- Open Access
Dissociation of neural correlates of verbal and non-verbal visual working memory with different delays
© Rothmayr et al; licensee BioMed Central Ltd. 2007
- Received: 04 September 2007
- Accepted: 25 October 2007
- Published: 25 October 2007
Dorsolateral prefrontal cortex (DLPFC), posterior parietal cortex, and regions in the occipital cortex have been identified as neural sites for visual working memory (WM). The exact involvement of the DLPFC in verbal and non-verbal working memory processes, and how these processes depend on the time-span for retention, remains disputed.
We used functional MRI to explore the neural correlates of the delayed discrimination of Gabor stimuli differing in orientation. Twelve subjects were instructed to code the relative orientation either verbally or non-verbally with memory delays of short (2 s) or long (8 s) duration.
Blood-oxygen level dependent (BOLD) 3-Tesla fMRI revealed significantly more activity for the short verbal condition compared to the short non-verbal condition in bilateral superior temporal gyrus, insula and supramarginal gyrus. Activity in the long verbal condition was greater than in the long non-verbal condition in left language-associated areas (STG) and bilateral posterior parietal areas, including precuneus. Interestingly, right DLPFC and bilateral superior frontal gyrus was more active in the non-verbal long delay condition than in the long verbal condition.
The results point to a dissociation between the cortical sites involved in verbal and non-verbal WM for long and short delays. Right DLPFC seems to be engaged in non-verbal WM tasks especially for long delays. Furthermore, the results indicate that even slightly different memory maintenance intervals engage largely differing networks and that this novel finding may explain differing results in previous verbal/non-verbal WM studies.
- Retention Interval
- Work Memory
- Inferior Frontal Gyrus
- Superior Temporal Gyrus
- Supramarginal Gyrus
Working Memory (WM) is the ability to keep a limited amount of information online for immediate use during short intervals . In typical WM experiments 1 to 10 items are maintained in memory for periods up to and including 60 s . The classical model of WM consists of the central executive and three subsidiary systems, namely the visuo-spatial sketchpad, the phonological loop, and the recently proposed episodic buffer .
A memory system related to the visuo-spatial sketchpad component of WM is perceptual memory, which has been described as a low-level memory process that is comprised of a series of independent parallel mechanisms for various basic stimulus dimensions. These attributes, such as spatial frequency, contrast, or orientation, are thought to be the building blocks of visual images . According to this theory, each attribute is stored with high precision in separate perceptual stores . These models of sensory-based WM emphasize the delay-related signals in sensory cortex and the reciprocal projections of these areas to parietal and prefrontal cortex .
Objects in visual WM may be encoded with the help of verbal or non-verbal strategies. Numerous studies have investigated verbal and non-verbal WM [7–9]. The stimuli to test verbal and non-verbal WM differ significantly, ranging from single letters, numbers, dots, squares to complex objects and scenes [10–12]. The extent to which these stimuli can be coded verbally represents a major confound in these studies , since the labels given by the observer to the material, and not their visual representations per se, will be stored.
Several brain areas have been identified as the neural correlates of visual WM by means of lesion studies [14–16], PET [17, 18], ERPs , and fMRI [20, 21]. Among these is posterior parietal cortex, which may reflect the neural capacity limit of visual WM . Recently, Xu and Chun  have proposed that the inferior intraparietal sulcus (IPS), the superior IPS, and lateral occipital cortex (LOC) work in parallel to support visual WM encoding and maintenance. They suggest that representations in inferior IPS may be limited to a fixed number of objects, whereas capacity in LOC and superior IPS is limited by object complexity. LOC and superior IPS may thus participate in storing detailed representations of stimuli in visual WM. In addition, various striate and extra-striate areas of the occipital cortex have been identified as visual WM correlates . Interestingly, relatively early visual areas beyond V1, which have previously only been associated with visual perception, are also active during visual WM delays . Virtually all studies that investigated visual WM found activity in prefrontal cortex (PFC). The dorsolateral prefrontal cortex (DLPFC; BA 46/9) seems to play a crucial role in WM-related processes [25–27]. DLPFC activity has foremost been found in studies that required the manipulation of relevant items in memory [20, 28]. Most of these studies have used n-back tasks in which the subject has to remember an item presented n-trials ago and match it to the present item. Delayed-discrimination tasks, on the other hand, show less DLPFC activity . During delayed-discrimination tasks an item has to be discriminated from the previously presented item. Thus, the mere maintenance of an item and not its manipulation is required. Several review articles point to a role for DLPFC in the active manipulation of material in visual WM [7–9]. However, other evidence suggests that DLPFC is involved in the storage of visual information for several objects .
Further studies have attempted to identify brain regions related to either verbal or non-verbal WM. Based on the identified neural networks a verbal/non-verbal dissociation has been suggested in either a ventral/dorsal or a left/right fashion [2, 31]. Using a 2-back task, Ikeda and Osaka  investigated memory for colours that could be coded either verbally or visually. Analysis of the results from the condition where colours could be coded verbally revealed activity in areas associated with the phonological loop, such as inferior frontal gyrus and inferior parietal lobule. The non-verbal coding of colours resulted in right inferior frontal gyrus activity, an area that has been associated with the visuo-spatial sketchpad of WM. These results stand in contrast to the results of the review article by Cabeza and Nyberg  of more than 60 visual WM studies. These authors concluded that there is little evidence for a dissociation of verbal and non-verbal WM in the human cortex. This could be explained by the observation that most paradigms allow for the verbal encoding of visual material. Although Ikeda and Osaka  revealed a possible dissociation between verbal and non-verbal WM-associated brain areas, their non-verbal stimuli may also have been coded verbally by the subjects. The words "lighter" or "darker" may have been used by the subjects for the intended non-verbal stimuli that all stemmed from one color category. Although their study revealed differing brain activity between verbal and non-verbal conditions this does not imply that this was due to their subjects' coding approaches. Mere differences in the visual appearance of the stimuli could also, at least in part, account for their results.
The effect of memory delay length on cortical activation has received less attention. In the visual WM studies reviewed above, inter-stimulus intervals (ISIs) in delayed discrimination paradigms varied from 350 ms  to 24 s . Barch et al.  reported on the impact of delay length on brain activation in visual WM tasks. In their verbal WM task the retention interval was either 1 or 8 s. The task used was a variant of the Continuous Performance Test . Subjects had to press a button whenever the letter X followed the letter A. The fMRI data revealed increased activation for the longer delay in inferior frontal gyrus, left posterior parietal lobe, and the left DLPFC. The previously mentioned conflicting results with respect to a dissociation of verbal/non-verbal WM may be, in part, due to the varying retention intervals used . Differences in task demands ranging from simple delayed-discrimination to demanding n-back tasks may also underlie the differences in brain activation.
The present study attempts to account for some of the inconsistencies in visual WM studies by systematically varying both delay length and coding strategies in the discrimination of simple grating stimuli. We used Gabor stimuli of differing orientation and instructed subjects to explicitly encode the relative orientations using a verbal code. The results from this condition were compared to those arising from a condition, where verbal coding could not be readily employed. We believe that we were able to create a paradigm in which non-verbal stimuli were virtually identical to the verbal stimuli but which could not be coded verbally as may have taken place in previous WM studies. Our findings suggest that the coding strategy used by the subjects has a profound effect on the pattern of brain activation exhibited during the delayed discrimination of similar stimuli. These differences are most pronounced for the long delay, where verbal stimuli seem to engage predominantly left-hemispheric temporo-parietal areas, whereas non-verbal memory is associated with medial and right-hemispheric frontal brain activity.
Twelve right-handed adults (6 male, 6 female), aged between 20 and 40 years (mean= 25.4 yrs), participated in the study. All participants gave their written informed consent. All had normal or corrected-to-normal vision and reported no prior psychiatric or neurological impairments.
In the experiment the participants had to decide whether two Gabor stimuli, which were presented sequentially and separated by a delay period, had the same or a different orientation. The inter-stimulus interval (ISI) between the reference and the test stimulus was either 2 or 8 seconds. Gabor pairs were constructed so that they could be coded either verbally or non-verbally. Thus, the experiment consisted of four conditions (verbal/non-verbal x ISI 2 s/ISI 8 s).
In 50% of all trials both the reference and the test stimulus had the same orientation, on the other trials the reference and test stimuli differed in orientation. Trials were presented in random order and subjects were instructed to maintain central fixation throughout the experiment.
Prior to the fMRI experiment subjects participated in a training session outside the scanner (n = 40 trials). In the fMRI experiment, each subject participated in one session that consisted of a total of 144 trials. At the end of the session, subjects were asked if and how often they had used verbal coding strategies in both the verbal and the non-verbal conditions.
Display and stimulus parameters
Stimuli were created with Matlab 6.5.1 software (Math Works Inc., Natick, MA) and presented with Presentation 9.13 software (Neurobehavioral Systems Inc., Albany, CA). Stimuli were back-projected on a screen inside the scanner with a D-ILA LCD-projector (JVC Corp., Japan) with a frame refresh rate of 60 Hz. The screen size subtended 16.4°C × 21.7°C of visual angle. Gabor stimuli had a diameter of approximately 6.5°C of visual angle and were presented in the lower left quadrant or the upper right quadrant of the screen at a visual angle of 8.6°C from central fixation, measured from the centre of the Gabors. Gabor patches had a maximum contrast close to 100% and a spatial frequency of 3.4 c/deg. The contrast of the Gabors was tapered with a Gaussian kernel (Gauss constant: 1.3 deg).
Subjects responded by pressing the buttons of a Lumitouch (Photon Control, Burnaby, Canada) optical response device with their index finger and the middle finger of their right hand. Reaction time (RT) and accuracy data were recorded and stored for offline analysis.
Blood-oxygen-level-dependant imaging data were acquired with a 3-Tesla Siemens Allegra head scanner (Siemens Inc., Erlangen, Germany) at the University of Regensburg. The scanner acquired echo-planar-imaging (EPI) sequences using fast gradients. A standard one-channel head coil was used. During T2* image acquisition 34 slices (whole brain) were scanned in interleaved order. Time-to-repeat (TR) was 2000 ms. Time-to-echo (TE) was set at 30 ms, with a flip angle of 90°C. Voxel-size was set to 3 × 3 × 3 mm. The field of view measured 192 × 192 mm. Trials in the experimental paradigm were synchronized with scanner pulses. In every experimental session, 1088 scans were acquired. In order to obtain a better estimate of the actual hemodynamic response function (hrf) a jitter was implemented during the acquisition of functional images. Therefore on half of the trials in the experimental paradigm the trial onset was shifted by a fixed amount of time. A 1000 ms fixation period was added at the beginning and at the end of each respective trial, thus shifting events in the jittered trials by 1000 ms. Anatomical T1-weighted images were obtained using a MPRAGE pulse sequence (Magnetization Prepared RApid Gradient Echo) with time-to-repeat (TR) of 2300 ms, a time-to-echo (TE) of 3.93 ms, and a flip angle of 12°C. A total of 176 slices were scanned, with isotropic voxels sized 1 × 1 × 1 mm. The field of view had a size of 256 × 256 mm.
Reaction time and accuracy data were analyzed statistically with SPSS for Windows 12.0 software (SPSS Inc., Chicago, IL). A repeated-measures ANOVA was conducted at a significance level of p ≤ 0.05.
Images were pre-processed and statistically analyzed with SPM2  which runs in MatLab (Math Works Inc., Natick, MA). Prior to pre-processing all obtained imaging data in DICOM format were transformed to ANALYZE file format. Functional data were slice timed and realigned. A T2*-weighted mean image of the unsmoothed images was co-registered with the corresponding anatomical T1-weighted image of the same individual. The individual T1-image was used to derive the transformation parameters for the stereotaxic space using the SPM2 template (Montreal Neurological Institute (MNI) Template), which was then applied to the individual single co-registered EPI images. The voxel sizes of the written normalised images were 1 mm3. Images were then smoothed with a 8-mm full-width half maximum (FWHM) isotropic Gaussian kernel.
Statistical evaluation consisted of modeling the onset times of the test Gabor-stimuli as events on individual first level. These onsets were modeled separately for each of the 4 conditions if the correct response was given. Another two regressors for incorrect responses after an ISI of 2 or 8 seconds, respectively, were also included amounting to a total of 7 regressors (including constant) for each individual analysis. Interesting effects were contrasted using T-statistics, generating the relevant contrast images for second level evaluation.
For the random-effects group level statistics, T-value maps were calculated with appropriate contrast images. Activation vs. baseline maps were thresholded at p < .05 corrected on cluster level (cluster-defining threshold t = 4.0). Thresholds were adjusted for differential contrasts as we expected only small differences of effect sizes. Clusters surpassing an individual threshold of p < .05 corrected on cluster level (cluster-defining threshold t = 2.0) are reported as significant differential activations. To visualize the results, the activations were overlaid on a normalized rendered image from one of the subjects.
The computation of each individual's performance revealed that all participants were able to discriminate the relevant stimuli reasonably well. Mean accuracy (proportion of correct responses) for the four conditions was as follows: verbal, 2s ISI: 0.958 (standard error of the mean, SE = 0.013); verbal, 8s ISI: 0.949 (SE = 0.017); non-verbal, 2s ISI: 0.775 (SE = 0.024) and non-verbal, 8s ISI: 0.778 (SE = 0.022). A repeated-measures ANOVA with the factors type of stimulus (verbal/non-verbal) and ISI (2 s/8 s) revealed a significant effect of type of stimulus [F(1,11) = 55.27, p ≤ 0.01]. Accuracy was correspondingly higher for the verbal conditions.
The examination of the responses given by the subjects after having been asked about their coding strategies revealed that the overwhelming majority of them used verbal coding for all verbal trials (92% of subjects) and refrained from doing so in the non-verbal trials (75% claimed to have used verbal coding at no time or only seldom). The few subjects who had attempted to use verbal coding for non-verbal trials reported to have used the words "tilted"/"more tilted". These subjects also claimed to have aborted the strategy soon after the onset of the experiment because they had felt that it was not successful. The different results for verbal versus non-verbal trials may therefore be regarded as a consequence of the participants' coding strategies. All participants claimed to have used the words "left" and "right" of vertical for the verbal coding trials in covert speech.
Functional imaging results
Brain areas showing significant activation. Contrasts: verbal 2s ISI > baseline, verbal 8s ISI > baseline, non-verbal 2s ISI > baseline, and non-verbal 8s ISI > baseline. The Montreal Neurological Institute (MNI) coordinates of the most active voxel is given for each cluster, along with the z-value of the magnitude of activation and the number of voxels contained within the cluster (in parentheses). Abbreviations for each brain structure assigned using the SPM2 extension MSU: MFG = middle frontal gyrus; IFG = inferior frontal gyrus; IPL = inferior parietal lobule; STG = superior temporal gyrus; SFG = superior frontal gyrus; MTG = middle temporal gyrus
Hemisphere & Region
Z-values of maxima (cluster size in number of voxels)
Verbal 2s ISI > baseline
cingulate gyrus, IPL, MFG, postcentral gyrus, precentral gyrus
cuneus, posterior cingulate, precuneus
IPL, postcentral gyrus, precuneus
Verbal 8s ISI > baseline
cingulate gyrus, insula, precentral gyrus
postcentral gyrus, precentral gyrus
Non-verbal 2s ISI > baseline
IPL, postcentral gyrus
cingulate gyrus, MFG
Non-verbal 8s ISI > baseline
IFG, insula, thalamus
IFG, insula, IPL, MFG, precentral gyrus
cingulate gyrus medial, frontal gyrus
Brain areas showing significant activation. Contrasts: verbal 2s ISI > non-verbal 2s ISI, verbal 8s ISI > non-verbal 8s ISI, and non-verbal 8s ISI > verbal 8s ISI, otherwise as in Table 1. No activity was detected in the contrast non-verbal 2s ISI > verbal 2s ISI. For abbreviations see Table 1
Hemisphere & Region
Z-values of maxima (cluster size in number of voxels)
Verbal 2s ISI > non-verbal 2s ISI
insula, IPL, STG
insula, STG, supramarginal gyrus
cingulate gyrus, posterior cingulate, precentral lobule
Verbal 8s ISI > non-verbal 8s ISI
cuneus, posterior cingulate, precuneus
IPL, MTG, STG, supramarginal gyrus
Non-verbal 8s ISI > verbal 8s ISI
medial frontal gyrus, SFG
This study investigated differences in cortical BOLD activity for a verbal and non-verbal delayed-discrimination WM paradigm for short and long retention intervals. The paradigm used here, a delayed orientation discrimination task, focused on the maintenance of visual memory representations without any manipulation process. In the verbal encoding condition, Gabor patches were oriented slightly to the left or to the right of vertical so that subjects could covertly use the terms "left" and "right" as verbal cues. The "non-verbal stimuli" were oriented to the left only and could not be readily related to the vertical or horizontal axes. Gabors were constructed in this manner so that they could not be easily coded in a verbal manner (i.e., reference to the principal axes did not ease the task) but demanded perceptual coding. Differences in orientation angle between the reference and test gratings, however, were the same for both encoding conditions. We believe that subjects coded verbal and non-verbal stimulus pairs with a verbal coding strategy in one case and refrained from doing so in the latter instance. Firstly, subjects were explicitly told in the instruction to code verbal stimuli with the words "left" and "right".
Secondly, non-verbal stimuli were constructed in a fashion that would not lend themselves to verbal coding. Gratings in these conditions differed by 22°C (for "different" trials) and were all oriented to the left.
Orientations were selected that were not near prominent positions of an analogue clock face and stimuli were presented for 200 ms only. Verbal stimuli were oriented to the left or to the right of the vertical plane, thus inevitably yielding the verbal codes "left" and "right". Although usually considered an unreliable measure of experimental control, subject debriefings conducted in our experiment confirmed that subjects had used verbal coding in the verbal condition, and refrained from doing so in the nonverbal condition, as intended.
We believe that the stimuli used in this study represent a novel approach in the investigation of verbal and non-verbal WM. Due to the virtually identical visual appearance of the verbal and the non-verbal stimuli, differences in brain activity in this experiment can be attributed entirely to the coding strategies applied by the subjects. Indeed, the trial-by-trial cues instructed the subjects to apply the appropriate strategies to the individual trial types. This manipulation may not have been properly achieved in previous studies.
The systematic variation of delay length, as conducted here, presents a novelty in verbal/non-verbal WM research and may explain differing results as well.
The behavioural data revealed slower reaction times and lower accuracies for the non-verbal conditions as opposed to the verbal conditions, suggesting the use of different neural mechanisms.
Non-verbal WM is typically associated with the engagement of the visuospatial sketchpad component of WM, whereas verbal WM additionally engages the phonological loop component. It has frequently been reported in previous studies that verbal coding, as opposed to non-verbal WM, enhances WM performance, a finding that is reflected in this study's behavioural results.
Accuracies and reaction times differed between the verbal and non-verbal conditions (Fig. 3). It could be argued that we should have adapted the stimulus differences in angle between stimulus pairs or presentation time to yield equivalent performance for the two trial types. By doing this, however, differences in brain activity could not have been attributed to underlying coding strategies used by the subjects but would have to be explained in terms of differing visual stimulus properties. Such a procedure (i.e., different stimuli for verbal and nonverbal trial types), which was knowingly avoided in this study, may have constituted a major confound in previous studies. We believe that, although accuracies differed between verbal and non-verbal trials, the results may be interpreted as a result subjects' coding strategies and not to differing stimulus properties, a major problem in previous WM studies.
The functional imaging results presented here reflect maintenance processes dependent on both delay period and coding strategy applied. Since a simple delayed-discrimination WM paradigm was used here, it does not reflect manipulation processes that are usually captured in n-back tasks and that are thus hard to disentangle from maintenance processes [7–9].
The random-effects group analysis (Table 1), in which all four conditions were contrasted with baseline activation levels, revealed activity in prefrontal, posterior parietal cortex and further areas that have previously been associated with WM. The main focus of this study, however, was on the dissociation between verbal and non-verbal WM at different delay lengths. Therefore we will not discuss these results in detail, but rather focus on the direct comparisons of verbal and non-verbal conditions. The differential analysis between the verbal and non-verbal conditions revealed differing activity for the comparisons between the conditions with the same delay duration. In the short retention interval, significantly more activity was detected in bilateral areas close to well-known language areas, such as the supramarginal gyrus, superior temporal gyrus, and inferior frontal gyrus, with preponderance in the left hemisphere. No additional activity was found when contrasting the short non-verbal to the short verbal condition. In the long interval, however, the non-verbal condition showed more activity in right DLPFC and medial frontal areas than the verbal condition. In the verbal long-retention condition more activity could be measured in left language associated areas (such as supramarginal gyrus, superior temporal gyrus, as well as in medial parietal areas) when compared to that found in the long non-verbal condition.
These results suggest an interaction in visual WM between the effects of memory delay length and modality of encoding. The right DLPFC is significantly more active in the non-verbal condition with the long retention interval when compared to the verbal condition of same retention interval (Fig. 4). In contrast, in the long delay conditions, parietal, temporal, and frontal areas in the immediate proximity of language areas of the left hemisphere, as well as medial parietal areas, especially precuneus, were more active in the verbal than in the non-verbal condition. The neural basis for the phonological loop component of WM has been localized in left supramarginal gyrus, Broca's area, inferior frontal gyrus, and the superior parietal lobule [18, 32]. Our study revealed relatively more activity in these same areas for the verbal coding condition and may thus indicate the engagement of the phonological loop for these conditions. On the other hand, the precuneus is a structure that has frequently been reported in connection with different forms of higher-order cognition including episodic memory retrieval . The exact role of the precuneus in the contrast between the verbal versus the nonverbal conditions with long retention interval requires further investigation.
The short verbal condition showed more brain activity bilaterally around the Sylvian fissure, such as the supramarginal gyrus, which have previously been associated with the phonological loop component of WM [18, 32]. Activity in the supramarginal gyrus has also been related to articulatory rehearsal . For short retention intervals, we were not able to detect any areas that were more active in the non-verbal when compared to the verbal condition (Fig. 4).
This finding suggests that non-verbal WM for shorter delay periods depends on different maintenance mechanisms than non-verbal WM for longer delay periods. Our study suggests that especially right DLPFC seems to play a crucial role in the maintenance of stimuli in non-verbal WM. Since our experiment required the mere maintenance of items without any manipulation process, the results also suggest that DLPFC plays not only a role in manipulation processes [7–9], but also in WM maintenance . The differential activity between the verbal and nonverbal conditions (Fig. 4) supports the idea of a dissociation between the left and right hemispheres for verbal and non-verbal WM, respectively. Our results are in line with the findings that point to a dominance of the right hemisphere for non-verbal material , and these hemispheric differences appear even more pronounced for long retention intervals. One possible reason for the controversy regarding a possible hemispheric specialization for verbal and nonverbal WM might be related to the different retention intervals used in different studies. In a study of Barch et al. , the left DLPFC was active for verbal WM only for long delay periods (8 s) as opposed to a short (1 s) retention interval.
In conclusion, the present study explored the neural correlates of verbal and non-verbal visual WM at different delay lengths. Our findings point to a dissociation between verbal and nonverbal WM processing, with a prominent activation of the left hemisphere in verbal coding and a right prefrontal activation associated with non-verbal coding. A recent study by Ikeda and Osaka  explored hemispheric differences in inferior frontal and posterior parietal cortex in the verbal and nonverbal encoding of colour stimuli. Together with our findings, these results point to a dissociation of left and right hemispheric processing for verbal and nonverbal working memory for visual stimuli. Furthermore, our findings give rise to the assumption that even slight differences in memory delay length have a significant effect on associated neural networks.
This work was supported by a grant from the Federal Ministry for Research and Technology (BMBF, Project "Visuospatial Cognition") to MWG and by a grant from the Norwegian Research Council to SM.
- Baddeley AD: Working memory: looking back and looking forward. Nat Rev Neurosci. 2003, 4: 829-839. 10.1038/nrn1201.View ArticlePubMedGoogle Scholar
- Smith EE, Jonides J, Marshuetz C, Koeppe RA: Components of verbal working memory: Evidence from neuroimaging. Proc Natl Acad Sci USA. 1998, 95: 876-882. 10.1073/pnas.95.3.876.PubMed CentralView ArticlePubMedGoogle Scholar
- Baddeley AD: The episodic buffer: a new component of working memory?. Trends Cogn Sci. 2000, 4: 417-423. 10.1016/S1364-6613(00)01538-2.View ArticlePubMedGoogle Scholar
- Magnussen S, Greenlee MW: The psychophysics of perceptual memory. Psychol Res. 1999, 62: 81-92. 10.1007/s004260050043.View ArticlePubMedGoogle Scholar
- Magnussen S: Low-level memory processes in vision. Trends Neurosci. 2000, 23: 247-251. 10.1016/S0166-2236(00)01569-1.View ArticlePubMedGoogle Scholar
- Pasternak T, Greenlee MW: Working memory in primate sensory systems. Nat Rev Neurosci. 2005, 6: 97-107. 10.1038/nrn1603.View ArticlePubMedGoogle Scholar
- Cabeza R, Nyberg L: Imaging Cognition II: An empirical review of 275 PET and fMRI studies. J Cogn Neurosci. 2000, 12: 1-47. 10.1162/08989290051137585.View ArticlePubMedGoogle Scholar
- Fletcher PC, Henson RNA: Frontal lobes and human memory. Insights from functional neuroimaging. Brain. 2001, 124: 849-881. 10.1093/brain/124.5.849.View ArticlePubMedGoogle Scholar
- Smith EE, Jonides J: Storage and executive processes in the frontal lobes. Science. 1999, 283: 1657-1661. 10.1126/science.283.5408.1657.View ArticlePubMedGoogle Scholar
- D'Esposito MD, Aguirre GK, Zarahn EM, Ballard D, Shin RK: Functional MRI studies of spatial and nonspatial working memory. Cogn Brain Res. 1999, 7: 1-13. 10.1016/S0926-6410(98)00004-4.View ArticleGoogle Scholar
- Postle BR, Stern CE, Rosen BR, Corkin S: An fMRI investigation of cortical contributions to spatial and nonspatial visual working memory. NeuroImage. 2000, 11: 409-423. 10.1006/nimg.2000.0570.View ArticlePubMedGoogle Scholar
- Walter H, Bretschneider V, Grön G, Zurowski B, Wunderlich AP, Tomczak R, Spitzer M: Evidence for quantitative domain dominance for verbal and spatial working memory in frontal and parietal cortex. Cortex. 2003, 39: 897-911.View ArticlePubMedGoogle Scholar
- Lages M, Treisman M: Spatial frequency discrimination: Visual long-term memory or criterion setting?. Vision Res. 1998, 38: 557-572. 10.1016/S0042-6989(97)88333-2.View ArticlePubMedGoogle Scholar
- Greenlee MW, Berg H, Stuhr V, Mergner T: Visual search and visual working memory in patients with chronic focal cortical lesions. Vision Res. 2000, 40: 3759-3773. 10.1016/S0042-6989(00)00199-1.View ArticlePubMedGoogle Scholar
- Greenlee MW, Koessler M, Cornelissen FW, Mergner T: Visual discrimination and short-term memory for random patterns in patients with a focal cortical lesion. Cereb Cortex. 1997, 7: 253-267. 10.1093/cercor/7.3.253.View ArticlePubMedGoogle Scholar
- Ploner CJ, Rivaud-Pechoux S, Gaymard BM, Agid Y, Pierrot-Deseiligny C: Errors of memory-guided saccades in humans with lesions of the frontal eye field and the dorsolateral prefrontal cortex. J Neurophysiol. 1999, 82: 1086-1090.PubMedGoogle Scholar
- Cornette L, Dupont P, Orban GA: The neural substrate of orientation short-term memory and resistance to distractor items. Eur J Neurosci. 2002, 15: 165-175. 10.1046/j.0953-816x.2001.01830.x.View ArticlePubMedGoogle Scholar
- Paulesu E, Frith CD, Frackowiak RSJ: The neural correlates of the verbal component of working memory. Nature. 1993, 362: 342-345. 10.1038/362342a0.View ArticlePubMedGoogle Scholar
- Reinvang I, Magnussen S, Greenlee MW, Larsson PG: Electrophysiological localization of brain areas involved in perceptual memory. Exp Brain Res. 1998, 123: 481-484. 10.1007/s002210050592.View ArticlePubMedGoogle Scholar
- Baumann O, Frank G, Rutschmann RM, Greenlee MW: Cortical activation during sequences of memory-guided saccades: a functional MRI study. Neuroreport. 2007, 18: 451-455. 10.1097/WNR.0b013e32805868ba.View ArticlePubMedGoogle Scholar
- Pessoa L, Gutierrez E, Bandettini PA, Ungerleider L: Neural correlates of visual working memory: fMRI amplitude predicts task performance. Neuron. 2002, 35: 975-987. 10.1016/S0896-6273(02)00817-6.View ArticlePubMedGoogle Scholar
- Todd JJ, Marois R: Capacity limit of visual short-term memory in human posterior parietal cortex. Nature. 2004, 428: 751-754. 10.1038/nature02466.View ArticlePubMedGoogle Scholar
- Xu Y, Chun MM: Dissociable neural mechnisms supporting visual short-term memory for objects. Nature. 2006, 440: 91-95. 10.1038/nature04262.View ArticlePubMedGoogle Scholar
- Greenlee MW, Magnussen S, Reinvang I: Brain regions involved in spatial frequency discrimination: evidence from fMRI. Exp Brain Res. 2000, 132: 399-403. 10.1007/s002210000385.View ArticlePubMedGoogle Scholar
- Fuster JM: prefrontal cortex – an update: time is of the essence. Neuron. 2001, 30: 319-333. 10.1016/S0896-6273(01)00285-9.View ArticlePubMedGoogle Scholar
- Goldman-Rakic PS: Architecture of the prefrontal cortex and the central executive. Ann N Y Acad Sci. 1995, 769: 71-83. 10.1111/j.1749-6632.1995.tb38132.x.View ArticlePubMedGoogle Scholar
- Zhang JX, Leung HC, Johnson MK: Frontal activations associated with accessing and evaluating information in working memory: an fMRI study. Neuroimage. 2003, 20: 1531-1539. 10.1016/j.neuroimage.2003.07.016.View ArticlePubMedGoogle Scholar
- Cornette L, Dupont P, Bormans G, Mortelmans L, Orban GA: Separate neural correlates for the mnemonic components of successive discrimination and working memory tasks. Cereb Cortex. 2001, 1: 59-72. 10.1093/cercor/11.1.59.View ArticleGoogle Scholar
- Faillenot I, Sunaert S, Van Hecke P, Orban GA: Orientation discrimination of objects and gratings compared: an fMRI study. Eur J Neurosci. 2001, 13: 585-596. 10.1046/j.1460-9568.2001.01399.x.View ArticlePubMedGoogle Scholar
- Leung HC, Gore JC, Goldman-Rakic PS: Sustained mnemonic response in the Human middle frontal gyrus during the on-line storage of spatial memoranda. J Cogn Neurosci. 2002, 14: 659-671. 10.1162/08989290260045882.View ArticlePubMedGoogle Scholar
- Smith EE, Jonides J, Koeppe RA: Dissociating verbal and spatial working memory using PET. Cereb Cortex. 1996, 6: 11-20. 10.1093/cercor/6.1.11.View ArticlePubMedGoogle Scholar
- Ikeda T, Osaka N: How are colors memorized in working memory? A functional magnetic resonance imaging study. Neuroreport. 2007, 18: 111-114. 10.1097/WNR.0b013e328010ff3f.View ArticlePubMedGoogle Scholar
- Barch DM, Braver TS, Nystrom LE, Forman SD, Noll DC, Cohen JD: Dissociating working memory from task difficulty in human prefrontal cortex. Neuropsychologia. 1997, 35: 1373-1380. 10.1016/S0028-3932(97)00072-9.View ArticlePubMedGoogle Scholar
- Rosvold HE, Mirsky AF, Sarason I, Bransome ED, Beck LH: A continuous performance test of brain damage. J Consult Psychol. 1956, 20: 343-350. 10.1037/h0043220.View ArticlePubMedGoogle Scholar
- SPM Homepage. London, Wellcome Dept Cogn Neurol, http://www.fil.ion.ucl.ac.uk/spm
- Magnussen S, Idas E, Myhre SH: Representation of orientation and spatial frequency in perception and memory: a choice reaction-time analysis. J Exp Psychol Hum Percept Perform. 1998, 24: 707-718. 10.1037/0096-15184.108.40.2067.View ArticlePubMedGoogle Scholar
- Cavanna AE, Trimble MR: The precuneus: a review of its functional anatomy and behavioural correlates. Brain. 2006, 129: 564-83. 10.1093/brain/awl004.View ArticlePubMedGoogle Scholar
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