Integrated Intelligence from Distributed Brain Activity
Authors: John Duncan, Moataz Assem, Sneha Shashidhara
Journal: Trends in Cognitive Sciences (2020)

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. 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. 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. Organized cognition of any kind arises from widely distributed brain activity. An immediate question is how such activity is integrated, allowing just the right cognitive contents to be combined in just the right way for current purposes. Though much is certainly unknown, an outline view of the relevant psychological and physiological mechanisms is beginning to appear. In this opinion article, we describe recent progress towards a whole-brain understanding of cognitive integration. We begin with recent work on the cognitive mechanisms of fluid intelligence (see Glossary). Theoretical accounts of fluid intelligence focus on processes of cognitive control [1.Duncan J. et al.Intelligence and the frontal lobe: the organization of goal-directed behavior.Cognit. Psychol. 1996; 30: 257-303Crossref PubMed Scopus (0) Google Scholar, 2.Kane M.J. Engle R.W. The role of prefrontal cortex in working memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective.Psychon. Bull. Rev. 2002; 9: 637-671Crossref PubMed Google Scholar, 3.Unsworth N. Robison M.K. A locus coeruleus-norepinephrine account of individual differences in working memory capacity and attention control.Psychon. Bull. 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In broad outline, these findings suggest how distributed brain activity builds organized cognition. We conclude with some of the many questions that this scheme raises for future work. A fundamental psychometric discovery is positive manifold: to some extent, all tests of different cognitive abilities tend to have positive correlations [11.Spearman C. General intelligence, objectively determined and measured.Am. J. Psychol. 1904; 15: 201-293Crossref Google Scholar,12.Spearman C. The Abilities of Man. Macmillan, 1927Google Scholar], even those that on the surface are dissimilar. In his foundational work, Spearman [11.Spearman C. General intelligence, objectively determined and measured.Am. J. Psychol. 1904; 15: 201-293Crossref Google Scholar] proposed that some general or g factor contributes to success in any task. If this model is fit to correlational data, novel problem-solving tests turn out to be excellent measures of g, reflecting the fact that, in a diverse task battery, it will be these tests that have the largest average correlations with a wide range of others. Well known examples are matrix problems (Figure 1A ) [13.Raven J.C. et al.Manual for Raven's Progressive Matrices and Vocabulary Scales. H.K. Lewis, 1988Google Scholar,14.Institute for Personality and Ability Testing Measuring Intelligence with the Culture Fair Tests. The Institute for Personality and Ability Testing, 1973Google Scholar], series completions [15.Thurstone L.L. Thurstone T.G. Examiner Manual for the SRA Primary Mental Abilities Test. Science Research Associates, 1949Google Scholar], etc. The ability measured in such tests has been called 'fluid intelligence'. Later, we consider several possible contributors to positive manifold, but meanwhile, the broad ability of fluid intelligence tests to predict success in many kinds of activity, from laboratory tests to life achievements, suggests cognitive mechanisms of widespread importance in mental life. We begin with models of fluid intelligence based on cognitive or executive control. Following early work linking fluid intelligence to frontal lobe functions [1.Duncan J. et al.Intelligence and the frontal lobe: the organization of goal-directed behavior.Cognit. Psychol. 1996; 30: 257-303Crossref PubMed Scopus (0) Google Scholar,16.Duncan J. Attention, intelligence and the frontal lobes.in: Gazzaniga M.S. The Cognitive Neurosciences. 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A different perspective on cognitive control comes from classical work in artificial intelligence, going back to the early problem-solving programs of Newell et al. [25.Newell A. et al.Elements of a theory of human problem solving.Psychol. Rev. 1958; 65: 151-166Crossref Scopus (519) Google Scholar]. As this work made clear, most complex problems cannot be solved in a single processing step. Instead, they must be divided into simpler parts, with these simpler parts solved in turn to produce final success on the whole problem. A familiar example organized as a goal–subgoal hierarchy is shown in Figure 1B. It is not possible simply to set the goal of traveling to Japan and then move immediately to the question of how to move one's hand; the statement of the goal brings too few constraints to lead immediately to motor commands. But beginning with the goal of travel to Japan, the problem solver can set progressive subgoals of flying, buying a ticket, and logging onto the internet, and now, sitting at a laptop, it becomes possible to plan a movement [26.Sacerdoti E.D. Planning in a hierarchy of abstraction spaces.Artif. Intell. 1974; 5: 115-135Crossref Scopus (0) Google Scholar]. This reasoning suggests that a core aspect of 'control' must be cognitive segmentation: based on knowledge of the problem domain and moves available to the problem solver, a complex whole must be organized into separate, more easily soluble parts. Early theoretical work [27.Carpenter P.A. et al.What one intelligence test measures: a theoretical account of the processing in the Raven Progressive Matrices Test.Psychol. Rev. 1990; 97: 404-431Crossref PubMed Google Scholar] emphasized the importance of cognitive segmentation in fluid intelligence problems. In a recent study [28.Duncan J. et al.Complexity and compositionality in fluid intelligence.Proc. Natl. Acad. Sci. U. S. A. 2017; 114: 5295-5299Crossref PubMed Scopus (26) Google Scholar], we modified traditional matrix problems so that little was left beyond this segmentation requirement. As shown in Figure 1C, entries in each cell of the matrix had three parts (in this case, short line straight or curved, longer line center or right, arrow pointing left or right). To minimize the working memory demands of traditional problems (Figure 1A), the participant simply drew their answer into a response box, allowing each part of the answer to be drawn as soon as it was determined. The problem appears trivial once attention is focused on a single part of the figures (e.g., focusing just on the arrow) but, nevertheless, participants with low scores on a standard fluid intelligence test struggled to solve these simplified problems (Figure 1E, blue). To confirm that segmentation was the only significant requirement, we used problems that were presegmented, with each part of the figure presented in its own matrix (Figure 1D). Though component problems were exactly the same as those that would be produced by attention to a single part in the original figures, now all participants performed well (Figure 1E, red). Sometimes, accounts of fluid intelligence based on cognitive control have been contrasted with an account based on cognitive integration [4.Wilhelm O. et al.What is working memory capacity, and how can we measure it?.Front. Psychol. 2013; 4: 433Crossref PubMed Scopus (104) Google Scholar,29.Chuderski A. Even a single trivial binding of information is critical for fluid intelligence.Intelligence. 2019; 77: 101396Crossref Scopus (0) Google Scholar]. According to integration accounts, the key process in fluid intelligence is binding together the different components of a cognitive process or representation. In this light it is instructive to consider what is needed to 'attend' to one part of the problem in Figure 1C (e.g., to determine that the arrow should point left). Evidently, information is needed on arrow direction in each cell of the matrix. This direction information must be correctly bound to positions within each figure and in the matrix as a whole. The layout of left and right arrows must be related to an overall conception of the problem to be solved and what it means for the solution to 'look right'. Doubtless, problem solving is guided by internal reward signals bound to each successful step. To create attention, multiple cognitive fragments must be integrated into precisely the correct combinations and relationships. As these computational structures are built, the task as a whole is segmented into useful parts, each representing a step closer to the overall goal. The need for attentional integration – creating the steps of complex behavior, each consisting of components assembled into just the right computational structure – is obvious in fluid intelligence problems. We would argue, however, that attentional integration lies at the heart of all organized cognition (Figure 2), helping to explain why fluid intelligence tests predict success in such a wide variety of behavior. Segmentation into parts is minimized in very simple tasks, but even here, just the right cognitive elements, organized in just the right way, must be assembled for behavioral control. Rather than conflicting, we suggest that accounts of fluid intelligence based on control and integration reflect two views of the same process. Focused attention, resistance to distraction, and integration are all important aspects of brain activity that defines and assembles the contents of an elementary cognitive operation. Our approach thus places popular accounts of fluid intelligence within a broader view of how 'cognitive control' in general should be conceived. Of course, the material to be integrated in creating a cognitive structure will be represented in widely distributed brain activity. Even attention to a visual object requires integration of activity across multiple cortical and subcortical brain areas, representing that object's different properties and action affordances [30.Duncan J. et al.Competitive brain activity in visual attention.Curr. Opin. Neurobiol. 1997; 7: 255-261Crossref PubMed Scopus (392) Google Scholar,31.Phaf R.H. et al.SLAM: a connectionist model for attention in visual selection tasks.Cognit. Psychol. 1990; 22: 273-341Crossref PubMed Google Scholar]. For a complex cognitive structure such as a single problem-solving step, relevant activity may be distributed across much or perhaps most of the brain. This calls for an integration mechanism with widespread, flexible access to whatever neural activity is needed in current cognition. Along with regions of brain activity linked to specific cognitive domains, brain imaging studies often show activity in a widely distributed MD system: a set of brain regions in which activity increases with almost any kind of cognitive load ([9.Duncan J. The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour.Trends Cogn. Sci. 2010; 14: 172-179Abstract Full Text Full Text PDF PubMed Scopus (853) Google Scholar,10.Duncan J. The structure of cognition: attentional episodes in mind and brain.Neuron. 2013; 80: 35-50Abstract Full Text Full Text PDF PubMed Scopus (239) Google Scholar,32.Duncan J. Owen A.M. 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To address this limitation, we turned recently to the methods and data of the Human Connectome Project (HCP) [44.Assem M. et al.A domain-general cognitive core defined in multimodally parcellated human cortex.Cereb. Cortex. 2020; 30: 4361-4380Crossref PubMed Scopus (0) Google Scholar]. In the HCP approach, surface-based processing using multimodal MRI features improves brain coregistration, much sharpening delineation of cortical areas. Multimodal data are further used to parcellate the cerebral cortex of each participant into 180 distinct regions per hemisphere [45.Glasser M.F. et al.A multi-modal parcellation of human cerebral cortex.Nature. 2016; 536: 171-178Crossref PubMed Google Scholar]. In a sample of 449 HCP participants, we used the conjunction of three cognitive contrasts – high versus low working memory load, relational reasoning versus perceptual matching, and arithmetic versus story comprehension – to examine MD regions and their properties. The results clarify the anatomy of known MD regions, as well as suggesting new regions. Across the cortex we see nine tightly defined patches of MD activity, distributed across frontal, parietal, and temporo-occipital cortex (Figure 3A,B ). The HCP parcellation further divides MD patches into 27 individual regions, which we separate into a ten-region core with strongest activity and a surrounding penumbra (Figure 3C). Five MD patches lie on the lateral frontal surface, the most dorsal lying just anterior to the frontal eye field (Figure 3A, i6-8/6a), the most ventral incorporating anterior insula and adjacent regions of the frontal operculum (Figure 3A, AVI), and between these, a chain of three extending from the inferior frontal junction posteriorly into the rostrolateral frontal cortex (Figure 3A, IFJp/6r; p9-46v; a9-46v). An additional frontal patch lies on the dorsomedial frontal surface (Figure 3A, SCEF/8BM). In the lateral parietal lobe, the primary MD patch is centered in the depths of the intraparietal sulcus (Figure 3A, IP1/IP2), with an additional patch in dorsomedial parietal cortex (Figure 3A, POS2/7Pm). Confirming previous indications, and despite the fact that one of our three contrasts used auditory rather than visual materials, we find additional MD activity in a region at the junction of anterior occipital and posterior temporal cortex (Figure 3A, TE1p). With this fine-scale definition of MD regions, we can ask also about functional differentiation. Despite very frequent coactivation of MD regions, there have also been many suggestions of functional differentiations (e.g., [46.Dosenbach N.U. et al.A core system for the implementation of task sets.Neuron. 2006; 50: 799-812Abstract Full Text Full Text PDF PubMed Scopus (1193) Google Scholar, 47.Yeo B.T. et al.Functional specialization and flexibility in human association cortex.Cereb. 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Though the MD system is commonly recruited as a whole, the exact pattern of this recruitment differs from task to task (see also [49.Shashidhara S. et al.Progressive recruitment of the frontoparietal multiple-demand system with increased task complexity, time pressure, and reward.J. Cogn. Neurosci. 2019; 31: 1617-1630Crossref PubMed Scopus (2) Google Scholar]). HCP resting state data also allowed us to examine patterns of functional connectivity. Commonly, resting state studies define a 'frontoparietal control network' (FPN), substantially overlapping with the MD system that is defined by activation data [50.Yeo B.T. et al.The organization of the human cerebral cortex estimated by intrinsic functional connectivity.J. Neurophysiol. 2011; 106: 1125-1165Crossref PubMed Scopus (3108) Google Scholar]. Based on HCP resting state data, Figure 4B shows average connectivity for all possible types of connection between core, penumbra, and non-MD regions. Strikingly, core–core connections were strongest, followed by connections of core to penumbra. Comparison with a canonical FPN, previously extracted by Ji et al. [51.Ji J.L. et al.Mapping the human brain's cortical-subcortical functional network organization.Neuroimage. 2019; 185: 35-57Crossref PubMed Scopus (0) Google Scholar] from the same HCP data, showed that all regions in our MD core lie within the FPN and, within that network, core–core connections are especially strong. Penumbra regions, by contrast, are spread across several networks. Recent analyses of brain connectivity have emphasized its small world structure [52.Bullmore E. Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems.Nat. Rev. Neurosci. 2009; 10: 186-198Crossref PubMed Scopus (6330) Google Scholar], with strong connectivity within local modules and long-range connector hubs linking between modules. Graph theory and other approaches suggest that MD regions are among the brain's widely connected hubs [53.Cole M.W. et al.Identifying the brain's most globally connected regions.Neuroimage. 2010; 49: 3132-3148Crossref PubMed Scopus (357) Google Scholar,54.Power J.D. et al.Evidence for hubs in human functional brain networks.Neuron. 2013; 79: 798-813Abstract Full Text Full Text PDF PubMed Scopus (399) Google Scholar], with dynamic, task-dependent connections to other brain networks [55.Cole M.W. et al.Multi-task connectivity reveals flexible hubs for adaptive task control.Nat. Neurosci. 2013; 16: 1348-1355Crossref PubMed Scopus (790) Google Scholar,56.Higo T. et al.Distributed and causal influence of frontal operculum in task control.Proc. Natl. Acad. Sci. U. S. A. 2011; 108: 4230-4235Crossref PubMed Scopus (90) Google Scholar]. Several previous accounts link fluid intelligence to this widespread MD connectivity [57.Cole M.W. et al.Global connectivity of prefrontal cortex predicts cognitive control and intelligence.J. Neurosci. 2012; 32: 8988-8999Crossref PubMed Scopus (356) Google Scholar], including a recent 'network neuroscience' proposal [58.Barbey A.K. Network neuroscience theory of human intelligence.Trends Cogn. Sci. 2018; 22: 8-20Abstract Full Text Full Text PDF PubMed Scopus (93) Google Scholar]. In line with such ideas, our results suggest that the MD system is well placed for attentional integration (Figure 5A ). Because local connections dominate in the brain, a system needing access to many modules needs widely distributed parts. Locally, we suggest, different kinds of information are preferentially fed into different MD regions, accounting for partial functional differentiations. Similarly, local connections allow MD outputs to influence processing in many modules. Creating integrated cognitive structures, however, requires that all types of information can be creatively selected and combined, in precisely the roles and relationships required by a particular cognitive demand. Strong connectivity between MD regions, in particular between regions of the core