely switch probability) influencing the preparatory strategy. However, other factors besides the switch probability might affect which preparatory strategy will be adopted. Manipulating these factors might add support to the dissociation put forward here. A first factor that might affect the preparatory strategy is cue validity (see, for instance, Wendt, LunaRodriguez, Reisenauer, Jacobsen, and Dreisbach 2012). In most task-switching studies, pre-knowledge about the upcoming task demands is perfectly valid. However, people might change their preparatory strategy if an informative cue is regularly followed by the other, un-cued task. By modulating the distribution of valid and invalid cues, one could further explore anticipatory task control by contextual manipulation (see e.g. Gajewski, Stoerig, and Falkenstein, 2008 for a similar reasoning in an ERP study on response conflict). The adopted preparatory strategy might also be triggered by the instructions. Emphasizing speed over accuracy or vice versa (see e.g. Vallesi, McIntosh, Crescentini, and Stuss, 2012), for instance, might lead to different strategies. The length of the repetition sequences is another variable that might affect the

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preparatory strategy. One could assume that longer repetition sequences might lead to an increase in rule perseveration that might affect preparatory processes (Ruge et al., 2013). One could also manipulate the difficulty of the tasks involved as well as the relative difficulty of one task compared to the other to examine contextual changes in preparatory control. One study nicely illustrating the effect of task difficulty on task preparation is the study by Wylie, Javitt, and Foxe (2006). They showed that subjects could prepare effectively for the easier color task, which was associated with robust preparatory activity in color-processing areas. However, people could not effectively prepare for the more difficult motion task, which was accompanied with a lack of preparatory activity in motion-processing areas. Important to note is that the strategy that is adopted does not only dependent on the experimental context. Several studies have demonstrated a role for motivational and affective states in task switching (e.g. De Jong, 2000; Dreisbach & Goschke, 2004; Heuer, Kleinsorge, Klein, & Kohlisch, 2004; Steinhauser, Maier, & Hubner, 2007). Steinhauser et al. (2007), for instance, showed that stress induced a strategy change leading to preparatory reductions of the switch cost for the low-stress group but not for the high-stress group. A key goal for future research will be to investigate whether motivational and affective states similarly modulate the preparatory strategy and whether this will lead to a similar functional dissociation within the preparatory network. As mentioned before, we use the term “strategy” in a very broad sense as it entails both passive, implicit processes as well as deliberate, conscious processes. We acknowledge, however, the fact that the awareness of the adopted strategy is an important issue. Several studies have focused on this. Gotler et al. (2003), for instance, showed that implicitly learned task sequences can affect performance in task switching. Also Koch, Philipp, and Gade (2006) found implicit tasksequence learning when no information was provided prior to the experiment. However, they also found differences between this group and a group that did receive explicit information about the order of the tasks in the task sequence. This suggests that the strategy used in task switching might depend on the way the information about the task sequences is acquired (but see Kleinsorge, Schmidtke, Gajewski, and Heuer 2003). It would be interesting to further investigate the role of implicit and explicit knowledge in strategy formation, for instance by manipulating the way information about task sequences is acquired (explicit or implicit).

Acknowledgements This research was made possible by the Research FoundationFlanders (FWO-Vlaanderen; FWO10/PDO/234 and FWO13/PDOH1/ 234), of which the first author is a postdoctoral research fellow, and further supported by the Special Research Fund (BOF) of Ghent University (BOF06/24JZAP and BOF08/GOA/011). References Allport, D. A., Styles, E. A., & Hsieh, S. (1994). Shifting intentional set: Exploring the dynamic control of tasks. In: C. Umilta, & M. Moscovitch (Eds.), Conscious and nonconscious information processing: Attention and performance XV (pp. 421– 452). Cambridge, MA: MIT Press. Allport, D. A., & Wylie, G. (1999). Task-switching: Positive and negative priming of task-set. In: G. W. Humphreys, J. Duncan, & A. M. Treisman (Eds.), Attention, space and action: Studies in cognitive neuroscience (pp. 273–296). Oxford, England: Oxford University Press. Altmann, E. M. (2004a). Advance preparation in task switching: What work is being done? Psychological Science, 15, 616–622. Altmann, E. M. (2004b). The preparation effect in task switching: Carryover of SOA. Memory & Cognition, 32, 153–163. Anderson, R. A. (1987). Inferior parietal lobule function in spatial perception and visuomotor integration. In Mountcastle, V. B., Plum, F., Geiger, S. R. (eds.), Handbook of physiology. Bethesda, MD: American Physiological Society.

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