Abstract

Orienting of attention produces a “sensory gain” in the processing of visual targets at attended locations and an increase in the amplitude of target-related P1 and N1 ERPs. P1 marks gain reduction at unattended locations; N1 marks gain enhancement at attended ones. Lateral targets that are preceded by valid cues also evoke a larger P1 over the hemisphere contralateral to the no-target side, which reflects inhibition of this side of space [Slagter, H. A., Prinssen, S., Reteig, L. C., & Mazaheri, A. Facilitation and inhibition in attention: Functional dissociation of pre-stimulus alpha activity, P1, and N1 components. Neuroimage, 125, 25–35, 2016]. To clarify the relationships among cue predictiveness, sensory gain, and the inhibitory P1 response, we compared cue- and target-related ERPs among valid, neutral, and invalid trials with predictive (80% valid/20% invalid) or nonpredictive (50% valid/50% invalid) directional cues. Preparatory facilitation over the visual cortex contralateral to the cued side of space (lateral directing attention positivity component) was reduced during nonpredictive cueing. With predictive cues, the target-related inhibitory P1 was larger over the hemisphere contralateral to the no-target side not only in response to valid but also in response to neutral and invalid targets: This result highlights a default inhibitory hemispheric asymmetry that is independent from cued orienting of attention. With nonpredictive cues, valid targets reduced the amplitude of the inhibitory P1 over the hemisphere contralateral to the no-target side whereas invalid targets enhanced the amplitude of the same inhibitory component. Enhanced inhibition was matched with speeded reorienting to invalid targets and drop in attentional costs. These findings show that reorienting of attention is modulated by the combination of cue-related facilitatory and target-related inhibitory activity.

INTRODUCTION

Selective attention prioritizes the processing of behaviorally relevant sensory events over irrelevant ones. In the domain of visual–spatial attention, this means facilitating and speeding up the processing of targets at attentionally cued locations over those at uncued locations (Posner, 1980). At the electrophysiological level, this facilitation is typically reflected in enhanced amplitude of the P1 and N1 target-related ERP components (Hillyard, Vogel, & Luck, 1998; Mangun & Hillyard, 1991). The P1 and N1 show dissociable attentional effects (Hillyard et al., 1998; Luck et al., 1994). With respect to targets that are preceded by spatially neutral cues, the amplitude of the P1 is reduced in response to targets that are presented at invalidly cued locations, though it is not increased when targets are presented at validly cued locations. Thus, the differential amplitude of the P1 component between invalid and neutral targets reflects the “cost” of reorienting, that is, the increase in RTs to invalid relative to neutral targets. The N1 shows an opposite effect: Relative to targets that are preceded by spatially neutral cues, its amplitude is enhanced when targets are presented at validly cued locations though it is not reduced when targets are presented at invalidly cued ones. Thus, the differential amplitude of the N1 component between valid and neutral targets reflects the “benefit” of orienting, that is, the RTs advantage in the detection of valid relative to neutral targets. Taken together, these results have suggested that the inhibitory and facilitatory mechanisms of spatial attention that are marked by the P1 and N1 components rely on different neural mechanisms (Hillyard et al., 1998; Luck et al., 1994).

In a series of recent studies, we have highlighted that the functional separation between attentional benefits and costs entails a relevant advantage in attentional processing. This conclusion was suggested by the finding that, compared with central spatial cues that predict target location in 80% of trials, cues that predict target location in 50% of trials produce a selective drop in attentional costs without causing a comparable loss of attentional benefits (Lasaponara, Fortunato, et al., 2019; Dragone et al., 2018; Lasaponara et al., 2017; Lasaponara, Chica, Lecce, Lupianez, & Doricchi, 2011; Doricchi, Macci, Silvetti, & Macaluso, 2009; for similar results with 50% cue predictiveness, see Hietanen, Leppänen, Nummenmaa, & Astikainen, 2008). At the electrophysiological level, the reduction in attentional costs is matched with a selective decrement in the differential amplitude between the P1 that is evoked by invalid relative to neutral targets. In contrast, the maintenance of the attentional benefits with both predictive and nonpredictive cues is matched with no variation in the amplitude of the differential N1 response between valid and neutral targets (Lasaponara et al., 2011, 2017). These results suggest that, when the probabilistic association between cues and targets is poor, the functional and anatomical segregation between attentional benefits and costs allows speeding up reorienting of attention to invalid targets, without cancelling out the benefits provided by valid spatial cueing.

Slagter, Prinssen, Reteig, and Mazaheri (2016) have recently refined the idea that the target-related P1 component helps suppressing sensory processing at unattended locations. Using a Posner task that included only valid central cues that always predicted the position of lateral targets, these authors found a complete-to-predominant lateralization of the P1 over the hemisphere ipsilateral to the target and contralateral to the no-target side of space. Slagter and colleagues advanced the hypothesis that the predominant P1 response over the hemisphere contralateral to the no-target side marks the blocking of sensory processing in this side of space and that this blocking is triggered by the very appearance of the target in the cued side. Following this suggestion, we have recently found that, compared with directional cues that correctly predict target position in the majority of trials, that is, 80% valid and 20% invalid trials, nonpredictive directional cues, that is, 50% valid and 50% invalid trials, reduce both the preparatory facilitation of the visual cortex contralateral to the no-target side and the amplitude of the target-related P1 over the hemisphere contralateral to the no-target side (Lasaponara et al., 2011, 2017). Following the proposal by Slagter et al. (2016), this latter result would point at a drop in the target-related blocking of sensory processing in the no-target side of space. In this study, we wished to expand on these findings and explore whether changes in predictive cueing also produce corresponding changes in the hemispheric distribution of the P1 evoked by neutrally cued and invalidly cued targets. This could not be explored in our previous study because the number of neutral and invalid trials was not adequate to run reliable comparative ERPs analyses. This methodological limitation precluded gaining insights into a number of important issues. First, comparing the hemispheric distribution in the latency and amplitude of P1 responses among valid, neutral and invalid targets might shed light on the relationship between the target-related P1 inhibitory function identified by Slagter et al. (2016) and the “sensory gain” that is produced by attentional orienting (Hillyard et al., 1998). Because available evidence shows that both these mechanisms are modulated by cue predictiveness (Lasaponara et al., 2011, 2017; Doricchi et al., 2009; Giessing, Thiel, Rösler, & Fink, 2006), one might argue that preparatory/facilitatory effects produced by endogenous cues interact with the target-driven inhibitory effects highlighted by Slagter et al. (2016). Second, studying the influence of cue predictiveness on the hemispheric distribution of P1 responses should clarify whether the reduction in the differential amplitude of the P1 between invalid and neutral targets that is observed with nonpredictive cues (Lasaponara et al., 2011), is driven by a specific drop in differential amplitude over the hemisphere contralateral to the no-target side of space or by a drop over both hemispheres. Third, and more generally, investigating whether and how variations in cue predictiveness modify the hemispheric distribution of the latency and amplitude of the P1 response to invalid targets, when the no-target side of space corresponds to the side that was facilitated during the cue period, relative to valid targets, when the no-target side of space corresponds to the side that was inhibited during the cue period, should provide interesting insights on the mechanisms that underlie reorienting of attention. Basing on converging ERPs and pupil dilation evidence that points at higher preparatory facilitation of target processing with predictive rather than nonpredictive directional cues (Lasaponara, Fortunato, et al., 2019; Dragone et al., 2018; Geva, Zivan, Warsha, & Olchik, 2013; Eimer, 1993), different types of interaction between cue predictiveness and the inhibitory P1 response evoked by invalid targets can be envisaged. A first hypothesis could be that cue predictiveness sets and optimizes the strength of the target-related P1 inhibitory response by influencing the activity of the P1 generators directly. If this would be the case, compared with nonpredictive cueing, invalid targets that follow predictive directional cues should produce a higher P1 response over the hemisphere contralateral to the no-target side because more inhibition would be required to suppress the strong preparatory activation of the cued side of space that was produced by predictive cueing. Alternatively, one might hypothesize that cue predictiveness modulates the strength of the P1 inhibitory response indirectly by setting the preparatory facilitation of sensory processing during the cue period and putting in contrast this preparatory activation with the target-related activation of P1 generators. In this case, compared with invalid targets that follow nonpredictive directional cues, those that follow predictive directional ones should produce a smaller P1 response over the hemisphere contralateral to the no-target side, because the full development of this component would be counteracted by the strong preparatory facilitation of the cued side of space that was produced by predictive cues.

To address these issues, we studied cue- and target-related ERPs during the performance of a Posner task with predictive (Pred; 80% valid trials/20% invalid trials) or nonpredictive (NoPred; 50% valid trials/50% invalid trials) central directional cues that over different trials were intermixed with nondirectional neutral cues. Cue-related ERPs provide important information about the temporal development and extent of pre-target attentional control and bias. Voluntary deployment of spatial attention with central endogenous cues is associated with three preparatory components that show higher amplitude over the hemisphere contralateral to the cued target location (Eimer, 2014): (1) an “early directing attention negativity” (EDAN) that occurs 200–400 msec postcue in parietal–occipital areas and that marks the early phases of the attentional shift (Nobre, Sebestyen, & Miniussi, 2000; Harter, Miller, Price, LaLonde, & Keyes, 1989) or, alternatively, the selection of task-relevant features in central spatial cues (van Velzen & Eimer, 2003); (2) an “anterior directing attention negativity” (ADAN) that reflects attentional engagement in frontal areas and occurs 500–900 msec postcue (Praamstra, Boutsen, & Humphreys, 2005; Eimer, van Velzen, & Driver, 2002); (3) a “lateral directing attention positivity” (LDAP; Hopf & Mangun, 2000; Harter et al., 1989) that occurs 500–1000 msec postcue onset and marks facilitatory effects in visual areas contralateral to the cued location (Eimer et al., 2002). More recently, preparatory activity with negative polarity over the hemisphere contralateral to the direction of attention was also reported. This activity has been labeled LDAN (“lateral directing attention negativity”; Van der Stigchel, Heslenfeld, & Theeuwes, 2006) or BRN (biasing-related negativity; Grent-'t-Jong & Woldorff, 2007). These components would reflect a combination of “pretarget oculomotor programming” and “attentional orienting” (Van der Stigchel et al., 2006) and mark changes in the baseline activity of attentional parietal–frontal areas and in perceptual sensitivity (Grent-'t-Jong & Woldorff, 2007). Finally, another typical mark of preparatory cued orienting is the decrease in α-band power (∼8–14 Hz) over parietal–occipital areas contralateral to the attended side of space (Lasaponara, Pinto, Aiello, Tomaiuolo, & Doricchi, 2019; Capotosto, Babiloni, Romani, & Corbetta, 2009; Kelly, Gomez-Ramirez, & Foxe, 2009; Thut, Nietzel, Brandt, & Pascual-Leone, 2006; Sauseng et al., 2005; Yamagishi, Goda, Callan, Anderson, & Kawato, 2005). Here, to allow comparisons with the results of previous studies that have investigated the influence of cue predictiveness on orienting of spatial attention (Lasaponara et al., 2011, 2017; Praamstra et al., 2005; van Velzen & Eimer, 2003; Eimer et al., 2002; Hopf & Mangun, 2000), we focused only on EDAN, ADAN, and LDAP components.

METHODS

Participants

Participants were included in the study only if they showed a reliable validity effect, that is, RTs advantage for validly versus invalidly cued targets (valid vs. invalid RTs tested with two-tailed t test, p < .05) during an initial training run with an endogenous Posner task that included 96 valid and 24 invalid trials. Two participants did not show a significant validity effect during training and were not included in the experimental sample. Eighteen healthy participants (14 women, 4 men; mean age = 23.5, SD = 3.4) were included and completed the study. Among these, one participant was further excluded from final analyses because of an excessive number of EEG and EOG artifacts. All participants were right-handed, had normal or corrected-to-normal visual acuity, and had normal color perception. They gave written informed consent for participating in the study. The experimental protocol was approved by the institutional ethics committee of the Fondazione Santa Lucia IRCCS-Rome and the Psychology Department of Sapienza University-Rome.

Procedure and Stimuli

Presentation of stimuli and recording of manual RTs was performed with E-Prime software (Schneider, Eschman, & Zuccolotto, 2002) through a PC connected to a 17-in. monitor. This system was connected to another computer, which recorded EEG signals. Each trial (see Figure 1) began with the presentation of a central fixation cross (size: 1° × 1°) surrounded by two overlapping arrowheads (size: 1.7° × 1.7°) and two lateral boxes (size: 3.5° × 3.5°), one centered 9° to the left and the other 9° to the right of central fixation. One of the arrows pointed to the box on the left side and the other arrow pointed to the box on the right side. All stimuli were white against a black background. This “fixation” period lasted 500–1000 msec (uniform distribution) and was followed by a “cue” period, lasting between 1400 and 1800 msec (uniform distribution). At the beginning of the “cue” period, the color of arrows changed. On directional “valid,” “invalid,” and “directional catch” trials, one arrow became yellow and the other became blue. In each experimental group (see below), half of the participants were instructed to pay attention to the box indicated by the yellow arrow, and the other half were instructed to pay attention to the box pointed by the blue one. In nondirectional “neutral” and “neutral catch” trials, the upper or lower half of each arrow became yellow and the other half became blue. Corresponding upper or lower halves of the two arrows never had the same color (Figure 1). Participants were instructed that, within nondirectional trials, central cues did not indicate any specific side of space (left/right), so that, in this case, they did not have to orient their attention to one of the two boxes before target occurrence. At the end of the “cue” period, a white asterisk (size: 1° × 1°) was presented as the target for 100 msec at the center of one of the two boxes, while the central cue remained colored. Once the target disappeared, the arrow cue turned white again for the entire 1100-msec period allowed for response collection (“response” period). Participants were required to hold their gaze on central fixation throughout the trial and try not to blink during the cue and target period. They were asked to detect the appearance of the target by pressing a button with their right index finger as soon as possible or withhold their response if no target was presented (catch trials). On “valid” trials, the target was presented in the box cued by the arrow with the relevant color. On “invalid” trials, the target was presented in the box opposed to the one cued by the arrow with the relevant color. On “neutral” trials, the target was presented with equal probability in one of the two boxes. In “directional catch” and “neutral catch” trials, no target followed cue presentation. Spatial arrangement, timing, and events of the different trial types used in the task are reported in Figure 1.

Figure 1. 

Time course of events during directional and nondirectional experimental trials. Flow of events is represented from up to down. Duration of events is reported in milliseconds.

Figure 1. 

Time course of events during directional and nondirectional experimental trials. Flow of events is represented from up to down. Duration of events is reported in milliseconds.

In the Pred condition, there were 480 Valid and 120 Invalid trials, whereas in the NoPred condition there were 300 valid and 300 invalid trials. In both conditions, there was an identical number of 300 neutral, 32 directional catch, and 32 neutral catch trials. A total of 1944 trials was administered to each participant in the whole experiment. The order of the administration of the Pred and NoPred conditions was counterbalanced across participants who received no information on cue predictiveness. Trials were presented in six consecutive ERP runs (162 trials per run, giving a total of 972 trials). The frequency of each trial type, the cued side and the target side were balanced within and across runs.

Electrophysiological Recording

The EEG was recorded from 64 electrodes placed according to the 10–10 system montage (BrainVision system). All scalp channels were referenced online to Cz. Horizontal eye movements were also monitored with a bipolar recording from electrodes at the left and right outer canthi. Blinks and vertical eye movements were recorded with an electrode below the left eye, which was offline referenced to site Fp1.

The EEG from each electrode site was digitized at 250 Hz with an amplifier bandpass of 0.1–1000 Hz, including a 50-Hz notch filter, and was stored for off-line averaging. The EEG was initially re-referenced against the average of all channels and then filtered offline with a bandpass of 0.1–30 Hz and successively segmented in epochs lasting 1600 msec for cue-locked analyses and 1000 msec for target-locked analyses of the P1/N1 early components and the late P300 component. In all cases, the 200-msec period preceding cues or targets was used as baseline. Before computerized artifact rejection, ocular correction was performed accordingly to the Gratton and Coles algorithm (Gratton, Coles, & Donchin, 1983). Artifact rejection was performed before signal averaging to discard epochs in which eye deviations, blinks, or amplifier blocking occurred. All epochs in which EOG amplitudes and EEG amplitudes were greater than ±100 μV were excluded from further analysis. In the final sample of 17 participants included in the analyses, less than ∼10% of the EEG signals were rejected for violating these artifact criteria. As a further control of data included in the final analyses, we computed separate averaged HEOG waveforms for the left and right cues epochs, left and right targets epochs, and their corresponding fixation periods, and we checked for the presence of a significant differential amplitude between leftward and rightward eye movements (Luck, 2014). This method allows disclosing for the presence of very small eye movements that will result in differences in the HEOG differential waveforms. Individual inspection of single-subject HEOG waveforms pointed out that, in both Pred and NoPred conditions, there were no differences in the amplitude of leftward and rightward eye movements during the fixation, cue, and target period (Experimental Condition (Pred, NoPred) × Epoch [fixation, cue, target]). Based on a conventional calibration procedure (i.e., 10° lateral eye movements; see Lins, Picton, Berg, & Scherg, 1993) during task performance, eye deviations were confined to less than 1° around central fixation, and their amplitude was comparable among the different phases in trial (0.24°, 0.31°, and 0.23° for the fixation, cue, and target periods, respectively). The amplitude of these eye shifts usually results in a propagated voltage of less than 0.1 μV at posterior scalp sites (Lins et al., 1993).

Statistical Analysis

Behavioral Data

For each type of trial, RTs exceeding 2 SD above and below the experimental group's mean were considered as outliers and not included in the analysis. This procedure resulted in the exclusion of less than 3% of responses in each type of trial. Individuals' average RTs were entered in an Experimental Condition (Pred, NoPred) × Target Type (valid, neutral, invalid) ANOVA.

ERP Data

Cue-related components.

The three lateralized, long lasting preparatory ERP components EDAN, ADAN, and LDAP that were elicited by central spatial cues were averaged within four conventional ROIs (Hong, Sun, Bengson, Mangun, & Tong, 2015): frontal left (F7, FC5), frontal right (F8, FC6), parieto-occipital left (P7, PO7), parieto-occipital right (P8, PO8). Each component was analyzed by entering individual data in an Experimental Condition (Pred, NoPred) × Cue Direction (left, right) × Hemisphere (ipsilateral, contralateral) repeated-measures ANOVA. The amplitude of these components was measured as mean activity with respect to a 200-msec prestimulus baseline in the following conventional time windows: EDAN (250–450 msec postcue; Hong et al., 2015; Kelly et al., 2009; Seiss, Driver, & Eimer, 2009), ADAN (450–1200 msec postcue; Hong et al., 2015; Seiss et al., 2009; Eimer et al., 2002), and LDAP (450–1200 msec postcue; Hong et al., 2015; Seiss et al., 2009; Eimer et al., 2002).

Target-related components.
P1 and N1.

Individual amplitudes of these small transitory ERP components were measured as mean activity with respect to a 200-msec prestimulus baseline in the following conventional time windows: contralateral P1 (110–190 msec), ipsilateral P1 (130–210 msec), and N1 (200–280 msec) over electrode derivations, that is, PO7/8, P7/8, P3/4, where these components showed maximal amplitude in the grand average (Di Russo, Aprile, Spitoni, & Spinelli, 2007).

Latency peaks of the same components were estimated through an automatic peak detection algorithm (Vision Analyzer 2.1.2) within the same time windows and over the same electrode derivations used for amplitude analysis. All peaks identified by the software were further verified through visual inspection. Time windows and derivation are consistent with those used in the large majority of previous studies (e.g., see Lasaponara et al., 2011, 2017; Slagter et al., 2016; Gomez Gonzalez, Clark, Fan, Luck, & Hillyard, 1994). Individual latency peaks and mean amplitudes of P1 were successively entered in an Experimental Condition (Pred, NoPred) × Trial Type (valid, neutral, invalid) × Hemisphere (ipsilateral, contralateral) repeated-measures ANOVA. Individual mean amplitudes and latency peaks of the N1 recorded over the hemisphere contralateral to target side were entered in an Experimental Condition (Pred, NoPred) × Trial Type (valid, neutral, invalid) repeated-measures ANOVA.

P3a and P3b.

The amplitude of P300 component was measured as mean activity with respect to a 200-msec prestimulus baseline in the 280–500 msec time windows over Cz for the anterior P3a component and in the 500–800 msec time windows over Pz for the posterior P3b component. The selection of time windows and derivations used for the analysis of these large amplitude components were based both on the results of previous studies (Saevarsson, Kristjánsson, Bach, & Heinrich, 2012; Polich, 2007) and on visual inspection of scalp topographies. Individual mean amplitudes data were entered in an Experimental Condition (Pred, NoPred) × Trial Type (valid, neutral, invalid) repeated-measures ANOVA.

RESULTS

Behavioral Results

A significant main effect of Trial Type was found, F(2, 42) = 23.7, p < .001, with significant benefits (14.8 msec; valid: 269.8 msec vs. neutral: 284.6, p < .001) and nonsignificant costs (5.3 msec; invalid: 289.8 msec vs. neutral: 284.6 msec, p = .09). The Experimental Condition × Target Type interaction was also significant, F(2, 42) = 10.01, p < .001, and showed a reduction of the validity effect, that is, the RTs difference between invalid and valid trials, with nonpredictive cueing (Pred: 28.1 msec vs. 11.8 msec, p < .0001). This interaction was explored further by evaluating the influence of experimental condition on benefits and costs separately. A first nonsignificant Experimental Condition × Target Type (valid, neutral) interaction highlighted the presence of benefits both with Pred and NoPred cues, F(1, 18) = 0.68, p = .42 (benefits: Pred = 17.3 msec, p < .001; NoPred = 14.7 msec, p < .001). In contrast, the Experimental Condition × Target Type (invalid, neutral) interaction was significant, F(1, 18) = 13.8, p = .002. Planned comparisons showed that costs were significant with Pred ones (13.4 msec, p < .001; see Figure 2) and not significant with NoPred cues (2.1 msec, p = .48).

Figure 2. 

Average RTs to valid, neutral, and invalid targets in predictive and nonpredictive conditions. Bars represent SE.

Figure 2. 

Average RTs to valid, neutral, and invalid targets in predictive and nonpredictive conditions. Bars represent SE.

Electrophysiological Results

Cue Period

EDAN.

A significant main effect of Hemisphere was found, F(1, 16) = 23.2, p < .001. This pointed at greater negativity over the occipital–parietal sites in the hemisphere contralateral to cue direction. At variance from previous studies (Lasaponara et al., 2011, 2017), the EDAN was not reduced by nonpredictive cueing (Pred: −0.21 μV ± 0.06 SEM vs. NoPred: −0.20 μV ± 0.05 SEM, p = ns).

ADAN.

A significant main effect of Hemisphere, F(1, 16) = 12.7, p = .002, highlighted greater negativity over frontal sites contralateral to cue direction. In agreement with previous studies (Lasaponara et al., 2011, 2017), a significant Experimental Condition × Hemisphere interaction, F(1, 16) = 4.6, p = .04, showed an increment in the amplitude of the ADAN with NoPred (−0.44 μV ± 0.11 SEM), as compared with Pred cues (−0.21 μV ± 0.09 SEM, p = .04).

LDAP.

A significant main effect of Hemisphere, F(1, 16) = 10.3, p = .005, demonstrated greater positivity over occipital–parietal sites contralateral to cue direction. In line with previous results (Lasaponara et al., 2011, 2017), a significant Experimental Condition × Hemisphere interaction, F(1, 16) = 4.2, p = .05, showed that the amplitude of the LDAP was reduced by nonpredictive cueing (Pred: 0.22 μV ± 0.05 SEM vs. NoPred: 0.07 μV ± 0.05 SEM; Figure 3).

Figure 3. 

EDAN, ADAN, and LDAP cue-related ERPs recorded in directional trials with arrow cues pointing to the left (red line) or to the right (green line) side of space, and corresponding scalp topographic maps representing the amplitude of the differential “cue-right minus cue-left” waveform in the predictive and nonpredictive conditions. Components recorded over the left and right hemisphere are reported separately for anterior and posterior pools of derivations. Time windows where significant statistical effects are present are highlighted by gray squares.

Figure 3. 

EDAN, ADAN, and LDAP cue-related ERPs recorded in directional trials with arrow cues pointing to the left (red line) or to the right (green line) side of space, and corresponding scalp topographic maps representing the amplitude of the differential “cue-right minus cue-left” waveform in the predictive and nonpredictive conditions. Components recorded over the left and right hemisphere are reported separately for anterior and posterior pools of derivations. Time windows where significant statistical effects are present are highlighted by gray squares.

Target Period

P1 amplitude.

A significant triple Experimental Condition × Trial Type (valid, neutral, invalid) × Hemisphere interaction was found, F(2, 32) = 3.4, p = .04 (see Figures 4 and 5).

Figure 4. 

Target-related P1 and N1 recorded over the hemisphere ipsilateral and contralateral to target side in the predictive (black line) and nonpredictive condition (red line) and corresponding scalp topographic maps (predictive condition: black outline; nonpredictive condition: red outline). Time windows are represented by gray squares (full square, significant difference between ipsilateral and contralateral waveforms; empty square, nonsignificant difference).

Figure 4. 

Target-related P1 and N1 recorded over the hemisphere ipsilateral and contralateral to target side in the predictive (black line) and nonpredictive condition (red line) and corresponding scalp topographic maps (predictive condition: black outline; nonpredictive condition: red outline). Time windows are represented by gray squares (full square, significant difference between ipsilateral and contralateral waveforms; empty square, nonsignificant difference).

Figure 5. 

Mean amplitudes of P1 recorded over the hemisphere ipsilateral (left) and contralateral (right) to target side, in response to valid, neutral, and invalid targets in predictive (black line) and nonpredictive (gray line) condition. Line bars represent SE.

Figure 5. 

Mean amplitudes of P1 recorded over the hemisphere ipsilateral (left) and contralateral (right) to target side, in response to valid, neutral, and invalid targets in predictive (black line) and nonpredictive (gray line) condition. Line bars represent SE.

To explore the strength of sensory gain (Hillyard & Anllo-Vento, 1998; Mangun & Hillyard, 1991) as a function of cue predictiveness, this triple interaction was further explored in a series of Trial Type × Hemisphere ANOVAs that were run separately for the Pred and NoPred experimental conditions. In the Pred condition, a significant main effect of Trial Type, F(2, 32) = 6.6, p = .003, indicated the presence of a conventional “sensory gain” with higher P1 amplitude in response to valid and neutral than invalid targets (both ps < .001; valid: 0.38 μV ± 0.08 SEM; neutral: 0.36 μV ± 0.06 SEM; invalid: 0.24 μV ± 0.09 SEM) whereas no difference was present between valid and neutral targets (p = .5; see Figures 5 and 6). A main effect of hemisphere, F(1, 16) = 17.2, p = .0001, showed that, independently of trial type, the P1 was larger over the hemisphere contralateral to the no-target side (0.42 μV ± 0.07 SEM) than over the hemisphere contralateral to the target (0.23 μV ± 0.05 SEM, p = .0007; see Figures 4 and 5). Also in the NoPred condition, the P1 was generally larger over the hemisphere contralateral to the no-target side (0.37 μV ± 0.05 SEM) than over the hemisphere contralateral to the target (0.24 μV ± 0.0 SEM, p = .0006), F(1, 16) = 18.1, p = .0006 (see Figures 4 and 5). A significant Trial Type × Hemisphere interaction, F(2, 32) = 12.3, p = .0001, pointed out a conventional “sensory gain” over the hemisphere contralateral to the target, where the amplitude of the P1 was higher in response to valid (0.31 μV ± 0.07 SEM) than invalid targets (0.13 μV ± 0.08 SEM, p = .006) and in response to neutral targets (neutral: 0.29 μV ± 0.05 SEM) compared with invalid ones (p = .02). In striking contrast, a “reversed sensory gain” was present over the hemisphere contralateral to the no-target side, with higher P1 amplitude in response to invalid than valid targets (valid: 0.29 μV ± 0.06 SEM vs. invalid: 0.51 μV ± 0.08 SEM, p < .001; see Figures 4 and 5).

Figure 6. 

Target-related P1 waveforms collapsed across ipsilateral and contralateral hemispheres in response to valid (black line), neutral (gray line), and invalid (blue line) targets in the predictive (left) and nonpredictive (right) experimental condition.

Figure 6. 

Target-related P1 waveforms collapsed across ipsilateral and contralateral hemispheres in response to valid (black line), neutral (gray line), and invalid (blue line) targets in the predictive (left) and nonpredictive (right) experimental condition.

Inspection of P1 waveforms showed that with valid and neutral targets the amplitude of the P1 contralateral to the no-target side was higher in the Pred than in the NoPred condition, whereas with invalid targets, a reversed pattern was present with higher amplitude in the NoPred than in the Pred condition (see Figure 4). To evaluate the statistical significance of these effects, the initial triple Experimental Condition × Trial Type (valid, neutral, invalid) × Hemisphere interaction was explored further through a series of Experimental Condition × Trial Type × Hemisphere ANOVAs that separately contrasted valid with invalid, neutral with invalid, and valid with neutral trials. The Experimental Condition × Trial Type (valid, invalid) × Hemisphere interaction was significant, F(1, 16) = 8.4, p = .01, whereas the Experimental Condition × Trial Type (neutral, invalid) × Hemisphere interaction only approached significance, F(1, 16) = 3.3, p = .08, and the Experimental Condition × Trial Type (Valid, Neutral) × Hemisphere interaction was not significant, F(1, 16) = 0.2, p = .63. Planned comparisons showed that, like in Lasaponara et al. (2017), the amplitude of the P1 evoked by valid targets over the hemisphere contralateral to the no-target side was higher in the Pred than in the NoPred condition (Pred: 0.47 μV ± 0.05 SEM vs. NoPred: 0.29 μV ± 0.06 SEM, p = .01). In contrast, with invalid targets, a reversed pattern was present, and the amplitude of the P1 over the hemisphere contralateral to the no-target side was higher in the NoPred rather than in the Pred condition (Pred: 0.35 μV ± 0.03 SEM vs. NoPred: 0.51 μV ± 0.07 SEM, p = .03). No difference was observed with neutral targets (Pred: 0.44 μV ± 0.06 SEM vs. NoPred: 0.32 μV ± 0.05 SEM, p = .1).

Differences in the amplitude of the P1 between the hemisphere contralateral to the target and the one contralateral to the no-target side (see Figures 4 and 5) were tested through post hoc comparisons gathered from the significant triple Experimental Condition (Pred, NoPred) × Trial Type (valid, neutral, invalid) × Hemisphere interaction. These comparisons showed that, in the Pred condition, the P1 was significantly higher over the hemisphere contralateral to the no-target side in all types of trials (all ps < .01). In contrast in the NoPred condition, the P1 was higher over the hemisphere contralateral to the no-target side only in invalid trials (p < .001) though not in valid (p = .6) and neutral trials (p = .6).

P1 latency.

A significant Hemisphere effect, F(1, 16) = 128, p < .001, showed that the P1 peaked first over the hemisphere contralateral to the target and then over the hemisphere contralateral to the no-target side (contralateral: ∼164 msec ± 1.4 SEM vs. ipsilateral/no-target side: ∼180 msec ± 1.5 SEM). The Experimental Condition × Trial Type × Hemisphere interaction was also significant, F(2, 32) = 83.6, p < .001. This interaction highlighted that the peak latency of the P1 was generally shorter over the hemisphere contralateral to the target, with the notable exception of the peak latency evoked by invalid targets in the NoPred condition (182 msec ± 3.3 SEM; see Figure 4). In this latter case, the peak latency was longer as compared with valid (165 msec ± 1.3 SEM, p < .001) and neutral targets (161 msec ± 1.8 SEM, p < .001) and not different from the peak latency recorded over the hemisphere contralateral to the no-target side of space [188 msec ± 1.2 SEM, p = ns].

N1 amplitude.

A significant main effect of Trial Type, F(2, 32) = 4.9, p = .01, pointed out that both in the Pred and NoPred conditions, the amplitude of the N1 was higher with valid targets (−0.23 μV ± 0.11 SEM) rather than with invalid (0.11 μV ± 0.14 SEM, p = .004) or neutral ones (−0.10 μV ± 0.11 SEM, p = .05; see Figures 4 and 6). The attentional enhancement of N1 amplitude in a purely detection task is in agreement with data reported by Hietanen et al. (2008) with suprathreshold stimuli (like those used in our task) and by Luck et al. (1994) in near-threshold luminance detection, while it is not in line with initial observations by Mangun and Hillyard (1991), who found N1 enhancement in a discrimination task though not in a detection one. Because the study of N1 is not the main focus of the present investigation, here we shall not discuss this issue in more detail.

N1 latency.

A significant main effect of Trial Type, F(2, 32) = 42.3, p < .001, showed that the peak latency of the N1 evoked by invalid targets (226 msec ± 1.3 SEM) was delayed as compared with valid (219 msec ± 1.6 SEM, p < .001) and neutral (214 msec ± 1.7 SEM, p < .001) targets.

P3a and P3b.
P3a.

A significant main effect of Trial Type, F(2, 32) = 8.8, p = .0008, showed that the amplitude of the P3a was higher in response to invalid (2.3 μV ± 0.31 SEM) than valid (1.9 μV ± 0.34 SEM, p = .001) and neutral (1.8 μV ± 0.32 SEM, p = .005) targets (see Figure 7). The Experimental Condition × Trial Type interaction was also significant, F(2, 32) = 11.3, p = .0001, and demonstrated that invalid targets evoked a larger P3a in the Pred condition (invalid: 2.6 μV ± 0.34 SEM vs. valid: 1.8 μV ± 0.38 SEM, p < .001; invalid: 2.6 μV ± 0.34 SEM vs. neutral: 1.7 μV ± 0.37 SEM, p < .001) though not in the NoPred one (invalid: 2.1 μV ± 0.38 SEM vs. valid: 2 μV ± 0.36 SEM, p = .8; invalid: 2.1 μV ± 0.38 SEM vs. neutral: 1.9 μV ± 0.35 SEM, p = .5). This result is easily explained by the fact that, in the Pred condition, invalid targets were rarer and unexpected, that is, novel (20% of invalid trials vs. 80% of valid trials), than in the NoPred condition (50% of invalid trials and 50% of valid trials).

Figure 7. 

(A) Target-related P3a recorded over Cz for valid (black line), invalid (red line), and neutral targets (blue line) in the predictive (left side) and nonpredictive condition (right side) with corresponding scalp topographic maps. (B) Target related P3b recorded over Pz for valid (black line), invalid (red line), and neutral targets (blue line) in the predictive (left side) and nonpredictive condition (right side) with corresponding scalp topographic maps. P3a and P3b time windows are represented by gray squares (full square, significant difference between experimental conditions; empty square, nonsignificant difference).

Figure 7. 

(A) Target-related P3a recorded over Cz for valid (black line), invalid (red line), and neutral targets (blue line) in the predictive (left side) and nonpredictive condition (right side) with corresponding scalp topographic maps. (B) Target related P3b recorded over Pz for valid (black line), invalid (red line), and neutral targets (blue line) in the predictive (left side) and nonpredictive condition (right side) with corresponding scalp topographic maps. P3a and P3b time windows are represented by gray squares (full square, significant difference between experimental conditions; empty square, nonsignificant difference).

P3b.

A significant main effect of Experimental Condition, F(2, 32) = 4.6, p = .04, showed that, independently of trial type, the P3b was higher in the Pred (1.4 μV ± 0.16 SEM) than in the NoPred condition (1.1 μV ± 0.13 SEM, p = .04; see Figure 7).

To summarize, these results show that infrequent invalid targets in the NoPred condition evoke a stronger novelty reaction and a larger P3a (Knight & Scabini, 1998; see Lasaponara, Fortunato, et al., 2019, for corresponding findings with pupil dilation measures). These results will be not discussed further, as they are tangential to the main focus of our study.

DISCUSSION

Here, we investigated how changes in predictive cueing influence behavioral performance in a Posner task with central endogenous cues and examined corresponding changes in cue- and target-related ERPs. A special focus was devoted to the study of the target-related P1 inhibitory component.

The study of cue-related ERP components EDAN, ADAN, and LDAP basically replicated our previous observations in healthy participants (Lasaponara et al., 2011). In particular, nonpredictive cues increased the amplitude of the ADAN, which marks amodal mechanisms of attentional engagement in frontal areas (Praamstra et al., 2005; Eimer et al., 2002), and reduced the amplitude of the LDAP, which reflects preparatory/facilitatory effects in visual areas contralateral to the cued side of space (Hopf & Mangun, 2000; Harter et al., 1989) and the filtering out of uncued spatial positions (Lasaponara et al., 2011, 2017; Doricchi et al., 2009).

The first relevant target-related finding from this study was that the comparison between validly and invalidly cued targets highlighted “sensory gain” effects, so that the amplitude of the P1 and N1 components recorded over the hemisphere contralateral to the target was higher for valid than invalid targets. Analyses of RT performance showed that these effects were matched with the presence of attentional benefits and costs in the predictive condition and with the selective reduction of costs in the nonpredictive condition, thus replicating our previous results (Lasaponara et al., 2011, 2017). The second finding was that, like in Slagter et al. (2016), in valid trials the amplitude of the P1 was higher in the hemisphere contralateral to the no-target side of space. Nonetheless, and most importantly, our study disclosed the presence of the same hemispheric bias also with neutral and invalid targets. This finding suggests the presence of a default inhibitory hemispheric asymmetry (DIHA) that is triggered whenever a target occurs in one of the two lateral sides of space that compete for the capture of attention. These results were qualified further by the study of the effects of predictive cueing. As in Lasaponara et al. (2017), we found that, compared with the predictive condition, in the nonpredictive condition valid targets evoked a weaker inhibition of the no-target side of space, as signaled by a relative reduction in the amplitude of the P1 component over the hemisphere contralateral to the no-target side of space. A likely interpretation of this finding is that nonpredictive valid cues produce a poor filtering out of the uncued/no-target side of space and that this counteracts the strength of the inhibition of the same side that is triggered by target appearance at the cued side (Slagter et al., 2016). Poor filtering out of uncued spatial position with nonpredictive cues is suggested homogenously by fMRI, ERPs and pupil dilation studies (Lasaponara, Fortunato, et al., 2019; Dragone et al., 2018; Lasaponara et al., 2011, 2017; Shulman et al., 2010; Doricchi et al., 2009; Shulman, Astafiev, McAvoy, d'Avossa, & Corbetta, 2007; Vossel, Thiel, & Fink, 2006; for a review, see Macaluso & Doricchi, 2013). In the present investigation, reduced filtering-out during nonpredictive cuing was supported by a reduction in the amplitude of the cue-related LDAP component that reflects the ratio between the preparatory facilitation of sensory processing in the visual cortex contralateral to the cued side of space and the inhibition of the cortex contralateral to the uncued side. Nonetheless, the most interesting finding was that, contrary to valid and neutral trials, in invalid trials the amplitude of the P1 recorded over the hemisphere contralateral to the no-target side of space was larger with nonpredictive rather than with predictive cues. A plausible explanation of this latter finding is that, in the predictive condition, directional cues produced a strong preparatory facilitation of the cued side of space. As a consequence, on invalid trials, when the originally cued side corresponds to the no-target side of space, the strength of this preparatory facilitation counteracted the strength of the target-driven inhibition of the no-target side of space and the amplitude of the corresponding P1. In contrast, in the nonpredictive condition directional cues produced a poorer preparatory facilitation of the cued side of space. Therefore, on invalid trials the poor preparatory/excitatory facilitation of the cued/no-target side of space did not counteract the strength of the target-driven inhibition of the no-target side and the amplitude of the corresponding P1. Taken together, these results seem more compatible with the hypothesis that cue predictiveness modulates indirectly the strength of the inhibitory target-related P1 response by changing the level of preparatory facilitation of sensory processing in visual attentional areas.

To summarize, both in the case of valid and invalid trials, changes in the amplitude of the P1 over the hemisphere contralateral to the no-target side that are linked to changes in cue predictiveness seem to be well accounted by the interaction between the strength of facilitatory effects generated during the cue period and the inhibitory effects driven by target occurrence. In a previous investigation, we have found that the abatement of attentional costs in the nonpredictive condition was matched with a drop in the differential amplitude of the P1 between invalidly and neutrally cued targets (Lasaponara et al., 2011): The results of this study suggest that this drop is mainly due to an increase in the amplitude of the P1 over the hemisphere contralateral to the no-target side of space.

Our study also highlighted changes in the hemispheric distribution of the P1 latency peak as a function of predictive cueing and type of trial. In line with conventional findings (Hillyard & Anllo-Vento, 1998; Gomez Gonzalez et al., 1994), because of callosal transfer of visual input the P1 peaked first over the hemisphere contralateral to the target and ∼20–30 msec later over the hemisphere contralateral to the no-target side. This finding was present both with valid and neutral targets independently of cue predictiveness and with invalid targets in the predictive condition. In contrast, in the nonpredictive condition, the peak latency of the P1 that invalid targets evoked over the contralateral hemisphere was delayed and not different from the peak latency of the P1 over the hemisphere contralateral to the no-target side of space. A reasonable post hoc explanation for the delayed development of the P1-related inhibition over the hemisphere contralateral to the target is that this delayed inhibition favors reorienting of attention that, in fact, was faster than with predictive cueing. The mechanism subtending this phenomenon should be matter for future investigation. In a previous study (Lasaponara et al., 2017), we observed that changes in cue predictiveness did not produce change in the hemispheric distribution of P1 peak latencies evoked by valid targets. Based on this finding, we have concluded that cue predictiveness modulates the amplitude and latency of the P1 through signals that are sent from higher attentional areas to the P1 generators in each hemisphere rather than by changes in the interhemispheric competition between the P1 generators of the two hemispheres (see Slagter et al., 2016). Here, we have found that, when compared with the other types of trials and the predictive condition, in the nonpredictive condition invalid targets evoked a delayed P1 over the contralateral hemisphere without producing significant changes in latency over the hemisphere contralateral to the no-target side of space. Although more direct evidence is needed to draw final conclusions, this dissociation supports the idea that the influence of cue predictiveness on the P1 is set independently in each hemisphere.

Inspecting the Attentional Brain's Brake: A New Look at the P1

Taken together, the ERP results of our study allow to formulate three main conclusions that expand on the classic “sensory gain” view of the attentional inhibitory P1 component and refine its understanding. The conclusions are as follows:

  • 1. 

    When two spatial positions, one located in the left and one in the right side of space, compete for attentional capture, the simple occurrence of an attentional target in one of these positions produces a relative reduction in the amplitude of the inhibitory P1 over the hemisphere contralateral to the target and a corresponding relative enhancement of the same component over the hemisphere contralateral to the no-target side. This DIHA does not depend on lateral bias of attention, because in a task context characterized by predictive directional cues, it is also evoked by targets preceded by neutral nondirectional cues and comparable among valid, neutral, and invalid targets (see Figure 4 and 5).

  • 2. 

    Assuming a positive relationship between the target-related inhibition and the amplitude of the P1, our data suggest that cued attention sets the level of the P1 inhibitory response in both hemispheres. This is suggested by the observation that with predictive attentional cues, compared both to valid and neutral targets, the amplitude of the P1 evoked by invalid targets was reduced over both hemispheres rather than only over the hemisphere contralateral to the target (see Figures 4 and 5). This reduction is in line with original observations by Luck et al. (1994), although in this study we show, for the first time, that (a) it is present over both hemispheres, (b) it is superimposed on a DIHA, and (c) it does not affect the magnitude of the DIHA. General reduction of target-related inhibitory responses might help speeding up reorienting attention from the originally cued to the actual target position.

  • 3. 

    Changes in cue predictiveness modify the amplitude of the P1 over the hemisphere contralateral to the no-target side though not over the hemisphere contralateral to the target (see Figures 4 and 5). Put in other words, cue predictiveness modifies the DIHA by changing the level of the inhibition of the no-target side that is generated by the occurrence of the target in the opposite side. More specifically, compared with predictive cueing, nonpredictive cueing abolishes the DIHA with valid and neutral targets while it enhances the DIHA with invalid ones. The absence of a significant DIHA with neutral targets in the NoPred condition might suggest that the predictive context determined by directional cues has an impact on the distribution/division of attention induced by neutral nondirectional cues. This point deserves further investigation.

Deconstructing Reorienting of Attention and Improving Its Understanding

It has been variously suggested that reorienting of attention might imply multiple operations that take place at different moments following the occurrence of an invalid target (Macaluso & Doricchi, 2013; Doricchi et al., 2009; Corbetta, Patel, & Shulman, 2008). A first “early” operation is the inhibition of the attentional–spatial vector that was set during the cue period toward the invalidly cued side and the activation of the new correct vector toward the actual target side. In the monkey, this operation could be linked to the early activation of neurons in the areas of the posterior parietal cortex (area 7a and lateral intraparietal area; Corbetta et al., 2008; Constantinidis & Steinmetz, 2001, 2005; Bisley, Krishna, & Goldberg, 2004; Robinson, Bowman, & Kertzman, 1995) that are homologous to the intraparietal sulcus and superior parietal lobule in humans. Activation of the superior parietal lobule during reorienting of attention in human participants has been described both in task triggering exogenous reorienting (Dragone, Lasaponara, Silvetti, Macaluso, & Doricchi, 2015) and endogenous shifts of attention (shift-stay task; Capotosto et al., 2013). A second and temporally “late” operation is that of signaling the “mismatch” between the cued and the actual target position (Doricchi et al., 2009). Together with “match” signals triggered by valid targets, that is, when the cued target position corresponds to the actual one, “mismatch” signals triggered by invalid targets help keeping updated the brain representation of the probabilistic link between cue direction and target position (Geng & Vossel, 2013; Macaluso & Doricchi, 2013; Doricchi et al., 2009). Although previous findings have suggested a predominant response of the right TPJ to invalid targets (Corbetta & Shulman, 2002; though see Asplund, Todd, Snyder, & Marois, 2010; Serences et al., 2005) more recent observations have highlighted a left TPJ response to invalid targets (Silvetti et al., 2016; Doricchi et al., 2009), with a spatial preference for targets presented in the right side of space (Dragone et al., 2015). A series of investigations has also suggested that valid targets selectively engage the left TPJ and that this area would be primarily involved in the generation of “match” signals between the cued and the actual target position (Abrahamse & Silvetti, 2016; Silvetti et al., 2016; DiQuattro & Geng, 2011; Doricchi et al., 2009). Within this theoretical framework, we think that the results of the present study improve the understanding of the “early” mechanisms that participate in the reselection of the attentional vector toward invalidly cued targets. Our findings show that cue predictiveness modifies the speed of reorienting and that this is matched with specific modifications in the hemispheric distribution of the latency and amplitude of the P1 response evoked by invalid targets. These modifications reflect, in turn, changes in the strength of inhibition in the processing of the no-target side of space that was originally indicated by the invalid cue. The lack of similar modifications in the facilitatory N1 component suggests that the effects of cue predictiveness on the processing of invalid targets are mainly, if not exclusively, conveyed by variations in the inhibitory components of reorienting.

To conclude, the findings of this study exemplify how the experimental manipulation of cue predictiveness might serve as a tool to “deconstruct” reorienting of attention and improve the understanding of its functional and neural bases.

Acknowledgments

This work was supported by grants PRIN-MIUR 2017 (“EnvironMag” Project No. 2017XBJN4F) and Ricerche di Ateneo 2018 (Universita' La Sapienza) to F. D. S. L. was supported by a research grant from the Department of Psychology, Universita' La Sapienza, Rome, Italy. M. P. was supported by research grant from the Fondazione Santa Lucia IRCCS, Rome, Italy.

Reprint requests should be sent to Fabrizio Doricchi, Dipartimento di Psicologia 39, Università degli Studi di Roma “La Sapienza,” Via dei Marsi 78, 00185, Rome, Italy, or via e-mail: fabrizio.doricchi@uniroma1.it, or Stefano Lasaponara, Fondazione Santa Lucia, Centro Ricerche di Neuropsicologia, IRCCS, Via Ardeatina 306, 00179, Rome, Italy, or via e-mail: lasaponara.stefano@gmail.com.

REFERENCES

Abrahamse
,
E. L.
, &
Silvetti
,
M.
(
2016
).
Commentary: The role of the parietal cortex in the representation of task–reward associations
.
Frontiers in Human Neuroscience
,
10
,
192
.
Asplund
,
C. L.
,
Todd
,
J. J.
,
Snyder
,
A. P.
, &
Marois
,
R.
(
2010
).
A central role for the lateral prefrontal cortex in goal-directed and stimulus-driven attention
.
Nature Neuroscience
,
13
,
507
512
.
Bisley
,
J. W.
,
Krishna
,
B. S.
, &
Goldberg
,
M. E.
(
2004
).
A rapid and precise on-response in posterior parietal cortex
.
Journal of Neuroscience
,
24
,
1833
1838
.
Capotosto
,
P.
,
Babiloni
,
C.
,
Romani
,
G. L.
, &
Corbetta
,
M.
(
2009
).
Frontoparietal cortex controls spatial attention through modulation of anticipatory alpha rhythms
.
Journal of Neuroscience
,
29
,
5863
5872
.
Capotosto
,
P.
,
Tosoni
,
A.
,
Spadone
,
S.
,
Sestieri
,
C.
,
Perrucci
,
M. G.
,
Romani
,
G. L.
, et al
(
2013
).
Anatomical segregation of visual selection mechanisms in human parietal cortex
.
Journal of Neuroscience
,
33
,
6225
6229
.
Constantinidis
,
C.
, &
Steinmetz
,
M. A.
(
2001
).
Neuronal responses in area 7a to multiple stimulus displays: II. Responses are suppressed at the cued location
.
Cerebral Cortex
,
11
,
592
597
.
Constantinidis
,
C.
, &
Steinmetz
,
M. A.
(
2005
).
Posterior parietal cortex automatically encodes the location of salient stimuli
.
Journal Neuroscience
,
25
,
233
238
.
Corbetta
,
M.
,
Patel
,
G.
, &
Shulman
,
G. L.
(
2008
).
The reorienting system of the human brain: From environment to theory of mind
.
Neuron
,
58
,
306
324
.
Corbetta
,
M.
, &
Shulman
,
G. L.
(
2002
).
Control of goal-directed and stimulus-driven attention in the brain
.
Nature Reviews Neuroscience
,
3
,
201
.
Di Russo
,
F.
,
Aprile
,
T.
,
Spitoni
,
G.
, &
Spinelli
,
D.
(
2007
).
Impaired visual processing of contralesional stimuli in neglect patients: A visual-evoked potential study
.
Brain
,
131
,
842
854
.
DiQuattro
,
N. E.
, &
Geng
,
J. J.
(
2011
).
Contextual knowledge configures attentional control networks
.
Journal of Neuroscience
,
31
,
18026
18035
.
Doricchi
,
F.
,
Macci
,
E.
,
Silvetti
,
M.
, &
Macaluso
,
E.
(
2009
).
Neural correlates of the spatial and expectancy components of endogenous and stimulus-driven orienting of attention in the Posner task
.
Cerebral Cortex
,
20
,
1574
1585
.
Dragone
,
A.
,
Lasaponara
,
S.
,
Pinto
,
M.
,
Rotondaro
,
F.
,
De Luca
,
M.
, &
Doricchi
,
F.
(
2018
).
Expectancy modulates pupil size during endogenous orienting of spatial attention
.
Cortex
,
102
,
57
66
.
Dragone
,
A.
,
Lasaponara
,
S.
,
Silvetti
,
M.
,
Macaluso
,
E.
, &
Doricchi
,
F.
(
2015
).
Selective reorienting response of the left hemisphere to invalid visual targets in the right side of space: Relevance for the spatial neglect syndrome
.
Cortex
,
65
,
31
35
.
Eimer
,
M.
(
1993
).
Effects of attention and stimulus probability on ERPs in a go/nogo task
.
Biological Psychology
,
35
,
123
138
.
Eimer
,
M.
(
2014
).
The time course of spatial attention: Insights from event-related brain potentials
. In
A. C.
Nobre
&
S.
Kastner
(Eds.)
The Oxford handbook of attention
(
Vol. 1
, pp.
289
317
),
Oxford University Press
.
Eimer
,
M.
,
van Velzen
,
J.
, &
Driver
,
J.
(
2002
).
Cross-modal interactions between audition, touch, and vision in endogenous spatial attention: ERP evidence on preparatory states and sensory modulations
.
Journal of Cognitive Neuroscience
,
14
,
254
271
.
Geng
,
J. J.
, &
Vossel
,
S.
(
2013
).
Re-evaluating the role of TPJ in attentional control: Contextual updating?
Neuroscience & Biobehavioral Reviews
,
37
,
2608
2620
.
Geva
,
R.
,
Zivan
,
M.
,
Warsha
,
A.
, &
Olchik
,
D.
(
2013
).
Alerting, orienting or executive attention networks: Differential patters of pupil dilations
.
Frontiers in Behavioral Neuroscience
,
7
,
145
.
Giessing
,
C.
,
Thiel
,
C. M.
,
Rösler
,
F.
, &
Fink
,
G. R.
(
2006
).
The modulatory effects of nicotine on parietal cortex activity in a cued target detection task depend on cue reliability
.
Neuroscience
,
137
,
853
864
.
Gomez Gonzalez
,
C. M.
,
Clark
,
V. P.
,
Fan
,
S.
,
Luck
,
S. J.
, &
Hillyard
,
S. A.
(
1994
).
Sources of attention-sensitive visual event-related potentials
.
Brain Topography
,
7
,
41
51
.
Gratton
,
G.
,
Coles
,
M. G.
, &
Donchin
,
E.
(
1983
).
A new method for off-line removal of ocular artifact
.
Electroencephalography and Clinical Neurophysiology
,
55
,
468
484
.
Grent-'t-Jong
,
T.
, &
Woldorff
,
M. G.
(
2007
).
Timing and sequence of brain activity in top–down control of visual–spatial attention
.
PLoS Biology
,
5
,
e12
.
Harter
,
M. R.
,
Miller
,
S. L.
,
Price
,
N. J.
,
LaLonde
,
M. E.
, &
Keyes
,
A. L.
(
1989
).
Neural processes involved in directing attention
.
Journal of Cognitive Neuroscience
,
1
,
223
237
.
Hietanen
,
J. K.
,
Leppänen
,
J. M.
,
Nummenmaa
,
L.
, &
Astikainen
,
P.
(
2008
).
Visuospatial attention shifts by gaze and arrow cues: An ERP study
.
Brain Research
,
1215
,
123
136
.
Hillyard
,
S. A.
, &
Anllo-Vento
,
L.
(
1998
).
Event-related brain potentials in the study of visual selective attention
.
Proceedings of the National Academy of Sciences, U.S.A.
,
95
,
781
787
.
Hillyard
,
S. A.
,
Vogel
,
E. K.
, &
Luck
,
S. J.
(
1998
).
Sensory gain control (amplification) as a mechanism of selective attention: Electrophysiological and neuroimaging evidence
.
Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences
,
353
,
1257
1270
.
Hong
,
X.
,
Sun
,
J.
,
Bengson
,
J. J.
,
Mangun
,
G. R.
, &
Tong
,
S.
(
2015
).
Normal aging selectively diminishes alpha lateralization in visual spatial attention
.
Neuroimage
,
106
,
353
363
.
Hopf
,
J. M.
, &
Mangun
,
G. R.
(
2000
).
Shifting visual attention in space: An electrophysiological analysis using high spatial resolution mapping
.
Clinical Neurophysiology
,
111
,
1241
1257
.
Kelly
,
S. P.
,
Gomez-Ramirez
,
M.
, &
Foxe
,
J. J.
(
2009
).
The strength of anticipatory spatial biasing predicts target discrimination at attended locations: A high-density EEG study
.
European Journal of Neuroscience
,
30
,
2224
2234
.
Knight
,
R. T.
, &
Scabini
,
D.
(
1998
).
Anatomic bases of event-related potentials and their relationship to novelty detection in humans
.
Journal of Clinical Neurophysiology
,
15
,
3
13
.
Lasaponara
,
S.
,
Chica
,
A. B.
,
Lecce
,
F.
,
Lupianez
,
J.
, &
Doricchi
,
F.
(
2011
).
ERP evidence for selective drop in attentional costs in uncertain environments: Challenging a purely premotor account of covert orienting of attention
.
Neuropsychologia
,
49
,
2648
2657
.
Lasaponara
,
S.
,
D'Onofrio
,
M.
,
Dragone
,
A.
,
Pinto
,
M.
,
Caratelli
,
L.
, &
Doricchi
,
F.
(
2017
).
Changes in predictive cuing modulate the hemispheric distribution of the P1 inhibitory response to attentional targets
.
Neuropsychologia
,
99
,
156
164
.
Lasaponara
,
S.
,
Fortunato
,
G.
,
Dragone
,
A.
,
Pellegrino
,
M.
,
Marson
,
F.
,
Silvetti
,
M.
, et al
(
2019
).
Expectancy modulates pupil size both during endogenous orienting and during re-orienting of spatial attention: A study with isoluminant stimuli
.
European Journal of Neuroscience
,
50
,
2893
2904
.
Lasaponara
,
S.
,
Pinto
,
M.
,
Aiello
,
M.
,
Tomaiuolo
,
F.
, &
Doricchi
,
F.
(
2019
).
The hemispheric distribution of α-band EEG activity during orienting of attention in patients with reduced awareness of the left side of space (spatial neglect)
.
Journal of Neuroscience
,
39
,
4332
4343
.
Lins
,
O. G.
,
Picton
,
T. W.
,
Berg
,
P.
, &
Scherg
,
M.
(
1993
).
Ocular artifacts in EEG and event-related potentials I: Scalp topography
.
Brain Topography
,
6
,
51
63
.
Luck
,
S. J.
(
2014
).
An introduction to the event-related potential technique
.
Cambridge, MA
:
MIT Press
.
Luck
,
S. J.
,
Hillyard
,
S. A.
,
Mouloua
,
M.
,
Woldorff
,
M. G.
,
Clark
,
V. P.
, &
Hawkins
,
H. L.
(
1994
).
Effects of spatial cuing on luminance detectability: Psychophysical and electrophysiological evidence for early selection
.
Journal of Experimental Psychology: Human Perception and Performance
,
20
,
887
904
.
Macaluso
,
E.
, &
Doricchi
,
F.
(
2013
).
Attention and predictions: Control of spatial attention beyond the endogenous–exogenous dichotomy
.
Frontiers in Human Neuroscience
,
7
,
685
.
Mangun
,
G. R.
, &
Hillyard
,
S. A.
(
1991
).
Modulations of sensory-evoked brain potentials indicate changes in perceptual processing during visual–spatial priming
.
Journal of Experimental Psychology: Human Perception and Performance
,
17
,
1057
1074
.
Nobre
,
A. C.
,
Sebestyen
,
G. N.
, &
Miniussi
,
C.
(
2000
).
The dynamics of shifting visuospatial attention revealed by event related brain potentials
.
Neuropsychologia
,
38
,
964
974
.
Polich
,
J.
(
2007
).
Updating P300: An integrative theory of P3a and P3b
.
Clinical Neurophysiology
,
118
,
2128
2148
.
Posner
,
M. I.
(
1980
).
Orienting of attention
.
Quarterly Journal of Experimental Psychology
,
32
,
3
25
.
Praamstra
,
P.
,
Boutsen
,
L.
, &
Humphreys
,
G. W.
(
2005
).
Frontoparietal control of spatial attention and motor intention in human EEG
.
Journal of Neurophysiology
,
94
,
764
774
.
Robinson
,
D. L.
,
Bowman
,
E. M.
, &
Kertzman
,
C.
(
1995
).
Covert orienting of attention in macaques. II. Contributions of parietal cortex
.
Journal of Neurophysiology
,
74
,
698
721
.
Saevarsson
,
S.
,
Kristjánsson
,
Á.
,
Bach
,
M.
, &
Heinrich
,
S. P.
(
2012
).
P300 in neglect
.
Clinical Neurophysiology
,
123
,
496
506
.
Sauseng
,
P.
,
Klimesch
,
W.
,
Stadler
,
W.
,
Schabus
,
M.
,
Doppelmayr
,
M.
,
Hanslmayr
,
S.
, et al
(
2005
).
A shift of visual spatial attention is selectively associated with human EEG alpha activity
.
European Journal of Neuroscience
,
22
,
2917
2926
.
Schneider
,
W.
,
Eschmann
,
A.
, &
Zuccolotto
,
A.
(
2002
).
E-Prime (v1. 1)
.
Pittsburgh, PA
:
Psychology Software
.
Seiss
,
E.
,
Driver
,
J.
, &
Eimer
,
M.
(
2009
).
Effects of attentional filtering demands on preparatory ERPs elicited in a spatial cueing task
.
Clinical Neurophysiology
,
120
,
1087
1095
.
Serences
,
J. T.
,
Shomstein
,
S.
,
Leber
,
A. B.
,
Golay
,
X.
,
Egeth
,
H. E.
, &
Yantis
,
S.
(
2005
).
Coordination of voluntary and stimulus-driven attentional control in human cortex
.
Psychological Science
,
16
,
114
122
.
Shulman
,
G. L.
,
Astafiev
,
S. V.
,
McAvoy
,
M. P.
,
d'Avossa
,
G.
, &
Corbetta
,
M.
(
2007
).
Right TPJ deactivation during visual search: Functional significance and support for a filter hypothesis
.
Cerebral Cortex
,
17
,
2625
2633
.
Shulman
,
G. L.
,
Pope
,
D. L.
,
Astafiev
,
S. V.
,
McAvoy
,
M. P.
,
Snyder
,
A. Z.
, &
Corbetta
,
M.
(
2010
).
Right hemisphere dominance during spatial selective attention and target detection occurs outside the dorsal frontoparietal network
.
Journal of Neuroscience
,
30
,
3640
3651
.
Silvetti
,
M.
,
Lasaponara
,
S.
,
Lecce
,
F.
,
Dragone
,
A.
,
Macaluso
,
E.
, &
Doricchi
,
F.
(
2016
).
The response of the left ventral attentional system to invalid targets and its implication for the spatial neglect syndrome: A multivariate fMRI investigation
.
Cerebral Cortex
,
26
,
4551
4562
.
Slagter
,
H. A.
,
Prinssen
,
S.
,
Reteig
,
L. C.
, &
Mazaheri
,
A.
(
2016
).
Facilitation and inhibition in attention: Functional dissociation of pre-stimulus alpha activity, P1, and N1 components
.
Neuroimage
,
125
,
25
35
.
Thut
,
G.
,
Nietzel
,
A.
,
Brandt
,
S. A.
, &
Pascual-Leone
,
A.
(
2006
).
α-Band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection
.
Journal of Neuroscience
,
26
,
9494
9502
.
Van der Stigchel
,
S.
,
Heslenfeld
,
D. J.
, &
Theeuwes
,
J.
(
2006
).
An ERP study of preparatory and inhibitory mechanisms in a cued saccade task
.
Brain Research
,
1105
,
32
45
.
van Velzen
,
J.
, &
Eimer
,
M.
(
2003
).
Early posterior ERP components do not reflect the control of attentional shifts toward expected peripheral events
.
Psychophysiology
,
40
,
827
831
.
Vossel
,
S.
,
Thiel
,
C. M.
, &
Fink
,
G. R.
(
2006
).
Cue validity modulates the neural correlates of covert endogenous orienting of attention in parietal and frontal cortex
.
Neuroimage
,
32
,
1257
1264
.
Yamagishi
,
N.
,
Goda
,
N.
,
Callan
,
D. E.
,
Anderson
,
S. J.
, &
Kawato
,
M.
(
2005
).
Attentional shifts towards an expected visual target alter the level of alpha-band oscillatory activity in the human calcarine cortex
.
Cognitive Brain Research
,
25
,
799
809
.