A Neural Model for Compositional Word Embeddings and Sentence Processing

We propose a new neural model for word embeddings, which uses Unitary Matrices as the primary device for encoding lexical information. It uses simple matrix multiplication to derive matrices for large units, yielding a sentence processing model that is strictly compositional, does not lose information over time steps, and is transparent, in the sense that word embed- dings can be analysed regardless of context. This model does not employ activation functions, and so the network is fully accessible to analysis by the methods of linear algebra at each point in its operation on an input sequence. We test it in two NLP agreement tasks and obtain rule like perfect accuracy, with greater stability than current state-of-the-art systems. Our proposed model goes some way towards offer- ing a class of computationally powerful deep learning systems that can be fully understood and compared to human cognitive processes for natural language learning and representation

The Structured Process Modeling Method (SPMM) : what is the best way for me to construct a process model?

More and more organizations turn to the construction of process models to support strategical and operational tasks. At the same time, reports indicate quality issues for a considerable part of these models, caused by modeling errors. Therefore, the research described in this paper investigates the development of a practical method to determine and train an optimal process modeling strategy that aims to decrease the number of cognitive errors made during modeling. Such cognitive errors originate in inadequate cognitive processing caused by the inherent complexity of constructing process models. The method helps modelers to derive their personal cognitive profile and the related optimal cognitive strategy that minimizes these cognitive failures. The contribution of the research consists of the conceptual method and an automated modeling strategy selection and training instrument. These two artefacts are positively evaluated by a laboratory experiment covering multiple modeling sessions and involving a total of 149 master students at Ghent University

Nature of Mathematical Modeling Tasks for Secondary Mathematics Preservice Teachers

This study investigated the nature of written modeling tasks reported by instructors of required courses in five secondary mathematics teacher education programs. These tasks were analyzed based on a framework addressing potential cognitive orientation (simple procedures, complex procedures, and rich tasks) and purpose (epistemological, educational, contextual, and socio-critical modeling) of the tasks. Our analysis suggests that most tasks included questions of more than one cognitive orientation and more than half of the tasks were coded as contextual modeling. We also found that tasks that were coded as contextual modeling offered opportunities for future teachers to engage with questions at all levels of cognitive orientation. The nature of several modeling tasks, along with the ideas for refining the current frameworks, are presented for future implications of analyzing and developing modeling tasks

Cognitive modeling of social behaviors

To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual mind as ways of carrying out activities. This requires for the psychologist a shift from only modeling goals and tasks —why people do what they do—to modeling behavioral patterns—what people do—as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). To illustrate these ideas, this article presents an extract from a Brahms simulation of the Flashline Mars Arctic Research Station (FMARS), in which a crew of six people are living and working for a week, physically simulating a Mars surface mission. The example focuses on the simulation of a planning meeting, showing how physiological constraints (e.g., hunger, fatigue), facilities (e.g., the habitat’s layout) and group decision making interact. Methods are described for constructing such a model of practice, from video and first-hand observation, and how this modeling approach changes how one relates goals, knowledge, and cognitive architecture. The resulting simulation model is a powerful complement to task analysis and knowledge-based simulations of reasoning, with many practical applications for work system design, operations management, and training

The Structured Process Modeling Theory (SPMT): a cognitive view on why and how modelers benefit from structuring the process of process modeling

After observing various inexperienced modelers constructing a business process model based on the same textual case description, it was noted that great differences existed in the quality of the produced models. The impression arose that certain quality issues originated from cognitive failures during the modeling process. Therefore, we developed an explanatory theory that describes the cognitive mechanisms that affect effectiveness and efficiency of process model construction: the Structured Process Modeling Theory (SPMT). This theory states that modeling accuracy and speed are higher when the modeler adopts an (i) individually fitting (ii) structured (iii) serialized process modeling approach. The SPMT is evaluated against six theory quality criteria

Modeling Life as Cognitive Info-Computation

This article presents a naturalist approach to cognition understood as a network of info-computational, autopoietic processes in living systems. It provides a conceptual framework for the unified view of cognition as evolved from the simplest to the most complex organisms, based on new empirical and theoretical results. It addresses three fundamental questions: what cognition is, how cognition works and what cognition does at different levels of complexity of living organisms. By explicating the info-computational character of cognition, its evolution, agent-dependency and generative mechanisms we can better understand its life-sustaining and life-propagating role. The info-computational approach contributes to rethinking cognition as a process of natural computation in living beings that can be applied for cognitive computation in artificial systems.Comment: Manuscript submitted to Computability in Europe CiE 201

Towards a quantitative evaluation of the relationship between the domain knowledge and the ability to assess peer work

In this work we present the preliminary results provided by the statistical modeling of the cognitive relationship between the knowledge about a topic a the ability to assess peer achievements on the same topic. Our starting point is Bloom's taxonomy of educational objectives in the cognitive domain, and our outcomes confirm the hypothesized ranking. A further consideration that can be derived is that meta-cognitive abilities (e.g., assessment) require deeper domain knowledge

Modeling the perceptual component of conceptual learning—A coordination perspective

Although a picture may be worth a thousand words, modeling diagrams as propositions and modeling visual processing as search through a database of verbal descriptions obscures what is problematic for the learner. Cognitive modeling of language learning and geometry has obscured the learner's problem of knowing where to look—what spaces, markings, and orientations constitute the objects of interest? Today we are launching into widespread use of multimedia instructional technology, without an adequate theory to relate perceptual processes to conceptual learning. Does this matter? In this article, I review the symbolic approach to modeling perceptual processing and show its limitations for explaining difficulties children encounter in interpreting a graphic display. I present an alternative analysis by which perceptual categorization is coupled to behavior sequences, where gesturing and emotional changes are essential for resolving impasses and breaking out of loops. I conclude by asking what kind of cognitive theory we need to exploit communication technology. Have we been correct to assume that pedagogy must be grounded in an accurate psychological model of knowledge, memory, and learning

Automatic Generation of Cognitive Theories using Genetic Programming

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming. Our approach evolves from experimental data cognitive theories that explain “the mental program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories