Potential Mechanims of Motivational Effects
Our study, together with the ones cited above and several others, illustrates attempts at understanding how motivation influences cognitive and sensory processes. More generally, what are the neural bases for these effects? At the outset, it is instructive to consider the relationship between motivation and cognition more abstractly. For concreteness, we can consider attention as the cognitive task. We know that attention affects behavior, and one possibility is that motivation has similar effects that take place independently of attention (Figure 3A). A second scenario would suggest that motivation affects behavior by engaging the same set of processes that are used by attention. In this case, the impact of motivation on behavior could be described as mediated by attention (Figure 3B). This mediation could be partial only, such that both direct (motivation → behavior) and indirect (via attention) effects take place. Finally, it is possible to imagine situations in which attention and motivation are more highly interactive, such that they jointly influence behavior (Figure 3C). In this latter case, although one may choose to describe certain processes as “attentional” and others as “motivational”, the interactions between the systems are sufficiently high, and their strict separation is possibly more semantic than real.
Figure 3  Mechanisms of motivational effects on attention. (A–C) Potential, abstract relationships between attention and motivation and their effects on behavior. (D) Modes of communication between cognitive and motivation networks illustrated for attentional-motivational interactions. (1) Interactions rely on “hub” regions, such as the anterior cingulate cortex, which are part of both attentional and motivational networks (indicated via the red outline in both the valuation-cortical and attentional networks). (2) In addition, specific regions may link the two networks, either directly or via the thalamus. (3) Finally, motivational signals are embedded within cognitive mechanisms via the action of diffuse neuromodulatory systems. Methodologically, disentangling attention and motivation may be quite hard, and many effects attributed to motivation could be attentional and vice versa (Maunsell, 2004). Although separating their potential contributions is challenging, some evidence suggests that these processes may be partially dissociable. For instance, Bendiksby and Platt (2006) suggested that cell responses in the lateral intraparietal area (LIP) in the monkey exhibit separate contributions from reward and attention – in the context of a cued-saccade reaction time task that was coupled with blocked rewards. For example, neuronal activity was positively correlated with saccade reaction times, which in their task was considered to reflect the cost of attentional re-orienting, in a way that was independent of reward size. At the same time, modulation by reward size was independent of which type of cue appeared in the neuronal receptive field, with some cues being more predictive of target location than others, therefore putatively capturing more attention. Thus, their results are consistent with the idea that motivation and attention independently contribute to responses in LIP – although a stronger case for their separation would require converging evidence (e.g., in their study, other variables may have affected saccade reaction times, and not only attentional processes).
Both monkey electrophysiology and human neuroimaging research suggest that the control of selective attention relies on a distributed set of fronto-parietal regions, including FEF in frontal cortex and IPS in parietal cortex. These regions, which in many cases appear to work together, are often conceptualized as “source” regions that exert control over sensory-processing areas to help select the information that is most relevant at a given time. One way to interpret the data from the attention studies described in the previous sections is to suggest that motivation acts on cognition to maximize potential reward in a way that relies on robust interactions between the attentional network and other reward/valuation networks. Among others, valuation regions include: (i) subcortically: the caudate, putamen and nucleus accumbens in the ventral striatum, and the amygdala; and (ii) cortically: the OFC, anterior insula, ACC and PCC. During trials in which reward (or punishment avoidance) is possible, valuation and attentional networks interact, resulting in enhanced behavioral performance that is supported by improved selection of sensory information. Critically, reward-related effects on cognitive function are specific (e.g., increased detection performance), as opposed to global (e.g., arousal). Whereas “independent” contributions from attention and motivation (Figure 3A) are not necessarily excluded, the above considerations are suggestive of the “mediation” and “integration” scenarios (Figures 3B,C) described above.
Indeed, we would like to propose that, more generally, the “integration” needs to be more seriously considered. Accordingly, the integration of motivational signals with those that are central to specific executive functions, including task switching, inhibition, and information maintenance, will rely on interactions between specific “cognitive” networks and those involved in determining the behavioral significance of the stimulus or task at hand. For instance, both dorsal (e.g., middle frontal gyrus) and ventral (e.g., inferior frontal gyrus) PFC sites, in addition to regions of parietal cortex (e.g., IPS), are important for maintaining and updating contextually relevant information “in mind.” As in the case of the control of attention, we suggest that working memory-related signals are integrated with motivational ones in these areas. Consistent with this notion, cells in monkey lateral PFC not only hold information concerning an object's shape and location, but are also modulated by reward magnitude (Leon and Shadlen, 1999; Watanabe and Sakagami, 2007). Human neuroimaging studies have shown similar modulations of working memory signals in lateral PFC by reward (e.g., Pochon et al., 2002; Taylor et al., 2004). Furthermore, motivational information does not act simply as an “additive” mechanism; instead, in lateral PFC, cognitive and motivational signals appear to be integrated (see Jimura et al. (in press). For instance, in monkeys, during the delay period of a task involving spatial information, spatial and reward information do more than just add, as there is an increase of the amount of transmitted information concerning target position, as quantified by information theory (Kobayashi et al., 2002). In other words, reward information increases the discriminability of target positions, leading to enhanced performance. In the context of our own studies, in sharp contrast with other proposals (Kouneiher et al., 2009), we have suggested that the effect of motivation goes well beyond an “energizing” (i.e., a generalized “additive”) function and, instead, involves enhancing and/or optimizing executive function (see also below) – a notion supported by the specific increases in detection sensitivity observed in our study; see also Small et al. (2005) and Mohanty et al. (2008).
The interaction between cognitive and motivation networks appears to take place via several modes of communication (Figure 3D). For instance, a specific brain region may function as a hub linking the two types of network. Recent advances in network theory (Guimera and Nunes Amaral, 2005) have shown that regions characterized by a high degree of connectivity, i.e., hubs (Sporns et al., 2007), are critical in regulating the flow and integration of information between regions. However, whereas the number of connections of a region is important in determining whether it will function as a hub, the structural topology of the region is also relevant. For instance, some regions are best characterized as “provincial” hubs (they occupy a central position within a single functional cluster; e.g., visual area V4, Sporns et al., 2007), whereas others act as “connector” hubs (they link separate region clusters). Hubs connecting cognitive and motivation networks would comprise examples of the latter type of region.
An intriguing suggestion by Mesulam et al. is that the PCC provides an important site for the integration of motivational and spatial attention information (Small et al., 2005; Mohanty et al., 2008; see also Platt and Huettel, 2008). In agreement with this suggestion in our neuroimaging study, as reviewed above, the PCC exhibited both motivation and attention signals. Specifically, not only did the PCC exhibit cue-related, target-related and sustained responses that increased with absolute incentive value, but increases in cue-related and sustained responses were correlated with individual trait measures tied to reward sensitivity (in this case, BAS-drive scores). Another, not mutually exclusive, possibility is that the ACC functions as a hub region linking the two types of network. The ACC is known to be important for integrating inputs from multiple sources, including affective and motivational inputs (Devinsky et al., 1995; Rushworth et al., 2007), and, in this respect, works in close cooperation with the anterior insula and OFC. The ACC has also been suggested to be involved in several executive processes, including conflict detection, error likelihood processing and error monitoring, and more generally helps determine the benefits and costs of acting. The ACC is also important for attentional control and controlling limited-processing capacity (Posner and DiGirolamo, 1998; Weissman et al., 2005; Pessoa, 2009). Thus, the ACC is a strong candidate for a hub connecting the two types of network.
In addition to interactions at specific connector hub regions, multiple “point-to-point” interactions may occur (indicated via the purple arrows in Figure 3D) that provide communication pathways between valuation and cognitive regions. For instance, in monkeys, the OFC projects to the ventral part of Brodmann area 46 on the lateral PFC surface (Barbas and Pandya, 1989). Another example includes the caudate nucleus, which is connected with several regions of frontal (including lateral PFC) and parietal cortices, in part via the thalamus (Alexander et al., 1986).
A third type of communication involves the diffuse action of neuromodulatory signals. Motivationally salient items engage dopaminergic cells in the ventral tegmental area (VTA) and SN. Widespread modulatory connections originating in these sites reach the entire cortical surface, thereby having the potential to rapidly influence cortical responses. Evidence from animal studies supports the notion that dopaminergic modulatory effects are associated with behavioral importance, generally (Schultz et al., 1992), and improve attentional accuracy, specifically (Granon et al., 2000; Seamans and Yang, 2004; Pezze et al., 2007). Several studies in humans, including ours, have also reported reward-related activation in dopaminergic centers (e.g., Bunzeck and Duzel, 2006; D'Ardenne et al., 2008) and, more commonly, their subcortical targets (e.g., caudate; both the head and body of the caudate have been reported). It is noteworthy that dopaminergic projections to the frontal lobe are much more significant than to posterior regions and, in particular, the occipital cortex appears to only minimally receive such projections (Oades and Halliday, 1987). These considerations are relevant to the understanding of the impact of motivation on both executive and sensory processes, and suggest that the impact of dopaminergic projection systems on visual function is likely to be relatively minor – though a complicating factor is that the effects could be strong though indirect. If this is correct, the effect on visual function reported in the studies above may be strongly dependent on “source” regions in frontal and parietal cortex that exert top-down modulatory signals on sensory processing.
Although neuromodulation can be viewed as simply another aspect of the suggested network interactions, it is worth separating it from the others for the following reasons. Because neuromodulatory signals target superficial (I–III) and deeper (V–VI) cortical layers, but tend to avoid layer IV (e.g., Raghanti et al., 2008), they appear to provide less of a “driving input” and instead may function to alter information processing. For instance, Goldman-Rakic et al. (1989) suggest that a major function of dopamine is to control cortical excitability, thereby possibly increasing the fidelity of signals computed within local networks (Douglas and Martin, 2007). More specifically, the effects of dopamine appear to enhance the neuronal signal-to-noise ratio (Sawaguchi and Matsumura, 1985), consistent with computational modeling results of the role of dopamine in working memory function (Gruber et al., 2006). Thus, it is intriguing to suggest that dopaminergic neuromodulation may be a key mechanism by which motivation sharpens attention and behavioral performance, for instance via the enhancement of the signal-to-noise ratio of relevant neurons. Therefore, the motivational context, which may be computed in valuation regions, may enhance the processing efficiency in cognitive regions via a dopaminergic signal. In the context of our task, valuation regions (e.g., OFC) signal behavioral relevance to neuromodulatory regions (e.g., VTA), which then enhance neuronal processing in relevant “cognitive” areas via dopamine signals (e.g., fronto-parietal regions). Future multi-site cell recordings may be able to more directly evaluate this working hypothesis. In any case, these specific effects on brain function are envisaged to be quite distinct from a simple “energizing” function.
Taken together, the available evidence suggests that motivation and cognition interact via multiple neural substrates to guide goal-directed behavior (Figure 3D). In particular, one or more of the above modes of communication may be operative at a given time depending on the particulars of the task at hand. More broadly, numerous opportunities for cognitive-emotional interactions exist in the brain, thereby allowing motivational significance to greatly shape complex behaviors.