Neuropsychologia 49 (2011) Contents lists available at ScienceDirect. Neuropsychologia

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1 Neuropsychologia 49 (2011) Contents lists available at ScienceDirect Neuropsychologia journal homepage: Modulation of working-memory maintenance by directed attention Jöran Lepsien a,b,, Ian Thornton a, Anna C. Nobre a a Brain & Cognition Laboratory, Department of Experimental Psychology, University of Oxford, United Kingdom b Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany article info abstract Article history: Received 3 November 2010 Received in revised form 14 December 2010 Accepted 10 March 2011 Available online 21 March 2011 Keywords: Attention fmri Maintenance Mental representations Working memory Many current models of working memory (WM) emphasize a close relationship between WM and attention. Recently it was demonstrated that attention can be dynamically and voluntarily oriented to items held in WM, and it was suggested that directed attention can modulate the maintenance of specific WM representations. Here we used event-related functional magnetic resonance imaging to test the effects of orienting attention to a category of stimuli when participants maintained a variable number of faces and scenes in WM. Retro-cues that indicated the relevant stimulus type for the subsequent WM test modulated maintenance-related activity in extrastriate areas preferentially responsive to face or scene stimuli fusiform and parahippocampal gyri respectively in a categorical way. After the retro-cue, the activity level in these areas was larger for the cued category in a load-independent way, suggesting the modulation may also reflect anticipation of the probe stimulus. Activity in associative parietal and prefrontal cortices was also modulated by retro-cues, and additionally co-varied with the number of stimuli of the relevant stimulus category that was being maintained. The findings suggest that these associative areas participate in maintaining the relevant memoranda in a flexible and goal-directed way to guide future behaviour Elsevier Ltd. All rights reserved. 1. Introduction The ability to maintain task-relevant representations in working memory (WM) is of critical importance for many cognitive tasks. Adaptive behaviour relies on flexible access and manipulation of these representations, and also requires prioritizing of particular items in WM in light of changing task goals and expectations. Recent studies have demonstrated that such prioritization can be controlled flexibly by means of directing attention to the internal representation of an item stored in WM (Bays & Husain, 2008; Griffin & Nobre, 2003; Landman, Spekreijse, & Lamme, 2003). Many models of WM propose a close relationship between WM and attention (Awh & Jonides, 2001; Courtney, 2004; Cowan, 1988; Curtis & D Esposito, 2003; Fuster, 2000; Jonides, Lewis, Nee, Lustig, Berman & Moore, 2008; McElree, 2006; Passingham & Sakai, 2004; Postle, 2006). On the neural level, a prevailing account suggests that top-down signals from prefrontal areas bias activity in posterior regions to maintain, monitor, and/or manipulate information in WM (Awh & Jonides, 2001; Curtis & D Esposito, 2003; Harrison & Tong, 2009), in close analogy to attentional top-down signals bias- Corresponding author at: Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, Leipzig, Germany. Tel.: ; fax: address: lepsien@cbs.mpg.de (J. Lepsien). ing activity in visual areas during perception (Desimone & Duncan, 1995; Kastner & Ungerleider, 2001; Treue, 2003). Directly supporting this notion, a recent study demonstrated that the activity during maintenance in sensory areas was modulated as a consequence of attentional orienting towards the type of information represented in this area. These effects were linked to improved retrieval of attended relative to unattended items, and suggest a direct influence of attentional orienting on the maintenance of the items themselves (Lepsien & Nobre, 2007). However, other brain areas beyond the sensory cortices may also be involved in representing task-relevant items in WM. Representations in higher order areas may be expected to be less tied to the specific perceptual nature of the stimuli and to be flexibly modulated according to task goals. Key candidate regions are the posterior parietal (PPC) and the prefrontal cortices (PFC), which have both been shown to be sensitive to WM load (Linden et al., 2003; Rypma, Berger, & D Esposito, 2002; Todd & Marois, 2004; Xu & Chun, 2006). However, it can be difficult to tease apart whether these areas play a role in WM maintenance or in complementary selective attention or executive-control functions. For example, activity in the PPC is sustained during working-memory delays (Chafee & Goldman-Rakic, 1998; Courtney, Petit, Maisog, Ungerleider, & Haxby, 1998), and is correlated with the amount of information stored in WM (Todd & Marois, 2004; Xu & Chun, 2006) and with individual differences in WM capacity (Todd & Marois, 2005). This has led to some authors to ascribe to the PPC a direct role in WM maintenance (e.g., Todd & Marois, 2004). However, other /$ see front matter 2011 Elsevier Ltd. All rights reserved. doi: /j.neuropsychologia

2 1570 J. Lepsien et al. / Neuropsychologia 49 (2011) authors (Magen, Emmanouil, McMains, Kastner, & Treisman, 2009) have suggested a more attentional role for the PPC and proposed that the load-related activity in PPC reflects different attentional demands on rehearsal of information in WM rather than storage. Similarly, the PFC has been implicated in a wide range of WM-related processes, such as maintenance of task-relevant information (Courtney, 2004; Funahashi, Bruce, & Goldman-Rakic, 1989; Fuster & Alexander, 1971; Fuster, 1973; Goldman-Rakic, 1987), manipulation of information in WM (Petrides, 1994; D Esposito, Aguirre, Zarahn, & Ballard, 1998; Owen et al., 1999), or other executive-control functions like monitoring multiple mnemonic representations (Petrides, 2000). PFC has also been suggested to support WM through attention-related functions. For example, Rowe, Toni, Josephs, Frackowiak, and Passingham (2000) have suggested that area 46 is associated with the selection of relevant items in WM via attention (Bledowski, Rahm, & Rowe, 2009; Lebedev, Messinger, Kralik, & Wise, 2004), and other authors emphasized the role of this region in protecting WM representations against distraction (Knight, Staines, Swick, & Chao, 1999; Miller, Erickson, & Desimone, 1996; Sakai, Rowe, & Passingham, 2002; also referred to as active maintenance, see Miller & Cohen, 2001). The present experiment investigated the extent to which brain activity in perceptual and high-level brain areas involved in maintaining items in WM is flexibly modulated by changes in the task relevance of the memoranda. Attentional orienting to specific categories of stimuli being maintained in WM (faces or scenes) was manipulated by presenting retro-cues during the maintenance period, which indicated the category of stimuli that would be relevant to perform a subsequent comparison judgement to a probe item (Griffin & Nobre, 2003; Lepsien & Nobre, 2007). In each trial, participants viewed four items to be held in WM, from a variable and complementary (1 3) number of faces and scenes. Upon the presentation of a retro-cue indicating the relevant stimulus category, the effective load of task-relevant memoranda was reduced to 1 3. If it is possible to adapt WM maintenance flexibly by orienting attention to the relevant items, the load-related activity before and after attentional allocation should differ, and should be expressed in brain areas coding for the effective load of relevant items in WM ( effective WM load ). In addition, investigating the interaction between attention to a stimulus category and different levels of WM load for each category provides an elegant way to demonstrate more directly the effect of attentional orienting on WM maintenance. Lepsien and Nobre (2007) previously demonstrated that orienting attention to one stimulus category modulates stimulus-specific activity during the maintenance period. However, it is increasingly clear that the mere expectation of a probe stimulus of the relevant stimulus category can also modulate activity in higher order visual areas processing these stimuli (Esterman & Yantis, 2010; Puri, Wojciulik, & Ranganath, 2009; Stokes, Thompson, Nobre, & Duncan, 2009). Accordingly, it could be argued that the retro-cue manipulation used by Lepsien and Nobre (2007) could also have caused anticipation of an upcoming probe in addition to or instead of modulation of WM maintenance. If, however, attentional orienting were to result in graded changes of activity that tracked the effective WM load, this would be difficult to explain by mere anticipation. 2. Materials and methods 2.1. Participants Eighteen healthy volunteers took part in two experimental sessions (aged (mean 22.4), six female, normal or corrected-to-normal vision). Data sets of six participants were partly lost due to technical failures of the fmri scanner, thus only the data of twelve participants were entered into the analysis. Participants gave written informed consents. The study protocol had approval from the Oxfordshire Research Ethics Committee (06/Q1606/70) Stimuli The stimuli comprised 120 faces and 120 scenes converted to greyscale and presented within a rectangular frame of pixels. Front-view face images had the ears and neck removed using a graphics program, and were centered on a grey rectangle. Scene stimuli depicted outdoor views of different types of landscapes with a clear spatial layout. Scenes comprised views of mountain landscapes, forests, deserts, fields, rivers/lakes, or coasts/beaches. During the course of the experiment each stimulus was presented four to five times. Since face stimuli were more similar in general to one another than scene stimuli, task difficulty in responding to face and scene stimuli was equated by using similar types of scenes within each trial, drawing from within the same category of place (forests, deserts, fields, etc.). Pictures of faces and scenes were chosen as stimuli because of the known relative selectivity of processing in distinct brain areas, i.e. the posterior fusiform gyrus (FG) for faces (Allison et al., 1994; Kanwisher, McDermott, & Chun, 1997; Puce, Allison, Gore, & McCarthy, 1995) and the parahippocampal gyrus (PHG) for scenes (Aguirre, Zarahn, & D Esposito, 1998; Epstein & Kanwisher, 1998) (see also Gauthier, Tarr, Anderson, Skudlarski, & Gore, 1999; Haxby et al., 2001). Moreover, it has been demonstrated that these areas show sustained and load-dependent activity during the maintenance interval in WM tasks (Druzgal & D Esposito, 2001, 2003; Postle, Druzgal, & D Esposito, 2003; Rama & Courtney, 2005; Ranganath, Cohen, Dam, & D Esposito, 2004; Ranganath, DeGutis, & D Esposito, 2004; Sala, Rama, & Courtney, 2003) WM object-cueing task The task manipulated attention to different categories of objects held within WM using retro-cues (Griffin & Nobre, 2003), in a similar way to a previous study by Lepsien and Nobre (2007). Critically for the present experiment, the array of objects held within WM always contained four items from two categories (faces and scenes), and the number of items from each category varied between one and three. Retrocues were used to direct attention to a category of items. See Fig. 1 for an illustration of the trial design. Each trial began with the presentation of a slightly larger fixation cross for 1 s to alert participants to the new trial. Subsequently, the four items of the stimulus array were presented sequentially (1 s per stimulus, no interval between stimuli). The array could be made up of: 1 face and 3 scenes, 2 faces and 2 scenes, or 3 faces and 1 scene. After the array, a blank screen with a fixation cross was presented from 4 to 9 s (mean of 5.8 s) (inter-stimulus interval 1 (ISI1)). After ISI1, a cue was presented for 1 s. This consisted of the letter S (scenes) or F (faces) appearing in the middle of the screen. After another ISI (ISI2, 4 9 s, mean 5.8 s), a probe stimulus appeared on the screen for 1 s. The participant made a forced-choice response as to whether the probe matched one of the stimuli in the array, using the index and middle fingers for Match and Non-Match respectively. This marked the end of the trial (mean length of a trial: 18.6 s). The inter-trial interval (ITI) varied in length from 4 to 12 s (mean 6.95 s). Cues were 100% valid (i.e. the probe stimulus always came from the category cued). The rationale for using fully predictive cues was to maximize the reliance upon the cues, and therefore to optimize the magnitude of the putative neural effects for fmri measures. The retro-cue provided no information about the correct response. There were 50% match and 50% non-match trials, according to the presence or absence of the stimulus in the original array. If the probe stimulus was a non-match stimulus it was always a new item from the same category. All delays used were skewed towards the shorter durations and were varied pseudo-randomly to approximate a logarithmic distribution (ISIs: 4 5 s (50%), 6 7 s (35%) and 8 9 s (15%); ITI: 4 6 s (50%), 7 9 s (35%) and s (15%)). This kept the task to an endurable length, and maintained constant temporal expectation for the appearance of the next stimulus across intervals (temporal conditional probability remained at 50% until the last interval) (see Nobre, Correa, & Coull, 2007). In addition, using long and variable intervals enabled the separation of hemodynamic responses of individual events within single trials (Friston et al., 1998; c.f. Nobre et al., 2004). There were 6 conditions of interest in this experiment, resulting from the combination of the factors array type (1 face and 3 scenes (1F/3S), 2 faces and 2 scenes (2F/2S), 3 faces and 1 scene (3F/1S)) and cued category (face (), scene ()): 1F/3S- F (1F/3S array and cued to F), 2F/2S, 3F/1S, 1F/3S, 2F/2S, and 3F/1S. Each participant completed two scanning sessions, on separate days. In each session, they completed one block of 120 trials (240 trials across both sessions) in which all six trial types were presented in a pseudo-randomized and unpredictable order (20 repetitions per condition), lasting approximately 52 min. All tasks were programmed and presented using the Presentation software package (Neurobehavioral Systems, Albany, CA) Localizer task A separate localizer experiment was conducted during the second fmri session to define functionally the regions in posterior FG and PHG that were responsive to faces and places, respectively. Participants viewed sequences of faces, scenes, or checkerboard-like grid stimuli, and indicated with a speeded choice response (right hand; index finger: yes, middle finger: no) whether the presented stimulus

3 J. Lepsien et al. / Neuropsychologia 49 (2011) Fig. 1. Category based Retro-cueing task. An array of four stimuli (faces and scenes) was presented, and, after a variable delay, participants were cued to orient their attention to one category of stimuli held on-line in working memory. After a second variable delay participants had to indicate whether the probe matched one of the stimuli from the initial array. All intervals varied pseudo-randomly within the given limits. On average, one trial lasted for 18.6 s. See Section 2 for details. matched the previous one (continuous 1-back task). In each block, 11 stimuli from one category were presented for 1 s, separated by 1 s inter-stimulus-interval (block length 22 s). Within each block there were 5 matches and 5 non-matches. There were 6 blocks of each condition (faces, scenes, checkerboards), separated by 6-s interblock-intervals. The order of stimuli within each block and the order of blocks was pseudo-randomized, with the exception of every fourth block being a 22 s baseline block without stimulation. The whole localizer lasted 10.5 min Image acquisition Magnetic-resonance images were acquired using the 3T Siemens TimTrio scanner at the Oxford Centre for Magnetic Resonance Research (OCMR), using a Siemens 12-channel head coil. Functional images were obtained with a standard Siemens echo-planar imaging (EPI) sequence (TE = 30 ms, angle = 90, TR = 2 s). Thirty-two axial slices, with a thickness of 4 mm covered almost the entire cortex and the cerebellum (64 64 matrix with a field of view of 19.2 cm, resulting in a notional voxel size of 3 mm 3mm 4 mm). In each scanning session, 1549 images were acquired for the main task. For the localizer task, 316 images were acquired (approx min). In all cases, the first 6 images contained no experimental manipulation and were discarded in order to allow the signal intensities to saturate. A mirror mounted on top of the head-coil allowed subjects to view a screen mounted at the rear of the bore. Stimuli were projected using a projector placed outside the scanning room. Subjects gave responses using a custom-made MRIcompatible button box. Structural images of each participant were acquired at the end of the first fmri session, using an inversion-recovery-prepared 3D T1-weighted FLASH sequence (TR=10ms;TE=4ms;angle = 8,1mm 1mm 1 mm voxel size) Image processing and analysis General fmri data analysis Data were analysed using statistical parametric mapping (SPM5 revision 1782, (Wellcome Trust Centre for Neuroimaging, UCL, London, UK)) and Matlab 7.7 (The MathWorks, Inc, Natick, MA). Data from the main experiment and the localizer task were processed separately. First images were corrected for slice timing, followed by estimating and correcting for motion and EPI deformation. Thereafter, the high-resolution anatomical image of the same subject was co-registered with the functional images, and normalization was performed using the unified segmentation approach (Ashburner & Friston, 2005). After normalization, the resulting voxel size of the functional images was interpolated to 3 mm 3mm 3 mm. In the final step of the pre-processing, the functional images were smoothed using a Gaussian smoothing kernel of 8-mm full width at half maximum (FWHM). The statistical evaluation was based on a least-squares estimation using the general linear model with serially correlated observations. Non-sphericity was characterized by restricted maximum likelihood (ReML) hyperparameters, which were used to whiten the data (Penny, Kiebel, & Friston, 2003). In addition to the whitening, the data were temporally filtered to eliminate slow signal drifts (high-pass filter: 128 s). The design matrix was generated using the canonical hemodynamic response function without derivatives (Friston et al., 1998). The model of the main task included explanatory variables for all phases of a trial: preparation, stimulus array, first retention period (ISI1), cue, second retention period (ISI2) and probe. In addition, the first retention period was separated for array types (1F/3S, 2F/2S, 3F/1S), and the second retention interval was separated for the 6 main conditions resulting from the crossing of array and retro-cue types (1F/3S, 2F/2S, 3F/1S, 1F/3S, 2F/2S, 3F/1S). Stimulus arrays and retention intervals were modelled as extended events, including their duration. Error trials, including trials with incorrect, late or no response, were modelled as extended events with the duration comprising the length of the whole trial, and were included into the model as regressors of no interest. Statistical comparisons were calculated using linear contrasts in analyses of single-subjects data, and group effects were determined using a random-effects analysis at a second level (Friston, Holmes, & Worsley, 1999). To determine maintenance-related effects in general during the first retention interval, all array types were compared to the implicit baseline using a one-sample t-test. Effects during the second retention interval were investigated using a flexible factorial approach, comprising the factors cue and array type. Except where otherwise noted, all comparisons were corrected for multiple comparisons using False Discovery Rate (FDR) at a statistical threshold of p < 5 (Genovese, Lazar, & Nichols, 2002), and only activations with a size of more than 10 voxels were reported. The localizer task was analysed in a comparable fashion, with the only exceptions of using a block design for modelling and a high-pass filter of 180 s. Faceresponsive areas were obtained by comparing face vs. scene, masked by face vs. checkerboard, thresholded at p < 01 (uncorrected) (threshold for mask: p < 5, uncorrected). Corresponding contrasts were used to define scene-responsive areas. For four out of twelve participants, it was not possible to find clear activations in all ROIs. In these cases the threshold was lowered. If still no maximum was identifiable, the mean coordinates derived from all other participants were used (two times for the right fusiform gyrus and three times for the left parahippocampal gyrus). In order to plot time courses of neural activity for the six different types of trials starting from the presentation of the array, an additional model was specified. This included explanatory variables for arrays and cues, each separated for the six conditions of interest (1F/3S, 2F/2S, 3F/1S, 1F/3S, 2F/2S, 3F/1S), as well as probes and error trials. Retention periods were not specified explicitly in this model ROI analysis To test for modulation of category-specific responses by attention during WM maintenance, we measured activity in several regions-of-interest (ROI). Parameter estimates in the fusiform (FG) and parahippocampal gyri (PHG) were extracted in spheres with 3-mm radius centered on peak activations within individual participants using the localizer task. For technical reasons, it was not possible to use individual participant coordinates in the time course analysis, and the spherical regions centered on the mean coordinates were used instead (mean coordinates: right PHG (26/-43/-13), left PHG (-26/-46/-10), right FG (41/-51/-20) and left FG (-40/-55/-22)). Additional ROIs were defined by the overlap of maintenance-related activity during ISI1 and the interaction of cue*array during ISI2. That is, activations triggered by the three array types during ISI1 were compared to the implicit baseline, and afterwards used as an implicit mask for the interaction contrast on ISI2 (both contrasts thresholded at p < 5, FDR). ROI analyses were performed on all surviving clusters. It should be noted that according to the strict guidelines of Kriegeskorte, Simmons, Bellgowan, and Baker (2009) this method of selection for the additional ROIs is not completely independent of the results of the whole-brain analysis. However, the method cannot distort the findings in a way that compromises the comparisons of interest. The isolation of areas related to WM maintenance during ISI1 does not in itself lead to any bias related to changes in the effective WM load according to attentional orienting. All ROI analyses including the extraction of time courses were performed using the rfxplot toolbox (Gläscher, 2009) Parametric analysis To test for a direct link between behavioural data and brain activations, a separate analysis was conducted, introducing reaction times (RT) as a covariate into the flexible factorial model described above. To reflect the relative increase in RT when being cued to an array containing two or three as compared to one relevant stimulus, the fraction of RT following a cue to two or three stimuli relative to the RT following a cue to one stimulus was calculated (separately for face, scenes and participants). The statistical evaluation was done by testing for activity related to this covariate only.

4 1572 J. Lepsien et al. / Neuropsychologia 49 (2011) Results 3.1. Behavioural results The behavioural results are summarized in Fig. 2. To investigate the effects of the retro-cue on the performance in the delayedmatch-to-sample task, mean reaction time (RT) and accuracy (AC) were analysed with repeated-measures analyses of variance (ANOVAs) with the factors cue (face, scene) and array type (1F/3S, 2F/2S, 3F/1S). Only trials with correct responses were entered into the RT analysis. Trials with RTs deviating more than 3 standard deviations from the condition mean were considered outliers and were excluded from the analysis (1.8% on average). The predicted interaction between cue and array type reached significance for both RT (F(2,22) = , p < 01) and AC (F(2,22) = 6.186, p < 07), indicating that participants were using the cues to orient their attention to the indicated category. To test for the specific, predicted effect of attentional orienting in reducing load to the number of stimuli in the task-relevant category, the linear interaction of cue and array type was investigated. The planned contrast reached significance for both RT and AC (p s < 08), indicating a significant load effect across categories. The analysis further yielded a main effect of array type for RT (F(2,22) = 157, p < 1) and AC (F(2,22) = 3.543, p < 46), indicating significantly different responses to the different array types. Close inspection of these results revealed that responses towards the array type F2S2 were slower and less accurate in general. No significant main effects of cue were observed (all p > 0.155). Table 1 Areas showing a significant interaction between cue and array type during the second retention period. Label H Size Z x,y,z (MNI) Parietal aips R IPS R lo IPS L a IPS R lo aips L lo Frontal IFG/IFS R a IFS R lo IFG R lo IFS L a IFS L lo PreCS L lo FEF R SFS R lo SFS R lo SFS L a Other RSC/Cun R a RSC/Cun L a Coordinates, maximum z-values, spatial extent and labels of significantly activated brain areas with the interaction of cue and array type during ISI2 (threshold p < 5 (FDR); >10 voxel). H = hemisphere; L = left; R = right; lo = local maximum within a larger activation cluster; IPS = intraparietal sulcus; aips = anterior part of the IPS; RSC/Cun = retrosplenial cortex/cuneus; IFS = inferior frontal sulcus; SFS = superior frontal sulcus; IFG = inferior frontal gyrus; FEF = frontal eye fields; PreCS = precentral sulcus. a Cluster showing overlap with maintenance related activity from the first retention interval fmri results The analysis of the fmri data was performed in three steps. The first analysis identified brain areas showing dynamic changes in effective WM load induced by orienting attention to a category of stimuli maintained in WM. In close analogy to the analysis of the behavioural data, the interaction of the factors cue (face, scene) and array type (1F/3S, 2F/2S, 3F/1S) was tested for the second retention interval (ISI2). Second, to chart how the load-related activity changed from ISI1 to ISI2, ROI analyses were conducted on the areas of overlap between ISI1 and ISI2. Third, additional ROI analyses were also carried out on posterior regions functionally specialised for processing face and scene stimuli that were defined by the localizer task. These analyses served to clarify if modulation of activity in these areas is primarily driven by a modulation of maintenancerelated activity, or by the anticipation of the category of the probe stimulus Changes of effective WM load induced by attentional orienting The interaction between cue and array type during ISI2 should be significant in brain areas showing a differential change in loadrelated activity following attentional orienting, i.e. the load-related activity should reflect the number of WM representations in the focus of attention. This contrast revealed a network of frontal and parietal brain areas (see Fig. 3). In the parietal cortex bilateral activations were found along the anterior and middle segment of the intraparietal sulcus (IPS). Frontal activations included the right junction of the precentral (PreCS) and the superior frontal sulcus (SFS), as well as foci in the bilateral anterior SFS. Additional activation foci were identified bilaterally along the inferior frontal sulci (IFS), spreading into the middle frontal gyrus (MFG) on the left and into the inferior frontal gyrus (IFG) on the right. In both hemispheres one activation spot was located in the more posterior section of the IFS, whereas the other spot was located more anteriorly in the middle segment. In the left hemisphere, the activation also covered the junction of the IFS with the PreCS (inferior frontal junction (IFJ), Brass, Derrfuss, Forstmann, & von Cramon, 2005). In addition, the interaction contrast revealed a bilateral activation in the retrosplenial cortex/cuneus (RSC/Cun). The results from the interaction contrast largely overlapped with the maintenance-related activity during ISI1. Overlapping areas were found in the left IFS and SFS, in the right IFG, in the left IPS and the bilateral RSC/Cun (see Table 1 and Fig. 3) reaction time [ms] accuracy [%] Fig. 2. Mean (±SEM) reaction times and mean (±SEM) accuracies.

5 J. Lepsien et al. / Neuropsychologia 49 (2011) Fig. 3. Neural correlates of maintaining four faces/scenes in WM, and category-specific modulation. Green = maintenance-related activity during ISI1; red = interaction of cue*array during ISI2; yellow = overlap. Displays are oriented following the neurological convention (left = left). The numbers indicate the coordinates of the sagittal and axial planes Regions-of-interest analyses To chart the pattern of modulation of maintenance-related activity by attention, parameter estimates (beta values) for the conditions of interest were extracted, and averaged over all voxels within each ROI. The resulting ROI values were submitted to repeated-measures ANOVAs testing for the factors of cue (face, scene) and array type (1F/3S, 2F/2S, 3F/1S). In all ROIs identified by the overlap of activity during ISI1 and ISI2, the interaction between cue and array type reached significance (all p < 22). To test specifically for the effects of attentional orienting on the load-related activity during ISI2, the linear interaction of cue and array type was investigated. The planned contrast reached significance in all ROIs (all p < 5), indicating a significant reduction of the effective WM load across categories as a result of attentional orienting (see Fig. 4). To follow the development of activity in PHG and FG, time courses were extracted starting from the presentation of the array (see Fig. 5a), and from the middle of ISI1 time-locked to the retrocue (see Fig. 5b). During the array phase, a separation for the different array types can be seen in both areas, reflecting the dif- left inferior frontal sulcus 0.4 right inferior frontal gyrus left superior frontal sulcus left intraparietal sulcus Fig. 4. Effective working-memory load during ISI2. Parameter estimates (±SEM) extracted from ISI2, in clusters showing overlap of activity for maintenance related activity during ISI1 and the interaction of cue*array during ISI2. Effect sizes were pooled over categories, the numbers (1, 2 or 3) indicate the numbers of items cued (i.e. the effective working-memory load).

6 1574 J. Lepsien et al. / Neuropsychologia 49 (2011) Fig. 5. Plots showing the time courses of activity for the six conditions of interest in right FG and right PHG starting from the onset of the array (a) and the middle of ISI1 (retro-cue appears at time 0) (b). The unit of the x-axis is seconds. ferences in load for the stimulus type driving activity in each of the ROIs. After the retro-cue the pattern gradually changes into a main effect of cue, signalling the relevant stimulus category. To substantiate these effects statistically, parameter estimates for the array phase and for ISI2 were extracted for the PHG and FG ROIs (see Fig. 6 for the effects in right PHG and FG). For the array phase, a significant main effect of array type (all p < 03) was found in all regions, but no significant main effects of cue (all p > 0.134) or significant interactions (p > 0.142). For ISI2, the main effect of cue reached significance in the bilateral PHG and right FG (all p < 01, p = 89 for a 6.0 right parahippocampal gyrus 6.0 right fusiform gyrus b 1.0 right parahippocampal gyrus 1.0 right fusiform gyrus Fig. 6. Parameter estimates (±SEM) extracted from right FG and right PHG during the array phase (a) and ISI2 (b).

7 J. Lepsien et al. / Neuropsychologia 49 (2011) the left FG). No significant main effects of array type (all p > 0.333) or significant interactions between cue and array type were found (all p > 0.576). There was only a trend towards a significant interaction in the left PHG (p = 52) Covariation between reaction times and brain activity Including RTs as a covariate in the assessment of activity during ISI2 revealed no significant activation clusters at using a familywise correction. Lowering the threshold to p < 01 (uncorrected) revealed two activations in the left intraparietal sulcus (-30/-60/45, Z = 3.62) and left inferior frontal sulcus (-39/27/21, Z = 3.44). These clusters overlap or are in the direct vicinity of activations revealed by the interaction contrast. 4. Discussion The present experiment probed the flexibility of WM representations, and investigated if activity in perceptual and high-level brain areas involved in WM maintenance can be modulated according to the task-relevance of the items held on-line. Cueing attention to a subset of items in WM, tagging them as relevant for subsequent probe comparison, resulted in an enhancement in WM performance, which increased linearly as the number of items in the focus of attention decreased. On the neural level, this effect was evident in the bilateral PFC around the IFS, and in the left PPC. The activity in these brain areas reflected the effective load of relevant items in WM. In contrast, activity in the sensory areas, like PHG and FG, was dominated by the anticipation of a specific type of probe item, which was triggered by the retro-cue Behavioural effects of orienting attention to WM representations Participants were able to use the retro-cues to orient their attention selectively to a subset of items represented in WM. The beneficial effect of cueing was evident in both shorter RT and higher AC when fewer items were in the focus of attention. This is a clear indication that directing attention towards a category of items held in WM leads to a reduced effective WM load, and extends the findings of Lepsien and Nobre (2007). No significant main effect was found for the different cues, i.e. both cues were equally effective in directing attention. In principle, these results are in line with contemporary models of WM, suggesting a strong relationship between WM and attention (Curtis & D Esposito, 2003; Postle, 2006). However, other studies have failed to demonstrate that attention can be focused on more than one memory item, and have argued that orienting attention is less flexible in visual short-term memory than in perception (Makovski & Jiang, 2007). Similarly, proposals based on Cowan s influential model (1988), suggesting that attention is a critical process in WM, acting to highlight temporarily parts of long-term memory, claimed that only one item from within WM may enter privileged state of activation (the focus of attention ) in which it is readily available for further processing (Garavan, 1998; Houtkamp & Roelfsema, 2009; McElree, 2001; Oberauer & Bialkova, 2009; Oberauer, 2002). Other items would have to be retrieved first in order to become available, i.e. to enter the focus of attention. The present results challenge these findings and show that the attentional deployment is much more flexible, and probably equally flexible as attentional orienting in the perceptual domain Modulation of maintenance-related activity by attention A widespread network of brain areas in PPC and PFC showed decreases in brain activity that were proportional to the decreases in the load of relevant stimuli signalled by the retro-cue; the smaller the effective load the weaker the activation. The pattern of activity largely overlapped with the pattern of sustained maintenancerelated activity during ISI1, further underscoring the point that attentional orienting directly modulated the maintenance of information in WM. The activation foci in the PPC are well known from previous WM studies, which related these areas to WM maintenance (Rowe et al., 2000; Sakai et al., 2002; Todd & Marois, 2004, 2005; Xu & Chun, 2006), or updating the attentional focus and selecting information from within WM (Bledowski et al., 2009; Johnson, Raye, Mitchell, Greene, & Anderson, 2003; Leung, Oh, Ferri, & Yi, 2007; Nobre et al., 2004). For spatial WM it was suggested that information is retained by attention-based-rehearsal, i.e. systematic shifts of attention the to-be-remembered positions (Awh & Jonides, 2001; Postle, Awh, Jonides, Smith, & D Esposito, 2004). Load-related changes in the PPC would then reflect changes of the attentional demands of shortterm memory rehearsal (Magen et al., 2009), and rehearsal could be reframed as a series of updating operations (Bledowski et al., 2009). Lepsien and Nobre (2007) also found left-lateralized activity in the PPC located in neighbouring areas as compared to the present experiment, and related them to shifts of attention to faces and scenes held in WM. All these notions would also apply for the present experiment. It is plausible to assume that attentional orienting to a subset of items held on-line in WM leads to a decrease in the overall load-related activity, or to reduce demands on rehearsal. At the same time, activity in the PPC could reflect updating and selecting information via attention. For example Leung et al. (2007) found linear increases in parietal as well as frontal areas with increasing demands on memory updating (i.e. the number of updates). Accordingly, the activity in the PPC may reflect the number of items attended/rehearsed, rather than their maintenance. However, all studies on updating and selection focused on the transient activity related to the instructive cue. In contrast, the sustained maintenance-related activity was investigated in the present experiment. The retro-cues were explicitly modelled as separate events, making it unlikely that the present results are dominated by cue-related activity. Thus in the present context, it is more plausible to interpret PPC activity as related to maintenance and/or rehearsal in WM. These functions, however, may be supported in turn by a series of updating operations. Areas in the PFC showing overlapping activity during ISI1 and ISI2 were observed along the left SFS and the bilateral IFS. The anterior part of the SFS has been suggested to participate in maintaining information in spatial WM (Courtney et al., 1998; Rowe et al., 2000; Rowe & Passingham, 2001; Sakai et al., 2002), but has been also reported in the context of updating and selecting information in WM (Bledowski et al., 2009). Areas along the bilateral IFS and in the MFG have also been reported to show a transient response following updating information in WM (Bledowski et al., 2009; Johnson et al., 2003, Johnson, Mitchell, Raye, D Esposito, & Johnson, 2007; Leung et al., 2007; Roth, Serences, & Courtney, 2006; Roth & Courtney, 2007; Rowe et al., 2000, 2001). The left IFJ has been previously implicated in cognitive control, especially when tasks require a high degree of control over potential interference from task-irrelevant information and when task rules need to be updated (Brass & von Cramon, 2004; Brass et al., 2005; Derrfuss, Brass, & von Cramon, 2004). Accordingly, left IFJ activation has been reported in a number of studies investigating WM updating (Roth et al., 2006, 2007; Leung et al., 2007), and also following retro-cues instructing participants to orient their attention to face or scenes in WM (Lepsien & Nobre, 2007). However, similar to the present study, Roth et al. (2006) also reported sustained activity in the IFJ, and speculated that it may play a role in WM maintenance. As discussed previously, the present study focused on the investigation of the sustained activity during maintenance, and not the

8 1576 J. Lepsien et al. / Neuropsychologia 49 (2011) transient activity following the retro-cue. Accordingly, the present findings reflect more sustained processes after a subset of representations has been selected from within WM. Miller et al. (1996) reported the maintenance-related signals in the macaque PFC to be resistant to distractors, while activity in extrastriate visual areas was vulnerable to interruption (see also Miller & Cohen, 2001; Petrides, 2000). This notion is in line with the proposal of Passingham and coworkers (Passingham & Rowe, 2002; Rowe et al., 2000, 2001), suggesting that the role of the PFC for WM is attentional selection, especially under high demands and in the face of interference (see also Sakai et al., 2002). The network of PFC and PPC appears to code for the items that are selected as task-relevant by attentional allocation to a subset of information held on-line in WM Modulation of activity in PHG and FG Contrary to our predictions, neither the FG nor the PHG showed a load-dependent pattern of activation following the retro-cue. The analysis of time courses and parameter estimates revealed that both areas clearly coded for the different composition of the array, showing a systematic effect of preferred-stimulus load before the retro-cue was presented. However, during ISI2 only a main effect of attentional orienting can be seen. No residual load effect remains, but instead activity is higher or lower according to whether the preferred or non-preferred stimulus category, respectively, is relevant for subsequent performance. This effect may be best explained by modulation of baseline activity linked to the anticipation of the probe-stimulus category (Puri et al., 2009; Esterman & Yantis 2010; Stokes et al., 2009). We believe that our findings do not exclude the possibility of attentional orienting modulating maintenancerelated activity. However, the effects of anticipatory attention dominate the results, and preclude much interpretation about the possible modulation of WM maintenance. Furthermore, presentation of the retro-cue in this task may have disrupted the lingering load-dependent level of activity related to the encoding and maintenance of the arrays (Miller et al., 1996; Miller & Cohen, 2001; Petrides, 2000). The dissipation of the loadrelated effects after the retro-cue, however, should not be taken as evidence against the continued involvement of these posterior sensory areas in WM maintenance. It is possible that activity or excitability in these areas continues to support WM maintenance, and that these states might be measurable though other, more sensitive multivariate methods (e.g., Harrison & Tong, 2009). Studies with larger trial numbers will be required to test for modulation of maintenance-related activity using multivariate methods. In contrast to the previous study investigating the modulation of WM maintenance by attention (Lepsien & Nobre, 2007), retrocues in the present study did not indicate one individual item, but instead highlighted a category of items. The present study therefore extends our understanding about the degree of flexibility of WM representations. Biasing of WM maintenance need not to be based on low-level perceptual information such as stimulus location or the specific object or object features, but may also occur through changes in information about which high level perceptual attributes are relevant to behaviour, such as category. Mechanisms for orienting to and selecting a category versus an individual stimulus or location may differ, and these remain to be explored. The shrinking of load-related maintenance activity in associative areas around the number of items in the relevant category suggests that attentional selection may act at least partially by preferentially maintaining relevant items while releasing items from the uncued, task-irrelevant category. More sensitive methods, such as multivariate pattern analysis, may also prove helpful in understanding whether and how the nature of maintained representations may be modulated by attention. 5. Conclusion The present study provides behavioural and neural evidence that subsets of items held on-line in WM can be highlighted as task-relevant by orienting attention towards them. A network of prefrontal and parietal areas directly reflected the changes of the effective WM load in a systematic fashion, suggesting that these areas directly contribute to the (active) maintenance of information in WM. Acknowledgements We would like to thank the OCMR for the help with data acquisition. The research was funded by 21st Century Research Award: Bridging Brain, Mind and Behaviour to Anna C. Nobre by the James S. McDonnell Foundation. References Aguirre, G. K., Zarahn, E., & D Esposito, M. (1998). Neural components of topographical representation. Proceedings of the National Academy of Sciences of the United States of America, 95, Allison, T., Ginter, H., McCarthy, G., Nobre, A. C., Puce, A., Luby, M., et al. (1994). Face recognition in human extrastriate cortex. Journal of Neurophysiology, 71(2), Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26, Awh, E., & Jonides, J. (2001). Overlapping mechanisms of attention and spatial working memory. Trends in Cognitive Sciences, 5(3), Bays, P. M., & Husain, M. (2008). Dynamic shifts of limited working memory resources in human vision. Science, 321(5890), Bledowski, C., Rahm, B., & Rowe, J. B. (2009). What works in working memory? Separate systems for selection and updating of critical information. Journal of Neuroscience, 29, Brass, M., Derrfuss, J., Forstmann, B., & von Cramon, D. Y. (2005). The role of the inferior frontal junction area in cognitive control. Trends in Cognitive Sciences, 9(7), Brass, M., & von Cramon, D. Y. (2004). Selection for cognitive control: a functional magnetic resonance imaging study on the selection of task-relevant information. Journal of Neuroscience, 24, Chafee, M. V., & Goldman-Rakic, P. S. (1998). Matching patterns of activity in primate prefrontal area 8a and parietal area 7ip neurons during a spatial working memory task. Journal of Neurophysiology, 79(6), Courtney, S. M. (2004). Attention and cognitive control as emergent properties of information representation in working memory. Cognitive, Affective & Behavioral Neuroscience, 4, Courtney, S. M., Petit, L., Maisog, J. M., Ungerleider, L. G., & Haxby, J. V. (1998). An area specialized for spatial working memory in human frontal cortex. Science, 279(5355), Cowan, N. (1988). Evolving conceptions of memory storage, selective attention, and their mutual constraints within the human information-processing system. Psychological Bulletin, 104, Curtis, C. E., & D Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7, Derrfuss, J., Brass, M., & von Cramon, D. Y. (2004). Cognitive control in the posterior frontolateral cortex: evidence from common activations in task coordination, interference control, and working memory. NeuroImage, 23(2), Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, D Esposito, M., Aguirre, G. K., Zarahn, E., & Ballard, D. (1998). Functional MRI studies of spatial and non-spatial working memory. Cognitive Brain Research, 7, Druzgal, T. J., & D Esposito, M. (2001). Activity in fusiform face area modulated as a function of working memory load. Brain Research. Cognitive Brain Research, 10, Druzgal, T. J., & D Esposito, M. (2003). Dissecting contributions of prefrontal cortex and fusiform face area to face working memory. Journal of Cognitive Neuroscience, 15, Epstein, R., & Kanwisher, N. (1998). A cortical representation of the local visual environment. Nature, 392, Esterman, M., & Yantis, S. (2010). Perceptual expectation evokes category-selective cortical activity. Cerebral Cortex, 20(5), Friston, K. J., Fletcher, P., Josephs, O., Holmes, A., Rugg, M. D., & Turner, R. (1998). Event-related fmri: characterizing differential responses. Neuroimage, 7, Friston, K. J., Holmes, A. P., & Worsley, K. J. (1999). How many subjects constitute a study? Neuroimage, 10, 1 5. Funahashi, S., Bruce, C. J., & Goldman-Rakic, P. S. (1989). Mnemonic coding of visual space in the monkey s dorsolateral prefrontal cortex. Journal of Neurophysiology, 61, Fuster, J. M. (1973). Unit activity in prefrontal cortex during delayed-response performance: neuronal correlates of transient memory. Journal of Neurophysiology, 36,

9 J. Lepsien et al. / Neuropsychologia 49 (2011) Fuster, J. M. (2000). Executive frontal functions. Experimental Brain Research, 133, Fuster, J. M., & Alexander, G. E. (1971). Neuron activity related to short-term memory. Science, 173, Garavan, H. (1998). Serial attention within working memory. Memory and Cognition, 26, Gauthier, I., Tarr, M. J., Anderson, A. W., Skudlarski, P., & Gore, J. C. (1999). Activation of the middle fusiform face area increases with expertise in recognizing novel objects. Nature Neuroscience, 2, Genovese, C. R., Lazar, N. A., & Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the False Discovery Rate. Neuroimage, 15, Gläscher, J. (2009). Visualization of group inference data in functional neuroimaging. Neuroinformatics, 7(1), Goldman-Rakic, P. S. (1987). Circuitry of primate prefrontal cortex and regulation of behaviour by representational memory. In V. B. Mountcastle, F. Plum, & S. R. Geiger (Eds.), Handbook of Neurobiology (pp ). Bethesda: American Physiological Society. Griffin, I. C., & Nobre, A. C. (2003). Orienting attention to locations in internal representations. Journal of Cognitive Neuroscience, 15, Harrison, S. A., & Tong, F. (2009). Decoding reveals the contents of visual working memory in early visual areas. Nature, 458(7238), Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, Houtkamp, R., & Roelfsema, P. R. (2009). Matching of visual input to only one item at any one time. Psychological Research, 73(3), Johnson, M. R., Mitchell, K. J., Raye, C. L., D Esposito, M., & Johnson, M. K. (2007). A brief thought can modulate activity in extrastriate visual areas: top-down effects of refreshing just-seen visual stimuli. Neuroimage, 37, Johnson, M. K., Raye, C. L., Mitchell, K. J., Greene, E. J., & Anderson, A. W. (2003). FMRI evidence for an organization of prefrontal cortex by both type of process and type of information. Cerebral Cortex, 13, Jonides, J., Lewis, R. L., Nee, D. E., Lustig, C. A., Berman, M. G., & Moore, K. S. (2008). The mind and brain of short-term memory. Annual Review of Psychology, 59, Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: a module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, Kastner, S., & Ungerleider, L. G. (2001). The neural basis of biased competition in human visual cortex. Neuropsychologia, 39, Knight, R. T., Staines, W. R., Swick, D., & Chao, L. L. (1999). Prefrontal cortex regulates inhibition and excitation in distributed neural networks. Acta Psychologica, 101, Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S., & Baker, C. I. (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nature Neuroscience, 12(5), Landman, R., Spekreijse, H., & Lamme, V. A. (2003). Large capacity storage of integrated objects before change blindness. Vision Research, 43, Lebedev, M. A., Messinger, A., Kralik, J. D., & Wise, S. P. (2004). Representation of attended versus remembered locations in prefrontal cortex. PLoS Biology, 2, Lepsien, J., & Nobre, A. C. (2007). Attentional modulation of object representations in working memory. Cerebral Cortex, 17, Leung, H. C., Oh, H., Ferri, J., & Yi, Y. (2007). Load response functions in the human spatial working memory circuit during location memory updating. Neuroimage, 35, Linden, D. E. J., Bittner, R. A., Muckli, L., Waltz, J. A., Kriegeskorte, N., Goebel, R., et al. (2003). Cortical capacity constraints for visual working memory: dissociation of fmri load effects in a fronto-parietal network. Neuroimage, 20, Magen, H., Emmanouil, T. A., McMains, S. A., Kastner, S., & Treisman, A. (2009). Attentional demands predict short-term memory load response in posterior parietal cortex. Neuropsychologia, 47(8 9), Makovski, T., & Jiang, Y. V. (2007). Distributing versus focusing attention in visual short-term memory. Psychonomic Bulletin & Review, 14, McElree, B. (2001). Working memory and focal attention. Journal of Experimental Psychology Learning, Memory, and Cognition, 27, McElree, B. (2006). Accessing recent events. In B. H. Ross (Ed.), The psychology of learning and motivation. San Diego: Academic. Miller, E. K., & Cohen, J. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, Miller, E. K., Erickson, C. A., & Desimone, R. (1996). Neural mechanisms of visual working memory in prefrontal cortex of the Macaque. Journal of Neuroscience, 16, Nobre, A., Correa, A., & Coull, J. (2007). The hazards of time. Current Opinion in Neurobiology, 17(4), Nobre, A. C., Coull, J. T., Maquet, P., Frith, C. D., Vandenberghe, R., & Mesulam, M. M. (2004). Orienting attention to locations in perceptual versus mental representations. Journal of Cognitive Neuroscience, 16, Oberauer, K. (2002). Access to information in working memory: exploring the focus of attention. Journal of Experimental Psychology Learning, Memory, and Cognition, 28, Oberauer, K., & Bialkova, S. (2009). Accessing information in working memory: can the focus of attention grasp two elements at the same time? Journal of Experimental Psychology: General, 138, Owen, A. M., Herrod, N. J., Menon, D. K., Clark, J. C., Downey, S. P., Carpenter, T. A., et al. (1999). Redefining the functional organization of working memory processes within human lateral prefrontal cortex. European Journal of Neuroscience, 11, Passingham, R. E., & Rowe, J. B. (2002). Dorsal prefrontal cortex: maintenance in memory or attentional selection? In D. T. Stuss, & R. T. Knight (Eds.), Principles of frontal lobe function (pp ). Oxford: Oxford University Press. Passingham, R. E., & Sakai, K. (2004). The prefrontal cortex and working memory: physiology and brain imaging. Current Opinion in Neurobiology, 14, Penny, W. D., Kiebel, S. J., & Friston, K. J. (2003). Variational Bayesian Inference for fmri time series. NeuroImage, 19, Petrides, M. (1994). Frontal lobes and behaviour. Current Opinion in Neurobiology, 4, Petrides, M. (2000). Dissociable roles of mid-dorsolateral prefrontal and anterior inferotemporal cortex in visual working memory. Journal of Neuroscience, 20, Postle, B. R. (2006). Working memory as an emergent property of the mind and brain. Neuroscience, 139, Postle, B. R., Awh, E., Jonides, J., Smith, E. E., & D Esposito, M. (2004). The where and how of attention-based rehearsal in spatial working memory. Brain Research. Cognitive Brain Research, 20, Postle, B. R., Druzgal, T. J., & D Esposito, M. (2003). Seeking the neural substrates of visual working memory storage. Cortex, 39, Puce, A., Allison, T., Gore, J. C., & McCarthy, G. (1995). Face-sensitive regions in human extrastriate cortex studied by functional MRI. Journal of Neurophysiology, 74, Puri, A. M., Wojciulik, E., & Ranganath, C. (2009). Category expectation modulates baseline and stimulus-evoked activity in human inferotemporal cortex. Brain Research, 1301, Rama, P., & Courtney, S. M. (2005). Functional topography of working memory for face or voice identity. Neuroimage, 24, Ranganath, C., Cohen, M. X., Dam, C., & D Esposito, M. (2004). Inferior temporal, prefrontal, and hippocampal contributions to visual working memory maintenance and associative memory retrieval. Journal of Neuroscience, 24, Ranganath, C., DeGutis, J., & D Esposito, M. (2004). Category-specific modulation of inferior temporal activity during working memory encoding and maintenance. Brain Research. Cognitive Brain Research, 20, Roth, J. K., & Courtney, S. M. (2007). Neural system for updating object working memory from different sources: sensory stimuli or long-term memory. Neuroimage, 38, Roth, J. K., Serences, J. T., & Courtney, S. M. (2006). Neural system for controlling the contents of object working memory in humans. Cerebral Cortex, 16, Rowe, J. B., & Passingham, R. E. (2001). Working memory for location and time: activity in prefrontal area 46 relates to selection rather than maintenance in memory. Neuroimage, 14, Rowe, J. B., Toni, I., Josephs, O., Frackowiak, R. S. J., & Passingham, R. E. (2000). The prefrontal cortex: response selection or maintenance within working memory? Science, 288, Rypma, B., Berger, J. S., & D Esposito, M. (2002). The influence of working memory demand and subject performance on prefrontal cortical activity. Journal of Cognitive Neuroscience, 14, Sakai, K., Rowe, J. B., & Passingham, R. E. (2002). Active maintenance in prefrontal area 46 creates distractor-resistant memory. Nature Neuroscience, 5, Sala, J. B., Rama, P., & Courtney, S. M. (2003). Functional topography of a distributed neural system for spatial and nonspatial information maintenance in working memory. Neuropsychologia, 41, Stokes, M., Thompson, R., Nobre, A. C., & Duncan, J. (2009). Shape-specific preparatory activity mediates attention to targets in human visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 106(46), Todd, J. J., & Marois, R. (2004). Capacity limit of visual short-term memory in human posterior parietal cortex. Nature, 428, Todd, J. J., & Marois, R. (2005). Posterior parietal cortex activity predicts individual differences in visual short-term memory capacity. Cognitive, Affective & Behavioral Neuroscience, 5(2), Treue, S. (2003). Visual attention: the where, what, how and why of saliency. Current Opinion in Neurobiology, 13, Xu, Y., & Chun, M. (2006). Dissociable neural mechanisms supporting visual shortterm memory for objects. Nature, 440,