Self-reconfiguration. While flexibility is an undeniable virtue, it is closely associated with reliance on control, and subject to a seriality constraint that makes it inefficient. For example, while the ability to mentally solve an arbitrary arithmetic problem is a classic example of human cognitive flexibility, most people cannot do so while carrying on a conversation. This is in stark contrast to other kinds of tasks, often referred to as “automatic,” that can be carried out simultaneously, such as walking and talking. While it has widely been assumed that the seriality constraints associated with control-dependent processes reflect reliance on a central, capacity-limited control mechanism (akin to the CPU of a traditional computer), work in our laboratory has suggested a radical alternative: that a fundamental factor that determines whether a set of processes must be performed serially (i.e. are control-dependent) or they can be performed in parallel (i.e., they are automatic) has to do with whether they rely on shared (general purpose) or separated (task-dedicated) representations. Tasks that share representations must be serialized in order to avoid conflicting simultaneous demands from those representations, and therefore require control. That is, serialization is the purpose, not the fault of control. This can be avoided by taking the time to learn new, task-dedicated representations that permit parallel execution, and thus more efficient performance. However, that comes at the cost of more training and poorer generalization.
Work in our lab has shown that the analysis of this tradeoff — between the representational flexibility of shared representations and the processing efficiency of separated representations — provides a normative, formally rigorous account of the distinction between control-dependent and automatic processing in human performance [6,19], that also suggests a fundamental tradeoff in network architectures between flexibility and efficiency, similar to the one between interpreted and compiled forms of processing in traditional symbolic architectures: more abstract representations are valuable precisely because they are shared — they can flexibly be used by a broad range of processes and task; but, as consequence, they require serialization and therefore regulation by control. The learning of abstract functions in the ESBN architecture provides an extreme example of this, and may explain why, in humans, the most abstract forms of processing (such mathematical reasoning) also appear to be the most control-dependent, requiring serial processing, and consistently engage frontal lobe function. However, as shown in classic cognitive studies, when the exact same task is performed repeated, it can become automatized , presumably through the formation of dedicated representations, allowing it be performed efficiently in parallel with others. In recent work, we have begun to consider how a system can strategically decide, when acquiring a new task, whether to rely on shared representations for more rapid acquisition but serial execution, or to expend the additional training effort to acquire task-dedicated representations for more efficient execution. To date, this work has been restricted to a formal analysis of an abstract form of the problem  and an implementation in a deep learning network trained on a set of simple sensorimotor tasks . In current work, we are integrating these strategic decision-making mechanisms into the ESBN architecture, allowing the system to make strategic decisions between the flexible use of abstract, symbolic forms of computation versus committing to reconfiguring itself through the acquisition of task-dedicated function approximation.