Types Of Learning Mechanisms Research Articles

Contributor biographies

Contributor biographies

Procuring infrastructure public-private partnerships: capability development and learning from an owner perspective

The expanding application of Public-Private Partnerships (PPPs) in infrastructure development and public service delivery has posed numerous commercial challenges for public sector owners (PSOs) regarding their capabilities to initiate, procure and manage PPP projects. This paper starts from the important role of owner project capabilities in infrastructure delivery and explores how PSOs learn to develop new commercial capabilities to match the PPP context. We draw on a qualitative single-case study of a state-owned enterprise in the Chinese context, which at the time of the study was in the early stages of applying the PPP approach in procuring metro line projects. The finding indicates the process of how the PSO learned to establish and develop the necessary owner commercial capabilities to procure infrastructure PPPs for the first time, such as the abilities to define project scope, procure, and manage suppliers. It sheds light on the roles of different types of learning mechanisms – experience accumulation, knowledge articulation, and knowledge codification – on capability development for PPPs over time from an owner perspective.

Multiplayer mechanism design for soil tillage serious game

The primary goal of Serious Games is not only for fun but also for lesson. In learning the first stage of soil tillage which using the mouldboard plow, a proper understanding is needed so that the soil tillage process will follow the needs of plant growth. The use of serious games as a study instrument for soil tillage is under the concept of digital game-based learning (DGBL). The problem of players when playing serious games is less motivated to play because the serious game system and scenario are less challenging. That challenges accelerate the shape of knowledge and experience when playing the games (user experience). By referring to the Learning Mechanics Gaming Mechanics (LM-GM) model, which is based on multiplayer in serious games, hopefully the learning process of land management using the mouldboard plow can be optimized. This process can increase learning motivation and elevate the user experience. This research results a design concept of a learning mechanism and a game mechanism for a serious multiplayer game of soil tillage with a mouldboard plow. There are three types of learning mechanisms in conceptual and concrete components, also six types of game mechanisms that can be used as a reference for the formation of multiplayer serious games and the increase player motivation.

The role of corrective feedback and lexical guidance in perceptual learning of a novel L2 accent in dialogue

AbstractPerceptual learning of novel accents is a critical skill for second-language speech perception, but little is known about the mechanisms that facilitate perceptual learning in communicative contexts. To study perceptual learning in an interactive dialogue setting while maintaining experimental control of the phonetic input, we employed an innovative experimental method incorporating prerecorded speech into a naturalistic conversation. Using both computer-based and face-to-face dialogue settings, we investigated the effect of two types of learning mechanisms in interaction: explicit corrective feedback and implicit lexical guidance. Dutch participants played an information-gap game featuring minimal pairs with an accented English speaker whose /ε/ pronunciations were shifted to /ɪ/. Evidence for the vowel shift came either from corrective feedback about participants’ perceptual mistakes or from onscreen lexical information that constrained their interpretation of the interlocutor’s words. Corrective feedback explicitly contrasting the minimal pairs was more effective than generic feedback. Additionally, both receiving lexical guidance and exhibiting more uptake for the vowel shift improved listeners’ subsequent online processing of accented words. Comparable learning effects were found in both the computer-based and face-to-face interactions, showing that our results can be generalized to a more naturalistic learning context than traditional computer-based perception training programs.

Concurrent changes in latecomer capability-building and learning: Firm-level evidence from the Thai biogas industry

Abstract The localisation of clean-tech industries allows developing countries to align their economic growth aspirations with efforts to reduce the associated environmental impacts. A key issue in this regard is the development of technological capabilities at local firms in these countries. Although much research has been conducted on this topic, we currently have only limited understanding of how the learning mechanisms that these so-called latecomer firms engage in change as they deepen their capability stocks. In the present paper, we offer insights into this issue by means of a comparative analysis of nine technology-supplying firms in the Thai industry for industrial-scale biogas systems over a period of about twenty years. Our results suggest that there are indeed shifts in the relative importance of different types of learning mechanisms as firms deepen their capabilities. The paper finds that such shifts tend to follow a co-evolutionary pattern involving shifts within and across learning mechanisms that involve different sources of knowledge. Based on our analysis, we provide examples of how decision-makers can adopt targeted interventions for the support of clean-tech firms and industries that are at particular levels of development.

Exploring and yet failing less: learning from past and current exploration in R&D

Exploration is both an important part of a firm’s innovation strategy and an activity that involves a high degree of uncertainty. This article investigates a duality in the exploratory component of R&D activity with regard to innovation failure: while exploration is likely to increase firms’ exposure to failure, it might also provide learning opportunities to reduce failure. Our study contributes to the innovation management and organizational learning literatures by demonstrating the value of exploratory R&D for enabling two types of learning mechanisms. The first, experience-based learning, is based on the learning opportunities derived from accumulated experience in exploratory R&D: it involves improvements to procedures associated with experimentation and provides guidance for current exploration and to navigate the search space. The second, inferential-based learning, is based on the learning opportunities derived from current exploratory R&D efforts, which are associated with improved interpretation of ill-defined problems and timely responses to unstructured information. We draw on a longitudinal data set of 2226 Spanish manufacturing companies and show that, when past experience is associated with current exploration, innovation failure in the conception phase is reduced. We also find an inverted U-shaped relation between current exploratory R&D and innovation failure, in both the conception and implementation phases of innovation activities, showing that increasing levels of investment in current exploration activities attenuate the initial positive association between exploratory R&D and failure.

Programmable Spike-Timing-Dependent Plasticity Learning Circuits in Neuromorphic VLSI Architectures

Hardware implementations of spiking neural networks offer promising solutions for computational tasks that require compact and low-power computing technologies. As these solutions depend on both the specific network architecture and the type of learning algorithm used, it is important to develop spiking neural network devices that offer the possibility to reconfigure their network topology and to implement different types of learning mechanisms. Here we present a neuromorphic multi-neuron VLSI device with on-chip programmable event-based hybrid analog/digital circuits; the event-based nature of the input/output signals allows the use of address-event representation infrastructures for configuring arbitrary network architectures, while the programmable synaptic efficacy circuits allow the implementation of different types of spike-based learning mechanisms. The main contributions of this article are to demonstrate how the programmable neuromorphic system proposed can be configured to implement specific spike-based synaptic plasticity rules and to depict how it can be utilised in a cognitive task. Specifically, we explore the implementation of different spike-timing plasticity learning rules online in a hybrid system comprising a workstation and when the neuromorphic VLSI device is interfaced to it, and we demonstrate how, after training, the VLSI device can perform as a standalone component (i.e., without requiring a computer), binary classification of correlated patterns.

Auditory perceptual category formation does not require perceptual warping.

Categorical perception occurs when a perceiver's stimulus classifications affect their ability to make fine perceptual discriminations and is the most intensively studied form of category learning. On the basis of categorical perception studies, it has been proposed that category learning proceeds by the deformation of an initially homogeneous perceptual space ("perceptual warping"), so that stimuli within the same category are perceived as more similar to each other (more difficult to tell apart) than stimuli that are the same physical distance apart but that belong to different categories. Here, we present a significant counterexample in which robust category learning occurs without these differential perceptual space deformations. Two artificial categories were defined along the dimension of pitch for a perceptually unfamiliar, multidimensional class of sounds. A group of participants (selected on the basis of their listening abilities) were trained to sort sounds into these two arbitrary categories. Category formation, verified empirically, was accompanied by a heightened sensitivity along the entire pitch range, as indicated by changes in an EEG index of implicit perceptual distance (mismatch negativity), with no significant resemblance to the local perceptual deformations predicted by categorical perception. This demonstrates that robust categories can be initially formed within a continuous perceptual dimension without perceptual warping. We suggest that perceptual category formation is a flexible, multistage process sequentially combining different types of learning mechanisms rather than a single process with a universal set of behavioral and neural correlates.

Learning Mechanisms and Creative Climate

A creative climate is the key to the long term success and survival of organizations in many industries. A number of different interventions for organizational development and change attempt to design and enhance an organizational creative climate. Our understanding of some of the organizational mechanisms that can create and enhance such a climate is limited. This paper reports on an organizational development effort, carried out as a collaborative management research project, to explore the most important types of organizational learning mechanisms and the possible relationship between them and a creative climate in an Italian fashion design company. The study analyses four different types of learning mechanisms, and confirms the impact of two of them – structural and procedural – on the creative climate within an organization. The implications for organization development research and practice are then discussed

Emerging Cooperation With Minimal Effort: Rewarding Over Mimicking

This paper compares supervised and unsupervised learning mechanisms for the emergence of cooperative multiagent spatial coordination using a top-down approach. By observing the global performance of a group of homogeneous agents-supported by a nonglobal knowledge of their environment-we attempt to extract information about the minimum size of the agent neurocontroller and the type of learning mechanism that collectively generate high-performing and robust behaviors with minimal computational effort. Consequently, a methodology for obtaining controllers of minimal size is introduced and a comparative study between supervised and unsupervised learning mechanisms for the generation of successful collective behaviors is presented. We have developed a prototype simulated world for our studies. This case study is primarily a computer games inspired world but its main features are also biologically plausible. The two specific tasks that the agents are tested in are the competing strategies of obstacle-avoidance and target-achievement. We demonstrate that cooperative behavior among agents, which is supported only by limited communication, appears to be necessary for the problem's efficient solution and that learning by rewarding the behavior of agent groups constitutes a more efficient and computationally preferred generic approach than supervised learning approaches in such complex multiagent worlds

Making Organizational Learning Operational: Implications from Learning Classifier Systems

The concepts of organizational learning in organization and management science cover a very wide range of organization-related activities in organization. Since socially situated intelligence is one of such activities, this paper makes the concept of organizational learning operational from the computational viewpoint for investigating socially situated intelligence. In particular, this paper focuses on the characteristics of multiagent learning as one kind of socially situated intelligence, and analyzes them using four operationalized learning mechanisms in organizational learning. A careful investigation on the characteristics of multiagent learning has revealed the following implications: (1) there are two levels in the learning mechanisms for multiagent learning (the individual level and organizational level) and each mechanism is divided into two types (single- and double-loop learning). The integration of these four learning mechanisms improves socially situated intelligences and (2) the following properties support socially situated intelligence: (a) different dimensions in learning mechanisms, (b) interaction among various levels and types of learning mechanisms in addition to interaction among agents, and (c) combination of exploration at an individual level and exploitation at an organizational level.