Neuropsychologia 51 (2013) 1838–1847

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Neuropsychologia

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Differential neural mechanisms underlying exogenous attentionto peripheral and central distracters

Luis Carretié a,n, Jacobo Albert b, Sara López-Martín a, Sandra Hoyos a, Dominique Kessel a,Manuel Tapia a, Almudena Capilla a

a Facultad de Psicología, Universidad Autónoma de Madrid, 28049 Madrid, Spainb Instituto Pluridisciplinar, Universidad Complutense de Madrid, 28040 Madrid, Spain

a r t i c l e i n f o

Article history:Received 5 March 2013Received in revised form26 May 2013Accepted 21 June 2013Available online 29 June 2013

Keywords:Distracter eccentricityN2/P3Biological saliencyDorsal attention networkDefault mode networkVentral/polar cortex

32/$ - see front matter & 2013 Elsevier Ltd. Ax.doi.org/10.1016/j.neuropsychologia.2013.06.0

esponding author. Tel.: +34 914975177.ail address: carretie@uam.es (L. Carretié).

a b s t r a c t

Mechanisms underlying exogenous attention to central and peripheral distracters were temporally andspatially explored while 30 participants performed a digit categorization task. Neural (event-relatedpotentials—ERPs-, analyzed both at the scalp and at the voxel level) and behavioral indices of exogenousattention were analyzed. Distracters were either biologically salient or neutral, in order to test whetherthe exogenous attention bias to the former observed in previous studies is independent of, or interactswith, distracter eccentricity. Two subcomponents of the N2 component of the ERPs, N2olp and N2ft,reflected processes related to peripheral distracters processing. N2olp effects, located in the dorsalattention network (supplementary motor area), were probably related to covert reorientation toperipheral distracters. N2ft effects, located in the default mode network (posterior cingulate cortex),appeared to reflect less effort in the ongoing task when peripheral distracters were presented. N2ft alsoshowed a biological saliency effect which was independent of eccentricity and was located in the polar/ventral prefrontal cortex. P3 showed greater amplitudes to centrally presented distracters. These lattereffects were located in TEO (visual cortex), and would be functionally associated with spatial interferencebetween the target and central distracters. Behavior showed the relevance of both central and peripheraldistracters in exogenous attention. These results indicate that exogenous attention to peripheraldistracters differed in temporal and spatial terms from exogenous attention to central distracters andthat it is biased towards biologically salient events irrespective of their eccentricity.

& 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Exogenous attention, also named automatic, stimulus-driven orbottom-up attention, among other terms, is the process by which adistracter automatically captures our attention diverting it fromthe task where endogenous (voluntary, controlled or top-down)attention is directed. In other words, events capturing exogenousattention appear outside the current focus of processing. In manysituations, they also appear out of the focus of gaze, projecting tonon-foveal areas of the retina. In these areas, perception is muchpoorer than in fovea, mainly due to the fact that density of cones(a type of retinal photoreceptor which is responsible for detectingvisual information in daylight; e.g., Sterling, 2003) progressivelyand dramatically drops as eccentricity from fovea increases (morethan 10 times in only 5 mm eccentricity; e.g., Jonas, Schneider, &Naumann, 1992; Packer, Hendrickson, & Curcio, 1989). The con-sequence is that each peripheral cone covers a much wider area of

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the visual field than each foveal cone, and details (i.e., high spatialfrequencies) are lost. At the brain level, central vision is repre-sented more densely in the parvocellular layers of the lateralgeniculate nucleus than in the magnocellular layers, while mag-nocellular density declines more slowly with eccentricity thandoes parvocellular density (Brown, Halpert, & Goodale, 2005).Consequently, parvocellular information is richer in color anddetails than magnocellular. Therefore, processing resources needto be reoriented to the peripheral event in order to be betterperceived.

In these cases, exogenous attention should be specially linkedto the neural mechanisms involved in the reorientation of gaze,head or even body. Indeed, one of the main brain circuits under-lying exogenous attention, the dorsal attention network (DAN),engages several superior parietal and dorso-frontal areas that arecritical for organizing and controlling eye movements, as well asbody reorientation, such as the frontal eye fields, parietal eyefields, or motor and premotor cortices (see reviews in Corbetta,Patel, & Shulman, 2008; Pierrot-Deseilligny, Milea, & Müri, 2004;Posner, Rueda, & Kanske, 2007; Smith & Schenk, 2012). Impor-tantly, areas associated with motor reorienting are recruited even

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in covert attention tasks (i.e., those in which attention, but notgaze, must be directed to the peripheral stimulus: Grosbras, Laird,& Paus, 2005).

Nevertheless, these motor reorienting mechanisms are notinherent in exogenous attention. Sometimes, events automaticallycapturing attention are not peripherally located. They might bedetected, for example, close to the currently attended stimulus, orin central positions at different planes (i.e., behind or in front ofthe attended stimulus). These situations do not necessarily requiremotor reorientation (i.e., redirection of body, head or even gaze) toprocess the distracter. However, these central distracters wouldtrigger specific mechanisms that are absent or less prominent inthe case of peripheral distracters. For example, they might be moredifficult to be ignored /perceptually ‘suppressed’ (in case they areconsidered irrelevant) or exogenously attended (in case they areconsidered relevant) than peripheral distracters, since they spa-tially compete with the endogenously attended target. Spatialcompetition causes a complex “push–pull” or “biased competition”pattern of neural activity in extrastriste visual areas, mainly at thestages of the visual ventral pathway where receptive fields ofindividual neurons are larger (i.e, V4 and TEO; for a characteriza-tion of these two areas in the human brain, see Kastner et al.,2001). This pattern favours the inhibition of the distracter whileenhancing the processing of the target (Beck & Kastner, 2005;Desimone, 1998; Kastner & Pinsk, 2004; Kastner & Ungerleider,2000; Miller, Gochin, & Gross, 1993; Reynolds, Pasternak, &Desimone, 2000).

Based on the above, it seems reasonable to hypothesize that theneural mechanisms underlying exogenous attention to peripheraldistracters might be different from those associated with centraldistracters. However, evidence of this dissociation is only indirectand scarce up to date. In the present study, and with the aim atexploring this question, distracters were presented at differenteccentricities while participants were engaged in a centrally-located task. Targets (task-relevant elements of the stimuli) loca-tion was explicitly described in task instructions, and never over-lapped distracters. In the visual domain, temporally agile neuralsignals such as event-related potentials (ERPs) have consistentlypointed to N2 as a reliable index of exogenous attention tovisual stimuli (Carretié, Hinojosa, Martín-Loeches, Mercado, &Tapia, 2004a; Daffner et al., 2000; Folstein & Van Petten, 2008;Kenemans, Verbaten, Roelofs, & Slangen, 1989; Kenemans,Verbaten, Melis, & Slangen, 1992; Pazo-Álvarez, Cadaveira, &Amenedo, 2003; Rozenkrants & Polich, 2008). However, the exactfunctional nature of the exogenous attention-related processesreflected in N2 is still an open issue, due in part to the limited dataon its neural origin. Particularly, this study explored the contribu-tion of covert motor reorienting (triggered in the case peripheraldistracters) and sensorial competition (present in the case ofcentral distracters) to N2 amplitude, making use of source locali-zation algorithms in order to elucidate the neural origin of theobserved effects.

In addition to the spatial location of distracters, their biologicalsaliency is an important factor modulating exogenous attention,and both factors could interact. For example, stimuli showingthreatening value enhance neural indices of attentional capture(Carretié et al., 2004a, 2009b; Carretié, Ruiz-Padial, López-Martín,& Albert, 2011; Constantine, McNally, & Hornig, 2001; Doallo,Holguin, & Cadaveira, 2006; Eastwood, Smilek, & Merikle, 2003;Huang & Luo, 2007; Pourtois, Grandjean, Sander, & Vuilleumier,2004; Thomas, Johnstone, & Gonsalvez, 2007; Yuan et al., 2007;Vuilleumier, Armony, Driver, & Dolan, 2001). A question that arisesat this point is whether our nervous system is able to evaluatethe biological relevance of peripheral/poorly perceived stimuli(i.e., mainly magnocellular information). Several data suggest thatthis might be the case. On one hand, categorization of stimuli

(indicating whether it contains an animal or not) is possible evenat 70.51 of eccentricity with respect to fixation (Thorpe,Gegenfurtner, Fabre-Thorpe, & Bülthoff, 2001). On the other hand,processing of pictures in which high spatial frequencies and colorinformation have been eliminated, but centrally presented, isenhanced for biologically salient stimuli with respect to neutral(Alorda, Serrano-Pedraza, Campos-Bueno, Sierra-Vázquez, &Montoya, 2007; Carretié, Hinojosa, López-Martín, & Tapia, 2007;Vuilleumier, Armony, Driver, & Dolan, 2003). Hence, from thisindirect evidence as well as from an evolutionary point of view, itwould be reasonable to expect that biological saliency should bedetected even for distracters appearing in the periphery. However,the existing literature on this issue does not provide convergentresults. While some data show that these stimuli evoke greaterattention-related signals than neutral stimuli independently oftheir eccentricity (emotional facial expressions: Bayle, Henaff, &Krolak-Salmon, 2009; Rigoulot, D'Hondt, Defoort-Dhellemmes,Despretz, & Honoré, 2011; Rigoulot, D'hondt, Honore, & Sequeira,2012; affective non-facial scenes: Calvo, Nummenmaa, & Hyönä,2008), others report no differences between biologically salientand neutral distracters when presented in the periphery(i.e., at48.211: De Cesarei, Codispoti, & Schupp, 2009, employingaffective scenes as stimuli). To shed light on this question, bothneutral and biologically salient distracters were included in theexperimental design (non-facial distracters in both cases, sinceprevious literature is specially divergent in this case; see Carretiéet al., in press, for a comparison on the effects of facial and non-facial biologically salient stimuli on exogenous attention). In sum,this study aimed to investigate the neural mechanisms underlyingexogenous attention to distracters presented at different eccentri-cities, and whether this factor interacts with biological saliency ofdistracters.

2. Material and methods

2.1. Participants

Thirty-five individuals participated in this experiment, although data from only30 of them could eventually be analyzed, as explained later (26 women, age rangeof 17 to 28 years, mean¼19.65, SD¼2.39). The study had been approved by theUniversidad Autónoma de Madrid's Ethics Committee. All participants werestudents of Psychology at that university and took part in the experimentvoluntarily after providing informed consent. They reported normal or corrected-to-normal visual acuity.

2.2. Stimuli and procedure

Participants were placed in an electrically shielded, sound-attenuated room,and stimuli were presented on a back-projection screen through a RGB projector.They were asked to place their chin on a chinrest maintained at a fixed distancefrom the screen throughout the experiment. According to the type of distracter,seven types of stimuli were presented to participants: no distracter (D0), biologi-cally salient distracter proximal (Sprox), neutral distracter proximal (Nprox),biologically salient distracter medium eccentricity (Smed), neutral distractermedium angle (Nmed), biologically salient distracter distant (Sdist), and neutraldistracter distant (Ndist). The size for all distracters was 5.151 (width). Proximaldistracters had their inner border at 01, horizontally, from the center of the screen,medium eccentricity distracters at 11.291, and distant distracters at 30.061.Biologically salient distracters consisted of spiders, and neutral consisted of wheels,both having similar spatial frequencies (see examples in Fig. 1 and in the web linkmentioned below). All distracters were black-coloured. They were presentedbilaterally (symmetrically) in order to ensure that both eyes perceived them(peripheral distracters may project to the blindspot of the contralateral eye).Moreover, bilateral distracters have been shown to elicit haemodynamic responsesas strong as those elicited by unilateral distracters throughout the encephalon inwhole brain analyses -even in visual cortices- except in the vicinity of temporo-parietal junction (Schwartz et al., 2005).

Each of these stimuli contained two red central digits (1.331 height each), oneabove the other, their inner borders being at 2.411, vertically, from the center of thescreen. Each stimulus was displayed on the screen for 350 ms, and stimulus onsetasynchrony was 3000 ms. The task was related to the central digits: participants

Fig. 1. Examples of stimuli belonging to several experimental conditions. Only “discordant” examples are shown. D0 (no distracter) stimuli consisted of the two reddigits alone.

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were required to press, “as accurately and rapidly as possible”, one key if both digitswere even or if both were odd (i.e., if they were “concordant”), and a different key ifone central digit was even and the other was odd (i.e., if they were “discordant”).There were 32 combinations of digits, half of them concordant and the other halfdiscordant. The same combination of digits was repeated across all eccentricity andbiological saliency conditions in order to ensure that task demands were the samefor the seven conditions. These 32�7 (224) trials of each type were presented intwo runs separated by a rest period. Stimuli were presented in semi-random orderin such a way that there were never more than five consecutive trials for the samedistracter or numerical category. Participants were instructed to look continuouslyat a fixation mark located in the center of the screen and to blink preferably after abeep that sounded 1300 ms after each stimulus onset. An animation reproducingthe stimulation sequence with examples of all experimental conditions is shown inhttp://www.uam.es/CEACO/sup/visualangle12.htm.

2.3. Recording and pre-processing

Electroencephalographic (EEG) activity was recorded using an electrode cap(ElectroCap International) with tin electrodes. Fifty-nine electrodes were placed atthe scalp following a homogeneous distribution. All scalp electrodes were refer-enced to the nosetip. Electrooculographic (EOG) data were recorded supra- andinfraorbitally (vertical EOG) as well as from the left versus right orbital rim(horizontal EOG). An online analog bandpass filter of 0.3 Hz to 10 kHz was applied.Recordings were continuously digitized at a sampling rate of 420 Hz. The contin-uous recording was divided into 1000 ms epochs for each trial, beginning 200 msbefore stimulus onset. Behavioral activity was recorded by means of a two-buttonkeypad whose electrical output was also continuously digitized at a sampling rateof 420 Hz. An offline digital bandpass filter of 0.3 to 30 Hz was applied using theFieldtrip software (http://fieldtrip.fcdonders.nl; Oostenveld, Fries, Maris, &Schoffelen, 2011).

Trials for which subjects responded erroneously or did not respond wereeliminated. Ocular artifact removal was carried out through an independentcomponent analysis (ICA)-based strategy (see a description of this procedure andits advantages over traditional regression/covariance methods in Jung et al., 2000),as provided in Fieldtrip. After the ICA-based removing process, a second stage ofvisual inspection of the EEG data was conducted. If any further artifact was present,the corresponding trial was discarded. This incorrect response and artifact rejectionprocedure led to the average admission of 90.21% D0 trials, 91.15% Nprox, 90.94%Nmed, 90.10% Ndist, 90.94% Sprox, 91.87% Smed, and 89.06% Sdist. The minimumnumber of trials accepted for averaging was 75% (24 trials) per participant andcondition. Data from one participant were eliminated since he/she did not meetthis criterion. The rest of non-analyzed participants (four) presented non solvableanomalies in the recordings of one or more EEG leads (the integrity of all channelswas necessary for spatial analyses described later).

2.4. Data analysis

2.4.1. Detection, spatio-temporal characterization, and quantification of relevant ERPcomponents

Detection and quantification of N2 and other relevant ERP components wascarried out through a covariance-matrix-based temporal principal componentsanalysis (tPCA), a strategy that has repeatedly been recommended for thesepurposes (e.g., Chapman, Hoag, & Giaschi, 2004; Chapman & McCrary, 1995;

Dien, 2010). In brief, tPCA computes the covariance between all ERP time points,which tend to be high between those involved in the same component and lowbetween those belonging to different components. Temporal factor score, the tPCA-derived parameter in which extracted temporal factors can be quantified, is linearlyrelated to amplitude. The decision on the number of factors to select was based onthe scree test (Cliff, 1987). Extracted factors were submitted to promax rotation(Dien, 2010; 2012).

Once quantified in temporal terms, and prior to statistical contrasts onexperimental effects, N2 (and other relevant components) topography at the scalplevel was decomposed into its main spatial regions via a spatial PCA (sPCA)performed on N2 (and other relevant components) temporal factor scores. Thisspatial decomposition is an advisable strategy prior to statistical contrasts, sinceERP components frequently behave differently in some scalp areas than they do inothers (e.g., they present opposite polarity or react heterogeneously to experi-mental manipulations). sPCA provides a reliable division of the scalp into differentregions or spatial factors. Basically, each spatial factor is formed with the scalppoints where recordings tend to covary. Moreover, each spatial factor can bequantified through the spatial factor score, a single parameter that reflects theamplitude of the whole spatial factor. Also in this case, the decision on the numberof factors to select was based on the scree test, and extracted factors weresubmitted to promax rotation.

2.4.2. Analyses on experimental effectsIn the contrasts described below, the Huynh-Feldt (HF) epsilon correction was

applied to adjust degrees of freedom where necessary. Effect sizes were computedusing the partial eta-square (ƞ2p) method. Post-hoc comparisons to determine thesignificance of pairwise contrasts were performed using the Bonferroni correctionprocedure. The specific characteristics of the analyses according to the nature of thedependent variable were as follows.

(i)

Behavior. Performance in the digit categorization task was analyzed. To thisend, reaction times (RTs) and error rate (percentage of incorrect responses) inresponse to each of the seven conditions were submitted to non-parametriccontrasts, because these two variables were not normally distributed (RTs:SW¼0.976, po0.01; errors: SW¼0.913, po0.001). The main scope of thisanalysis was to test whether distracter conditions (Nprox, Sprox, Nmed, Smed,Ndist, Sdist) showed behavioral indices of exogenous attention when com-pared with the non-distracter condition (D0). In the case of RTs, outliers,defined as responses over 2000 or below 200 ms, were omitted in theanalyses.

(ii)

Scalp amplitudes (2D). Experimental effects on N2 and other relevantcomponents at the scalp level (2D) were also tested. Eccentricity (Proximal,Medial, Distal) and Biological Saliency (Neutral, Salient) were introduced asfactors in a repeated-measures ANOVA. Prior to analyses, amplitudes inresponse to D0 were subtracted from the six distracter conditions in everysubject in order to discount the effect of target.

(iii)

Source (3D) analyses. Temporal factor scores corresponding to relevantcomponents were submitted to exact low-resolution brain electromagnetictomography (eLORETA). eLORETA is a 3D, discrete linear solution for the EEGinverse problem (Pascual-Marqui, 2002). Although solutions provided by EEG-based source-location algorithms should be interpreted with caution due totheir potential error margins, the use of tPCA-derived factor scores instead ofdirect voltages (which leads to more accurate source-localization analyses:Carretié et al., 2004b; Dien, Spencer, & Donchin, 2003) and the relatively large

Fig.distrmed

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sample size employed in the present study (n¼30) contribute to reducingsuch error margins. Pairwise contrasts at the voxel level between stimulusconditions showing significant differences at the scalp level were computed inorder to detect potential sources of variability using the non-parametricmapping (SnPM) tool provided by eLORETA (Nichols & Holmes, 2001). Inorder to further explore these differences through full factorial contrasts,regions of interest (ROIs) were defined for those sources, and their currentdensities were exported and submitted to a repeated-measures ANOVA usingEccentricity (Proximal, Medial, Distal) and Biological Saliency (Neutral, Salient)as factors. Also in this case and prior to ANOVAs, amplitudes in response to D0were subtracted from the rest of conditions in every subject in order todiscount the effect of target.

3. Results

3.1. Detection, spatiotemporal characterization and quantification ofERP components

Fig. 2 shows a selection of grand averages after subtracting thebaseline (prestimulus) activity from each ERP. These grandaverages correspond to midline frontal and lateral parieto-occipital areas, where the experimental effects (discussed later)were most prominent. An important pattern of response alreadyobservable in the grand averages is that N2 can be decomposed intwo different components labeled N2olp and N2ft in this study.The former was maximal at occipital and lateral parietal regions(olp), and its latency was slightly shorter than that of N2ft,maximal at frontal and temporal areas (ft) (see also maps inFig. 3). Additionally, the wave labeled as ‘P3’ in the grand averages,presenting the typical posterior distribution and following N2, alsosuggests sensitivity to the experimental manipulations. As aconsequence, this component was also analyzed.

The first analytical step consisted in detecting and quantifyingthese components through tPCA (see section on Data Analysis).Seven temporal factors (TF) were extracted by tPCA and submittedto promax rotation (Fig. 3). Factor peak-latency and topography

2. Grand averages at frontal and lateral parietooccipital areas, where the experimacter, Nprox¼neutral distracter proximal, Nmed¼neutral medium angle, Ndist¼ium angle, Sdist¼biologically salient distant.

characteristics revealed TF6, TF8, and TF1 as the critical components,since they were associated with N2olp and N2ft and P3, respectively(Fig. 2). These labels will be employed hereafter to make resultseasier to understand. sPCA applied to the three temporal factorsdefined five different spatial factors (SFs) or regions in each case. TheSF score (equivalent to amplitude, as previously explained) of each SFwas extracted per subject and condition.

3.2. Experimental effects on scalp N2olp, N2ft, and P3 (2D)

As explained above, repeated-measures ANOVAs on Eccentri-city (Prox, Med, Dist)�Biological Saliency (Neutral, Salient) werecarried out for each spatial factor within each temporal compo-nent (N2olp, N2ft and P3), once factor scores to D0 were sub-tracted from all conditions. According to our main objective,relevant SFs were those in which significant central vs peripheraleffects were observed. Table 1 shows means and standard error ofthe means for N2olp, N2ft and P3 relevant SF scores in the sixdistracter conditions once those in response to D0 (no distracter)were subtracted.

3.2.1. N2olpANOVAs yielded significant results in SF5, presenting bilateral

distribution (Fig. 4). Concretely, main effects of Eccentricity wereobserved (F(2,58)¼12.262, po0.001, ƞ2p¼0.297), maximal ampli-tudes being elicited by Med and Dist eccentricities, which sig-nificantly differed from Prox distracters. Neither the main effect ofBiological Saliency nor the interaction Eccentricity�BiologicalSaliency were significant.

3.2.2. N2ftMain effects of Biological Saliency (F(1,29)¼6.318, po0.05,

ƞ2p¼0.179) and Eccentricity (F(2,58)¼21.732, G−G po0.001, ƞ2p¼0.428), but not the interaction, were patent in SF5, characterized by

ental effects are prominent. The tree relevant components are signaled. D0¼noneutral distant, Sprox¼biologically salient proximal, Smed¼biologically salient

Fig. 3. Temporal factors extracted by tPCA. The three relevant factors are drawn in red, blue and black. Their factor scores in the form of scalp maps are also represented; bluemeans negative amplitudes and red means positive amplitudes (maps elaborated through eLoreta software: http://www.uzh.ch/keyinst/loreta.htm). (For interpretation ofthe references to color in this figure legend, the reader is referred to the web version of this article.)

Table 1Means and standard error of the means (in parenthesis) of scalp neural responses (factor scores, linearly related to amplitudes) to each distracter type, once responses to thenon-distracter condition (D0) was subtracted to each of them. SF¼spatial factor.

Neutral Biologically salient

Proximal Medial Distal Proximal Medial Distal

N2olp (SF5) 0.536 (0.169) −0.180 (0.179) −0.070 (0.192) 0.346 (0.161) −0.098 (0.146) −0.052 (0.189)N2ft (SF5) −0.650 (0.146) 0.028 (0.184) 0.034 (0.147) −0.829 (0.195) −0.205 (0.148) −0.147 (0.129)P3 (SF4) 0.873 (0.228) 0.254 (0.187) 0.274 (0.174) 0.501 (0.235) 0.374 (0.168) 0.251 (0.217)

Table 2Means and standard error of the means (in parenthesis) of behavioral responses (reaction times –RTs- and error rate) to each experimental condition.

No distracter Neutral Biologically salient

Proximal Medial Distal Proximal Medial Distal

RTs (ms) 943.276 (32.388) 987.262 (30.789) 961.001 (33.283) 983.092 (30.512) 968.135 (29.969) 964.138 (30.854) 962.946 (33.417)Error rate (%) 7.292 (1.220) 6.250 (1.016) 6.563 (0.852) 6.458 (0.959) 6.667 (1.098) 6.667 (0.991) 7.083 1.048

Fig. 4. Loads of relevant spatial factors in the form of scalp maps in order to illustrate their topography (the more intense the red color, the higher the load).(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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a central/midline topography (Fig. 4). In the case of Biological Saliency,amplitudes elicited by salient distracters were greater than thoseelicited by neutral distracters. In the case of Eccentricity, post-hoc testsshowed that the maximal amplitudes (i.e., the most N2ft negativevalues) were elicited by Prox distracters, which significantly differedfrom Med and Dist distracters.

3.2.3. P3ANOVAs yielded significant results in SF4, which presented

bilateral distribution (Fig. 4). Differences were produced withrespect to Eccentricity (F(2,58)¼5.257, po0.01, ƞ2p¼0.153).Maximal amplitudes were elicited by Prox distracters, whichsignificantly differed from Med and Dist distracters according topost-hoc contrasts. No main effects of Biological Saliency nor theinteraction Eccentricity�Biological Saliency were observed.

3.3. Experimental effects on 3D sources

3.3.1. Region-of-interest (ROI) analysesFirst, and in order to localize the effects observed at the surface

level, eLoreta maps were computed for N2olp, N2ft and P3temporal factor scores per subject and condition. Subsequently,and as explained in the Methods section, several ROIs weredefined following data-driven criteria. In the case of N2olp, in

Fig. 5. ROIs in which current densities were sensitive to the experimental conditiondistracters; Med¼medium angle distracters; Dist¼maximal angle distracters). Error ba

which main effects of Eccentricity (Prox distracters eliciting mini-mum amplitudes) were observed at the surface level, a ROI wasdefined as those voxels in which maximal ProxoDist currentdensities were observed in the SnPM contrasting tool provided byeLoreta (in which only pairwise contrasts are possible). Withrespect to N2ft, ROIs were those comprising voxels showing thegreatest Sal4Neu and Prox4Dist differences, since main effectsof both Eccentricity and Biological Saliency were significant at thesurface level. Finally, P3 showed Eccentricity effects (proximaldistracters eliciting the greatest amplitudes) at the scalp level, soROIs were configured including those voxels showing maximalProx4Dist current densities.

Fig. 5 shows the ROIs obtained in the three cases. Currentdensities within these ROIs in every subject and condition werethen quantified through the ad hoc tool provided by eLoreta.Afterwards, and in order to submit these current densities toricher, full factorial statistical constrasts, they were exported andsubmitted to ANOVAs using Eccentricity (Prox, Med, Dist) andBiological Saliency (Neutral, Salient) as factors after subtractingcurrent densities elicited by D0 condition.

3.3.2. N2olpA ROI was defined in BA6 (Fig. 5), involving those voxels in

which maximal ProxoDist differential current densities had been

s. Average current densities for each condition are also shown (Prox¼proximalrs represent standard error of the means.

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observed. The main effect of Eccentricity was confirmed byANOVAs to be significant in this ROI (F(2,58)¼5.456, GG correctedpo0.005, ƞ2p¼0.158). Post-hoc analyses carried out through theBonferroni procedure indicated that significant differences wereproduced between Prox and Dist conditions (Fig. 5). No effect ofBiological Saliency (Neutral, Salient) nor the interaction betweenEccentricity and Biological Saliency were observed (p40.05).

3.3.3. N2ftFirst, the ROI corresponding to voxels showing maximal

Sal4Neu differences was located at BAs 10, 11 and 47 (Fig. 5).The ANOVA on this ROI's current densities revealed a significanteffect of Biological Saliency (F(1,19)¼4.354, po0.05, ƞ2p¼0.131).Maximal current densities were elicited by salient distracters.Main effect of Eccentricity was not significant. Second, anotherROI was defined for those voxels showing maximal Prox4Distdifferential current densities. As shown in Fig. 5, this ROI waslocated in the posterior parietal cortex (PCC; BA31). ANOVAs on itscurrent densities showed a main effect of Eccentricity (F(2,58)¼4.525, GG epsilon corrected p40.025, ƞ2p¼0.135). Post-hoc testsshowed differences between Prox and Dist conditions (Med vs Distdifferences being marginally significant: p¼0.054).

3.3.4. P3Inferior temporal cortex, comprising middle and inferior tem-

poral gyri (BA 20, 21 and 37) formed the relevant ROI (Fig. 5),which involved those voxels in which maximal Prox4Dist differ-ential current densities were observed. Main effects of Eccentricitywere confirmed by ANOVAs to be significant in this ROI (F(2, 58)¼3.486, GG corrected po0.05, ƞ2p¼0.107). Post-hoc analyses carriedout through the Bonferroni procedure indicated that significantdifferences were produced between Prox and Dist conditions(Fig. 5). No effects of Biological Saliency nor the interactionbetween Eccentricity and Biological Saliency were observed(p40.05).

3.4. Experimental effects on behavior

Error rate and RTs in the digit categorization task are shown inTable 2. Non-parametric contrasts were performed due to non-normality (see section on Data Analysis). First, the seven condi-tions (D0, Nprox, Sprox, Nmed, Smed, Ndist, Sdist) were submittedto a Friedman contrast. Differences between conditions weresignificant in the case of RTs (chi2¼18.629, po0.01), but not inthe case of errors (p40.05). Subsequently, pairwise contrasts onRTs were tested through the Wilcoxon method. On the one hand,and with the aim of testing whether distracters elicited a sig-nificant effect, D0 was compared with the average of the distracterconditions, differences being significant (Z¼−3.198, po0.001). Onthe other hand, each of the six conditions presenting distracterswas compared to each other. No differences were observed withrespect to Eccentricity (Nprox vs Nmed, Nprox vs Ndist, Nmed vsNdist, Sprox vs Smed, Sprox vs Sdist, Smed vs Sdist) nor withrespect to Biological Saliency (i.e., Nprox vs Sprox, Nmed vs Smedand Ndist vs Sdist) (p40.05 in all cases).

In order to test the linkage between RTs and critical ERPcomponents, a multiple regression analysis was carried out usingthe Enter method. RTs were the dependent variable, and predictorvariables were N2olp, N2ft and P3 amplitudes at the scalp level(i.e., spatial factor scores). The regression model was shown to besignificant (corrected R2¼0.129, F(3,176)¼9.809, po0.001).A strong association of RTs with N2olp and P3 was observed(beta¼0.211 and −0.368, respectively, po0.005 in both cases), butnot with posterior N2ft (beta¼0.009, p40.05). The positiveassociation of RTs with Nolp (a negative component showing

maximal amplitudes to peripheral—medial and distal-distracters)and their negative association with P3 (a positive componentshowing maximal amplitudes to central distracters) point to aninfluence of eccentricity processing on behavior, not detected inANOVAs described above. Concretely, neural processes linked tocentral distracters caused longer RTs.

4. Discussion

The first scope of this study was to explore whether mechan-isms underlying exogenous attention to peripheral distractersdiffer to some extent from those triggered by centrally presenteddistracters. In convergence with previous literature and as laterexplained, behavior (reaction times) showed the capability ofcentral and peripheral distracters to capture exogenous attention,but was unable to provide strong discriminative information.Importantly, neural results clearly confirmed the central-peripheral segregation, as suggested both by temporal and spatialelectrophysiological data. N2, an ERP component indexing exo-genous attention (Carretié et al., 2004a; Daffner et al., 2000;Folstein & Van Petten, 2008; Kenemans et al., 1989; 1992; Pazo-Álvarez et al., 2003; Rozenkrants & Polich, 2008), reflectedprocesses related to processing of peripheral distracters, asrevealed by scalp amplitude and source analyses of both N2olp(peak at 217 ms), and N2ft (peak at 274 ms). Indeed, both N2olpamplitude increment and N2ft amplitude decrement observed inresponse to peripheral distracters appear to signal greater exo-genous attention. Later, P3 showed enhanced activity in responseto centrally presented distracters. Latency, amplitude and sourceanalyses suggest that this component would be functionallyassociated with spatial interference between target and centraldistracters. In the next paragraphs, these results will be discussedstep by step.

As indicated in the Results section, N2 was subdivided into twodifferent components by principal component analyses. The firstone, labeled here as N2olp, showed maximal amplitudes inresponse to peripheral distracters, particularly in occipital andlateral parietal scalp regions. Source localization analyses on theexperimental N2olp effects observed in scalp indicated thatperipheral distracters recruited the DAN to a greater extent thancentral distracters. This network is considered to be responsiblefor overt and covert reorienting of processing resources towardsdistracters according to their priority (Bisley & Goldberg, 2010;Gottlieb, 2007; Ptak, 2012). Interestingly, DAN areas involved inmotor planning and execution are more clearly linked to exogen-ous than to endogenous attention, according to recent proposals(Smith & Schenk, 2012). For example, experimental preventionfrom planning or executing eye movements (Smith, Schenk, &Rorden, 2012) or lesions affecting normal oculomotor behavior(Gabay, Henik, & Gradstein, 2010) impair exogenous attention todistracters.

Specifically, and at least in the temporal window correspondingto N2olp, this activation was generated in supplementary motorarea (SMA; medial/dorsal BA6), one of the frontal nodes of DAN(Kincade, Abrams, Astafiev, Shulman, & Corbetta, 2005). The SMAplays an important role in the development of the intention-to-actand the specification and elaboration of action (Goldberg, 1985;Tanji, 1994). Importantly, SMA presents significant activity evenin situations of covert attention (i.e., even when no motion—e.g.,eye movement- is performed), as in the present case (Fairhall,Indovina, Driver, & Macaluso, 2009). The greater involvement ofDAN areas in response to peripheral distracters is not a surprisingresult. As indicated in the introduction, non-foveal visual proces-sing is mainly carried out by the magnocellular system. It conveysrudimentary visual information, poor in color and luminance

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details (i.e., only low spatial frequencies), but rapidly reaches“high-level” areas such as prefrontal and parietal cortices(Bar et al., 2006; Bullier, 2001). In fact, it has been recently shownthe preferential involvement of magnocellular-type information inDAN to a greater extent than in VAN (i.e., the ventral attentionnetwork; Carretié, Ríos, Periáñez, Kessel, & Álvarez-Linera, 2012).

N2ft peak appeared 57 ms after N2olp peak. This second N2component presented minimal amplitudes in response to trialscontaining peripheral distracters. This amplitude bias was mainlyobserved at midline parietal scalp regions. Source reconstructionshowed lower activation of posterior cingulate cortex (PCC) inresponse to peripheral compared to central distracters. Thispattern probably reflects attentional load in the digit categoriza-tion task. Indeed, an inverse correlation between the activity ofPCC—one of the key nodes of the default mode network (e.g.,Fransson & Marrelec, 2008; Greicius, Krasnow, Reiss, & Menon,2003)- and attentional load has been reported. For instance,higher activity in PCC is seen under lower attentional load(Rauss, Pourtois, Vuilleumier, & Schwartz, 2012), and lower PCCactivity is observed with higher sensory competition (Geng et al.,2006) and perceptual load (Xu, Monterosso, Kober, Balodis, &Potenza, 2011). Therefore, N2ft-associated PCC activity wouldindicate lesser perceptual/ cognitive effort in the ongoing task inperipheral distracter conditions. As may be appreciated from datadescribed up to this moment, present results suggest fasterprocessing of distracters (mainly reflected in N2olp) than oftargets (reflected in N2ft), in line with previous literature(Hickey, McDonald, and Theeuwes, 2006).

N2ft involved an additional source of experimental effects thatinformed on the second aim of this experiment, consisting of testingthe effect of biological saliency of distracters. The main result at thisrespect was that biologically salient distracters elicited greater N2ftamplitudes regardless of their eccentricity. These results are con-vergent with previous studies indicating that the emotional contentof stimulation is detected irrespective of its eccentricity with respectto fixation (Calvo et al., 2008; Rigoulot et al., 2011; 2012), and confirmthat magnocellular information is sufficient to recognize objects(Thorpe et al., 2001) and to evaluate their associated relevance (Baret al., 2006). Studies leaded by Rigoulot also recorded ERPs and,though experimental conditions were very different from currentones (e.g., stimuli consisted of facial expressions, eliciting the face-specific ERP topography, and no distracter vs relevant stimuli wereemployed), present data converge with them in that biologicalsaliency modulation was reflected very early in ERPs (less than300 ms from stimulus onset; see also Bayle et al., 2009). Importantly,even if attention was not explicitly directed towards the emotionalcontent of stimuli, as in the present study, biological saliencymodulation irrespective of eccentricity remained (Rigoulot et al.,2012). However, there is evidence of biological saliency/emotionneural insensitivity when stimulation appears over 8.211 from fixa-tion (De Cesarei et al., 2009). These latter data, along with the factthat we observed the biological saliency effect at the neural but not atthe behavioral level (this result will be discussed later), suggest thatother factors can modulate this effect and that further research onthis issue is needed.

The neural origin of this biological saliency effect was located inthe polar/ventral prefrontal cortex (PFC). Enhanced activity inthese regions while exogenous attention is directed to biologicallysalient stimulation has been previously reported (Carretié,Hinojosa, Mercado, & Tapia, 2005; Carretié, Hinojosa, Albert, &Mercado, 2006; Vuilleumier et al., 2001). This prefrontal area hasneurons specializing in the identification of objects and faces(Tanibuchi & Goldman-Rakic, 2003). Several findings indicate thatthe ventral PFC receives inputs from early stages of the visualcortex (e.g., V2: see Bar, 2003; Bar et al., 2006), allowing thisprefrontal area to rapidly extract and detect significant elements of

the visual scene. Although this rapid visual information thatreaches the ventral PFC is of magnocellular nature, it is sufficientfor the development of rapid evaluation processes (Bar, 2003; Baret al., 2006; Kveraga, Boshyan, & Bar, 2007). The fact that polar/ventral PFC activity is not dependent on the eccentricity ofdistracters reinforces the idea that it manages magnocellularinformation, which is present both in central and peripheraldistracters (contrarily, parvocellular information is mainly mana-ged in the case of centrally presented stimuli). Furthermore, it alsosupports the notion that this cortical area is in charge of evaluatingbiological saliency taking into account this imprecise information(Bar et al., 2006; Carretié, Albert, López-Martín, & Tapia, 2009a).

Finally, P3 showed greater amplitudes in response to centraldistracters. Source analyses revealed inferior temporal cortex (approxi-mately TEO region) to be the origin of the experimental effectsobserved in this component. Enhanced activations in response tobilateral distracters presented at low eccentricities -as compared withthose elicited by distracters presented at high eccentricities- havebeen previously reported in retinotopic cortices (Schwartz et al.,2005). As indicated in the introduction, spatial competition causes acomplex “push–pull” or “biased competition” pattern of neural activityat extrastriate visual areas, mainly at V4 and TEO, aimed at suppres-sing or inhibit the processing of the distracter and to enhance theprocessing of the target (Beck & Kastner, 2005; Desimone, 1998;Kastner & Pinsk, 2004; Kastner & Ungerleider, 2000; Miller et al.,1993; Reynolds et al., 2000). Biased competition neutralizes themutual suppression that typically affects responses to different stimulipresented in the same (large) receptive field of V4 and inferiortemporal neurons, among other retinotopic areas. Thus, when top-down mechanisms prioritizes one of these stimuli as being taskrelevant -target- and the others become task irrelevant -distracters-,the neural response to the former is as strong as if presented alone(Chelazzi, Duncan, Miller, & Desimone, 1998; Desimone, 1998). Thefact that increased responses towards targets concurring with centraldistracters was produced relatively late in time (on average P3 peakedat 386 ms from stimulus onset) supports the idea that this effectreflects top-down control from hierarchically superior structures ofthe brain, which may influence or bias the level of sensory processingof stimulation (see also Pessoa, Kastner, & Ungerleider, 2003).

Present results are a good example of the greatest sensitivity ofERPs to the effects of certain exogenous attention tasks ascompared to behavioral responses (see also Holmes, Kiss, &Eimer, 2006). Reaction times showed the interfering effect ofdistracters, since they were longer when they were present thanin the non-distracter condition. Additionally, regression analysesbetween behavior and ERP amplitudes indicated that neuralprocesses triggered by central distracters caused longer reactiontimes than those triggered by peripheral distracters. However,ANOVAs did not detect significant direct effects of eccentricityon behavior. Indeed, previous literature is not consistent at thisrespect. Whereas some previous data suggest that exogenousattention is particularly efficient for peripheral distracters(i.e., they capture attention to a greater extent than centraldistracters: Chen & Treiseman, 2008; Juola, Koshino, & Warner,1995), others report exogenous attention biases towards centraldistracters (Beck & Lavie, 2005). This discrepancy may beexplained by the variable processing demands associated withthe specific ongoing cognitive task. In the present study, and asdescribed above, both an advantage for peripheral and for centraldistracters was patent at the neural level (at different momentsand neural sources). Taking into account (i) that behavior is thefinal single output of the complete set of neural processes (notalways convergent), and (ii) that, from an evolutionary point ofview, there should not exist a clear behavioral unbalance betweenexogenous attention captured by peripheral and central distracterssince both situations may be relevant for survival, the absence of

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clear behavioral advantages as a function of distracter eccentricityis not surprising. Additionally, present results suggest that, at leastusing the current experimental conditions, the complementaryneural effects of low and high eccentricity contribute to a greaterextent to behavioral performance than the neural effects ofbiological saliency.

Additional research is needed in order to evaluate how factorsother than those explored here may modulate exogenous atten-tion to peripheral and central distracters. One of them, particularlyrelevant in the case of biologically salient distracters, is theindividual state/trait profile. Several studies have illustrated howindividual patterns such as anxiety or depression enhance thecapability of threatening information to capture attention (formeta-analyses see Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van IJzendoorn, 2007 and Peckham, McHugh, &Otto, 2010, respectively), but whether these biases present a“narrow” (i.e., mainly affecting foveal and parafoveal locations ofthe visual scene) or “wide” pattern (i.e., including periphery) is stillunexplored. Another pertinent issue worth to be explored iswhether perceptual aspects regarding spatial distribution charac-teristics of stimulation is explaining, at least in part, the observedeffects (e.g., there is a cortical magnification for central vision inV1 and probably in ventral visual extrastriate cortices: Brownet al., 2005).

Two main conclusions can be extracted from present results.First, they indicate that exogenous attention to peripheral dis-tracters differed in temporal and spatial terms from exogenousattention to central distracters. The former was significantlyreflected in N2, both N2olp and N2ft. Whereas N2olp indexedthe processing of peripheral distracters themselves, N2ft wasprobably associated with the processing of the target whenperipheral distracters were presented. The origin of these effectswas located, respectively, in SMA (belonging to DAN) and PCC(a part of the default mode network). Exogenous attention tocentral distracters was reflected in P3, whose amplitude appearedto be linked to the sensory competition inherent in visual events inwhich both target and distracter compete in spatial terms. Thiseffect was originated in the ventral visual cortex, in areas corre-sponding to TEO, where sensory competition has been previouslyreported to cause maximal effects. The second conclusion is that,in line with previous studies, exogenous attention is biasedtowards biologically salient events irrespective of their eccentri-city. This bias, reasonable in evolutionary terms, was mainlyreflected in N2 (particularly, in N2ft) and was originated inpolar/ventral PFC, a structure which previous literature links tobiological saliency/emotion evaluation. Present results supportprevious studies suggesting that magnocellular information iscrucial in exogenous attention, and particularly in its bias towardsbiologically salient events.

Acknowledgements

This work was supported by the grants PSI2011-26314 from theMinisterio de Economía y Competitividad of Spain (MINECO) andCEMU-2012-004 from the Universidad Autónoma de Madrid.MINECO also supports Jacobo Albert through a Juan de la Ciervacontract (JCI-2010-07766). The funders had no role in studydesign, data collection and analysis, decision to publish, orpreparation of the manuscript.

References

Alorda, C., Serrano-Pedraza, I., Campos-Bueno, J. J., Sierra-Vázquez, V., & Montoya, P.(2007). Low spatial frequency filtering modulates early brain processing ofaffective complex pictures. Neuropsychologia, 45, 3223–3233.

Bar, M. (2003). A cortical mechanism for triggering top-down facilitation in visualobject recognition. Journal of Cognitive Neuroscience, 15, 600–609.

Bar, M., Kassam, K. S., Ghuman, A. S., Boshyan, J., Schmidt, A. M., Dale, A. M.,Hämäläinen, M. S., Marinkovic, K., Schacter, D. L., Rosen, B. R., & Halgren, E.(2006). Top-down facilitation of visual recognition. Proceedings of the NationalAcademy of Sciences of the United States of America, 103, 449–454.

Bar-Haim, Y., Lamy, D., Pergamin, L., Bakermans-Kranenburg, M. J., & van IJzen-doorn, M. H. (2007). Threat-related attentional bias in anxious and nonanxiousindividuals: a meta-analytic study. Psychological Bulletin, 133, 1.

Bayle, D. J., Henaff, M., & Krolak-Salmon, P. (2009). Unconsciously perceived fear inperipheral vision alerts the limbic system: a MEG study. PloS One, 4, e8207.

Beck, D. M., & Kastner, S. (2005). Stimulus context modulates competition in humanextrastriate cortex. Nature Neuroscience, 8, 1110–1116.

Beck, D. M., & Lavie, N. (2005). Look here but ignore what you see: effects ofdistractors at fixation. Journal of Experimental Psychology: Human Perception andPerformance, 31, 592.

Bisley, J. W., & Goldberg, M. E. (2010). Attention, intention, and priority in theparietal lobe. Annual Review of Neuroscience, 33, 1–21.

Brown, L. E., Halpert, B. A., & Goodale, M. A. (2005). Peripheral vision for perceptionand action. Experimental Brain Research, 165, 97–106.

Bullier, J. (2001). Integrated model of visual processing. Brain Research Reviews, 36,96–107.

Calvo, M. G., Nummenmaa, L., & Hyönä, J. (2008). Emotional scenes in peripheralvision: selective orienting and gist processing, but not content identification.Emotion, 8, 68–80.

Carretié, L., Albert, J., López-Martín, S., & Tapia, M. (2009a). Negative brain: anintegrative review on the neural processes activated by unpleasant stimuli.International Journal of Psychophysiology, 71, 57–63.

Carretié, L., Hinojosa, J. A., Albert, J., & Mercado, F. (2006). Neural response tosustained affective visual stimulation using an indirect task. Experimental BrainResearch, 174, 630–637.

Carretié, L., Hinojosa, J. A., López-Martín, S., Albert, J., Tapia, M., & Pozo, M. A.(2009b). Danger is worse when it moves: neural and behavioral indices ofenhanced attentional capture by dynamic threatening stimuli. Neuropsycholo-gia, 47, 364–369.

Carretié, L., Hinojosa, J. A., López-Martín, S., & Tapia, M. (2007). An electrophysio-logical study on the interaction between emotional content and spatialfrequency of visual stimuli. Neuropsychologia, 45, 1187–1195.

Carretié, L., Hinojosa, J. A., Martín-Loeches, M., Mercado, F., & Tapia, M. (2004a).Automatic attention to emotional stimuli: neural correlates. Human BrainMapping, 22, 290–299.

Carretié, L., Hinojosa, J. A., Mercado, F., & Tapia, M. (2005). Cortical response tosubjectively unconscious danger. NeuroImage, 24, 615–623.

Carretié, L., Kessel, D., Carboni, A., López-Martín, S., Albert, J., Tapia, M., Mercado, F.,Capilla, A., & Hinojosa, J. A. (2013). Exogenous attention to facial vs non-facialemotional visual stimuli. Social Cognitive and Affective Neuroscience in press

Carretié, L., Ríos, M., Periáñez, J. A., Kessel, D., & Álvarez-Linera, J. (2012). The role oflow and high spatial frequencies in exogenous attention to biologically salientstimuli. PLoS One, 7, e37082.

Carretié, L., Ruiz-Padial, E., López-Martín, S., & Albert, J. (2011). Decomposingunpleasantness: differential exogenous attention to disgusting and fearfulstimuli. Biological Psychology, 86, 247–253.

Carretié, L., Tapia, M., Mercado, F., Albert, J., López-Martin, S., & de la Serna, J. M.(2004b). Voltage-based versus factor score-based source localization analysesof electrophysiological brain activity: a comparison. Brain Topography, 17,109–115.

Chapman, C., Hoag, R., & Giaschi, D. (2004). The effect of disrupting the humanmagnocellular pathway on global motion perception. Vision Research, 44,2551–2557.

Chapman, R. M., & McCrary, J. W. (1995). EP component identification and measure-ment by principal components analysis. Brain and Cognition, 27, 288–310.

Chelazzi, L., Duncan, J., Miller, E. K., & Desimone, R. (1998). Responses of neurons ininferior temporal cortex during memory-guided visual search. Journal ofNeurophysiology, 80, 2918–2940.

Chen, Z., & Treiseman, A. (2008). Distractor inhibition is more effective at a central thanat a peripheral location. Attention, Perception, & Psychophysics, 70, 1081–1091.

Cliff, N. (1987). Analyzing multivariate data. San Diego, Calif: Harcourt BraceJovanovich.

Constantine, R., McNally, R. J., & Hornig, C. D. (2001). Snake fear and the pictorialemotional stroop paradigm. Cognitive Therapy and Research, 25, 757–764.

Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reorienting system of thehuman brain: from environment to theory of mind. Neuron, 58, 306–324.

Daffner, K. R., Mesulam, M. M., Scinto, L. F. M., Calvo, V., Faust, R., & Holcomb, P. J.(2000). An electrophysiological index of stimulus unfamiliarity. Psychophysiol,37, 737–747.

De Cesarei, A., Codispoti, M., & Schupp, H. T. (2009). Peripheral vision andpreferential emotion processing. Neuroreport, 20, 1439.

Desimone, R. (1998). Visual attention mediated by biased competition in extra-striate visual cortex. Philosophical Transactions of the Royal Society of London.Series B: Biological Sciences, 353, 1245–1255.

Dien, J. (2010). Evaluating two‐step PCA of ERP data with geomin, infomax, oblimin,promax, and varimax rotations. Psychophysiology, 47, 170–183.

Dien, J. (2012). Applying principal components analysis to event-related potentials:a tutorial. Developmental Neuropsychology, 37, 497–517.

Dien, J., Spencer, K. M., & Donchin, E. (2003). Localization of the event-relatedpotential novelty response as defined by principal components analysis.

L. Carretié et al. / Neuropsychologia 51 (2013) 1838–1847 1847

Cognitive Brain Research, 17, 637–650, http://dx.doi.org/10.1016/S0926-6410(03)00188-5.

Doallo, S., Holguin, S. R., & Cadaveira, F. (2006). Attentional load affects automaticemotional processing: evidence from event-related potentials. Neuroreport, 17,1797–1801.

Eastwood, J. D., Smilek, D., & Merikle, P. M. (2003). Negative facial expressioncaptures attention and disrupts performance. Attention, Perception, & Psycho-physics, 65, 352–358.

Fairhall, S., Indovina, I., Driver, J., & Macaluso, E. (2009). The brain networkunderlying serial visual search: comparing overt and covert spatial orienting,for activations and for effective connectivity. Cerebral Cortex, 19, 2946–2958.

Folstein, J. R., & Van Petten, C. (2008). Influence of cognitive control and mismatchon the N2 component of the ERP: a review. Psychophysiology, 45, 152–170.

Fransson, P., & Marrelec, G. (2008). The precuneus/posterior cingulate cortex plays apivotal role in the default mode network: evidence from a partial correlationnetwork analysis. NeuroImage, 42, 1178–1184, http://dx.doi.org/10.1016/j.neuroimage.2008.05.059.

Gabay, S., Henik, A., & Gradstein, L. (2010). Ocular motor ability and covert attentionin patients with Duane Retraction Syndrome. Neuropsychologia, 48, 3102–3109.

Geng, J. J., Eger, E., Ruff, C. C., Kristjánsson, Á., Rotshtein, P., & Driver, J. (2006). On-line attentional selection from competing stimuli in opposite visual fields:effects on human visual cortex and control processes. Journal of Neurophysiol-ogy, 96, 2601–2612.

Goldberg, G. (1985). Supplementary motor area structure and function: review andhypotheses. Behavioral and Brain Sciences, 8, 567–588.

Gottlieb, J. (2007). From thought to action: the parietal cortex as a bridge betweenperception, action, and cognition. Neuron, 53, 9–16.

Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivityin the resting brain: a network analysis of the default mode hypothesis.Proceedings of the National Academy of Sciences, 100, 253.

Grosbras, M. H., Laird, A. R., & Paus, T. (2005). Cortical regions involved in eyemovements, shifts of attention, and gaze perception. Human Brain Mapping, 25,140–154.

Hickey, C., McDonald, J. J., & Theeuwes, J. (2006). Electrophysiological evidence ofthe capture of visual attention. Journal of Cognitive Neuroscience, 18, 604–613.

Holmes, A., Kiss, M., & Eimer, M. (2006). Attention modulates the processing ofemotional expression triggered by foveal faces. Neuroscience Letters, 394, 48–52.

Huang, Y. X., & Luo, Y. J. (2007). Attention shortage resistance of negative stimuli inan implicit emotional task. Neuroscience Letters, 412, 134–138.

Jonas, J. B., Schneider, U., & Naumann, G. O. (1992). Count and density of humanretinal photoreceptors. Graefe's Archive for Clinical and Experimental Ophthal-mology, 230, 505–510.

Jung, T. P., Makeig, S., Humphries, C., Lee, T. W., Mckeown, M. J., Iragui, V., &Sejnowski, T. J. (2000). Removing electroencephalographic artifacts by blindsource separation. Psychophysiology, 37, 163–178.

Juola, J. F., Koshino, H., & Warner, C. B. (1995). Tradeoffs between attentional effectsof spatial cues and abrupt onsets. Attention, Perception, & Psychophysics, 57,333–342.

Kastner, S., De Weerd, P., Pinsk, M. A., Elizondo, M. I., Desimone, R., & Ungerleider, L.G. (2001). Modulation of sensory suppression: implications for receptive fieldsizes in the human visual cortex. Journal of Neurophysiology, 86, 1398–1411.

Kastner, S., & Pinsk, M. A. (2004). Visual attention as a multilevel selection process.Cognitive, Affective, & Behavioral Neuroscience, 4, 483–500.

Kastner, S., & Ungerleider, L. G. (2000). Mechanisms of visual attention in thehuman cortex. Annual Review of Neuroscience, 23, 315–341.

Kenemans, J. L., Verbaten, M. N., Melis, C. J., & Slangen, J. L. (1992). Visual stimuluschange and the orienting reaction: event-related potential evidence for a two-stage process. Biological Psychology, 33, 97–114.

Kenemans, J. L., Verbaten, M. N., Roelofs, J. W., & Slangen, J. L. (1989). Initial- andchange-orienting reactions: an analysis based on visual single-trial event-related potentials. Biological Psychology, 28, 199–226.

Kincade, J. M., Abrams, R. A., Astafiev, S. V., Shulman, G. L., & Corbetta, M. (2005). Anevent-related functional magnetic resonance imaging study of voluntary andstimulus-driven orienting of attention. The Journal of Neuroscience, 25, 4593.

Kveraga, K., Boshyan, J., & Bar, M. (2007). Magnocellular projections as the trigger oftop-down facilitation in recognition. Journal of Neuroscience, 27, 13232–13240.

Miller, E. K., Gochin, P. M., & Gross, C. G. (1993). Suppression of visual responses ofneurons in inferior temporal cortex of the awake macaque by addition of asecond stimulus. Brain Research, 616, 25–29.

Nichols, T. E., & Holmes, A. P. (2001). Nonparametric permutation tests forfunctional neuroimaging: a primer with examples. Human Brain Mapping, 15,1–25.

Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J. M. (2011). FieldTrip: open sourcesoftware for advanced analysis of MEG, EEG, and invasive electrophysiologicaldata. Computational Intelligence and Neuroscience, 2011, 1.

Packer, O., Hendrickson, A. E., & Curcio, C. A. (1989). Photoreceptor topography ofthe retina in the adult pigtail macaque (Macaca nemestrina). The Journal ofComparative Neurology, 288, 165–183.

Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetictomography (sLORETA): technical details. Methods and Findings in Experimentaland Clinical Pharmacology, 24(Suppl. D), 5–12.

Pazo-Álvarez, P., Cadaveira, F., & Amenedo, E. (2003). MMN in the visual modality: areview. Biological Psychology, 63, 199–236.

Peckham, A. D., McHugh, R. K., & Otto, M. W. (2010). A meta-analysis of themagnitude of biased attention in depression. Depression and Anxiety, 27,1135–1142.

Pessoa, L., Kastner, S., & Ungerleider, L. G. (2003). Neuroimaging studies ofattention: from modulation of sensory processing to top-down control. TheJournal of Neuroscience, 23, 3990–3998.

Pierrot-Deseilligny, C., Milea, D., & Müri, R. M. (2004). Eye movement control by thecerebral cortex. Current Opinion in Neurology, 17, 17–25.

Posner, M. I., Rueda, M. R., & Kanske, P. (2007). Probing the mechanisms ofattention. In: J. T. Cacioppo, J. G. Tassinary, & G. G. Berntson (Eds.), The handbookof psychophysiology (3rd ed.). Cambridge: Cambridge University Press.

Pourtois, G., Grandjean, D., Sander, D., & Vuilleumier, P. (2004). Electrophysiologicalcorrelates of rapid spatial orienting towards fearful faces. Cerebral Cortex, 14,619–633.

Ptak, R. (2012). The frontoparietal attention network of the human brain: action,saliency, and a priority map of the environment. The Neuroscientist, 18, 502–515.

Rauss, K., Pourtois, G., Vuilleumier, P., & Schwartz, S. (2012). Effects of attentionalload on early visual processing depend on stimulus timing. Human BrainMapping, 33, 63–74.

Reynolds, J. H., Pasternak, T., & Desimone, R. (2000). Attention increases sensitivityof V4 neurons. Neuron, 26, 703–714.

Rigoulot, S., D'Hondt, F., Defoort-Dhellemmes, S., Despretz, P., & Honoré, J. (2011).Fearful faces impact in peripheral vision: behavioral and neural evidence.Neuropsychologia, 49, 2013–2021.

Rigoulot, S., D'hondt, F., Honore, J., & Sequeira, H. (2012). Implicit emotionalprocessing in peripheral vision: behavioral and neural evidence. Neuropsycho-logia, 50, 2887–2896.

Rozenkrants, B., & Polich, J. (2008). Affective ERP processing in a visual oddball task:arousal, valence, and gender. Clinical Neurophysiology, 119, 2260–2265.

Schwartz, S., Vuilleumier, P., Hutton, C., Maravita, A., Dolan, R. J., & Driver, J. (2005).Attentional load and sensory competition in human vision: modulation of fMRIresponses by load at fixation during task-irrelevant stimulation in the periph-eral visual field. Cerebral Cortex, 15, 770–786.

Smith, D. T., Schenk, T., & Rorden, C. (2012). Saccade preparation is required forexogenous attention but not endogenous attention or IOR. Journal of Experi-mental Psychology.Human Perception and Performance, 38, 1438–1447.

Smith, D. T., & Schenk, T. (2012). The premotor theory of attention: time to moveon? Neuropsychologia, 50, 1104–1114.

Sterling, P. (2003). How retinal circuits optimize the transfer of visual information.In: L. Chalupa, & J. Werner (Eds.), The visual neurosciences (pp. 234–259).Cambridge, MA: MIT Press.

Tanibuchi, I., & Goldman-Rakic, P. S. (2003). Dissociation of spatial-, object-, andsound-coding neurons in the mediodorsal nucleus of the primate thalamus.Journal of Neurophysiology, 89, 1067–1077.

Tanji, J. (1994). The supplementary motor area in the cerebral cortex. NeuroscienceResearch, 19, 251–268.

Thomas, S. J., Johnstone, S. J., & Gonsalvez, C. J. (2007). Event-related potentialsduring an emotional Stroop task. International Journal of Psychophysiology, 63,221–231.

Thorpe, S. J., Gegenfurtner, K. R., Fabre-Thorpe, M., & Bülthoff, H. H. (2001).Detection of animals in natural images using far peripheral vision. EuropeanJournal of Neuroscience, 14, 869–876.

Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2001). Effects of attention andemotion on face processing in the human brain an event-related fMRI study.Neuron, 30, 829–841.

Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2003). Distinct spatialfrequency sensitivities for processing faces and emotional expressions. NatureNeuroscience, 6, 624.

Xu, J., Monterosso, J., Kober, H., Balodis, I. M., & Potenza, M. N. (2011). Perceptualload-dependent neural correlates of distractor interference inhibition. PloS One,6, e14552.

Yuan, J., Zhang, Q., Chen, A., Li, H., Wang, Q., Zhuang, Z., & Jia, S. (2007). Are wesensitive to valence differences in emotionally negative stimuli? Electrophy-siological evidence from an ERP study. Neuropsychologia, 45, 2764–2771.