Building Functional Systems: Components, Connectivity and Cascades

Building functional systems: Components, connectivity and cascades Linda B. Smith

What we know: The human brain consists of many heterogeneous components. Performing any task – reading, throwing a ball, making a sandwich --recruits a subset of these components. In the real time performance of a task, these components form a functional network. The robust, inventive, and open-ended nature of human intelligence may be linked to five properties of these functional networks. 1. Coupled systems in a task drive change. When component systems are coupled in a real- time task, they change as a product of that interaction (1,2). One cogent example concerns visual letter recognition and the writing of letters (3). In adults, letter recognition, a high precision skill, is linked to the functional specialization of regions of the visual cortex. Experimental and functional imaging studies with pre-school children show that children who are taught to recognize letters by writing those letters versus by merely seeing them, developed more mature visual neural responses to purely visual information, a result that shows that the coupling of motor and visual systems in writing letters led to changes in the visual system itself. The real time coupling of heterogeneous systems in a task, does not just make a system, it changes the components. 2. Creating novel systems. Remarkably, the human brain is capable of creating novel functional networks that become highly skilled and stable. Humans read, write do algebra, invent programming languages, and excel in chemistry only because of the plasticity of the human brain and its ability to build novel and task specific functional networks (4). 3. A processing cascade. Within these networks, information is shared in a particular way that has been variously characterized as “interactive activation” or “incremental” or a “dynamic cascade” (5). Components share information incrementally. For example, in on-line language processing, the fraction of a second sound that corresponds to “ba” activates a whole neighborhood of word candidates that stand in readiness for further information. Moreover, the flow of information proceeds in all directions and over nested time scales so given “ba” and that knowledge that the topic is about a child’s party will lead to greater activation of the candidates “ball” and “balloon” than the candidates “ballot” and “ballet”. Because the flow of information within these complex networks in continuous and interactive, these systems are robust, work even when the available information is less than optimal, and can be inventive. However, it also means that a sluggish component may degrade the performance (and the learning) of the whole network. 4. Automaticity and precision. The perceptual components of these functional networks are strongly changeable by experience – extracting statistical regularities, becoming more sharply tuned, faster, and highly context-sensitive (6,7). Precision and speed in these component perceptual processes may enable higher-level solutions within a whole functional network, including what we might all “insight” and “concepts” and lack of precision and speed may place strong constraints on what can be learned from educational experiences. 5. Overlapping integrations. The component systems in any functional network (say reading) may also play a role in other functional networks (say music, 8). Theory and empirical evidence suggest that the overlapping couplings of component systems in multiple functional networks may play a critical role in in building higher order abstractions (9).

What we do not know: How do we structure learning experiences to build robust, efficient, and flexible functional networks in specific domains? Citations

(1) Bullmore, E. T., & Sporns, O. (2009). Complex brain networks: Graph-theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186– 198. http://ccn.ucla.edu/wiki/images/6/6a/Bullmore_Sporns_NatRevNeuro_2009.pdf

(2) Smith, L. B. & Sheya, A. (2010) Is Cognition Enough to Explain Cognitive Development? Topics in Cognitive Science, 1-11. http://cogsci.msu.edu/DSS/2010- 2011/Smith/CogEnough_smithsheya.pdf

(3) James K.H. (2010) Sensori-motor experience leads to changes in visual processing in the developing brain. Developmental Science. 13:279–288.

(4) Dehaene S., Cohen, L., Sigman, M.,& Vinkier, F. (2005) The neural code for written words: a proposal. Trends in Cognitive Science, 9:335–341. http://www.unicog.org/publications/DehaeneCohenSigmanVinckier_LCDmodelReading_ TICS2005.pdf

(5) Price, C.J., & Devlin, J.T. (2011) The Interactive Account of ventral occipito-temporal contributions to reading. Trends in Cognitive Science, 15:246–253. http://ukpmc.ac.uk/articles/PMC3223525

(6) Jeter PE, et al. (2009) Task precision at transfer determines specificity of perceptual learning. Journal of Vision. 9, 1–13. http://ww.journalofvision.org/content/9/3/1.full

(7) Samuelson, L., & Smith, L. B. (2000). Grounding development in cognitive process. Child Development, 71, 98–106. http://www.psychology.uiowa.edu/labs/lcdlab/samuelson-smith-2000a.pdf

(8) Anvari, S.H., Trainor, L.J., Woodside, J., & Levy, B.A. (2002). Relations among musical skills, phonological processing and early reading ability in pre- school children. Journal of Experimental Child Psychology, 83, 111–130. http://www.hearinglab.org/print/anvari2002.pdf

(9) Smith, L.B. (2010) More than concepts: How multiple integrations make human intelligence. In D. Mareschal, P. Quinn, & S. Lea (eds), The making of human intelligence, New York: Oxford Unviersity Press.