1 Department of Psychological and Brain Sciences, Johns Hopkins University (JHU), Baltimore, MD 21218, USA, 2 Department of Neurocognition, Universiteit Maastricht, The Netherlands, 3 F.C. Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands, 4 F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA, 5 Department of Neuroscience, JHU School of Medicine, Baltimore, MD 21205, USA, 6 Department of Radiology, JHU School of Medicine, Baltimore, MD 21205, USA
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Abstract |
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Key Words: attentional control, fMRI, object-based attention, parietal cortex
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Introduction |
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Studies of the effects of attention have shown both behavioral facilitation (e.g. faster and more accurate responses) and enhanced cortical responses to attended locations or features (Posner, 1980; Egeth et al., 1984
; Moran and Desimone, 1985
; O'Craven et al., 1997
; Chawla et al., 1999
; Treue and Martinez Trujillo, 1999
; Saenz et al., 2002
). Because visual systems must represent objects that are often spatially overlapping and partly occluded, a selective mechanism that operates on perceptual objects, and not just spatial locations, is required for natural vision. Behavioral and neurophysiological evidence has confirmed that object-based selection plays a central role in primate vision (Rock and Gutman, 1981
; Duncan, 1984
; Roelfsema et al., 1998
; O'Craven et al., 1999
).
Complementing these studies of the effects of attention have been investigations of the sources of attentional control signals. These studies have implicated a network of areas in posterior parietal and frontal cortex as crucial for the control of spatial attention (Mesulam, 1981; Posner et al., 1984
; Wojciulik and Kanwisher, 1999
; Corbetta et al., 2000
; Hopfinger et al., 2000
; Beauchamp et al., 2001
; Vandenberghe et al., 2001
; Yantis et al., 2002
; Bisley and Goldberg, 2003
) and feature based attention (Le et al., 1998
; Shulman et al., 2002
; Giesbrecht et al., 2003
; Liu et al., 2003
). However, little is known about the control of object-based attention. Although two studies have investigated how objects in the scene can guide the selection of a region of space (Fink et al., 1997
; Arrington et al., 2000
), there has been no investigation of the control of object-based attention using, for example, spatially superimposed objects (as in Fig. 1) where spatial selection is not possible (Duncan, 1984
; O'Craven et al., 1999
). This type of selection is distinct from non-spatial feature-based attention examined in previous work (Le et al., 1998
; Liu et al., 2003
), where shifts of attention between feature dimensions (e.g. color and motion), not segmented object representations, were examined. This difference is important because in object-based attention, all the attributes of an attended object are bound together into a unitary representation (O'Craven et al., 1999
).
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Embedded within the morphing stimulus stream were targets that directed the subject to shift attention between the face and house stream or to maintain attention on the currently attended stream. A cortical area that is selective for a particular object type (e.g. faces) should become selectively more active when attention is directed to the preferred object stream. In contrast, an area controlling shifts of attention between objects should yield a pattern of increasing activity following a shift of attention, regardless of the direction of the shift (Yantis et al., 2002).
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Experiment 1 |
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Subjects
Fifteen neurologically intact young adults (10 females), age 2333 years, gave written informed consent to participate in the study, which was approved by the Johns Hopkins University institutional review boards. Data from one of the subjects were discarded because of abnormally low accuracy on the behavioral task during scanning (>2 SD from the group mean).
Behavioral Tasks
We first defined cortical regions of interest (ROIs) exhibiting greater activity to pictures of faces than places and vice versa (Kanwisher et al., 1997; Aguirre et al., 1998
; Epstein et al., 1999
). In this localizer task, subjects passively viewed pictures of faces, places, and common objects in 30s blocks, interleaved with 20s of fixation (Kanwisher et al., 1997
; Epstein et al., 1999
). The stimulation procedures approximated those of Kanwisher et al. (1997
) except that each stimulus was presented for 750 ms followed by a blank interstimulus interval of 250 ms and there was an 8 s ready period at the beginning of each run.
Next, the same set of subjects completed eight event-related fMRI runs while performing the attention-switching task (Fig. 1a,b). Before scanning began, subjects memorized two face targets and two house targets and then completed a series of practice runs. The specific target items differed from one subject to the next. One face and one house were designated hold targets, and the other face and house were designated shift targets. Each run began with a superimposed face and house in the center of the screen, and the participant was instructed to begin the run by attending to either the faces or the houses. When the run began, each face spatially morphed into another face and each house spatially morphed into another house. Each morph lasted for 240 ms, followed by the static superimposed stimulus pair for 760 ms, yielding one morph per second (spatial morphing was implemented using MorphMan 2000, Stoik Software). The order of target events was randomized within an experimental run and targets were separated in time by 3, 4 or 5 s (equally often) to introduce temporal jittering (Burock et al., 1998). In each of the eight experimental runs, there were nine instances of each target type (targets that were missed by subjects were not included in the analysis). Targets never appeared in the unattended object stream.
The subjects were to press buttons held in both hands whenever any shift or hold target was detected. Hold targets required that attention remain on the currently attended object stream (e.g. faces); shift targets required that attention be shifted to the other stream (e.g. from faces to houses). No fixation point was provided for the subjects, nor were any instructions given to maintain a specific gaze position. We chose to allow free viewing because under most circumstances, the locus of eye-gaze and the locus of spatial attention are yoked; therefore, we could observe changes in the locus of spatial attention indirectly by monitoring eye-gaze position (see Methods and Results concerning eye-tracking below).
The 15 face stimuli were selected from the face database developed by the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. The 15 house stimuli were randomly chosen photographs of single-family homes. All stimuli subtended a visual angle of 5° at a viewing distance of
65 cm and were grayscale bitmaps. Each stimulus from a set could spatially morph into any of the other 14 stimuli from that set.
Data Acquisition and Analysis
MRI scanning was carried out with a Philips Gyroscan 1.5 T scanner in the F.M. Kirby Research Center for Functional Brain Imaging at the Kennedy Krieger Institute, Baltimore, MD. Anatomical images were acquired using an MP-RAGE T1-weighted sequence that yielded images with a 1 mm isotropic voxel resolution (TR = 8.1 ms, TE = 3.7 ms, flip angle = 8°, time between inversions = 3 s, inversion time = 748 ms). Whole brain echoplanar functional images (EPI) were acquired with a Philips quatrature head coil in 21 transverse slices (TR = 2000 ms, TE = 35 ms, flip angle = 90°, matrix = 64 x 64, FOV = 240 mm, slice thickness = 6 mm, no gap). The same EPI parameters were used for both the localizer and attention scans.
BrainVoyager software (Brain Innovation, Maastricht, The Netherlands) was used for the fMRI analyses. Images from each data collection run were slice-time and motion corrected before high pass temporal frequency filtering was applied to the functional time series to remove components that occurred three or fewer times per run. No spatial smoothing was performed on the functional data. The images were then transformed into Talairach space (Talairach and Tournoux, 1988) and resampled into 3 mm isotropic voxels. To correct for motion across the scans, each EPI volume for a subject was co-registered to the first volume in the fourth (middle) functional scan acquired for that subject.
Functional localizer data were collected in three runs that consisted of 164 time points each. A group random effects general linear model (GLM) was performed with regressors specified for each of the three stimulus types (faces, places and common objects). Each regressor consisted of a boxcar model of each respective stimulation epoch convolved with a gamma function (delta = 2.5 s, tau = 1.25 s; Boynton et al., 1996). The resulting regression vector was cross correlated with the BOLD time series, yielding scalar beta weights corresponding to the relative changes in signal strength associated with that particular stimulus type. ROIs responding more strongly to faces were defined by contrasting the beta weights for the face stimuli with the weighted sum of the beta weights for the common objects and places. ROIs responding more strongly to places were defined by contrasting the beta weights for the place stimuli with the weighted sum of the beta weights for the face and the common object stimuli (Kanwisher et al., 1997
; Epstein et al., 1999
). All ROIs were defined based on a threshold of t(13) = 3.0, P < 0.01 for individual voxels.
Data from the attention task were collected in eight runs conducted in a single session, with 86 time points in each run. To evaluate the BOLD response to each of the four event types (face hold, shift house-to-face, hold house, shift face-to-house), we created separate boxcar regressors for each of the 12 time points following each stimulus type and estimated the least squares fit at each time point; thus the magnitude of the beta weight associated with each time point reflects the relative change in the BOLD signal at that time point following each event (B.D. Ward, http://afni.nimh.nih.gov/afni/docpdf/3ddeconvolve.pdf). This approach has the advantage that the data can determine (within limits) the functional form of the estimated hemodynamic response function (HRF); no a priori assumptions about the shape and parameters of the HRF are required. We then pooled the estimated beta weights across stimulus identity (face, house) and performed a repeated measures t-test comparing the BOLD response to shift and hold events collapsed across timepoints 25 (410s) post-stimulus. This t-test identified voxels exhibiting a main effect of shift versus hold events on the BOLD response over this time period. The single voxel threshold in the group data was set at t(13) = 3.0, P < 0.01. A minimum cluster size of.590 ml (7 voxels in the original acquisition space) was adopted to correct for multiple comparisons, yielding a corrected statistical threshold of P < 0.004 [determined using the program AlphaSim (B.D. Ward, http://afni.nimh.nih.gov/afni/docpdf/ALPHASim.pdf) which was used to run 2000 Monte Carlo simulations that took into account the entire EPI acquisition matrix].
We next computed event-related averages (e.g. Figs 2eg, 3gj and 4c,d) of the BOLD signal from each activated cluster by defining a temporal window around each target event (extending from 6 s before to 16 s after event onset) and averaging the BOLD signal within the window for all events of that type across all subjects. The baseline, or 0% signal change, for the event-related average plots was defined as the average BOLD signal during the 6 s preceding each target event. Because the 6 s preceding each event could contain both shift and hold events, negative deflections in the event-related averages following hold (or shift) events should be interpreted as relative decreases in activity, not as absolute inhibition. Error bars on all event-related average plots represent the between subject error at each timepoint.
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Eye Position Monitoring
To evaluate the possibility that switch-related activity was related to overt or covert shifts of spatial attention (rather than shifts of object-based attention), six of the subjects completed eight runs of the attention switching task outside of the scanner while their eye position was monitored at 250 Hz with an EyeLink video-based eye tracking system (SMI, Teltow, Germany). An eye movement was registered if the velocity of the eyes exceeded 30°/s or the acceleration of the eyes exceeded 9600°/s2 for >8 ms and the total distance traversed was >0.25° (excluding blinks) during a 1.5 s temporal window following the onset of each target stimulus. The end of an eye-movement was registered when the velocity and acceleration fell below threshold for at least five contiguous samples (20 ms). In addition to computing the number of eye-movements following each target type, we also calculated the mean Cartesian coordinates of eye-gaze during temporal epochs in which the subject was attending to faces or houses; these epochs were defined as all timepoints during which the subject was attending to a particular stimulus-type, not just the 1.5 s following the presentation of a target. Paired t-tests were then used to compare the mean X and Y eye-gaze position while subjects were attending to faces and houses.
Results
Behavioral Data
A two-way repeated measures ANOVA (stimulus identity x target type) revealed no significant differences in the behavioral detection rates for the shift and hold targets [percentage detected ± SE: 94 ± 1.9, 91 ± 2.4, 87 ± 3.5 and 94 ± 1.8 for hold house, hold face, shift face-to-house, and shift house-to-face events, respectively, F(1,13) = 2.9, P > 0.10] or in response times [in ms ± SE: 681 ± 22.5, 735 ± 15.9, 753 ± 20 and 701 ± 28 for hold house, hold face, shift face-to-house and shift house-to-face events, respectively, F(1,13) = 2.1, P > 0.15]. Note that we did not emphasize response speed, so these response times should be interpreted with caution.
Attention Effects in Localizer ROIs
As shown in previous studies, a region of right lateral fusiform gyrus (LatFus, Fig. 2a,b) was more active when subjects viewed faces than when they viewed objects or places and bilateral regions of the medial fusiform gyrus (MedFus) and the parahippocampal gyrus (Fig. 2c,d) were more active when they viewed places than when they viewed faces or objects (Kanwisher et al., 1997; Aguirre et al., 1998
; Epstein et al., 1999
). In the following text, we sometimes refer to these regions as being face selective or house selective; by this we mean only that they respond more strongly to faces or to houses, respectively. In fact, these regions respond in a characteristic way to many classes of visual stimuli (Gauthier et al., 1999
; Haxby et al., 2001
).
Figure 2eg show the group mean event-related timecourses of the BOLD signal in the localizer-defined right LatFus and the bilateral MedFus for the four event types in the attention task (hold house, hold face, shift face-to-house and shift house-to-face). A repeated measures ANOVA (time points 410 s x stimulus identity x target type) was used to evaluate the event-related timecourses from each area (see Table 1).
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The event related average plots for the face and house hold events (open and closed circles, respectively) indicate that the BOLD signal following these events was already different at the moment the events occurred (i.e. time zero in the figure). This is because subjects were already attending either to houses or to faces at time zero. For instance, a face hold target was necessarily preceded by a shift target that instructed subjects to direct attention to the face object stream. Paired one-tailed t-tests on the timecourse data at time 0 confirmed the early onset of the attention effect in these areas [right LatFus, t(13) = 2.67, P < 0.01; right MedFus, t(13) = 1.8, P < 0.05; left MedFus, t(13) = 1.9, P < 0.05].
The BOLD timecourse following shift targets reveals a markedly different temporal pattern: in right LatFus, for example, a shift from (nonpreferred) houses to (preferred) faces was associated with a relative increase in the BOLD signal and a shift from faces to houses was associated with a relative decrease in the BOLD signal (Fig. 2e, open and closed triangles, respectively). A complementary pattern of activation was observed in right and left MedFus (Fig. 2f,g). The crossover pattern following shift events and the sustained activity following hold events corroborates previous reports (O'Craven et al., 1999) that the deployment of attention to one of two spatially superimposed objects can significantly modulate the strength of the sensory representation in areas that respond more strongly to different object types.
Shift-related Activity
Figure 3af shows cortical regions exhibiting a stronger BOLD response following shift than following hold targets (Table 2, top). In frontal cortex, shift-related activity was observed near the junction of the right superior frontal sulcus (SFS) and the precentral gyrus (PreCeG, Fig. 3a,d), near the putative human homologue of the frontal eye field. In parietal cortex, two major activation foci were observed: the first was in the medial aspect of the superior parietal lobule (SPL, including the precuneus, Fig. 3a,c,e) and a second more ventral activation that extended bilaterally through precuneus cortex into the left intraparietal sulcus (IPS, Fig. 3b,c,e). In addition, an area of the left occipital pole (not shown, see Table 2) and an activation in the left lingual and fusiform gyri exhibited a main effect of shifting attention (Fig. 3f).
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Figure 4 shows areas exhibiting a greater BOLD response following hold as compared to shift events: a region of left superior frontal gyrus (SFG, Fig. 4a,c) and a region near the left intraparietal sulcus (IPS, Fig. 4b,d).
ANOVAs on the event-related averages revealed that none of the areas exhibiting a main effect of shift versus hold showed a main effect of stimulus identity. However, some regions exhibited a larger difference in the BOLD response to shift face-to-house and hold house events than to shift house-to-face and hold face events. This difference was manifested in an interaction between stimulus type and stimulus identity (see Table 2) and is most graphically exemplified in Figure 4c,d.
Eye Movements
A two-way ANOVA (stimulus identity x target type) revealed no differences in the mean number of eye movements occurring in a 1.5 s window following the onset of shift and hold targets, [mean number of eye movements ± SE: 1.1 ± 0.1, 1.0 ± 0.17, 1.1 ± 0.17 and 1.2 ± 0.18 for hold house, hold face, shift face-to-house and shift house-to-face events, respectively, F(1,5) = 2.7,P > 0.16]. The main effect of stimulus identity and the interaction term also failed to reach significance (Ps > 0.39). As an additional check, we examined the number of eye movements made during the temporal interval when the target stimuli were physically present on the display screen (1 s): no significant differences were observed (main effect of shift versus hold, F(1,5) = 0.04, P > 0.84). Finally, we examined the spatial distribution of the subjects gaze position during epochs of attention to faces and houses; paired t-tests were used to compare the mean Cartesian coordinates of eye gaze and no differences were found [t(5) = 0.83, P > 0.4, t(5) = 1.75, P > 0.14 for the comparisons of X and Y mean eye position, respectively].
Discussion
An instruction to shift attention led to systematic modulation of activity in ventral visual areas that respond differentially to face and house stimuli, corroborating a previous report (O'Craven et al., 1999). In addition, regions of posterior parietal and dorsal frontal cortex are transiently more active when attention is shifted between segmented perceptual objects, suggesting a role for these regions in object-based attentional control.
We also observed shift-related activity in occipital and ventral temporal visual regions (e.g. Fig. 3f) that are generally believed to be the recipients of attentional biasing signals (Desimone and Duncan, 1995; Kastner and Ungerleider, 2000
) and not the source of such signals. We speculate that accompanying each attention switch was a brief interval of object segmentation during which the motion of low-level visual features caused by the spatial morphing of the to-be-attended object stream may have been particularly salient. This may in turn have evoked heightened responses in early sensory areas following switch signals compared to hold signals. Such responses to low-level features would not be expected to occur in parietal and frontal regions. Further measurements will be required to test this possibility.
Finally, the difference between the BOLD response to shift and hold targets was larger in some regions when attention was switched to houses compared to faces (see Table 2). In addition, two areas were identified that responded strongly to hold house stimuli, leading to a main effect of hold greater than shift (Fig. 4). While hold house targets evoked the strongest responses in these regions, the response profiles were at least nominally similar to the patterns observed in MedFus (e.g. Fig. 2f,g). That is, there was a stronger response to hold house targets, and an increasing response to shift face-to-house targets (with the complementary pattern observed for face hold and shift stimuli). These findings echo previous reports showing that house stimuli evoke a strong response in parietal and superior frontal regions (Ishai et al., 2000; Sala et al., 2003
).
An alternative account of the present results might assert that the switch-related activity observed in frontal and parietal areas is a manifestation of spatial shifts of attention to different regions of the face and the house stimuli, rather than to nonspatial object-based shifts of attention. For instance, subjects may have been attending to the roof-line of the house stimuli and to the eyesnosemouth region of the face stimuli, and this would have led to a discrete shift of spatial attention following each shift target. Such a shift would be expected to activate the very regions observed here (i.e. posterior parietal and dorsal prefrontal cortices).
To evaluate this hypothesis, six of the original subjects completed eight runs of the attention switching task outside of the scanner and while their eye position was monitored. During free viewing, the locus of spatial attention is closely yoked to the locus of eye position (Hoffman and Subramaniam, 1995). Therefore, in free viewing, one can effectively monitor the locus of spatial attention by monitoring the position of overt eye movements. Subjects did make eye movements during the task; however, there was no significant difference in the mean number of eye movements made in the 1.5 s following shift and hold targets. In addition, no systematic differences were found in the Cartesian coordinates of gaze position during epochs of attention to faces and houses. Based on these results, we conclude that changes in the spatial extent of gaze position (and by implication, spatial attention) cannot account for the observed switch related activity in the present study.
While the eye-tracking data suggest that subjects were not attending to different spatial locations during epochs of attention to faces and houses, and were not systematically shifting the locus of spatial attention following shift targets, a more subtle spatial attention account of the present results cannot be ruled out solely with eye movement data. For instance, subjects may attend only to the central regions of the display when attending to faces (e.g. the eyesnosemouth region) and they may attend to the peripheral parts of the stimulus when attending to houses. This alternative account echoes recent evidence suggesting that the lateral and medial fusiform face-selective and house-selective regions of ventral visual cortex may be retinotopically organized visual areas corresponding to the center and the periphery of the visual field, respectively (Levy et al., 2001; Malach et al., 2002
). The eye movement data do not bear on this spatial zoom-lens account of the present results because the center of gaze could remain constant under a spatial zooming account; only the extent of spatial attention should covertly vary while attending faces and houses, respectively.
To test this alternative account, we designed a second experiment in which the house stimuli overlapped with the central portion of the face stimuli (Fig. 5). This stimulus arrangement would render an attentional zooming mechanism ineffective because the relevant object features are now spatially overlapping. If the results of Experiment 1 were due to attentional zooming, then a different temporal and spatial BOLD activation pattern should be observed here.
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Experiment 2 |
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Subjects
Eight neurologically intact young adults (five females), age 1930 years, gave written informed consent to participate in the study, which was approved by the Johns Hopkins University institutional review boards. Three of the subjects from Experiment 1 also participated in Experiment 2; the other five subjects had no prior experience with the paradigm. There were no systematic differences between the experienced and naïve subjects.
Behavioral Tasks
The functional localizer task used to identify regions of lateral and medial fusiform gyrus was identical to that used in Experiment 1. The object-based attention task was identical to Experiment 1, except that the house stimuli were reduced in size to fit within the central 3° of the face stimuli (see Fig. 5). On average, the eyesnosemouth region of the faces subtended 3° vertically, thus the house stimuli overlapped with these foveal features of the face stimuli.
Data Acquisition and Analysis
All data acquisition and analysis methods were identical to Experiment 1 except where noted. MRI scanning was carried out with a Philips Intera 3 T scanner (rather than the 1.5 T scanner used in Experiment 1) in the F.M. Kirby Research Center for Functional Brain Imaging at the Kennedy Krieger Institute, Baltimore, MD. Anatomical images were acquired using an MP-RAGE T1-weighted sequence and a SENSE (MRI Devices Inc., Waukesha, WI) head coil (TR = 8.2 ms, TE = 3.7 ms, flip angle = 8°, pre-pulse TI delay = 852.5 ms, SENSE factor = 2, scan time = 385 s). Whole brain echoplanar functional images (EPI) were acquired in 26 transverse slices (TR = 2000 ms, TE = 30 ms, flip angle = 70°, matrix = 80 x 80, FOV = 240 mm, slice thickness = 3 mm, SENSE factor = 2, 1 mm gap). The same EPI parameters were used for both the functional localizer and attention scans. Two runs of functional localizer data were collected and each subject participated in 9 or 10 runs of the experimental task. Two experimental runs were discarded from one subject and one functional localizer run was discarded from another subject due to excessive head movement.
The EPI images from the functional localizer task were spatially smoothed with a 6 mm FWHM Gaussian kernel and the data from the experimental attention task were smoothed with a 4 mm FWHM Gaussian kernel before statistical maps were computed. We opted to spatially smooth the data in Experiment 2 (and not in Experiment 1) to help offset the power loss that accompanies a smaller sample size. A group fixed effects GLM was performed on the data from the functional localizer runs to identify face and house selective ROIs in lateral and medial fusiform gyrus, respectively [single voxel threshold of t(2421) = 5.0, P < 0.00001].
A separate group random effects GLM was used to identify brain areas that exhibited heightened responses to shift versus hold events during the object-based attention task [single voxel threshold of t(7) = 3.5, P < 0.01]. A minimum cluster size of.405 ml (15 voxels in the original acquisition space) was adopted to correct for multiple comparisons, yielding a corrected statistical threshold of P < 0.05 [determined using the program AlphaSim, (B.D. Ward, http://afni.nimh.nih.gov/afni/docpdf/alphasim.pdf) which was used to run 2000 Monte Carlo simulations that took into account the entire EPI acquisition matrix and a 4 mm FWHM Gaussian smoothing kernel].
Eye Position Monitoring
Eye movements were monitored for six of the eight subjects during fMRI data acquisition using a custom built 30 Hz MRI-compatible video camera that provided input to ViewPoint EyeTracker software (Arrington Research Inc., Scottsdale, AZ). Due to difficulties maintaining stable eye-tracker calibration over the course of the scanning session (because repositioning the subject in the middle of a session is undesirable), a variable amount of eye-tracking data was collected from each subject. For two subjects, three runs of data were collected; four, six, seven and nine runs of data were collected from the remaining subjects, respectively. Because the eye tracker in the scanner had a lower temporal resolution (30 Hz) than the eye tracker we used outside of the scanner in Experiment 1 (250 Hz), the criterion for determining an eye movement was changed. In Experiment 2, an eye movement was registered if the eyes moved >0.25° between successive video frames, which were acquired every 33 ms, during a 1.5 s temporal interval following the onset of each hold and shift target (excluding blinks). The end of an eye movement was registered when the eye gaze fell below the displacement threshold for at least two sequential video frames. As in Experiment 1, we also computed the mean Cartesian coordinates of the eyes during epochs of attention to faces and houses (these epochs included all timepoints in which attention was focused on faces or houses, not just the 1.5 s following the presentation of a target).
Results
Behavioral Data
A two-way repeated measures ANOVA (stimulus identity x target type) revealed that detection accuracy was higher for shift versus hold targets [percentage detected ± SE: 84 ± 4.3, 86 ± 3.9, 92 ± 1.8 and 92 ± 1.6 for hold house, hold face, shift face-to-house and shift house-to-face events, respectively, F(1,7) = 14.1, P < 0.01]. No other significant effects were observed for detection rates or for the response times (in ms ± SE: 791 ± 36, 818 ± 36, 834 ± 35 and 801 ± 40 for hold house, hold face, shift face-to-house and shift house-to-face events, respectively, F(1,7) = 2.6, P > 0.14).
Attention Effects in Localizer ROIs
Figures 6ce show the group mean event-related timecourses of the BOLD signal in the functional localizer defined right LatFus and the bilateral MedFus for the four event types in the attention task (hold house, hold face, shift face-to-house and shift house-to-face; see Table 3). Figure 6c shows the time course of activity in right LatFus during the attention task. The main effect of attending to faces versus attending to houses failed to reach significance in this region when considering timepoints 410 s post-stimulus (open circles and triangles, respectively). However, a paired one-tailed t-test revealed a significantly greater response to hold face compared to hold house stimuli at time 0, confirming the early onset of stimulus-specific attention effects in this region [t(7) = 2.6, P < 0.05]. In addition, the right LatFus region exhibited an interaction between stimulus identity and time, revealing that face selectivity evolved after attention was shifted from a house to a face stimulus [F(3,21) = 10.5, P < 0.001].
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Shift-related Activity
Figure 7a,b shows regions of medial SPLprecuneus and right SFSPreCeG that showed greater BOLD responses following shift compared to hold targets (Table 4). Importantly, these regions exhibit a high degree of spatial correspondence with the SPL and SFSPreCeG activations observed in Experiment 1. In contrast to Experiment 1, no significant clusters were observed outside of SPL and right SFSPreCeG. While this null result does not rule out the involvement of other areas in object-based attentional control (such as the lingual gyrus activation depicted in Fig. 3f), it suggests that the occipital and ventral temporal shift-related activations seen in Experiment 1 are not as robust as the frontoparietal activations.
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We also observed a region in the inferior medial-frontal cortex, extending up the longitudinal fissure to the medial bank of superior frontal cortex that responded more strongly to hold targets than to shift targets. However, because the inferior medial-frontal cortex is highly susceptible to imaging artifacts (e.g. EPI image distortion) and because no such region was observed in Experiment 1, we will not further discuss this activation.
Eye Movements
A two-way ANOVA (stimulus identity x target type) revealed no differences in the mean number of eye movements occurring in a 1.5 s window following the onset of shift and hold targets [mean number of eye-movements ± SE: 2.8 ± 0.34, 2.8 ± 0.43, 3.0 ± 0.45 and 2.9 ± 0.33 for hold house, hold face, shift face-to-house and shift house-to-face events, respectively, F(1,5) = 0.36, P > 0.57]. The main effect of stimulus identity and the interaction term were also non-significant (Ps > 0.72). Note that the mean number of eye movements registered in Experiment 2 is higher than in Experiment 1; however, since different eye trackers and motion criteria were used, the results can not be compared directly. As in Experiment 1, we also examined the number of eye-movements made in a 1 s temporal window corresponding to the time when the target stimuli were physically present on the display: no significant differences were observed [main effect of shift versus hold: F(1,5) = 0.003, P > 0.95]. Paired t-tests revealed no differences between the mean Cartesian coordinates of eye gaze during epochs of attention to faces and houses [t(5) = 0.47,P > 0.65, t(5) = 0.6,P > 0.56 for the comparisons of X and Y mean eye gaze position, respectively].
Discussion
The results of Experiment 2 replicate the key findings reported in the first experiment: activation levels in regions of object selective ventral visual cortex are modulated by attention to faces and houses, respectively, and these stimulus-specific modulations are accompanied by transient activation increases in regions of dorsal parietal and frontal cortex that are commonly thought to mediate voluntary attentional control in other domains (Kastner and Ungerleider, 2000; Corbetta and Shulman, 2002
; Yantis et al., 2002
).
In addition to the main effect of shifting attention observed in SPL and SFSPreCeG regions, there was a main effect of attending to houses. This heightened response to house stimuli was also observed in Experiment 1 and most likely reflects our choice of stimuli. For instance, regions of dorsal parietal and frontal cortex have been shown to be more active in working memory tasks for houses compared to faces (Sala et al., 2003). Evidently these areas have a modest selective preference for houses over faces.
While a spatial zoom lens model might account for the data in Experiment 1, the stimuli used in the second experiment were designed to rule out this explanation. The house stimuli were reduced in size so that they overlapped with the eyesnosemouth region of the face stimuli, which corresponds to the region of space most relevant for discriminating faces (Yarbus, 1967; Levy et al., 2001
; Malach et al., 2002
). Thus, zooming out when attending to houses and zooming in when attending to faces would be ineffective in Experiment 2.
Eye movement data collected in the scanner revealed no difference either in the number of eye-movements made following shift and hold events, or in the spatial distribution of gaze during epochs of attention to faces or houses. Therefore, we conclude that the shift-related parietal and dorsal frontal activations observed in the present study can be attributed to the control of object-based, and not space-based, attentional control.
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General Discussion |
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Because fMRI is a correlational method, the present data do not rule out the possibility that parietal and frontal regions are the recipient of attention-related changes in extrastriate visual areas, rather than the source of the control signals as we have suggested. However, convergent evidence from the human neurospychology literature supports the role of parietal cortex in controlling object-based attention. Patients with Balints syndrome, a neurological deficit typically observed following bilateral lesions in the parieto-occipital area, can perceive only one object in the visual field at a time, even if the objects are spatially superimposed (Luria, 1959). Furthermore, unilateral damage to parietal cortex can cause visual neglect in an object-based frame of reference and not just in the contralesional hemifield of space (Driver and Halligan, 1991
; Behrmann and Tipper, 1999
). The present results, together with these neuropsychological findings, strongly suggest that parietal cortex is causally involved in the control of object-based attention.
Mechanisms of Attentional Control
Previous reports have shown that regions of extrastriate, parietal, and frontal cortex exhibit a sustained increase in neural activity, the so-called baseline shift, when attention is directed to a location in the periphery or a to-be-fixated object, even before the stimulus itself appears (Luck et al., 1997; Chelazzi et al., 1998
; Kastner et al., 1999
). The baseline shift is greater in parietal and frontal regions than in extrastriate visual cortex (Kastner et al., 1999
).
The task employed in the present report could not reveal the presence of a nonlateralized baseline shift because subjects were always attending to an object; there were no periods of rest or passive fixation. Consequently, any baseline shift would have been continuously present throughout the task. Instead, the current paradigm isolates signals associated with attention shifts from other cortical activity by equating perceptual, memory load, and motor requirements following shift and hold targets. If an area only participates in the static maintenance of attention, then no additional increase in the BOLD signal would be observed following shifts of attention. Therefore, we conclude that the brain regions exhibiting more activity following shift targets than following hold targets were transiently active (in addition to any sustained baseline shift they may have exhibited).
Corbetta and colleagues have reported sustained BOLD responses in parietal and frontal compared to occipital regions during a delay period following a spatial attention cue; they concluded that parietal and frontal areas influence attention by tonically maintaining the current locus of attention via a sustained baseline shift (Corbetta et al., 2000; Corbetta and Shulman, 2002
). We have found that parietal and frontal regions are transiently active during shifts of attention between objects; functionally similar parietal areas were also found in previous studies of spatial attention shifts (Vandenberghe et al., 2001
; Yantis et al., 2002
).
Together with data from other laboratories, the present results suggest a dual role for parietal and frontal regions in attentional control involving the initiation (via the transient signal) and the maintenance (via the baseline shift) of a desired attentive state. This functional distinction may involve superior and more inferior regions of parietal cortex, respectively (Corbetta et al., 2000; Hopfinger et al., 2000
; Vandenberghe et al., 2001
; Yantis et al., 2002
).
Conclusions
The present results reveal that regions in dorsal frontal and parietal cortex mediate nonspatial shifts of object-based attention. Similar areas have previously been implicated in the control of spatial attention (Corbetta et al., 2000; Hopfinger et al., 2000
; Beauchamp et al., 2001
; Vandenberghe et al., 2001
; Yantis et al., 2002
). The transient timecourse of parietal and frontal activity suggests that these regions may serve to abruptly change the attentive state of the brain; this new state may then be maintained via sustained increases in activity elsewhere in the brain (Luck et al., 1997
; Kastner et al., 1999
; Corbetta et al., 2000
; Vandenberghe et al., 2001
). The functional similarity of the neural systems associated with shifts of spatial and object-based visual attention suggests a domain-general mechanism of attentional control.
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Notes |
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Address correspondence to John T. Serences, Department of Psychological and Brain Sciences, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD, 21218-2686, e-mail: serences{at}jhu.edu.
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References |
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