Network mechanisms of intentional learning.

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus-response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.This work was supported by Medical Research Council Grant (U1055.01.002.00001.01) and a European Research GrantPCIG13-GA-2013-618351 to AH. JBR is supported by the Wellcome Trust (103838). The authors report no conflicts of interest.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.neuroimage.2015.11.06

Building routines for non-routine events: Supply chain resilience learning mechanisms and their antecedents.

Organisations must build resilience to be able to deal with disruptions or non-routine events in their supply chains. While learning is implicit in definitions of supply chain resilience, there is little understanding of how exactly organisations can adapt their routines to build resilience. The aim of this study is to address this gap. An in-depth qualitative case study based on 28 interviews across five companies exploring learning to build supply chain resilience. This study uncovers six learning mechanisms and their antecedents that foster supply chain resilience. The learning mechanisms identified suggest that, through knowledge creation within an organisation and knowledge transfer across the supply chain and broader network of stakeholders, operating routines are built and/ or adapted both intentionally and unintentionally during three stages of a supply chain disruption: preparation, response and recovery. This study shows how the impact of a supply chain disruption may be reduced by intentional and unintentional learning in all three disruption phases. By being aware of the antecedents of unintentional learning organisations can more consciously adapt routines. Furthermore, findings highlight the potential value of additional attention to knowledge transfer, particularly in relation to collaborative and vicarious learning across the supply chain and broader network of stakeholders not only in preparation for, but also in response to and recovery from disruptions. This study contributes novel insights about how learning leads both directly and indirectly to the evolution of operating routines that help an organisation and its supply chains to deal with disruptions. Results detail six specific learning mechanisms for knowledge creation and knowledge transfer and their antecedents for building supply chain resilience. In doing so, this study provides new fine grained theoretical insights about how supply chain resilience can be improved through all three phases of a disruption. Propositions are developed for theory development.n/

Explicit learning in ACT-R

A popular distinction in the learning literature is the distinction between implicit and explicit learning. Although many studies elaborate on the nature of implicit learning, little attention is left for explicit learning. The unintentional aspect of implicit learning corresponds well to the mechanistic view of learning employed in architectures of cognition. But how to account for deliberate, intentional, explicit learning? This chapter argues that explicit learning can be explained by strategies that exploit implicit learning mechanisms. This idea is explored and modelled using the ACT-R theory (Anderson, 1993). An explicit strategy for learning facts in ACT-R’s declarative memory is rehearsal, a strategy that uses ACT-R’s activation learning mechanisms to gain deliberate control over what is learned. In the same sense, strategies for explicit procedural learning are proposed. Procedural learning in ACT-R involves generalisation of examples. Explicit learning rules can create and manipulate these examples. An example of these explicit rules will be discussed. These rules are general enough to be able to model the learning of three different tasks. Furthermore, the last of these models can explain the difference between adults and children in the discrimination-shift task

Adaptive Resonance Theory

SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378

Neuroethology, Computational

Over the past decade, a number of neural network researchers have used the term computational neuroethology to describe a specific approach to neuroethology. Neuroethology is the study of the neural mechanisms underlying the generation of behavior in animals, and hence it lies at the intersection of neuroscience (the study of nervous systems) and ethology (the study of animal behavior); for an introduction to neuroethology, see Simmons and Young (1999). The definition of computational neuroethology is very similar, but is not quite so dependent on studying animals: animals just happen to be biological autonomous agents. But there are also non-biological autonomous agents such as some types of robots, and some types of simulated embodied agents operating in virtual worlds. In this context, autonomous agents are self-governing entities capable of operating (i.e., coordinating perception and action) for extended periods of time in environments that are complex, uncertain, and dynamic. Thus, computational neuroethology can be characterised as the attempt to analyze the computational principles underlying the generation of behavior in animals and in artificial autonomous agents

Consciousness CLEARS the Mind

A full understanding of consciouness requires that we identify the brain processes from which conscious experiences emerge. What are these processes, and what is their utility in supporting successful adaptive behaviors? Adaptive Resonance Theory (ART) predicted a functional link between processes of Consciousness, Learning, Expectation, Attention, Resonance, and Synchrony (CLEARS), includes the prediction that "all conscious states are resonant states." This connection clarifies how brain dynamics enable a behaving individual to autonomously adapt in real time to a rapidly changing world. The present article reviews theoretical considerations that predicted these functional links, how they work, and some of the rapidly growing body of behavioral and brain data that have provided support for these predictions. The article also summarizes ART models that predict functional roles for identified cells in laminar thalamocortical circuits, including the six layered neocortical circuits and their interactions with specific primary and higher-order specific thalamic nuclei and nonspecific nuclei. These prediction include explanations of how slow perceptual learning can occur more frequently in superficial cortical layers. ART traces these properties to the existence of intracortical feedback loops, and to reset mechanisms whereby thalamocortical mismatches use circuits such as the one from specific thalamic nuclei to nonspecific thalamic nuclei and then to layer 4 of neocortical areas via layers 1-to-5-to-6-to-4.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624