Summary and Keywords
Natural scenes are cluttered and contain many objects that cannot all be processed simultaneously. Due to this limited processing capacity, neural mechanisms are needed to selectively enhance the information that is most relevant to one’s current behavior and to filter unwanted information. We refer to these mechanisms as “selective attention.” Attention has been studied extensively at the behavioral level in a variety of paradigms, most notably, Treisman’s visual search and Posner’s paradigm. These paradigms have also provided the basis for studies directed at understanding the neural mechanisms underlying attentional selection, both in the form of neuroimaging studies in humans and intracranial electrophysiology in non-human primates. The selection of behaviorally relevant information is mediated by a large-scale network that includes regions in all major lobes as well as subcortical structures. Attending to a visual stimulus modulates processing across the visual processing hierarchy with stronger effects in higher-order areas. Current research is aimed at characterizing the functions of the different network nodes as well as the dynamics of their functional connectivity.
The capacity limit of the visual system to process multiple objects at the same time poses a fundamental problem and necessitates the instantiation of mechanisms that can preferentially route visual information important for ongoing behavior through large-scale brain networks. We will refer to this set of mechanisms that mediates the prioritization of certain neural representations over others and their associated behaviors as “visual attention.” Attention research has been central to the fields of cognitive neuroscience, psychology, and systems neurophysiology. This has led to the discovery of a large number of attention effects at each of these levels of observation, which will be reviewed in this article.
Our review will focus on visual attention, as it has been the most widely studied and is currently the best understood modality. However, the knowledge gained from understanding visual attention is likely to reveal principles of neural processing that may be fundamental to selective processing in other modalities as well as to cognition more generally. Furthermore, even though attention is often studied in isolation, a mechanism that prioritizes task-relevant information will likely interface with many cognitive domains such as action control and decision making, motivation and emotions, memories at different time scales, and awareness. While a discussion of these interactions is beyond the scope of this article, it is important to note that understanding the interaction of selective attention with other cognitive domains will ultimately lay the foundation for reaching a cohesive understanding of the general principles of cognition and their associated neural mechanisms (see Nobre & Kastner, 2014, Handbook of Attention, specifically chaps. 25–30).
The two most commonly used paradigms to study visual attention are visual spatial orienting (Posner, Snyder, & Davidson, 1980) and visual search (Treisman & Gelade, 1980), which will be reviewed initially. In the later parts of this section, we will also highlight more recent behavioral studies that have begun to question some of the notions gained from the classical attention paradigms.
In spatial orienting tasks, subjects are instructed by a predictive cue to direct attention to a particular spatial location where they must detect or discriminate a target stimulus. The classic finding is that subjects benefit from the cue as they respond faster and more accurately to stimuli occurring at the cued location than to stimuli occurring at other locations. This facilitation comes at the expense of other objects in the visual environment, reflecting the competitive nature of attention.
While orienting tasks typically involve only a single target stimulus, visual search tasks more closely relate to our everyday experience, where we typically face cluttered scenes. In search tasks, subjects are given an array of stimuli and asked to find a particular target stimulus defined by one or more features in the array (e.g., find the green “T” in an array of green and blue “T”s and “L”s; see Figure 1A). Hence, in visual search, the selection process is informed by features of the target (i.e., feature-based attention), which then guides spatial attention.
Performance on visual search tasks is affected by how many features the target shares with other stimuli in the array. If the target has a unique feature, such as being of a different color from the distracters, the search is completed quickly and effortlessly, regardless of the number of elements in the array. This phenomenon is known as “pop-out” or efficient (parallel) search. However, just by changing the distractors in the search array, the search for the same target can be made much more difficult. For example, if the target is defined by a conjunction of features that each are shared by distracters (as in Figure 1A), search time increases as a function of the number of elements in the array. This is known as inefficient search and the increase in search times is thought to reflect a serial target search. However, under some circumstances, only a subset of the array needs to be searched. Simple features, such as color, can be used to guide the search to just those elements that share a particular target feature (Wolfe, Cave, & Franzel, 1989). Search difficulty also depends on the similarity of the target to the distracters and to the dissimilarity of the distracters to each other (Duncan & Humphreys, 1989).
Classic attention theories (Posner et al., 1980; Treisman & Gelade, 1980) propose a unique and indivisible “spotlight” of attention that highlights a selected item during visual search, or when attention is allocated in space. To process an entire scene, this spotlight was thought to be continuously moving from location-to-location, shifting at a rate of approximately 20 Hz (Wolfe, Võ, Evans, & Greene, 2011). Previous studies suggested that this shifting may be regular, moving the spotlight of attention in a rhythmic fashion around a visual scene (Buschman & Miller, 2009). Surprisingly, recent evidence shows that even when this spotlight is sustained at one location, it is not static, but rather appears to flash rhythmically. Using EEG, Busch and VanRullen (2010) demonstrated that the detection of a visual target at threshold was systematically related to the phase of an ongoing theta oscillation (~7 Hz). This phase-behavior relationship was contingent on the allocation of attentional resources following a cue and was absent at other locations in the visual field. The cue served not only to guide the deployment of attention, but caused the timing of the high- and low-excitability states of the oscillation to align across trials (see also Lakatos et al., 2009). Thus, it appears that the selection mechanism periodically samples the attended location, with the degree of selection fluctuating with the phase of the neural rhythm. Intriguingly, recent behavioral studies suggest that there may be at least two concurrent spatial mechanisms: the first is the “classic” focusing of attention at a selected location, while the second mechanism rhythmically monitors other locations outside this focus (Figure 1B; Fiebelkorn, Saalman, & Kastner, 2013; Landau & Fries, 2012). Such rhythmic monitoring of other locations and objects may be an important mechanism for flexibly gating the reallocation of attentional resources. It is important to note that the rhythmic monitoring appears to be an automatic process that is distinct from voluntarily splitting or dividing attentional resources across multiple locations. Together, these findings suggest that selective attention falls into the class of rhythmic behaviors and is a highly dynamic and flexible resource. The neural basis of the rhythmic properties of selective attention is unclear and awaits future investigation. Thus, as much as the results of studies using classical attention paradigms have shaped our current theoretical concepts and have been foundational for investigations at the neural level that will be reviewed in the sections on Principles of Attentional Modulation and Functional Network Characteristics, they did not consider the temporal dynamics of attentional selection, a dimension that will need to be included in future theoretical accounts.
Attention mechanisms have evolved to function in real-world scenarios. Recently, there has been a growing number of studies that have asked whether the knowledge and concepts gained from simplified laboratory conditions translate to more ecologically relevant situations. An important first step to investigate attentional prioritization under more naturalistic conditions has been to study the selection of categorical object information from natural scenes (for an in-depth review, see Peelen & Kastner, 2014). In daily life, we select meaningful objects from meaningful scenes such as looking for cars when crossing a street. What would be the behavioral prediction for detecting a car in the scene of Figure 1C based on classic search paradigms? Typical scenes contain dozens of distracter objects with highly variable appearance, and there is not one feature that uniquely defines a target. Based on classical attention theories one would predict a long response time reflecting a particularly inefficient search. However, the opposite is the case. The detection of familiar object categories in scenes is extremely rapid (Thorpe, Fize, & Marlot, 1996) and search is highly efficient—adding additional items to a scene has little cost (Wolfe et al., 2011). Furthermore, one can accurately perform such real-world search tasks while simultaneously performing a second, demanding attention task at fixation (Figure 1D; Li, VanRullen, Koch, & Perona, 2002). This suggests that real-world search of object categories does not require focused spatial attention.
Neuroimaging studies in humans have begun to investigate the neural basis of real-world search by having subjects detect the presence of objects from a target category in briefly presented photographs (Peelen & Kastner, 2011; Peelen, Fei-Fei, & Kastner, 2009), or short movie segments (Çukur, Nishimoto, Huth, & Gallant, 2013). It was found that the pattern of neural activity in object-selective cortex evoked by the scenes fully depended on task-relevance: target objects embedded in natural scenes were only represented when one was actively searching for them. Responses in many parts of the brain increased with the appearance of a stimulus in the target category, or a semantically similar category, suggesting that category-based attention may have widespread influences on brain activity. Together, these results provide neural evidence that the attentional selection mechanism that biases the processing of scenes acts at the level of natural categories. Future work is needed to extend our traditional concepts of attention to incorporate mechanisms that are optimized for naturalistic conditions. Key to this will be the development of appropriate paradigms in animal models in order to study the underlying neural mechanisms in greater detail.
Studies based on careful observations of behavior have provided the foundation not only for theoretical accounts of selective visual processing, but also for the investigations that are aimed at revealing its underlying neural mechanisms. As mentioned earlier, many of these studies use variations of either the Posner’s or Treisman’s visual search tasks. Thus, it is important to consider that our understanding of the neural basis of attention is currently limited to the behavioral effects observed in these particular tasks.
Principles of Attentional Modulation
In the primate brain, attentional selection is mediated by a large-scale network of regions, including the frontal, parietal, temporal, and occipital cortex as well as thalamic and midbrain regions (Corbetta & Shulman, 2002; Ungerleider & Kastner, 2000). As we will review in the section on Functional Network Anatomy, there is a functional dissociation between higher- and lower-order areas as well as between cortical and thalamic regions. What is common to all areas involved in this large-scale network is that their neural responses—as observed at the single-cell and the population level—get modulated by selectively attending or anticipating a visual stimulus. These modulatory effects have been prominently studied in primate extrastriate cortex, but they constitute a set of possible principles that will likely extend to selective processing in other modalities as well as to other species.
When attention is allocated to a spatial location, feature, or object, its neural representation is enhanced relative to when attending elsewhere. This enhancement occurs in many different ways, ranging from changes in the responses of single neurons to changes in the dynamics of populations of neurons. We briefly review these enhancements next.
Spatial Attention Enhances Neural Responses
In one of the first studies probing attention effects in the primate brain, it was reported that directing spatial attention into the receptive field of a single parietal cortex neuron increased its response to a stimulus (Bushnell, Goldberg, & Robinson, 1981). Since then, studies in monkeys and humans have shown that spatial attention increases neural responses to a selected stimulus across many levels of processing. This includes cortical visual areas, such as V1, V2, V4, MT, MST, and IT (e.g., Chelazzi, Miller, Duncan, & Desimone, 1993; Luck, Chelazzi, Hillyard, & Desimone, 1997; Spitzer, Desimone, & Moran, 1988; Treue & Maunsell, 1999), as well as subcortical regions such as the lateral geniculate nucleus, pulvinar, reticular nucleus of the thalamus, and superior colliculus (McAlonan, Cavanaugh, & Wurtz, 2006; O’Connor, Fukui, Pinsk, & Kastner, 2002; Zénon & Krauzlis, 2012). The magnitude of the spatial attention effect increases along the cortical hierarchy, reaching its strongest effect in higher-order regions, such as prefrontal and parietal cortex (Rainer, Asaad, & Miller, 1998). Similarly, spatial attention effects seem to occur first in higher cortical regions and then cascade backwards (Buffalo, Fries, Landman, Liang, & Desimone, 2010). However, spatial attention does not simply increase the response rate of neurons but also increases a neuron’s sensitivity to stimuli. For example, spatial attention shifts the contrast-response function of single neurons in V4 and MT such that a neuron is more sensitive to low-contrast stimuli (Figure 2A, Martínez-Trujillo & Treue, 2002; Reynolds, Pasternak, & Desimone, 2000). By increasing the neuronal sensitivity, the perceived contrast of a stimulus can be increased due to attentional allocation, thus improving behavioral performance (Carrasco, Ling, & Read, 2004).
Attending to features. Attention can not only be directed to a specific location, but also to a stimulus feature. Such featural attention influences single neuron responses in much the same way as spatial attention, increasing the sensitivity of neurons that respond preferentially to stimuli matching the sought-after feature (in V1 and V4, Haenny & Schiller, 1988; in MT, Treue & Trujillo, 1999). Similar results have been reported in human neuroimaging studies (Saenz, Buracas, & Boynton, 2002). Selection of a desired feature also suppresses neurons with response properties of differing selectivity (Martínez-Trujillo & Treue, 2004).
Increases of baseline activity. Thus far, we have discussed the effects of attention on visually evoked activity; that is, attention can modulate the degree to which a visual stimulus evokes activity in visually responsive areas. However, it is also the case that effects of attention have been measured in these areas in absence of visual stimulation. Specifically, spontaneous firing rates, or so-called baseline responses, in V2 and V4 increased when an animal was cued to attend to the location within the cell’s RF but before a visual stimulus actually appeared (Lee et al., 2007; Luck et al., 1997). Similar baseline increases were observed in human visual cortex using fMRI when attention was directed to a spatial location, or a feature was anticipated (Fig. 3C, area V4; Kastner, Pinsk, De Weerd, Desimone, & Ungerleider, 1999; O’Connor et al., 2002; McMains, Fehd, Emmanouli, & Kastner, 2007; Müller et al., 2003; Serences & Boynton, 2007; Sylvester et al., 2007). These fMRI studies showed that, in fact, every visual area that typically shows an effect of attention on visually evoked activity also showed a baseline increase. It is interesting to note that the magnitude of the baseline increases does not predict the size of the attention effect during visual presentations (McMains et al., 2007), indicating that the effects of attention on visually evoked activity are not simply the addition of a baseline increase but rather result from some nonlinear interaction of effects on the baseline and the visually evoked activity. Indeed, because baseline increases occur in the absence of stimulation, they are by nature purely additive gains. They cannot then account for multiplicative and contrast gain responses that, by definition, vary as a function of the visually evoked activity, although the baseline increases may still contribute to those gains.
Attention changes population codes. Although many of the effects of attention are observed at the level of single neurons, they also impact representations at the population level. For example, increasing the sensitivity of selected neurons will lead to an increase in the selectivity of the entire population. In addition, attention also acts to directly change the way information is represented in populations of neurons. One way that attention improves the encoding of information in a neuronal population is by decreasing noise correlations (Cohen & Maunsell, 2009; Mitchell, Sundberg, & Reynolds, 2009). Noise correlations measure the degree to which neurons share uninformative signals that vary from trial-to-trial. Since each neuron has a limited bandwidth, correlations in their signal reduces the information-carrying capacity of the population as a whole. This is perhaps most easily seen in the extreme: If each neuron carried the same signal, then the amount of information carried by the entire population would be the same as by any one neuron. Therefore, by reducing noise correlations, attention can significantly increase the information capacity of the population (Figure 2B).
However, not all correlations have a negative impact. Attention also increases the synchrony of selected populations of neurons, particularly at high frequencies (~40–50 Hz, Fries et al., 2001; Figure 2C). This is thought to boost the transmission of information from the selected population. It is important to note that such temporal synchrony is orthogonal to noise correlations: Information is carried in the pattern of firing across a population of neurons, and redundancy in that population (such as in the case of noise correlations) reduces the information capacity of a network; synchrony is local coincidence in time and ensures the temporal precision of the firing pattern in order to drive downstream neurons. Exactly how such synchrony arises remains unknown. However, it may be under top-down control. For example, microstimulation of FEF induces high-frequency oscillations in parietal cortex in a topographic manner (Premereur, Vanduffel, Roelfsema, & Janssen, 2012).
Attention resolves competition. Thus far we have largely discussed how attention impacts the representation of isolated stimuli. However, the need for attention is greatest when multiple stimuli are present and thus there is competition among stimuli for neural representation. In a now classic experiment, Moran and Desimone demonstrated how competition is resolved within single V4 neurons (1985). When two stimuli were simultaneously presented in the receptive field of a V4 neuron they competed with one another, reducing the overall response of the neuron (Figure 2D, purple line). However, this effect was counteracted by attention: When attention was directed to one of the two stimuli in the receptive field, the neuron responded as if only the attended stimulus was presented (Figure 2D, pink dashed line). Similar results have been found in MT and MST (Treue & Maunsell, 1999), and corroborating evidence has been obtained in human neuroimaging studies (reviewed in Beck & Kastner, 2009). Biasing the competition between stimuli can also be conceived as a shift in a neuron’s selectivity: Spatial attention collapses the receptive field of neurons toward the attended location (Connor, Preddie, Gallant, & Essen, 1997; Womelsdorf, Anton-Erxleben, Pieper, & Treue, 2006) while featural attention shifts the tuning curve of neurons toward an attended feature (David, Hayden, Mazer, & Gallant, 2008; Martínez-Trujillo & Treue, 2004).
Biasing competition through normalization. Competition between stimuli is likely the result of a normalization process. Responses in cortex are normalized such that a constant level of overall activity in a region is maintained. For example, the response of V1 neurons to multiple stimuli of varying contrasts closely fits the predictions of a normalization model (Busse, Wade, & Carandini, 2009). The exact neural mechanisms responsible for normalizing responses remain unknown, although several possibilities have been discussed (for review, see Carandini & Heeger, 2012). In particular, one account that fits well with other observed effects of attention is that divisive normalization is the result of lateral inhibition within a cortical region (Lee et al., 2012; Wilson, Runyan, Wang, & Sur, 2012; but also see Nassi, Gómez-Laberge, Kreiman, & Born, 2014, for a role for feedback). Normalization is thought to be important for cortical functioning for several reasons. First, by bounding the overall activity level, normalization may reduce the risk of pathologically high levels of excitation. Second, normalization acts to sparsify responses in a cortical region, possibly contributing to the formation of the optimal, sparse, responses described above (Schwartz & Simoncelli, 2001). Finally, as we will detail, a wide range of the attention effects reviewed above have been modeled as the result of attention modulating the gain of normalization (Reynolds & Heeger, 2009; Reynolds, Chelazzi, & Desimone, 1999).
The normalization model is able to explain how spatial attention can increase the contrast gain of neurons (Reynolds, Pasternak, & Desimone, 2000), the response gain (Williford & Maunsell, 2006), or multiplicatively scale responses (McAdams & Maunsell, 2000). Further, it captures the sharpening of tuning curves with featural attention (David et al., 2008; Martínez-Trujillo & Treue, 2004). Finally, like the biased competition model, the normalization model of attention also predicts the response to multiple stimuli in a single receptive field (Moran & Desimone, 1985).
Lateral inhibition carried out by inhibitory interneurons is a candidate mechanism that may instantiate the normalization computation. If so, one would expect significant attentional modulation of the inhibitory neurons that are computing the normalization signal. Indeed, attention has a much larger impact on the responses of putative inhibitory interneurons compared to putative pyramidal cells (Mitchell, Sundberg, & Reynolds, 2007). Direct evidence for top-down targeting of inhibitory interneurons comes from a recent study showing that long-range projections from cingulate cortex to visual cortex in mice increase center-surround modulation via local inhibitory interneuron circuits (Zhang et al., 2014).
Furthermore, a model that centers on lateral inhibition has the potential to explain a diverse set of attention effects. For example, as noted by Reynolds and Heeger (2009), such recurrent models of normalization may capture the temporal dynamics of attention effects (namely, the lack of an attention effect on the visual transient) or the alterations of the size and center of receptive fields. Lateral inhibition has also been shown to increase the sparsity of neural representations (Schwartz & Simoncelli, 2001). As sparser signals are more likely to be independent to one another, this will lead to a reduction in noise correlations (as observed; Cohen & Maunsell, 2009).
Detailed mechanistic accounts of attentional modulation have been established for processing in visual cortex. Next we will review the functional neuroanatomy and the involvement of areas beyond visual cortex.
Functional Network Anatomy and Dynamic Connectivity
Fronto-parietal attention network. Early evidence that attentional selection involves a distributed large-scale network that encompasses higher-order cortex comes from neuropsychological studies of human patients showing that unilateral brain lesions, especially of higher-order cortex, may cause impairment in spatially directing attention to the contralateral hemifield. This syndrome is known as visuospatial hemineglect. In severe cases, patients suffering from neglect will completely disregard the visual hemifield contralateral to the side of the lesion (e.g., Bisiach & Vallar, 1988). This leads to deficits in everyday behaviors; patients will read from only one side of a book, apply make-up to only one half of their face, or eat from only one side of a plate.
Visuospatial neglect may follow unilateral lesions at different sites, including most frequently the temporo-parietal junction (Mort et al., 2003) and superior temporal cortex (e.g., Karnath, Ferber, & Himmelbach, 2001). Neglect is also, but less frequently, observed following damage of the frontal lobe (e.g., Damasio, Damasio, & Chui, 1980), the anterior cingulate cortex (e.g., Janer & Pardo, 1991), other sites in parietal cortex such as the superior parietal lobule (Kenzie et al., 2015), the basal ganglia (e.g., Damasio et al., 1980), and the thalamus, in particular, the pulvinar (e.g., Karnath, Himmelbach, & Rorden, 2002). The syndrome is not confined to cortical lesions, but can also result from white matter lesions that affect structural connections between nodes of the attention network (Lunven et al., 2015). Importantly, neglect occurs more often with right-sided lesions than with left-sided lesions, which has been taken as evidence for a specialized role of the right hemisphere in attentional selection. This observed hemispheric asymmetry led to the “hemispatial” theory, which proposes that the right hemisphere directs attention to both visual hemifields, whereas the left hemisphere directs attention to the right visual field only (Heilman & Van Den Abell, 1980). Thus, while left hemispheric damage can be compensated for by the right hemisphere, such compensation will not be possible with right hemispheric damage, thereby resulting in neglect of the left visual field.
Human neuroimaging studies of the intact brain have provided a more detailed account of the neuroanatomy of the attention network than the patient studies. When subjects attend to a location in space in anticipation of the appearance of a stimulus, neural signals increase in a fronto-parietal network consisting of regions within the superior parietal lobule (SPL), the intraparietal sulcus (IPS), the frontal eye field (FEF), and the supplementary eye field (SEF; see Figure 3A for full map). This dorsal fronto-parietal attention network has been implicated in many visuospatial tasks, regardless of whether target stimuli were detected, discriminated, or tracked in visual space (Ungerleider & Kastner, 2000) and regardless of whether the task required spatial attention, spatial working memory, or planning saccades (Jerde, Merriam, Riggall, Hedges, & Curtis, 2012).
The fronto-parietal attention network is also activated when subjects select nonspatial information. In studies of feature-based attention, similar activations have been found when subjects shift attention from one feature to another (e.g., from color to direction of motion in a display of colored, moving dots (Greenberg, Esterman, Wilson, Serences, & Yantis, 2010), or when subjects shift attention between two spatially overlapping objects and perform object-based selections (Serences, Schwarzbach, Courtney, Golay, & Yantis, 2004). Together, these studies suggest that the fronto-parietal network is a “domain-general” controller without much functional specialization. However, it is not clear whether distributed subpopulations within this network subserve specific functions needed for space-, feature-, or object-based attentional control.
It is important to note that the fronto-parietal network consists of a large number of topographically organized areas that coordinate their functional operations (Figure 2A). Thus far, nine topographically organized areas have been found in posterior parietal and frontal cortex, each containing a continuous representation of the contralateral visual field (for review, Silver & Kastner, 2009). The delineation of topographic organization in higher-order cortex in individual subjects has permitted a more systematic study of the dorsal attention network in the human brain.
In line with the topographic organization, spatial attention increased responses more strongly when directed contra- rather than ipsilaterally (Szczepanski, Konen, & Kastner, 2010), thus generating a contralateral spatial biasing signal in each topographic region. The sum of the biasing signals across areas was of similar magnitude across the two hemispheres, suggesting a balanced spatial control system in the intact brain. Thus, visual space within a hemifield appears to be largely controlled by the contralateral hemisphere, contradicting the hemispatial theory of attention control. Instead, these studies have provided evidence for an alternative model of neglect, first proposed by Kinsbourne (1977). According to this “interhemispheric competition account,” spatial attention uses an opponent processor control system, in which each hemisphere directs attention toward the contralateral visual field. In an intact system, the two hemispheric processors are balanced through mutual reciprocal inhibition, possibly through direct callosal connections, or through cortico-subcortical interactions of parietal cortex and superior colliculus. The interhemispheric competition account of attention control has received further empirical support in transcranial magnetic stimulation studies showing that attentional biasing signals can be altered in predictable ways by perturbing the fronto-parietal control system (Szczepanski & Kastner, 2013).
While Kinsbourne’s original model was not able to account for the right hemispheric dominance observed with the neglect syndrome, the functional brain imaging studies in the intact brain have shown several asymmetries in the strengths of attentional biasing signals across the nodes of the dorsal attention network (Szczepanski et al., 2010). These asymmetries can theoretically account for the observed right hemispheric dominance. Further support for the Kinsbourne model comes from clinical studies in patients suffering from hemineglect following a stroke to the right superior temporal cortex, who show reduced activity in the right relative to the left dorsal parietal attention network, even though these brain regions are structurally intact (Corbetta et al., 2005; Corbetta, Kincade, Lewis, Snyder, & Sapir, 2005). Thus, the attentional deficits observed in these patients may be explained by a distal impact of the lesion. This results in an imbalance of attentional biasing signals generated by each hemisphere and, thus, an imbalance in the ability to control contralateral space. This imbalance is also accompanied by a breakdown of functional connectivity within the dorsal network between the two hemispheres (He et al., 2007).
The ventral attention network. In humans, selective attention appears to involve two large-scale networks that are spatially and functionally segregated, a dorsal fronto-parietal network that we have discussed thus far and a ventral fronto-parietal network, respectively. While the dorsal network engages both hemispheres with the few asymmetries that we noted, the ventral network is largely lateralized to the right hemisphere and consists of areas in the region of the temporo-parietal junction (TPJ) and inferior frontal cortex (Corbetta & Shulman, 2002). Studies investigating activity patterns during a resting state confirmed that these networks constitute separate entities that were identifiable by characteristic coherence of signal fluctuations during the resting state that can be used as neural signatures of functional connectivity within large-scale networks (He et al., 2007).
Initial studies suggested that the ventral network reflected a reorienting of spatial attention to a new location in the visual field, where a stimulus had unexpectedly occurred (Corbetta, Kincade, Ollinger, McAvoy, & Shulman, 2000). Particularly, it was shown that the dorsal, but not the ventral network, became activated when spatial attention was directed to and maintained at a cued location, thus when a specific attentional set was instantiated and maintained. In contrast, the ventral network was activated when spatial attention needed to be redirected to a location that was not previously cued, where a target had appeared unexpectedly. Subsequently it was shown that such activation did not require a spatial redirection of attention, but occurred also in so-called oddball tasks performed at a fixed spatial location, in which infrequently presented target stimuli had to be detected within a series of visual stimuli. Importantly, the ventral network is engaged during the detection of infrequently occurring target stimuli independent of their sensory modality, or the specific response demands of the task (Downar, Crawley, Mikulis, & Davis, 2000). Together, these studies indicated that the ventral attention network constituted a general domain mechanism for the detection of salient and infrequently occurring events in the environment. It should be noted that the ventral attention network is considered a “nonspatial” network, that is, a network that does not represent spatial information systematically. Indeed, topographic organization has not yet been identified in the brain regions that constitute the ventral attention network.
Importantly, it has been demonstrated that activation of the ventral attention network does not occur with any salient or infrequent sensory stimulus, but only with stimuli that are relevant to the current task set (Downar, Crawley, Mikulis, & Davis, 2001). Further, the TPJ has been shown to be deactivated depending on task relevance and subjects’ focus (Shulman, Astafiev, McAvoy, d’Avossa, & Corbetta, 2007), which has been interpreted as a filter mechanism that prevents reorienting to unimportant sensory stimuli. Such deactivation of the ventral network may be controlled by the dorsal attention network to maintain and optimize its attentional task set.
The ventral attention network, and in particular the TPJ, have also been shown to be activated in a number of tasks that involve self-referencing and are important in social cognition. For example, the TPJ is involved in “theory of mind” cognition; these are tasks during which subjects reason about other people’s mental states (Fletcher et al., 1995; Gallagher & Frith, 2003) or feel empathy for others (Decety & Lamm, 2007). While it is not clear whether the same populations of neurons in the region of the TPJ are involved with tasks that involve redirecting attention versus those that involve referencing oneself to another person’s state of mind, it is conceivable that the reorienting function of the TPJ is not restricted to sensory events in the environment, but can also be applied to changing a self-referential context (Graziano & Kastner, 2011). Thus, the ventral network may be a general purpose network for re-referencing social context that could involve events driven by the environment as well as by cognitive states.
Functional dissociations of the network. Thus far we have highlighted the distributed nature of attentional processing, which is mediated by the strongly interconnected anatomy of the brain, thereby ensuring that any information is quickly shared between regions. In this framework, computations and behavior do not arise from a single brain region but rather emerge through interactions between regions. However, this does not imply that each brain region does exactly the same computation. There are important functional dissociations that can be drawn between regions.
One broad functional dissociation that has been made is that the higher-order fronto-parietal cortex acts as the “source” of modulatory attention-related signals that are fed back to sensory cortex. This dissociation was observed in early human neuroimaging studies showing that when attention was directed to the location of an upcoming stimulus, activity in frontal and parietal cortex was sustained relative to activity in visual cortex, reflecting the attentional operations of the task and not sensory processing (Figure 3C; Kastner et al., 1999). This notion was later elegantly extended in seminal studies by Moore and colleagues establishing causal influences of frontal cortex onto extrastriate processing areas using electrical microstimulation in combination with electrophysiological recordings in non-human primates (Moore & Armstrong, 2003). To understand the different contributions of frontal and parietal cortex in controlling attention, we will now discuss electrophysiological studies in non-human primates in greater depth.
The large-scale (dorsal) fronto-parietal attention network seen in humans is generally conserved in non-human primates. For spatial selection, important parts of the network include frontal cortex (lateral prefrontal; lPFC; and the frontal eye fields, FEF) as well as a region within the intraparietal sulcus (lateral intraparietal area, LIP). In addition, a recent neuroimaging study has shown evidence for a role of medial posterior parietal cortex including areas V6 and V6A in mediating dynamic shifts of attention across the visual field (Premereur, Janssen, & Vanduffel, 2015). Shifts in attention are reflected in single neuron responses in all of these regions (e.g., FEF, Bichot & Schall, 1999; LIP, Bisley & Goldberg, 2003).
What then distinguishes these regions? To answer this question, Buschman and Miller (2007) used large-scale, multiple electrode recording techniques to simultaneously record the activity of neurons in lPFC, FEF, and LIP. They found that, when a monkey’s attention was externally captured by a salient stimulus (i.e., by a “pop-out” stimulus, see above), this was reflected first in LIP neurons and then in FEF neurons, suggesting a flow of information from parietal to frontal cortex. In contrast, when attention was internally directed by the memory of the target stimulus (i.e., during a conjunction search), such voluntary control of attention originated in the frontal cortex, and information flowed back to the parietal cortex. These results are corroborated by similar results in humans (Li, Gratton, Yao, & Knight, 2010) and by simultaneous recording studies showing that PFC exerts greater executive control relative to PPC in a rule-based spatial categorization task (Crowe et al., 2013). Another important study recording simultaneously from lPFC and LIP showed that both regions exhibited similar responses during target selection, but differed with respect to their contributions to distracter suppression. Distracters were more strongly suppressed in lPFC relative to LIP, and the activity of prefrontal neurons also correlated more closely with behavioral performance (Suzuki & Gottlieb, 2013).
These results suggest that frontal and parietal cortex play different roles in guiding attention. First, parietal cortex (LIP) encodes a “saliency” map of the visual scene, encoding which locations in space are of potentially high significance. Such saliency is largely defined by the properties of the stimuli. Consistent with this model, LIP neurons will respond to a highly salient, transiently flashed stimulus (Bisley & Goldberg, 2006; Gottlieb et al., 1998) and encode the saliency of stimuli in a visual scene (Arcizet, Mirpour, & Bisley, 2011). In contrast, neurons in frontal cortex carry information about task-relevant stimuli, not necessarily the most salient stimulus (Hasegawa, Matsumoto, & Mikami, 2000) and serve to filter distracter information. Furthermore, inactivating lateral PFC disrupts tasks requiring top-down, internal direction of attention (Iba & Sawaguchi, 2003). However, inactivation of LIP also appears to lead to deficits in target selection, which makes this distinction less clear (Wardak, Olivier, & Duhamel, 2004).
Important evidence that the prefrontal cortex is the source of top-down signals comes from the work of Moore and colleagues, who found that electrical stimulation of the frontal eye fields (FEF) can induce attention-like effects. Stimulation of FEF increases the animal’s behavioral discriminability at the location of the FEF receptive fields, as if attentional resources had been directed there (Moore & Fallah, 2004). Furthermore, attention-like effects were observed in V4 neurons whose receptive fields overlapped with the stimulated FEF neurons (Figure 2D; Moore & Armstrong, 2003). Causal manipulations in humans using TMS have corroborated these findings by showing qualitatively similar effects (Ruff et al., 2006).
Dynamic functional connectivity. Despite these functional dissociations, it is clear that the fronto-parietal network works as a cohesive unit to direct attention based on a multitude of factors. This then raises the question—how can one network dynamically adapt to changing requirements as the situation or goals change? More globally, how might the fronto-parietal network induce attention by biasing connections throughout the brain? This isn’t likely due to anatomical changes; changes in behavior simply happen too quickly. Instead, changes in the effective connectivity between interconnected regions allows for the large-scale network to adapt as needed.
Changing the synchrony of neurons is one mechanism that may modulate effective connectivity. Theoretical and experimental work has shown that increasing the synchrony of inputs into a single neuron has a super-additive effect (Azouz & Gray, 2000; Salinas & Sejnowski, 2001). Therefore, modulating the synchrony of a population of neurons will dynamically change their downstream impact. Therefore, one way to increase the strength of an attended stimulus would be to increase the synchrony of neurons representing that stimulus. Early experimental support for such a model came from the somatosensory system, where Steinmetz and colleagues (2000) found that attending to tactile stimuli increased the synchrony of neurons. Studies on visual attention showed that neural synchrony increased in a highly specific way, that is, attention increased the high-frequency (40–80 Hz) synchronous oscillations and decreased the low-frequency (<10 Hz) oscillations in populations of neurons representing the attended location (Fries, Reynolds, Rorie, & Desimone, 2001; Womelsdorf, Fries, Mitra, & Desimone, 2006).
In addition to boosting the effectiveness of local neuronal populations, increasing synchrony between brain regions may also change inter-areal effective connectivity. Oscillations in population activity likely reflect the ebb-and-flow of inhibition in a local network. Therefore, aligning such oscillations across regions could ensure that populations of neurons in interconnected regions will be in a co-excitable state, which is one possible way to boost effective connectivity (Figure 4A; Bressler, 1996; Fries, 2005). There is growing evidence for such a model (Buschman & Miller, 2007; Gregoriou, Gotts, Zhou, & Desimone, 2009a; Saalmann, Pigarev, & Vidyasagar, 2007; Siegel, Donner, Oostenveld, Fries, & Engel, 2008). In particular, a recent study by Fries and colleagues demonstrated that synchrony between regions can be highly selective, acting on a single visual object (Bosman et al., 2012). By recording simultaneously from populations of V1 neurons with receptive fields encompassing one of two stimuli as well as from V4 neurons whose receptive field overlapped both stimuli (Figure 3B, middle), they showed that, when attention was directed to a single stimulus, gamma-band oscillations were selectively synchronized between V4 and only those V1 neurons that encoded the attended stimulus location (Figure 4B, left and right).
These effects of synchronization are not limited to visual cortex or specifically to the gamma frequency band. Buschman and Miller (2007) found that synchrony between prefrontal and parietal cortex differed depending on whether attention was being externally captured by a salient stimulus or internally directed based on a remembered target (Figure 4C). When attention was externally captured and information flowed in a bottom-up manner (from parietal cortex to prefrontal cortex), synchrony was observed at gamma-band frequencies (the same frequency found in visual cortex). In contrast, when attention was internally controlled, and information flowed “top-down” from prefrontal to parietal cortex, synchrony between prefrontal and parietal cortex was at a lower-frequency “beta” band. These results suggest that attention modulates synchrony between brain regions in order to guide information flow between regions in a task-specific manner. Furthermore, these results provided the first evidence that specific frequency bands might serve specific purposes: beta-band oscillations may increase “top-down” signaling while gamma-band oscillations increase “bottom-up signals.”
Role of the thalamus. Importantly, communication in cortical large-scale networks is not only mediated through cortico-cortical interactions, but also through thalamo-cortical interactions (see Sherman, 2016, for review). In particular, there is growing evidence that the pulvinar, the largest nucleus in the primate thalamus, plays a key role in attention. The pulvinar is considered a higher-order thalamic nucleus, because it forms input-output loops almost exclusively with the cortex, thereby forming cortico-thalamo-cortical pathways. As a general principle, directly connected cortical areas will be indirectly connected via the pulvinar (Jones, 2001; Shipp, 2003). This indirect connectivity may be used to facilitate information transfer between cortical areas. During spatial selection, increased synchrony of slow frequency oscillations in the alpha band between two interconnected visual cortical areas (V4 and TEO) resulted from pulvino-cortical rather than cortico-cortical communication (Figure 4D; Saalmann, Pinsk, Wang, Li, & Kastner, 2012). In addition, these slow oscillations were coupled to higher-frequency oscillations in the gamma band in each cortical region. Such cross-frequency coupling may be an effective mechanism for coordinating long-range communication across a network, with lower-frequency oscillations controlling the excitability of local neural populations in order to facilitate the coupling of higher-frequency oscillations (Canolty et al., 2006; Lakatos, Karmos, Mehta, Ulbert, & Schroeder, 2008). This mechanism may provide a bridge between cortico-cortical and thalamo-cortical mechanisms for large-scale communication. These studies have begun to provide a mechanistic framework for behavioral observations showing that pulvinar lesions or inactivations impair orienting responses and the exploration of visual space (Ward, Danziger, Owen, & Rafal, 2002; Wilke, Turchi, Smith, Mishkin, & Leopold, 2010).
Attention research has moved from laboratory scenes to the real world at the behavioral level, and from the single neuron to local populations and functional interactions across large-scale networks at the neural level. Future work is needed to continue building a detailed, mechanistic understanding of attention. Detailed circuit models of attention will require continuing efforts to identify cell types and quantify their role in attention. In addition, a unified theory must account for the dynamics of attention. Studying these dynamics will require accurately following the time-course of neural correlates of attention across large populations of neurons throughout the brain. Such large-scale recordings will require continued improvements in multi-electrode electrophysiology techniques and/or imaging approaches. This appears particularly important as neural dynamics may be key to understanding the enormous flexibility of attentional resources. Yet, the neural mechanisms supporting these dynamics remain largely unknown. Finally, we must continue to integrate attention with other cognitive domains. The parcellation of behavior and brain into different cognitive domains has yielded important insights into the neural mechanisms of many behaviors. However, cognition emerges from the interactions of these “cognitive domains”; thus, a complete understanding of cognition will require a more integrative approach.
This article builds upon and extends a recent review of the authors (Buschman & Kastner, 2015). We thank the National Institutes of Health, Office of Naval Research, National Science Foundation, and the James S. McDonnell Foundation for grant support of our research (SK: NIH 1RO1-EY017699, NIH 2R01MH064043, NSF BCS-1328270; TJB: NIH R00-MH092715, NIH DP2-EY025446, and ONR N000141410681).
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