Perceptual bias following visual target selection

www.elsevier.com/locate/ynimg NeuroImage 25 (2005) 1168 – 1174

Perceptual bias following visual target selection Matthijs Vink,* Rene´ S. Kahn, Mathijs Raemaekers, and Nick F. Ramsey Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands Received 23 August 2004; revised 14 December 2004; accepted 17 December 2004 Available online 23 February 2005 Attending to a relevant item in a visual display is thought to require not only selective attention to this item, but also active inhibition of surrounding distractor items. As a consequence of this spatial inhibition, selection of a relevant item in a previous distractor location is slowed (i.e., the spatial inhibition effect). The goal of this study is to identify brain regions that are involved in this spatial inhibition effect using functional magnetic resonance imaging (fMRI). Subjects had to select a target from a display which also included a distractor, while that target was presented in either a new location (control) or in a location previously occupied by a distractor (spatial inhibition). A region of interest analysis revealed decreased activation in the superior parietal lobe (SPL), but increased activation in the motor areas (supplementary motor area, putamen) when the target was presented in a previously inhibited compared to a new location. We take these results to suggest that presenting a target in a previously inhibited location negatively biases the selection of that target in favor of an accompanying distractor. This may result in an initially more efficient selection process, resulting in lower activation in the SPL. Counteracting this perceptual bias possibly requires additional motor activation. This study provides evidence for the notion that to make selection more efficient, prior information concerning an item is used. When this prior information conflicts with the current stimulus demands, compensatory motor actions are taken to correct this perceptual bias. D 2004 Elsevier Inc. All rights reserved. Keywords: Spatial inhibition; fMRI; Superior parietal lobe

Introduction Most visual environments are complex as there are many things that attract attention. Yet, people are quite capable of picking out the most relevant pieces of information, and to act quickly upon it. However, the mechanism by which the selection of particular visual items is accomplished is not yet fully understood. A number of models on attention have been proposed in which selection involves a filter (Broadbent, 1958, 1971) or a spotlight (Posner et al., 1980; Treisman, 1991) mechanism, and * Corresponding author. E-mail address: [email protected] (M. Vink). Available online on ScienceDirect (www.sciencedirect.com).

they imply that only those visual items or features are processed that fall within the scope of attention. However, evidence is accumulating that irrelevant objects are not simply ignored. For example, using the flanker paradigm, Eriksen and Eriksen (1974) and Eriksen and Schultz (1979) showed that responses to the target are influenced by irrelevant distractor items flanking that target. Based on this notion, alternative models on attention propose that active inhibition of irrelevant information forms a critical part in the process of selective attention (Neill, 1977; Tipper and Cranston, 1985). To examine active inhibition, Tipper devised a negative priming paradigm (Tipper, 1985). He showed that responding to a target is slowed when it is presented in a location previously occupied by a distractor, as compared to being presented in either a new location or a previously relevant location. This reaction time slowing is referred to as the spatial inhibition effect and is caused by the carry-over effects of distractor inhibition on subsequent target processing (Tipper, 2001). Spatial inhibition forms an essential element of normal information processing (Tipper, 1985), given the fact that attending to a previously inhibited location is counterproductive for fast target detection (e.g., (Pashler and Harris, 2001; Posner and Cohen, 1984). Spatial inhibition may serve to make visual selective attention more efficient by negatively biasing those locations which were previously inhibited (Fu et al., 2001). Hence, attending to such locations may require additional processing, as suggested by the increase in reaction times to these locations (i.e., the spatial inhibition effect). Neuroimaging studies have linked visual selective attention to a distributed neural network, which includes among others the occipital, frontal, and parietal lobe (Arrington et al., 2000; Gitelman et al., 1999; Mesulam, 1981; Mesulam et al., 2001). Visual information concerning the location of an object is projected from the occipital lobe to the frontal lobe through the parietal cortex (Ungerleider and Mishkin, 1982). The frontal eye fields and the surrounding premotor cortex provide access to motor maps needed to guide eye movements during visual search (Gitelman et al., 1999; Mesulam, 1981). Within this attention network, the parietal lobe plays a crucial part in converting visual information into motor behavior (Buchel et al., 1999; Goodale and Milner, 1992) through direct connections to frontal motor areas (Hamzei et al., 2002). For example, the parietal lobe has been implemented not

1053-8119/$ - see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2004.12.042

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only in visually guided movements (Ellermann et al., 1998; Hamzei et al., 2002), but also in response selection based on visual cues (Schumacher et al., 2003). Furthermore, the parietal lobe is engaged during shifts of attention to cued locations (Corbetta and Shulman 1998; Corbetta et al., 1993), and is also involved in target detection (Corbetta et al., 1995; Coull and Nobre, 1998). The goal of the present study is to identify brain regions that are involved in the spatial inhibition effect. We designed a spatial inhibition (SI) task in which subjects have to respond to the larger (i.e., the target) of two simultaneously presented dots (see Fig. 1). In the SI condition, the target is presented in the location previously occupied by the distractor stimulus, while the accompanying distractor stimulus is presented in a new location. To measure the effects of location inhibition on target selection, this condition is compared to a control condition in which both target and distractor are presented in new locations. Brain responses for each of the conditions are assessed in an event-related manner using functional MRI. Based on earlier studies on target selection, we hypothesize that during control trials, target detection relies upon the parietal lobe. If visual selective attention is negatively biased towards the target during SI trials, additional processing may be required to overcome this bias. Such additional processing may result in increased activation either in the parietal areas involved in target selection or in the frontal motor areas involved in preparing of the response. Methods Subjects Twenty-five right-handed (Edinburgh Handedness Inventory (Oldfield, 1971); 0.93, SD 0.14) subjects (twelve males, thirteen females, mean age 22 years, SD 5.7 years) participated in the experiment. All gave written informed consent. The Ethical

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Committee of the University Medical Center of Utrecht approved this study. Spatial inhibition task The spatial inhibition (SI) task is based on the spatial negative priming task of Tipper et al. (1995), experiment 1. The task consists of stimuli being presented in 5 blocks of 144 trials, alternated with rest periods of 30 s. Stimuli were projected onto a screen placed across the bore of the MR magnet 1.8 m from the subject’s eyes. Subjects were positioned in supine position and could observe the screen through a mirror placed at 458 above their eyes. Four circles formed the background that was displayed continuously throughout the task (see Fig. 1). The circles had a diameter of 5.78 and were located 28 from the center of the display. Each trial consisted of the presentation of two filled white dots within these outer circles; one small (i.e., the distractor) and one large (i.e., the target). Responses were registered using an MRIcompatible air pressure buttonbox with four buttons corresponding to the four locations on the screen. Subjects had to press the button matching the location of the large dot (i.e., target) with their right thumb as quickly as possible, and to return their thumb to the middle of the buttonbox (i.e., between all buttons) after responding. In total, five different sizes for targets and distractors were used (ranging between 1.158 and 5.78 in steps of 0.918). The distractor was always one size smaller that the target. Stimulus size was randomly chosen for each trial. Stimuli were presented for 1000 ms, during which time a response had to be made. Then the display was cleared, leaving the background in place while a fixation cross was presented in the middle of the circles for 500 ms. With the onset of the next trial, this fixation cross disappeared. Target and distractor location for each trial was pseudo-randomly chosen from the two experimental and five random stimulus configurations (i.e., filler trials). In the experimental configura-

Fig. 1. Schematic display of the spatial inhibition (SI) task. The first stimulus display represents the prime display, whereas the other stimulus displays function as both probe and prime display for the previous and next display, respectively. In the second stimulus display, the target is presented in a location previously occupied by a distractor (i.e., SI condition). The third display depicts a Control trial.

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tions, the distractor was always presented in a new location, while the target was presented in either a new location (i.e., control) or in a location previously occupied by a distractor (i.e., spatial inhibition). In the filler trials, the other possible stimulus configurations were used. Including these filler trials ensured that subjects could not predict the stimulus configuration in the probe display from the prime display. Within each experimental block, trials of each experimental condition were grouped together in runs of 3 to 5 trials, followed by 3 to 5 trials semi-randomly chosen the filler trials. As a result, in a sequence of eight trials, no more than five trials of the same condition could occur in a row (see Fig. 2). The grouping of trials from the same condition was done to improve the statistical power by maximizing signal difference between conditions. As multiple trials from the same condition follow each other in close temporal proximity, the individual hemodynamic response curves (i.e., BOLD) will add up in a linear fashion (Bandettini and Cox, 2000; Ramsey et al., 2002), thereby maximizing the total signal associated with that condition. Image acquisition Brain imaging data were collected on a 1.5-T Philips ACS-NT scanner (Philips Medical Systems, Best, the Netherlands) with fast gradients (PT6000). The head was held in place with a strap and padding. Structural and functional images were acquired in transverse orientation from the same section of the brain. For functional scans, a navigated 3D-PRESTO pulse sequence (Ramsey et al., 1998) was used with the following parameters: echo time 35 ms; repetition time 24 ms, flip angle 108; matrix 48  64  24, field of view 192  256  96 mm; voxel size 4 mm isotropic; scan duration 1500 ms per 24-slice volume. Immediately after functional scans, an additional PRESTO scan of the same volume of brain tissue was acquired with a high (308) flip angle

(FA30) for the image coregistration routine (Ramsey et al., 1998). Finally, a T1-weighted structural image was acquired. A total of 840 functional images were acquired within a single dynamic scan. Data analysis Data analysis of fMRI scans was performed with customwritten programs and software developed by the Montreal Neurological Institute (MNI, Canada). All functional scans were registered to the FA30 volume using a rigid-body affine transformation. Next, the structural image was registered to FA30 using a least-squares difference routine (Thevenaz et al., 1998), so that functional and structural images were spatially aligned. The structural image was then registered to MNI standard space. For each individual subject, regressor coefficients for each voxel were obtained from a general linear model regression analysis using a factor matrix that contained the factors representing event-related changes time-locked to the presentation of control (215 events) and spatial inhibition (215 events) trials. A third factor was included to model the trials of the remaining filler conditions (275 events). Rest periods were not separately modeled in the regression factor matrix and therefore effectively constituted the baseline activation level to which the task conditions were compared. To correct for drifts in the signal, a high-pass filter (discrete cosine transform basis functions) was applied to the data with a cut-off frequency of 0.002 Hz. Furthermore, a factor modeling mean volume intensity was included. Trials (1500 ms) and scans were time-locked to each other. Factors for all three conditions were convolved with a hemodynamic response function with a fixed delay of 6 s for the peak (Friston et al., 1995). Regressor coefficient volumes per subject were spatially registered to a T1-weighted MNI standard brain to enable group-wise comparisons using transformation parameters of the MNI registered structural volume. A 3-dimensional Gaussian filter (8 mm full width at half maximum) was then

Fig. 2. Schematic representation of trial sequence (gray bars) convolved with the hemodynamic response function (Friston et al., 1995) (black lines) (for details, see Methods).

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applied to these statistical volumes. Group activation maps were generated for each factor using the pooled standard deviation approach (Worsley, 1994). This constitutes a random effects model analysis. Group results were tested for significance (P b 0.05) with Bonferroni correction for the number of voxels (approximately 16,000, resulting in a critical z value of 4.51 for each voxel). A region of interest (ROI) approach was adopted to test differences in activation between the control and spatial inhibition condition, limited to those areas showing task-related activation. Such an analysis allows the detection of small differences between task conditions, without the need to correct for statistical testing in brain areas which are not involved in any of the task conditions (Nieto-Castanon et al., 2003). ROIs were selected from the contrast map between task activation and rest (baseline). Task activation was defined as activation above the significance threshold of P b 0.05 (z N 4.51) occurring in at least one of the task conditions (i.e., spatial inhibition, control, filler trials). For each individual subject, the mean regressor coefficient over all voxels per ROI was calculated for the SI and control trials. These data were entered in SPSS (SPSS Inc.) to perform group-wise statistical testing of condition effects on ROI activation levels. Results Behavioral results Mean reaction times for correct responses on spatial inhibition trials were significantly slower (577 ms) than on control trials (557 ms; t(24) = 3.73, P = 0.002). The filler trials, which were used to balance the design to prevent stimulus predictability, consisted of multiple conditions and were therefore not included in the analyses. Accuracy was above 95% for all conditions and was not different across task conditions (F b 1). fMRI results Task-related activation Task-related activation was defined as those regions being activated above the significance threshold of P b 0.05 corrected for multiple comparisons in at least one of the task conditions (see Fig. 3A). Brain responses were obtained for both correct and incorrect responses, as accuracy was above 95%. Performing the task was associated with activation predominantly in motor and visual areas (see Table 1). Within the occipital lobe, we found significant activation bilaterally in the middle occipital gyrus (MOG). More dorsal activation was observed in the inferior parietal lobe (IPL) and superior parietal lobe (SPL). Motor activation was found in the left precentral gyrus (M1) and supplementary motor area (SMA), the latter extending to the anterior cingulate gyrus (AC). Also the putamen activated bilaterally. More anterior, both the middle frontal gyrus (MFG) and the medial frontal gyrus (MdFG) were activated. Spatial inhibition A repeated-measures general linear model with task condition (two levels; control and spatial inhibition) and ROI (13 levels) as within-subject factors was applied. The main effect of condition was not significant (F(1,24) = 0.658, P = 0.425), suggesting no overall difference in activation levels between the task conditions. The main effect of ROI also was not significant (F(12,13) = 1.628, P = 0.198), indicating that the mean level of activation did not

Fig. 3. (A) Task versus rest activation overlaid on selected slices of the mean anatomical group image, thresholded at P b 0.05 corrected for multiple tests (i.e., z = 4.51). (B) Mean activation levels in three regions of interest (ROIs) showing a significant difference between control and spatial inhibition (for details, see Table 1). *P b 0.05; SPL, superior parietal lobe; SMA, supplementary motor area.

differ over the ROIs. The interaction between condition and ROI did reach significance (F(12,13) = 4.24, P = 0.007), indicating that the activation levels over the ROIs differed between control and spatial inhibition trials. Subsequent paired-samples t tests revealed that activation was significantly higher during spatial inhibition compared to control in the left supplementary motor area and the right putamen (t(24) = 2.26, P = 0.033 and t(24) = 2.25, P = 0.034, respectively). In contrast, activation in the right superior parietal lobe was significantly reduced during spatial inhibition as compared to control activation (t(24) = 2.44, P = 0.022). Mean activation levels in the other ROIs did not differ significantly between conditions. The results of the significant t tests are presented in Fig. 3B. Discussion The goal of the present study was to identify brain regions that are involved in the spatial inhibition (SI) effect. Overall, respond-

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Table 1 Details of the regions of interest (ROIs) obtained for the task versus rest contrast, thresholded at P b 0.05 corrected for multiple tests (i.e., z = 4.51) Region (Brodmann area)

Side

Pre- and postcentral gyrus Superior parietal lobe (SPL) Inferior parietal lobe (IPL) Supplementary motor area (SMA) Middle occipital gyrus (MOG)

L

3

395

41

19

52

L R** L

7 40

166 132 73

22 22 42

62 62 39

54 54 53

L*

6

87

2

0

51

L R R R

19

62 83 8 22

30 32 46 30

83 81 68 3

18 18 3 59

16 47 13 6

2 53 26 26

42 4 1 5

42 29 4 0

Middle frontal gyrus (MFG) Medial frontal gyrus (MdFG) Putamen

L L L R*

BA

37 6 8 9

No. of voxels

X

Y

Z

Talairach coordinates (X, Y, Z) reflect center of the region. BA, Brodmann area. * Paired-samples t test: Control b Spatial Inhibition. ** Paired-samples t test: Control N Spatial Inhibition (for details, see Results).

ing to a target presented simultaneously with a distractor engaged a distributed network consisting of middle occipital lobe, inferior (IPL) and superior parietal lobe (SPL), anterior cingulate gyrus, supplementary motor area (SMA), bilateral putamen, and left primary motor cortex (see Fig. 3A). These data are consistent with recent fMRI studies on visual target detection (Coull and Nobre, 1998; Hazeltine et al., 2000; Pollmann et al., 2003). We showed that presenting a target in a previously inhibited location (i.e., SI trial) leads to a reduction in activity in the SPL and an increase in activity in motor areas. Whereas during control trials activation was found predominantly in the SPL, activation shifted more towards the motor areas (i.e., putamen and SMA) during SI trials. Previous primate studies on corticocortical pathways have demonstrated that the primary visual cortex (area 17) lacks direct projections to the primate frontal lobe and motor areas (Jones and Powell, 1970; Pandya and Kuypers, 1969). The primary visual cortex does project onto the parietal cortex which in turn projects to the frontal motor areas. The superior parietal lobe (SPL) plays a major role in visuomotor transformations in that it provides information from visual cortex concerning stimulus locations to the medial (i.e., SMA; (Johnston et al., 2004) and dorsal part of the premotor cortex (Colby and Duhamel, 1991; Johnson et al., 1996; Wise et al., 1997). In our study, the SPL was most active when a response had to be made towards a target in a new location, while simultaneously a distractor was presented in a new location. In this case, selecting and responding to the target depends primarily upon external visual information being projected from the SPL to the premotor areas (Rowe et al., 2000). This is consistent with other studies showing that the SPL is implemented not only in targetdirected movements (Ellermann et al., 1998), but also in shifts of attention (Corbetta and Shulman, 1998; Corbetta et al., 1993, 1995, 2000; Shapiro et al., 2002). Damage to the SPL commonly results in erratic reaching for objects located in the contralateral visual hemi-field (De Renzi, 1982; Mesulam, 1981). Overall, this fronto-

parietal network may function as a visuomotor controller (Wise et al., 1997), which combines visual and perceptual information, as well as gaze and attention, to produce an output feeding selection, preparation, and execution of movements. In addition to providing information concerning the location of stimuli, the SPL has also been implemented in visual (short term) memory (Arrington et al., 2000; Cornette et al., 2002; Linden et al., 2003; Munk et al., 2002), suggesting that the SPL does more than simply relay visual information to the frontal motor regions. Neurons in the parietal lobe are sensitive to the location of the stimulus on the retina or with respect to the head (Andersen et al., 1997). Furthermore, neurons in the posterior parietal lobe show an enhanced response to attended targets within their receptive fields (Deco and Rolls, 2004). These data suggest that the information within the SPL is encoded in terms of location, whereby different neurons encode different parts of the visual field (a/o Pollmann et al., 2003). As a consequence of such encoding, presenting a stimulus in a previously used location may entail re-engagement of the same neurons. For example, in SI trials, the target is presented in a previously inhibited part of the visual field, while the accompanying distractor is presented in a new (i.e., previously unused) part of that visual field. Houghton et al. (1996) suggested that subsequent re-activation of these previously inhibited neurons (above the firing threshold) takes more time compared to neurons which were not inhibited previously (i.e., encode a different location in the visual field). Accordingly, an initial positive bias towards the distractor may arise (Cole et al., 2004), rather than to the target. This notion is consistent with the finding that the SPL is involved in processes leading to the efficient segmentation of old from new items (visual marking; Watson and Humphreys, 1997, 1998). In sum, by re-engagement of previously inhibited neurons to encode the new target location, target processing within the SPL is likely negatively biased by the carry-over effects of such inhibition. In contrast, during control trials, both stimuli are presented in new locations within the visual field. Therefore, target processing is not hampered by previous inhibition and hence requires additional processing, such as comparing the size of the stimuli, to determine the location of the target. As a consequence of the negative bias towards the target location in SI trials, the motor response may initially be directed towards the distractor location. Although accuracy did not differ between control trials and SI trials, reaction times were significantly increased on the latter trials. This suggests that there is some form of compensation for the biased parietal input to the motor system. Indeed, we found increased activation in both the putamen and the SMA. These parts of the motor system are closely interconnected and play a role mainly in preparing and planning of motor responses (Cunnington et al., 2002; Deiber et al., 1996; Tanji, 1994). The putamen is the main subcortical input region of the medial motor loop (Alexander et al., 1986) and has been associated with initiation as well as inhibition of movements (Raemaekers et al., 2002). The SMA is the main cortical region of the medial motor loop (Alexander and Crutcher, 1990; Tanji, 1994). This region has been implicated in self-initiated movements (Cunnington et al., 2002) and in the execution of memorized movements rather than visually guided movements (Mushiake et al., 1991; Tanji and Shima, 1994). Together with the SPL, the SMA is thought to be involved in converting visual information to motor programs (Johnston et al., 2004). During SI trials, the negative bias towards the target location (Pollmann et al., 2003) has to be overcome. This may involve the inhibition of the initial response

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towards the distractor location and the subsequent generation of the correct response. As the behavioral effect was only 15 ms, it is likely that rather than generating a new response, a shift towards the correct location occurred early in motor preparation. This shift may be triggered by additional information coming from the SPL, once the initial bias towards the target location is overcome. We hypothesize that, although delayed, the correct information regarding the target remains available. Although a subtle behavioral effect is not necessarily associated with subtle changes in brain activation patterns, we failed to detect large differences between conditions using a group-wise pairedsamples t test over the entire brain. Although we found a clear shift of activation within the fronto-parietal network, we cannot rule out the potential influence of other regions. Improving the signal to noise ratio by using higher field strengths or using multiple RF coils may possibly contribute to the detection of changes in brain activation associated with subtle, yet reliable and significant, behavioral processes. Our data are consistent with the notion that visual selective attention not only depends upon increased attention to relevant stimuli, but also involves the processing and subsequent active blocking of distractor stimuli. The consequences of such active blocking become apparent as subsequent responding to stimuli presented in previous distractor locations is slowed and requires compensatory premotor activation. The inhibitory mechanism constitutes a normal part of attention as it serves to bias selective attention based on a priori knowledge. This allows the brain to respond more efficiently to changes within the environment, thereby requiring less activation, especially in the superior parietal lobe. When such a change occurs within a previous distractor location, additional processing in premotor areas is required to respond to that location. In conclusion, our data support the idea that whereas the brain is set to perform tasks as efficient as possible by using prior knowledge to bias the processing of incoming information, all information remains available, be it after some delay, to maintain a high degree of cognitive flexibility. References Alexander, G.E., Crutcher, M.D., 1990. Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends Neurosci. 13, 266 – 271. Alexander, G.E., DeLong, M.R., Strick, P.L., 1986. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, 357 – 381. Andersen, R.A., Snyder, L.H., Bradley, D.C., Xing, J., 1997. Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annu. Rev. Neurosci. 20, 303 – 330. Arrington, C.M., Carr, T.H., Mayer, A.R., Rao, S.M., 2000. Neural mechanisms of visual attention: object-based selection of a region in space. J. Cogn. Neurosci. 12 (Suppl. 2), 106 – 117. Bandettini, P.A., Cox, R.W., 2000. Event-related fMRI contrast when using constant interstimulus interval: theory and experiment. Magn. Reson. Med. 43, 540 – 548. Broadbent, D.E., 1958. Perception and Communication. Pergamon Press, London. Broadbent, D.E., 1971. Decision and Stress. Academic Press, London. Buchel, C., Coull, J.T., Friston, K.J., 1999. The predictive value of changes in effective connectivity for human learning. Science 283, 1538 – 1541. Colby, C.L., Duhamel, J.R., 1991. Heterogeneity of extrastriate visual areas and multiple parietal areas in the macaque monkey. Neuropsychologia 29, 517 – 537.

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