A distributed neural network model called SPEC for processing sentences with recursive relative clauses is described. The model is based on separating the tasks of segmenting the input word sequence into clauses, forming the case-role representations, and keeping track of the recursive embeddings into di erent modules. The system needs to be trained only with the basic sentence constructs, and it generalizes not only to new instances of familiar relative clause structures, but to novel structures as well. SPEC exhibits plausible memory degradation as the depth of the center embeddings increases, its memory is primed by earlier constituents, and its performance is aided by semantic constraints between the constituents. The ability to process structure is largely due to a central executive network that monitors and controls the execution of the entire system. This way, in contrast to earlier subsymbolic systems, parsing is modeled as a controlled high-level process rather than one based on automatic re ex responses. 1

The authors present a theory of how relational inference and generalization can be accomplished within a cognitive architecture that is psychologically and neurally realistic. Their proposal is a form of symbolic connectionism: a connectionist system based on distributed representations of concept meanings, using temporal synchrony to bind fillers and roles into relational structures. The authors present a specific instantiation of their theory in the form of a computer simulation model, Learning and Inference with Schemas and Analogies (LISA). By using a kind of self-supervised learning, LISA can make specific inferences and form new relational generalizations and can hence acquire new schemas by induction from examples. The authors demonstrate the sufficiency of the model by using it to simulate a body of empirical phenomena concerning analogical inference and relational generalization. A fundamental aspect of human intelligence is the ability to form and manipulate relational representations. Examples of relational thinking include the ability to appreciate analogies between seemingly different objects or events (Gentner, 1983; Holyoak & Thagard, 1995), the ability to apply abstract rules in novel situations (e.g., Smith, Langston, & Nisbett, 1992), the ability to understand and learn language (e.g., Kim, Pinker, Prince, & Prasada, 1991), and even the ability to appreciate perceptual similarities

In this paper, a novel model of object-based visual attention extending Duncan's Integrated Competition Hypothesis [24] is presented. In contrast to the attention mechanisms used in most previous machine vision systems which drive attention based on the spatial location hypothesis, the mechanisms which direct visual attention in our system are object-driven as well as feature-driven. The competition to gain visual attention occurs not only within an object but also between objects. For this purpose, two new mechanisms in the proposed model are described and analyzed in detail. The first mechanism computes the visual salience of objects and groupings; the second one implements the hierarchical selectivity of attentional shifts. The results of the new approach on synthetic and natural images are reported.

A set of visual search experiments tested the proposal that focused attention is needed to detect change. Displays were arrays of rectangles, with the target being the item that continually changed its orientation or contrast polarity. Five aspects of performance were examined: linearity of response, processing time, capacity, selectivity, and memory trace. Detection of change was found to be a self-terminating process requiring a time that increased linearly with the number of items in the display. Capacity for orientation was found to be about five items, a value comparable to estimates of attentional capacity. Observers were able to filter out both static and dynamic variations in irrelevant properties. Analysis also indicated a memory for previously attended locations. These results support the hypothesis that the process needed to detect change is much the same as the attentional process needed to detect complex static patterns. Interestingly, the features of orientation and polarity were found to be handled in somewhat different ways. Taken together, these results not only provide evidence that focused attention is needed to see change, but also show that

Participants memorized briefly presented sets of digits, a subset of which had to be accessed as input for arithmetic tasks (the active set), whereas another subset had to be remembered independently of the concurrent task (the passive set). Latencies for arithmetic operations were a function of the setsize of active but not passive sets. Object-switch costs were observed when successive operations were applied to different digits within an active set. Participants took2stoencode a passive set so that it did not affect processing latencies (Experiment 2). The results support a model distinguishing 3 states of representations in working memory: the activated part of long-term memory, a capacity limited region of direct access, and a focus of attention. Working memory is commonly described as a system for simultaneous storage and processing of information. The relation between “storage ” and “processing, ” however, is rarely specified. Resource models generally posit a common resource (e.g., activation) that must be shared between the two functions (Just & Carpenter, 1992). Evidence from dual task studies, however, casts doubt on the resource-sharing hypothesis: There are numerous examples in the literature of processing that is largely unimpaired by a concurrent short-term memory demand, even when the memory demand is close to the maximum span (e.g., Foos & Wright,

In the single-store model of memory, the enhanced recall for the last items in a free-recall task (i.e., the recency effect) is understood to reflect a general property of memory rather than a separate short-term store. This interpretation is supported by the finding of a long-term recency effect under conditions that eliminate the contribution from the short-term store. In this article, evidence is reviewed showing that recency effects in the short and long terms have different properties, and it is suggested that 2 memory components are needed to account for the recency effects: an episodic contextual system with changing context and an activation-based short-term memory buffer that drives the encoding of item–context associations. A neurocomputational model based on these 2 components is shown to account for previously observed dissociations and to make novel predictions, which are confirmed in a set of experiments.

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The effective control of attentional focus is an essential requirement in mental reasoning based on mental models and mental images, as well as in the interaction with external diagrams. In this paper, we argue for spatial organization principles common to various mental subsystems that entail a noncentralistic control of focus. We give a brief overview of mental spatial reasoning and present a review of psychological findings related to cognitive control. We review existing modeling approaches that realize control of focus in imagery, scene recognition, and mental animation. Based on these foundations, we identify basic spatial organizing principles that are shared by the diverse subsystems collaborating in mental spatial reasoning. We discuss the implications of these principles in the framework of a computational modeling approach and give an outline of the conception of control of focus in our computational architecture Casimir.

This article presents a constrained review of human factors issues relevant to adaptive automation (AA), including designing complex system interfaces to support AA, facilitating human–computer interaction and crew interactions in adaptive system operations, and considering workload associated with AA management in the design of human roles in adaptive systems. Unfortunately, these issues have received limited attention in earlier reviews of AA. This work is aimed at supporting a general theory of human-centered automation advocating humans as active information processors in complex system control loops to support situation awareness and effective performance. The review demonstrates the need for research into user-centered design of dynamic displays in adaptive systems. It also points to the need for discretion in designing transparent interfaces to facilitate human awareness of modes of automated systems. Finally, the review identifies the need to consider critical human–human interactions in designing adaptive systems. This work describes important branches of a developing framework of AA research and contributes to the general theory of human-centered automation. 1.

We present an attention system that models the dynamics that occur in memory in response to stimuli, which includes habituation, novelty detection, and forgetting. We demonstrate how such an attention system can be used as a trigger for learning perception-action mappings. We discuss the value of social situatedhess in the form demonstrator-learner interactions, and show results fi'om both simulations and robot-human experiments of a simple wall-following task.