We present Embodied Construction Grammar, a formalism for linguistic analysis designed specifically for integration into a simulation-based model of language understanding. As in other construction grammars, linguistic constructions serve to map between phonological forms and conceptual representations.

In our research, we start with a constructivist belief in the importance of an active learner interacting with a variety of resources, developing his or her own understanding through a mixture of experimentation, experience and expert guidance. However, we supplement this constructivist outlook with a sociocultural commitment to the impportance of communication and collaboration with other learners throughout the knowledge construction process. In this paper, we describe a learning environment that we have developed that combines constructivist-inspired tools for open-ended investigation with communication and collaboration tools that support both expert guidance and multi-learner collaboration.

Abstract: The participatory simulations project brings together three lines of research—student understanding of complex dynamic systems, the use of participatory activities to augment student experience and the use of computer-based technologies to enable exploration of and reflection upon a domain of inquiry. These trio of goals can be significantly enabled and advanced through emerging network technologies. We argue that the study of dynamic systems stands as a new form of literacy for all – enabling us to track and make sense of the evolution of systems across time. Participatory Simulations Activities, on their own, are a powerful means for studying dynamic systems – and they can also support new forms of classroom interaction and can serve to prepare the way for engagement with computer-based systems modeling. To accomplish these goals, we introduce a new architecture, HubNet. HubNet is an open client-server architecture, which enables many users at the “Nodes ” (typically handheld devices) to control the behavior of individual objects or agents and to view the aggregated results on a central computer known as the Hub. This network of nodes is integrated with a powerful suite of modeling, analysis and display tools that together give users the capacity to “fly ’ the system in intuitive mode, to reflect on the emergent result of their simulation and, also, to encode their strategies as rules which the system can then run independently. The HubNet system is in use in several middle and secondary classrooms. Two illustrative cases of classroom use are presented and analyzed.

Working memory resources are needed for processing and maintenance of information during cognitive tasks. Many models have been developed to capture the effects of limited working memory resources on performance. However, most of these models do not account for the finding that different individuals show different sensitivities to working memory demands, and none of the models predicts individual subjects' patterns of performance. We propose a computational model that accounts for differences in working memory capacity in terms of a quantity called source activation, which is used to maintain goal-relevant information in an available state. We apply this model to capture the working memory effects of individual subjects at a fine level of detail across two experiments. This, we argue, strengthens the interpretation of source activation as working memory capacity. 2001 Cognitive Science Society, Inc. All rights reserved.

Two experiments are reported in which Ss were required to determine whether a random, angular form, presented at any of a number of picture-plane orientations, was a “standard ” or “reflected ” version. Average time required to make this determination increased linearly with the angular departure of the form from a previously learned orientation. The slope and intercept of the reaction-time (RT) function were virtually constant, regardless of the perceptual complexity of the test form and the orientation selected for initial learning. When Ss were informed in advance as to the identity and the orientation of the upcoming test form and, further, were permitted to indicate when they were prepared for its external presentation, RT for determining the version of the form was constant for all test-form orientations. However, the time needed to prepare for the test-form presentation increased linearly with the angular departure of the form from the learned orientation. It is argued that the processes both of preparing for and of responding to a disoriented test form consist of the mental rotation of an image, and that both sorts of mental rotation (pre-stimulus and post-stimulus) are carried out at essentially the same constant rate. During the past several years, experimental and theoretical investigation of nonverbal internal representation, particularly mental imagery, has proliferated. The primary focus of this renewed experimental effort has been directed toward questions concerning the functional significance of mental imagery (e.g., Bower, 1972; Paivio, 1971). With the exception of evidence concerning the modality or the coded form of internal representations- deriving primarily from the “selective interference”

The paper outlines a computational theory of human plausible reasoning constructed from analysis of people's answers to everyday questions. Like logic, the theory is expressed in a content-independent formalism. Unlike logic, the theory specifies how different information in memory affects the certainty of the conclusions drawn. The theory consists of a dimensionalized space of different inference types and their certainty conditions, including a variety of meta-inference types where the inference depends on the person's knowledge about his own knowledge. The protocols from people's answers to questions are analyzed in terms of the different inference types. The paper also discusses how memory is structured in multiple ways to support the different inference types, and how the information found in memory determines which inference types are triggered.

In this paper we explore a symbiosis between Dynamic Logic Programming and Metaphor to solve the problem of inconsistency in metaphorical knowledge integration. The problem of solving inconsistencies that may arise when knowledge from two different domains is combined, given a metaphorical mapping, is crucial, be it at the stage where we want to evaluate the appropriateness of the mapping function, or at a subsequent stage when we want to reason with the combined knowledge. We propose, in a formal and rigorous manner, a transformation and its semantics that solves the problem of inter-domain inconsistencies by employing the principle of inertia to the rules of the source domain. This work is part of an ongoing larger project, Dr. Divago, whose final goal is to develop a system to perform automatic creative reasoning.

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Emotional states, such as happiness or sadness, pose particular problems for information processing theories of mind. Hedonic components of states, unlike cognitive components, lack representational content. Research within Artificial Life, in particular the investigation of adaptive agent architectures, provides insights into the dynamic relationship between motivation, the ability of control sub-states to gain access to limited processing resources, and prototype emotional states. Holland's learning classifier system provides a concrete example of this relationship, demonstrating simple `emotion-like' states, much as a thermostat demonstrates simple `belief-like' and `desire-like' states. This leads to the conclusion that valency, a particular form of pleasure or displeasure, is a self-monitored process of credit-assignment. The importance of the movement of a domain-independent representation of utility within adaptive architectures is stressed. Existing information processing theor...

Education under Contract No. US-NIE-C-400-76-0116. I would