Highlights
. Fluid intelligence tests predict success in many activities, suggesting cognitive mechanisms of broad importance.
. We propose a core process of attentional integration. Complex problems must be segmented into simpler parts. Attention to each part integrates cognitive fragments into a computational structure.
. Fluid intelligence is linked to the brain’s multiple-demand (MD) system, defined by common activity across different cognitive demands. Across the brain, MD patches shows anatomical and physiological properties adapted to attentional integration.
. Neurophysiology of putative MD regions shows adaptive coding of task-relevant information. Suiting attentional integration, many neurons show conjunctive coding (e.g., binding cognitive operations to their target objects).
. In broad outline, these results suggest how distributed brain activity builds organized cognition.
Abstract: How does organized cognition arise from distributed brain activity? Recent analyses of fluid intelligence suggest a core process of cognitive focus and integration, organizing the components of a cognitive operation into the required computational structure. A cortical ‘multiple-demand’ (MD) system is closely linked to fluid intelligence, and recent imaging data define nine specific MD patches distributed across frontal, parietal, and occipitotemporal cortex. Wide cortical distribution, relative functional specialization, and strong connectivity suggest a basis for cognitive integration, matching electrophysiological evidence for binding of cognitive operations to their contents. Though still only in broad outline, these data suggest how distributed brain activity can build complex, organized cognition.
Keywords: intelligenceattentioncognitive controlbrain networksneural coding
Concluding Remarks and Future Directions
Many issues are raised by the integration account. Here we discuss two: the interface of short-term cognitive activity and long-term knowledge, and the nature of attentional capacity limitations.
As implied by our discussion of positive manifold, a core question is interface between on-line cognition and long-term knowledge. As in classical symbolic artificial intelligence (e.g., [97]), a complex problem is divided into simple parts on the basis of long-term knowledge of the structure of the world and relations within it. It is knowledge that tells us how travel to Japan can be divided into component steps, how a useful move can be made in proving a mathematical theorem, or where we should look in seeking a solution to a spatial puzzle. In the brain, knowledge that might shape current cognition is distributed across multiple brain systems. Semantic memory, for example, may be based around a proposed hub in the temporal pole [98], while episodic memory, spatial knowledge, and social knowledge are linked to distinct components of the default mode network [99]. To understand MD activity in constructing solutions to cognitive problems, we need to know how multiple aspects of knowledge feed into this process. Again, this is reminiscent of the widespread connectivity of MD regions (Figure 5) and our finding that multiple networks have representatives in the MD penumbra.
In classical artificial intelligence, problem solutions were often built up in an unlimited working memory, keeping track of a progressively more complex structure of goals and subgoals. For biological cognition this is not plausible; for goals such as travel to Japan or solving a scientific problem, only a small fraction can be represented in active neural firing at any one time, with the rest of the structure in long-term memory, ready for retrieval when required. At the same time, the current active focus of attention must remain bound to the long-term structure, so that, for example, a failure to progress to a goal by one route can trigger a search for an alternative. The issue is reminiscent of recent biological accounts of working memory, combining active neural firing with storage through short-term synaptic change [100,101]. It is presently unknown how the focus of attention in active cognition can be situated within a complex, long-term representation of the larger-scale problem.
A further open issue concerns the well-known capacity limitations of ‘attention’, reflected in difficulty carrying out several tasks at once [102,103]. Shared demands on MD activity could provide an obvious basis for such limits and, indeed, various authors have linked capacity limitations to the functions of frontal and parietal cortex [16,22,104,105]. Such proposals find support in neurophysiological studies, showing that, in frontal and parietal cortex, there is interference between representations of different visual stimuli [106], working memory items [107], or task components [86,108]. Further work is needed, however, to understand the physiological basis of this interference. In the visual system, capacity limits in representing multiple stimuli are thought to arise through a process of competition or divisive normalization [109., 110., 111.]. In such models, each stimulus attempts to drive the activity of a neuron to a particular value, appropriate to representing the properties of this stimulus; with multiple stimuli in the field, opposing forces bring activity to a compromise value, reducing the fidelity of representation for any one. Similar patterns can be seen in the visual responses of prefrontal neurons [112,113], raising the possibility that divisive normalization is a general principle in MD cortex. Recurrent neural networks have become popular as models of working memory and cognitive control (e.g., [114]), and in a recent model, divisive normalization is the basis for limited working memory capacity [115]. Further experimental work is needed to test whether divisive normalization models may be extended to the broader attentional limits of MD activity and cognitive control.
Of course, our account of cognitive integration leaves much unknown. That said, like an early map of the globe, it provides an outline sketch of how distributed brain activity can assemble complex cognition. This sketch, we suggest, provides the skeleton we need to guide future, more detailed physiological study (see Outstanding Questions).
Outstanding Questions
How do different MD regions interact? Across the extended MD system, we know little of the dynamics of information representation and exchange during task performance. The very different connectivities of MD regions imply separate functional contributions to cognitive integration, but in fMRI, the dominant picture is one of corecruitment. This picture may reflect the low temporal resolution of fMRI, rendering the method blind to high-speed information development and exchange. Elucidating how task-relevant information arises and is distributed across the network calls for simultaneous electrophysiological recordings in separate MD regions, either in experimental animals or patients implanted for intracranial recordings.
How does MD activity bind together coherent processing across multiple brain regions? Again, this calls for electrophysiological studies, addressing questions that include directional information flow at different stages of a cognitive operation, and the role of precise timing relations (e.g., oscillatory synchrony) across brain regions.
How are brief segments of cognition combined into complex goal–subgoal structures? For example, we know little of how sustained goal maintenance directs brain activity in pursuit of a series of subgoals. Especially for complex behavior, a critical question is interaction between immediate cognitive activity and long-term knowledge of goals, subgoals, and their relationships.
What is the role of prominent MD foci seen outside cerebral cortex, especially in caudate and cerebellum? Almost nothing is known of cortical-subcortical and cerebro-cerebellar interaction as cognitive operations are carried out. High-field imaging may bring the spatial resolution needed for studies of small subcortical structures.
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