Behavioural Brain Research 224 (2011) 58–64
Contents lists available at ScienceDirect
Behavioural Brain Research journal homepage: www.elsevier.com/locate/bbr
The inﬂuence of extrinsic motivation on competition-based selection Jessica Sänger a,∗ , Edmund Wascher b a b
Institute of Experimental Psychology II, Heinrich-Heine-University, D-40225 Düsseldorf, Germany Leibniz Research Centre for Working Environment and Human Factors, D-44139 Dortmund, Germany
a r t i c l e
i n f o
Article history: Received 31 January 2011 Received in revised form 13 May 2011 Accepted 18 May 2011 Keywords: Attention Competition Motivation Event-related potentials N2pc EEG ERP
a b s t r a c t The biased competition approach to visuo-spatial attention proposes that the selection of competing information is effected by the saliency of the stimulus as well as by an intention-based bias of attention towards behavioural goals. Wascher and Beste (2010)  showed that the detection of relevant information depends on its relative saliency compared to irrelevant conﬂicting stimuli. Furthermore the N1pc, N2pc and N2 of the EEG varied with the strength of the conﬂict. However, this system could also be modulated by rather global mechanisms like attentional effort. The present study investigates such modulations by testing the inﬂuence of extrinsic motivation on the selection of competing stimuli. Participants had to detect a luminance change in various conditions among others against an irrelevant orientation change. Half of the participants were motivated to maximize their performance by the announcement of a monetary reward for correct responses. Participants who were motivated had lower error rates than participants who were not motivated. The event-related lateralizations of the EEG showed no motivationrelated effect on the N1pc, which reﬂects the initial saliency driven orientation of attention towards the more salient stimulus. The subsequent N2pc was enhanced in the motivation condition. Extrinsic motivation was also accompanied by enhanced fronto-central negativities. Thus, the data provide evidence that the improvement of selection performance when participants were extrinsically motivated by announcing a reward was not due to changes in the initial saliency based processing of information but was foremost mediated by improved higher-level mechanisms. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Our visual environment contains too much information for us to perceive at once. Hence, multiple objects that are simultaneously present in the visual ﬁeld compete for neural representation. The important question concerning attention research is how we selectively process information that is relevant, while ignoring information that is irrelevant in the current task. The selection of relevant visual information can be directed by a deliberate intent on the part of the observer (top-down), or it can be captured by salient events in the scene (bottom-up; for a review see ). Consequently, visual attention may be either voluntarily directed to particular locations or features (voluntary control), or it may be captured by salient stimuli such as the sudden appearance of a new object or a strong feature change of a perceptual object (stimulus-driven control). Most often, the deployment of attention is the result of a dynamic interplay between voluntary attentional control settings (e.g., based on prior knowledge about
∗ Corresponding author at: Heinrich-Heine-Universität, Institut für Experimentelle Psychologie II, Universitätsstraße 1, D-40225 Düsseldorf, Germany. Tel.: +49 0211 811 2011; fax: +49 0211 811 4522. E-mail address: [email protected]
(J. Sänger). 0166-4328/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.bbr.2011.05.015
a target’s properties), the degree to which stimuli in the visual scene match these voluntary control settings and the distribution of feature saliency. The biased competition theory of attention  serves as a neuronal model which proposes that most of the different brain systems that represent visual information (sensory and motor; cortical and subcortical) integrate this information competitively. Multiple stimuli that are presented simultaneously in the visual ﬁeld compete for representation in the visual cortex by mutually suppressing neural responses to the competitors . Thus, a gain of representation for a particular visual object will always be at the expense of other objects’ representations . This process of competition is initially driven by the interrelation of the saliency of all incoming signals (, but see also ). Furthermore, competition can be controlled or biased within and across brain systems in favor of a particular object. Units matching the internal ‘template’ of an intended object will be pre-activated and will therefore gain an advantage (bias) by receiving an increased processing weight (see  for a review). While the initial saliency-based competition is assigned to sensory areas, the voluntary top-down-induced bias has been assigned to frontal cortical structures that impinge on those structures [8–10]. Duncan  proposed that attention is an emergent property of the various mechanisms (top-down or bottom-up) that converge to select an item for further processing.
J. Sänger, E. Wascher / Behavioural Brain Research 224 (2011) 58–64
However, the top-down inﬂuence on the competition is not sufﬁciently described by the term of intention. It additionally refers to biases that are generated by the cognitive demands of the task that includes mechanisms related to memory processes or emotional and motivational behaviour [11,12]. In this context, attentional effort has been conceptualized as a motivated activation of attentional systems (top-down) in order to stabilize or recover performance according to the detection of errors and reward loss which was caused by the presence of distractors, prolonged timeon-task, changing target stimulus characteristics and stimulus presentation parameters, circadian phase shifts, stress or sickness . Increases in attentional effort are motivated by the expected performance outcome; in the absence of such motivation attentional performance continues to decline or may cease altogether. Sarter et al.  described ‘attentional effort’ as a form of cognitive incentive that serves to optimize goal-directed behavioural and cognitive processes . The recovery or stabilization of performance under challenging conditions or the enhancement of attentional processes in response to increased incentives requires mechanisms which act to optimize input processing, noise ﬁltering, and the redistribution and focusing of processing resources. Such functions have been conceptualized as being orchestrated by prefrontal and anterior cingulate regions ([15,16], see also [17,18–20]) by modulating the activity of cholinergic terminals elsewhere in the cortex via cortico-cortical (or associational) projections [13,21,22]. Depending on the quality of stimuli and task characteristics, cortical cholinergic activity reﬂects the combined effects of signal-driven and task-driven modulation of detection . This cholinergic input system is directly and indirectly controlled by the mesolimbic dopaminergic (reward) systems . The link between these two neuronal systems is hypothesized to mediate the motivational activation of attentional systems. Accordingly, Servan-Schreiber et al.  could show that a dopamine agonist can improve reaction times and accuracy. Although numerous studies have demonstrated the neural basis of the interaction between attention and motivation the functional nature of such an interaction remains unclear (but see ). Eventrelated potentials of the EEG (ERPs) may provide such functional insights; hitherto this method has not been applied to systematically investigate the interactive processing within an attentional network. Most ERP studies in this ﬁeld focus on the effect of attention upon sensory processing. Attention has been demonstrated to act as a gain control modifying the magnitude of sensory responses to incoming information, visible in the N1, which is generated in sensory areas [27–29]. Also stimulus driven allocation of attention may be already reﬂected in such an early ERP component [30–34]. The subsequent N2pc, which reﬂects increased negativity contralateral to relevant information in a stimulus matrix  is assumed to reﬂect top-down driven allocation of attention [36,37] and respectively the ﬁltering of relevant from irrelevant stimuli . Recently, Wascher and Beste  could show how such stimulus driven and intention based attentional processes interact when competing stimuli are presented simultaneously. By means of event-related lateralization, namely the N1pc and the N2pc, the need of attentional re-allocation has been demonstrated when attention is misguided by salient distracters. This process was accompanied by a fronto-central negativity that arose whenever conﬂicting information was presented. Thus, that study demonstrates a direct interaction of sensory and supervisory structures in the attentional network. In the present study we investigated how extrinsic motivation in form of a positive incentive (reward) interacts with competitive attentional selection. In terms of the concept of attentional effort, as outlined above, it can be assumed that the cholinergic input system modulates exogenous attention (bottom-up) by enhancing the arousal state and resulting in a higher perceptual sensitivity. It is
also thought to additionally interact with endogenous attention (top-down) by increasing the controllability of distracter stimuli. 2. Methods 2.1. Participants Twenty-six students of the Heinrich-Heine University of Düsseldorf (4 males) ranging in age between 19 and 29 years (M = 22.15, SD = 2.89) took part in this study. All participants were right-handed and had normal or corrected-to-normal vision. They were in good physical health and had no history of neurological or psychiatric disorders. Participants were recruited by announcements and were paid in form of course credits. All participants gave written informed consent prior to entering the experiment. 2.2. Stimuli and procedure The stimuli were identical to those used in the study of Wascher and Beste . Two bars, presented at the left and right side from a ﬁxation cross (see Fig. 1). The bars were differentially illuminated compared to the background (30 cd/m2 ) with a Fechner contrast of 0.2 (i.e. 20 cd/m2 and 45 cd/m2 , respectively). Luminance and orientation were randomly intermixed to any possible combination for the ﬁrst frame. In each trial two frames of these stimuli were presented, one after the other. Each frame was shown for 100 ms interrupted by a short blank of 50 ms, during which only the ﬁxation cross was visible. Between the two frames either the luminance (LUM) or the orientation (ORI) of one single bar, luminance and orientation of one bar (LO-U = Luminance-Orientation Unilateral), or luminance and orientation distributed across the two bars (LO-B = Luminance-Orientation Bilateral) could change. The latter condition will also be referred to as the “conﬂict” condition, because in this condition relevant and irrelevant information are spatially separated. The subjects’ task was to detect changes in luminance and to ignore orientation changes. To indicate the luminance change they had to press one out of two buttons of a response box (RB-620, Cedrus Corporation, San Pedro, USA) with the index ﬁnger of the left or right hand at the side where the change had appeared. Trials in which only the orientation of one bar had changed were no-go trials. All participants were instructed to respond as fast as possible but not at cost of accuracy. To address the inﬂuence of a top-down modulation half of the participants got motivated by announcing a reward for every correct response. Those participants were able to earn a maximum of 10 D extra depending on how many correct responses they gave. At the end of the motivation block subjects were informed about their gain. The two groups (no-reward and reward) were matched with respect to age and sex. A total of 768 trials were presented, 192 for each condition, in a random order. The inter-trial interval was varied between 1900 and 2200 ms. Stimulus presentation and behavioural data recording was controlled by Presentation (Version 11; Neurobehavioral Systems Inc., Albany, USA). During the task all participants were seated in a comfortable armchair inside a sound attenuated and electrically shielded chamber in front of a 20 computer monitor (Mitsubishi–DiamondPro 2070SB ), that subtended 14.03 × 18.65◦ at the viewing distance of 120 cm. Ambient luminance in the testing room was 0.5–1.5 cd/m2 . All subjects performed 2 blocks of 384 trials. Each block took approximately 15 min and there was a short resting period in between the two experimental blocks. To control for the effects of motivation by the reward instruction, participants had to ﬁll in the Positive and Negative Affect Schedule (PANAS) before and after the motivation manipulation. This questionnaire was ﬁlled in by both groups to avoid effects related to the experimental course and related to the questionnaire by itself. 2.3. Data recording and analysis 2.3.1. Behavioural data Responses were recorded from the onset of the second frame. Button presses between 80 and 1500 ms after stimulus onset were taken as valid responses. Fast guesses (1500 ms) were excluded from response time and EEG analyses. Both of these kinds of errors hardly ever occurred ( .86), error rates were decreased for unilateral orientation changes (ORI) and conﬂict trials (LO-B) when participants were rewarded (LO-B: 25.7%, ORI: 28.5%) than when they were not rewarded (LO-B: 39.4%, t(24) = 2.58, p < .01, ORI: 28.5%, t(24) = 2.18, p < .05). Response times (see Fig. 2, right panel) also revealed a main effect for the Type of change, F(2,48) = 29.49, p < .001, ε = .77, with longest response times for LO-B (796 ms) compared to LOU (746 ms) and LUM (744 ms). As shown for the error rates, LO-B was the only condition that differed signiﬁcantly from the two other response conditions (p < .001). No main effect of Motivation, F < 1, and no interaction of the factors Type of change by Motivation, F(2,48) = 1.04, p = .36, was observed. Thus, no direct evidence for a speed-accuracy trade-off can be derived, although, at least numerically, participants slightly slowed down probably in order to increase accuracy when reward was given for correct responses. The results of the PANAS conﬁrmed the effectiveness of the motivation manipulation. While, the positive affect got enhanced (pre score: 18.62 vs. post score: 20.23, t(12) = −2.27, p < .043), the negative affect was signiﬁcantly reduced (pre score: 21.85 vs. post score: 17.23, t(12) = 12.00, p < .001) by the reward announcement.
3.2. ERP data
3.1. Behavioural data
The presentation of the second stimulus display elicited an asymmetric N1 that varied across the conditions LUM, ORI, LOU and LO-B, with an enhanced contralateral negativity to the most salient stimulus of the display, F(3,72) = 97.86, p < .001, ε = .48.
The behavioural data (see Fig. 2) corroborated previous results : Error rates (see Fig. 2, left panel) varied across the different
J. Sänger, E. Wascher / Behavioural Brain Research 224 (2011) 58–64
Fig. 2. Detection performance in form of mean error rates (A) and response times (B; error bars depict the standard error of the mean). Both parameters varied with the strength of the conﬂict. In addition, error rates but not response times are modulated by the extrinsic motivation manipulation.
Whereas for the non-conﬂict trials, LUM and LO-U, the asymmetry in the N1 points to the relevant luminance change, for the conﬂict condition LO-B, the N1 shows an enhanced activation ipsilateral to the attended luminance change, i.e. contralateral to the irrelevant orientation change (see Fig. 3). For all conditions this pos-
terior asymmetry in the N1 (N1pc) differed reliably from zero, all t(12) > 3.9, all p’s < .002. However, this early asymmetry, which is thought to reﬂect a ﬁrst orientation of attention, was not modulated by motivation, F < 1. Pairwise comparisons showed that this was not the case for any condition, all t < 1, p > .6.
Fig. 3. Posterior (PO7/PO8) event-related lateralizations (ERLs) of the EEG separately for each type of change. The increase of negativity contralateral to a unilateral transient (LUM, ORI, LO-U) is plotted upward. For ERLs of the central conﬂict condition (LO-B) negativity contralateral to the target stimulus is plotted upward. Point-wise effects of motivation are indicated by the bars on the bottom of each graph (p < 01). In the time window of the N1 ERLs indicate the capture of attention toward the more salient element. Subsequent asymmetries in the N2 range (N2pc) were enhanced in the conﬂict condition LO-B when the initial orientation of attention did not point toward the target element. This N2pc was increased for the motivated group.
J. Sänger, E. Wascher / Behavioural Brain Research 224 (2011) 58–64
Fig. 4. Event-related potentials derived from electrode site FCz, referred to linked mastoids. Point-wise effects of motivation are indicated by the bars on the bottom of each graph (p < 01). For all conditions, extrinsic motivation goes along with an increased frontal negativity which starts immediately after the presentation of the change (and possibly even before), whereas an effect in the N2 range appears to be selective for the increased ability to resolve the conﬂict (LO-B).
N1 was followed by a second asymmetric negative component. A phasic N2 was visible contralateral to the position of the attended luminance change. For all conditions except in no-go trials with unilateral orientation changes (ORI) this N2pc differed reliably from zero, all t(12) > 2.7, all p’s < .02. Moreover, indicated by pairwise comparisons this N2pc response was additionally magniﬁed for the reward group (LUM: t(24) = 2.08, p < .04; LO-U: t(24) = 2.07, p < .04; LO-B: t(24) = 2.12, p < .04). Across all conditions, increased fronto-central negativity was observed for the rewarded group in the time range of the N1 (measured between 140 and 190 ms, F(1,24) = 14.99, p < .01, see also Fig. 4). In a later time window, 270–320 ms, this enhanced negativity for the motivated group was still evident for the conﬂict condition (LO-B), t(24) = 2.79, p < .01 but not for any other condition (all t-values < 1 and all p’s > .8). 4. Discussion The present study investigated the inﬂuence of extrinsic motivation on competitive attentional selection. Participants had to detect the changes in luminance of a bar shown on a display where it could be accompanied by a change in orientation of the same or of another bar located at the contralateral side of ﬁxation. In this particular task, bottom-up driven attentional allocation, engagement of the anterior control system and re-allocation has been reported to be speciﬁcally measurable by means of separate ERP components . The behavioural results show that even though a luminance change was the only task relevant feature of the stimuli, its detection was strongly impaired by the irrelevant orientation change if both were spatially separated (conﬂict) or when there was a change in orientation only. Both types of errors were markedly reduced in participants who were rewarded. At least numerically (but not sta-
tistically), this increase in accuracy was accompanied by a slight increase in response times. Since motivation was a between subject factor, it is hard to decide whether this reﬂects a shift of the response criterion (i.e. speed accuracy trade-off) or an increase in availability of stimulus information. However, correlating response times and accuracy across participants in a post test, no interrelation between response times and accuracy was found for this task. This indicates that response criteria play a minor role. This saliency-based (bottom-up driven) processing of incoming information, as it is reﬂected by posterior asymmetries in the N1 range, was not affected by extrinsic motivation. Across all conditions this initial component was comparable for rewarded and non-rewarded subjects. On a ﬁrst sight this ﬁnding might be at odds with other studies that report substantial inﬂuence of attention upon early visual structures [44,45]. However, despite the fact that processing in this task can be modulated by changing the sensitivity to particular features involved via passive stimulation , the detection of a change needs the comparison of features across frames. Thus, higher order processing is essential to resolve the task and the gating mechanism can contribute only when information is fed back to those areas . Consequently, motivation-related alterations in the ERL were observed in the time range of the N2pc, not earlier. The N2pc, that has been proposed to reﬂect ﬁltering of irrelevant information and/or selection of relevant targets [33,34,36], increased in amplitude when a reward was prospected. This effect was visible across all conditions. When considering those conditions in which most errors were committed, improved accessibility of required information might account for this effect in the best way. While in the conﬂict trials both increased processing of the target and better suppression of distracters  could explain the effect, an improved performance for single orientation changes does not directly ﬁt into
J. Sänger, E. Wascher / Behavioural Brain Research 224 (2011) 58–64
this framework. With respect to the biased competition approach intention driven re-allocation of attention should be necessary only when the initial selection was insufﬁcient to accomplish the task. Accordingly, the N2pc was largest for conﬂict trials in which the asymmetry in the time range of the N1 did not point towards the target element. However, one has to be careful with the interpretation of this second asymmetry in the EEG, since its latency appears quite late for spatial processing in such a simple display. Whereas the N2pc is often observed with a peak latency around 250 ms after stimulus-onset the parieto-occipital negativity of the current study appeared slightly later in time (between 300 and 400 ms after stimulus-onset). This later time window is sometimes already associated with the sustained posterior contralateral negativity (SPCN) of the ERPs, which reﬂects visual short term memory (VSTM) processing. Compared to the component reported in the current study the SPCN is, as indicated by its name, a sustained and long-lasting negativity which persists for the duration of a retention interval or engages VSTM as an intermediate processing buffer [49–52]. Thus, parieto-occipital negativities in the present study can be assigned to the N2pc which is assumed to reﬂect the processing of the stimuli in the display via re-entrant loops in order to identify the spatial locations and the features of the relevant stimulus. This process was improved by the extrinsic motivation. Although no explicit N2pc was observed for those trials in which no task relevant stimulus (nogo trials = ORI) was presented, a motivation-related modulation of the event-related asymmetries in the time range of the N2pc was visible. This alteration refers to an increased allocation of attention at the location where no change was presented. This way, when a reward was given participants might have avoided illusionary perception; that is to say, the impression there might have been a luminance change. Extrinsic motivation additionally led to enhanced fronto-central negativities. The increase of fronto-central activity indicates that greater cognitive control has been applied to obtain all relevant information from the competitive display. Two phases have to be distinguished. Initially, already in the N1 range, an increase in excitability is visible across all conditions, indicating a general preparedness for enhanced processing when participants were motivated. This ﬁrst fronto-central activity might have had an unspeciﬁc impact on posterior perceptual areas via feedback projections. Studies in humans and macaque monkeys have demonstrated that those control signals are mediated and maintained within circumscribed networks of the parietal and frontal cortex (e.g. [53–58]). These areas control attentional selection and inﬂuence sensory processing and perception via feedback projections [2,59], as demonstrated by micro-stimulation studies [60–62] and by simultaneous recordings in sensory and source areas . In the context of those feedback projections, the neuromodulator acetylcholine (Ach), originating in the basal forebrain (e.g. [22,64,65]), is likely to contribute to attention. Although cholinergic modulation of cortical processes can be signal driven, it can equally be top-down driven [23,66]. Top-down-driven modulation of the corticopetal cholinergic system is under the control of ‘executive’ areas in the prefrontal cortex, thereby inﬂuencing their own ACh levels, those in parietal  and sensory cortical regions. This form of top-down control enhances the cognitive modulation of the detection processes . Within this framework the ascending cholinergic system can be conceptualized as part of the top-down control system emanating within the prefrontal cortex through which control is exerted over processing resources in the parietal attention system and in sensory cortices . This cholinergic input system is controlled by the mesolimbic dopaminergic system, which is substantial for the processing of motivation. The data propose a second more speciﬁc mechanism. The motivationrelated increase in amplitude in the time range of an N2-proper was restricted to the conﬂict trials. Thus, increased ability to recruit
controlling instances to resolve a perceptual conﬂict might be an outcome of the mechanisms outlined above. In summary, we replicated the ﬁndings of Wascher and Beste  that the processing of competitive stimuli is initially based on the saliency of the target compared to the saliency of a distracter (see also ), reﬂected in a parieto-occipital N1. While this initial processing step is not affected by increased motivation, subsequent intention driven stages are, reﬂected in the N2pc. This change of efﬁciency in sensory areas was accompanied by an increase of activity measured over fronto-central areas. The observed data pattern can be discussed within the framework of an attentional network in which the anterior cortex is capable of changing the signal processing properties of sensory areas via cholinergic neurons originating in the basal forebrain and innervating all cortical areas and participating in the gating of cortical information processing. This cholinergic system is closely related to the dopaminergic reward system, which explains the massive impact of extrinsic motivation upon the processing of competitive stimuli. Thus, our results support the concept of attentional effort and its foundation in the associational cholinergic system that connects frontal executive structures with rather sensory-related areas. However, the lack of an effect upon early components of the EEG cannot rule out a DA/ACh inﬂuence but suggests that extrinsic motivation does not speciﬁcally impact the processing of particular feature changes already in early stages of processing.
Acknowledgement This study was partially supported by the Deutsche Forschungsgemeinschaft (DFG WA 987/14-1 and DFG BE 4045/6-1).
References  Yantis S. Goal-directed and stimulus-driven determinants of attentional control. In: Monsell S, Driver J, editors. Attention and Performance. Cambridge, MA: MIT Press; 2000. p. 73–103.  Desimone R, Duncan J. Neural mechanisms of selective visual attention. Annual Reviews of Neuroscience 1995;18:193–222.  Kastner S, Ungerleider LG. The neural basis of biased competition in human visual cortex. Neuropsychologia 2001;39:1263–76.  Duncan J. Cooperating brain systems in selective perception and action. In: Inui T, McClelland JL, editors. Attention & Performance. Cambridge: MIT Press; 1996. p. 549–78.  Theeuwes J. Top-down and bottom-up control of visual selection. Acta Psychologica 2010;135:77–99.  Egeth HE, Leonard CJ, Leber AB. Why salience is not enough: reﬂections on top-down selection in vision. Acta Psychologica 2010;135:130–2, discussion 3–9.  Beck DM, Kastner S. Top-down and bottom-up mechanisms in biasing competition in the human brain. Vision Research 2009;49:1154–65.  Knudsen EI. Fundamental components of attention. Annual Reviews of Neuroscience 2007;30:57–78.  Reynolds JH, Desimone R. Interacting roles of attention and visual salience in V4. Neuron 2003;37:853–63.  Reynolds JH, Pasternak T, Desimone R. Attention increases sensitivity of V4 neurons. Neuron 2000;26:703–14.  Della Libera C, Chelazzi L. Learning to attend and to ignore is a matter of gains and losses. Psychological Science 2009;20:778–84.  Raymond JE, O’Brien JL. Selective visual attention and motivation: the consequences of value learning in an attentional blink task. Psychological Science 2009;20:981–8.  Sarter M, Gehring WJ, Kozak R. More attention must be paid: the neurobiology of attentional effort. Brain Research Reviews 2006;51:145–60.  Berridge KC, Robinson TE. Parsing reward. Trends in Neuroscience 2003;26:507–13.  Gehring WJ, Knight RT. Prefrontal–cingulate interactions in action monitoring. Nature Neuroscience 2000;3:516–20.  Hickey C, Chelazzi L, Theeuwes J. Reward changes salience in human vision via the anterior cingulate. Journal of Neuroscience 2010;30:11096–103.  Friedman-Hill SR, Robertson LC, Desimone R, Ungerleider LG. Posterior parietal cortex and the ﬁltering of distractors. Proceedings of the National Academy of Sciences of the United States of America 2003;100:4263–8.  Hopﬁnger JB, Buonocore MH, Mangun GR. The neural mechanisms of top-down attentional control. Nature Neuroscience 2000;3:284–91.
J. Sänger, E. Wascher / Behavioural Brain Research 224 (2011) 58–64
 Pessoa L, Kastner S, Ungerleider LG. Neuroimaging studies of attention: from modulation of sensory processing to top-down control. Journal of Neuroscience 2003;23:3990–8.  Serences JT, Schwarzbach J, Courtney SM, Golay X, Yantis S. Control of objectbased attention in human cortex. Cerebral Cortex 2004;14:1346–57.  Nelson CL, Sarter M, Bruno JP. Prefrontal cortical modulation of acetylcholine release in posterior parietal cortex. Neuroscience 2005;132:347–59.  Sarter M, Givens B, Bruno JP. The cognitive neuroscience of sustained attention: where top-down meets bottom-up. Brain Research Reviews 2001;35: 146–60.  Sarter M, Hasselmo ME, Bruno JP, Givens B. Unraveling the attentional functions of cortical cholinergic inputs: interactions between signal-driven and cognitive modulation of signal detection. Brain Research Reviews 2005;48:98–111.  Berridge KC, Robinson TE. What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Research Reviews 1998;28:309–69.  Servan-Schreiber D, Bruno RM, Carter CS, Cohen JD. Dopamine and the mechanisms of cognition. Part I. A neural network model predicting dopamine effects on selective attention. Biological Psychiatry 1998;43:713–22.  Engelmann JB, Pessoa L. Motivation sharpens exogenous spatial attention. Emotion 2007;7:668–74.  Posner MI, Dehaene S. Attentional networks. Trends in Neuroscience 1994;17:75–9.  Hillyard SA, Vogel EK, Luck SJ. 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 1998;353:1257–70.  Mangun GR. Neural mechanisms of visual selective attention. Psychophysiology 1995;32:4–18.  Shedden JM, Nordgaard CL. ERP time course of perceptual and post-perceptual mechanisms of spatial selection. Cognitive Brain Research 2001;11:59–75.  Wascher E, Hoffmann S, Sänger J, Grosjean M. Visuo-spatial processing and the N1 component of the ERP. Psychophysiology 2009:46.  Wascher E, Beste C. Tuning perceptual competition. Journal of Neurophysiology 2010;103:1057–65.  Wolber M, Wascher E. Visual search strategies are indexed by event-related lateralizations of the EEG. Biological Psychology 2003;63:79–100.  Wolber M, Wascher E. The posterior contralateral negativity as a temporal indicator of visuo-spatial processing. Journal of Psychophysiology 2005;19: 182–94.  Luck SJ, Hillyard SA. Spatial ﬁltering during visual search: evidence from human electrophysiology. Journal of Experimental Psychology: Human Perception and Performance 1994;20:1000–14.  Eimer M. The N2pc component as an indicator of attentional selectivity. Electroencephalography and Clinical Neurophysiology 1996;99:225–34.  Wascher E, Wauschkuhn B. The interaction of stimulus- and response-related processes measured by event-related lateralizations of the EEG. Electroencephalography & Clinical Neurophysiology 1996;99:149–62.  Luck SJ, Woodman GF, Vogel EK. Event-related potential studies of attention. Trends in Cognitive Science 2000;4:432–40.  Pivik RT, Broughton RJ, Coppola R, Davidson RJ, Fox N, Nuwer MR. Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology 1993;30:547–58.  Hanslmayr S, Klimesch W, Sauseng P, Gruber W, Doppelmayr M, Freunberger R, et al. Alpha phase reset contributes to the generation of ERPs. Cerebral Cortex 2007;17:1–8.  Gratton G, Coles MG, Donchin E. A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology 1983;55:468–84.  Coles MGH, Gratton G, Donchin E. Detecting early communication: using measures of movement-related potentials to illuminate human information processing. Biological Psychology 1988;26:69–89.
 Vasey MW, Thayer JF. The continuing problem of false positives in repeated measures ANOVA in psychophysiology: a multivariate solution. Psychophysiology 1987;24:479–86.  Ekstrom LB, Roelfsema PR, Arsenault JT, Bonmassar G, Vanduffel W. Bottomup dependent gating of frontal signals in early visual cortex. Science 2008;321:414–7.  Roelfsema PR, Spekreijse H. The representation of erroneously perceived stimuli in the primary visual cortex. Neuron 2001;31:853–63.  Beste C, Wascher E, Güntürkün O, Dinse HR. Improvement and impairment of visually guided behavior through LTP- and LTD-like exposure-based visual learning. Current Biology 2011.  Lamme VAF, Roelfsema PR. The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neuroscience 2000;23:571–9.  Hickey C, Di Lollo V, McDonald JJ. Electrophysiological indices of target and distractor processing in visual search. Journal of Cognitive Neuroscience 2009;21:760–75.  Jolicoeur P, Brisson B, Robitaille N. Dissociation of the N2pc and sustained posterior contralateral negativity in a choice response task. Brain Research 2008;1215:160–72.  Luria R, Sessa P, Gotler A, Jolicoeur P, Dell’Acqua R. Visual short-term memory capacity for simple and complex objects. Journal of Cognitive Neuroscience 2010;22:496–512.  Vogel EK, Machizawa MG. Neural activity predicts individual differences in visual working memory capacity. Nature 2004;428:748–51.  Vogel EK, McCollough AW, Machizawa MG. Neural measures reveal individual differences in controlling access to working memory. Nature 2005;438:500–3.  Steinmetz MA, Constantinidis C. Neurophysiological evidence for a role of posterior parietal cortex in redirecting visual attention. Cerebral Cortex 1995;5:448–56.  Fan J, McCandliss BD, Fossella J, Flombaum JI, Posner MI. The activation of attentional networks. Neuroimage 2005;26:471–9.  Everling S, Tinsley CJ, Gaffan D, Duncan J. Selective representation of taskrelevant objects and locations in the monkey prefrontal cortex. European Journal of Neuroscience 2006;23:2197–214.  Shulman GL, d’Avossa G, Tansy AP, Corbetta M. Two attentional processes in the parietal lobe. Cerebral Cortex 2002;12:590–600.  Duncan J, Humphreys G, Ward R. Competitive brain activity in visual attention. Current Opinion in Neurobiology 1997;7:255–61.  Constantinidis C, Steinmetz MA. Posterior parietal cortex automatically encodes the location of salient stimuli. Journal of Neuroscience 2005;25:233–8.  Luck SJ, Chelazzi L, Hillyard SA, Desimone R. Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. Journal of Neurophysiology 1997;77:24–42.  Moore T, Armstrong KM. Selective gating of visual signals by microstimulation of frontal cortex. Nature 2003;421:370–3.  Armstrong KM, Moore T. Rapid enhancement of visual cortical response discriminability by microstimulation of the frontal eye ﬁeld. Proceedings of the National Academy of Sciences of the United States of America 2007;104:9499–504.  Armstrong KM, Fitzgerald JK, Moore T. Changes in visual receptive ﬁelds with microstimulation of frontal cortex. Neuron 2006;50:791–8.  Gregoriou GG, Gotts SJ, Zhou H, Desimone R. Long-range neural coupling through synchronization with attention. Progress in Brain Research 2009;176:35–45.  Voytko ML. Cognitive functions of the basal forebrain cholinergic system in monkeys: memory or attention? Behavioural Brain Research 1996;75:13–25.  Dalley JW, McGaughy J, O’Connell MT, Cardinal RN, Levita L, Robbins TW. Distinct changes in cortical acetylcholine and noradrenaline efﬂux during contingent and noncontingent performance of a visual attentional task. Journal of Neuroscience 2001;21:4908–14.  Parikh V, Kozak R, Martinez V, Sarter M. Prefrontal acetylcholine release controls cue detection on multiple timescales. Neuron 2007;56:141–54.