Integrated Intelligence from Distributed Brain Activity

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: attention; brain networks; cognitive control; intelligence; neural coding.

Figure 1. Fluid intelligence and cognitive segmentation.

(A) Matrix problem. The task is to choose which of the response alternatives (bottom) would correctly complete the matrix (top). (B) Goal-subgoal hierarchy. (C) Modified matrix problem in typical format. The task is to decide what figure would fill the empty cell of the matrix (dotted outline), with the answer to be drawn in the response box below. (D) Same problem segmented into separate parts. (E) Proportion of problems correctly solved as a function of fluid intelligence. Blue – typical format; red – segmented format. Adapted with permission from [28].

Figure 2. Integration and segmentation in complex cognition.

(A) Attention to one part of a complex task integrates multiple component fragments (right). As a series of steps is created, the problem is progressively segmented into simpler sub-problems (left). This example is the “travel to Japan” problem from Figure 1B. (B) To create each step, fragments must be selected from many potential candidates and assembled into precisely the correct computational structure.

Figure 3. Anatomy and physiology of the MD system.

(A) Patches of cortical MD activity defined in data of 449 participants from the Human Connectome Project, using a conjunction of fMRI contrasts for working memory, reasoning and arithmetic. Left hemisphere data are shown; largely similar patches are also seen on the right. Regional parcellation (black outlines) and selected anatomical labels are taken from [45]. (B) The same data shown on a flat map of the left hemisphere. Numbering shows 9 MD patches distributed across lateral frontal (1-5), dorsomedial frontal (6), lateral (7) and medial (8) parietal, and temporo-occipital (9) cortex. (C) Individual MD regions using the HCP parcellation. Data are averaged from left and right hemispheres, and for illustration projected onto the left. The extended MD system (27 regions) is divided into core (10 regions, yellow), with activity above the mean of all 27 regions in at least 2/3 contrasts, and penumbra (remaining 17 regions, red). Adapted with permission from [44].

Figure 4. Functional profiles and connectivity of MD regions.

(A) Profiles of activation across the extended MD system for each task contrast. To show reliability, right panels show overlaid plots for 2 independent groups of 210 participants each. (B) Resting state connectivity (correlation of time series), calculated for every pair of cortical regions and then averaged for connections of each type. Left – left hemisphere; right – right hemisphere. Adapted with permission from [44].

Figure 5. The MD system and cognitive integration.

(A) Depending on its pattern of connectivity, each MD region (colored circles) has direct access to different information and brain operations, here illustrated with just a single link (black bidirectional arrows) for each region. Strong connectivity between MD regions allows assembly of these fragments into the required computational structure. The scheme suggests a basis for partial functional differentiations within a broad context of coactivation. (B) With their varying external connectivity, MD regions may show quantitative differences in neural coding for different task features. Against this background, however, communication between MD regions provides a strong basis for mixed selectivity.

Figure 6. Neurophysiology of binding object to role.

(A) Object selection task, two-target version. On each trial, the monkey touches a single object in a visual display. For each new problem, in the first set of trials (cycle 1), the monkey selects one object after another, learning which 2 objects (targets) are associated with reward. Targets are indicated here by green circles (not present on actual display). In subsequent trials (cycles 2-4), the animal can reselect the same targets for further rewards. After 4 cycles, targets are redefined for the next problem. In another task version, problems have only a single target. Recording areas in each animal are shown at upper right. (B) Correlation of population firing patterns for feedback (FB) and choice (CH) periods, separated by target object and cycle. Data from correct trials only. For CH, data are shown only for cycles 2-4, as correct choices were not known in cycle 1. Frontal lobe data are separated into dorsal and ventral regions (separated at fundus of principal sulcus). Small negative correlations between FB and CH patterns are an artefact of data normalization [74]. (C) Independence of object preference at CH and FB. For each recording area, left panel shows data for neurons identified as object-selective during FB. For each neuron, “best” (highest firing rate) and “worst” (lowest) were identified based on FB data. Plots show activity for these “best” and ”worst” objects while selecting them at CH (normalized for each neuron, averaged across neurons). Across the population, “best” and “worst” objects defined at FB did not give significantly different responses at CH. Right panels show reverse analysis, defining “best” and “worst” at CH and plotting data from FB. Adapted with permission from [86].