Modular connectionist architectures and the learning of quantification skills.

Modular connectionist systems comprise autonomous, communicating modules, achieving a behaviour more complex than that of a single neural network. The component modules, possibly of different topologies, may operate under various learning algorithms. Some modular connectionist systems are constrained at the representational level, in that the connectivity of the modules is hard-wired by the modeller; others are constrained at an architectural level, in that the modeller explicitly allocates each module to a specific subtask. Our approach aims to minimise these constraints, thus reducing the bias possibly introduced by the modeller. This is achieved, in the first case, through the introduction of adaptive connection weights and, in the second, by the automatic allocation of modules to subtasks as part of the learning process. The efficacy of a minimally constrained system, with respect to representation and architecture, is demonstrated by a simulation of numerical development amongst children. The modular connectionist system MASCOT (Modular Architecture for Subitising and Counting Over Time) is a dual-routed model simulating the quantification abilities of subitising and counting. A gating network learns to integrate the outputs of the two routes in determining the final output of the system. MASCOT simulates subitising through a numerosity detection system comprising modules with adaptive weights that self-organise over time. The effectiveness of MASCOT is demonstrated in that the distance effect and Fechner's law for numbers are seen to be consequences of this learning process. The automatic allocation of modules to subtasks is illustrated in a simulation of learning to count. Introducing feedback into one of two competing expert networks enables a mixture-of-experts model to perform decomposition of a task into static and temporal subtasks, and to allocate appropriate expert networks to those subtasks. MASCOT successfully performs decomposition of the counting task with a two-gated mixture-of-experts model and exhibits childlike counting errors

Modular connectionist architectures and the learning of quantification skills.

Modular connectionist systems comprise autonomous, communicating modules, achieving a behaviour more complex than that of a single neural network. The component modules, possibly of different topologies, may operate under various learning algorithms. Some modular connectionist systems are constrained at the representational level, in that the connectivity of the modules is hard-wired by the modeller; others are constrained at an architectural level, in that the modeller explicitly allocates each module to a specific subtask. Our approach aims to minimise these constraints, thus reducing the bias possibly introduced by the modeller. This is achieved, in the first case, through the introduction of adaptive connection weights and, in the second, by the automatic allocation of modules to subtasks as part of the learning process. The efficacy of a minimally constrained system, with respect to representation and architecture, is demonstrated by a simulation of numerical development amongst children. The modular connectionist system MASCOT (Modular Architecture for Subitising and Counting Over Time) is a dual-routed model simulating the quantification abilities of subitising and counting. A gating network learns to integrate the outputs of the two routes in determining the final output of the system. MASCOT simulates subitising through a numerosity detection system comprising modules with adaptive weights that self-organise over time. The effectiveness of MASCOT is demonstrated in that the distance effect and Fechner's law for numbers are seen to be consequences of this learning process. The automatic allocation of modules to subtasks is illustrated in a simulation of learning to count. Introducing feedback into one of two competing expert networks enables a mixture-of-experts model to perform decomposition of a task into static and temporal subtasks, and to allocate appropriate expert networks to those subtasks. MASCOT successfully performs decomposition of the counting task with a two-gated mixture-of-experts model and exhibits childlike counting errors

Introducing Feedback into a Mixture-of-Experts Model

The mixture-of-experts model is a static neural network architecture in that it learns input-output mappings where the output is directly influenced by the current input but not previous inputs. We explore a dynamic version of the mixture-of-experts model by introducing feedback into the architecture, enabling it to learn temporal behaviour. The model's ability to decompose a task into static and temporal subtasks and to allocate those subtasks to relevant expert networks is examined. The performance of the model on learning the two subtasks involved in verbal counting is presented. A potentially useful outcome of the simulation is that simplifying the topology of the expert networks tends to improve allocation of subtasks to the most appropriate expert networks. Keywords: Mixture-of-experts model, Decomposition, Feedback, Recurrency, Counting 3 1 Introduction The mixture-of-experts model is a modular connectionist architecture capable of automatic allocation of tasks to neural ex..

Towards a "Nervous System-Level" Model of Early Numerical Development

The feasibility of simulating child development with connectionist models is discussed with particular emphasis on numerical and linguistic development. The connectionist model which we use involves one or more connectionist architectures, including supervised and unsupervised algorithms, reflecting, perhaps, the symbiosis of supervised (teacher-controlled) and unsupervised learning capabilities in infancy and early childhood. We refer to these models as "nervous system-level", a term coined after Kohonen (1990). In this paper, we review a "nervous system-level" model of child language development. We then investigate a further domain in which a similar model provides a more plausible account than a model operating under a single learning strategy. In the field of early numerical abilities, stages of development vary according to those for which learning is biologically-determined, and those for which it is environmentally-dependent. We examine two models, each requiring different arch..

The Brain Debate

Neuroscientists use a broad range of organisms (from worms, flies and zebrafish to rodents and primates, and now to organoids) to study brain function and behavior. This Brain Debate aims at illustrating why different scientists study different systems and what can be achieved with different approaches. Animal preparations are frequently presented as ‘model organisms’. Does this suggest that the ultimate goal of animal work should be to understand and treat the human brain? Translating animal brain research to clinical implications is certainly a key goal of a large part of the research carried out. The neuroscientific community may, however, have diverse views on the purpose of studying many systems, on what can be achieved with different brain ‘models’, and on what the ultimate goals of each research field and envisioned applications may be. In this Brain Debate, we will discuss the merits of focused and comparative approaches and reflect on our scientific mission and on our vision for the field

Choosing `codebooks' for self-organising maps: A Case Study

Statistical pattern recognition techniques, supervised and unsupervised classification techniques being two good examples here, rely on the computations of similarity and distance metrics. These metrics are computed for an unknown pattern, say, x, and a reference vector, x k, selected usually by human beings. This means that when the training regimen leads to a finite set of categories it would not be possible to determine the bias in the choice of the reference vectors and their concomitant efforts in the ensuing learning. This is despite there being no a priori knowledge of categories and despite the fact that some components of the reference vectors may be missing. A method of automatically generating the reference vectors is discussed, with specific reference to streams of freetext news items. The reference vectors were generated by a procedure that automatically selects a group of keywords based on a lexico-semantic analysis. Three different kinds of news streams -- headlines onl..

The effectiveness of critical time intervention for abused women and homeless people leaving Dutch shelters: study protocol of two randomised controlled trials

Contains fulltext : 117787.pdf (publisher's version ) (Open Access)BACKGROUND: One of the main priorities of Dutch organisations providing shelter services is to develop evidence-based interventions in the care for abused women and homeless people. To date, most of these organisations have not used specific intervention models and the interventions which have been implemented rarely have an empirical and theoretical foundation. The present studies aim to examine the effectiveness of critical time intervention (CTI) for abused women and homeless people. METHODS: In two multi-centre randomised controlled trials we investigate whether CTI, a time-limited (nine month) outreach intervention, is more effective than care-as-usual for abused women and homeless people making the transition from shelter facilities to supported or independent housing. Participants were recruited in 19 women's shelter facilities and 22 homeless shelter facilities across The Netherlands and randomly allocated to the intervention group (CTI) or the control group (care-as-usual). They were interviewed four times in nine months: once before leaving the shelter, and then at three, six and nine months after leaving the shelter. Quality of life (primary outcome for abused women) and recurrent loss of housing (primary outcome for homeless people) as well as secondary outcomes (e.g. care needs, self-esteem, loneliness, social support, substance use, psychological distress and service use) were assessed during the interviews. In addition, the model integrity of CTI was investigated during the data collection period. DISCUSSION: Based on international research CTI is expected to be an appropriate intervention for clients making the transition from institutional to community living. If CTI proves to be effective for abused women and homeless people, shelter services could include this case management model in their professional standards and improve the (quality of) services for clients. TRIAL REGISTRATION: NTR3463 and NTR3425