www.elsevier.com/locate/cognit This paper argues that a theory of situated vision, suited for the dual purposes of object recognition and the control of action, will have to provide something more than a system that constructs a conceptual representation from visual stimuli: it will also need to provide a special kind of direct (preconceptual, unmediated) connection between elements of a visual representation and certain elements in the world. Like natural language demonstratives (such as `this ' or `that') this direct connection allows entities to be referred to without being categorized or conceptualized. Several reasons are given for why we need such a preconceptual mechanism which individuates and keeps track of several individual objects in the world. One is that early vision must pick out and compute the relation among several individual objects while ignoring their properties. Another is that incrementally computing and updating representations of a dynamic scene requires keeping track of token individuals despite changes in their properties or locations. It is then noted that a mechanism meeting these requirements has already been proposed in order to account for a number of disparate empirical phenomena, including subitizing, search-subset selection and multiple object tracking

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We summarize selected recent developments and promising directions for improving the quality of models of human performance in synthetic environments. The potential uses and goals for behavioral models in synthetic environments are first summarized. Within that context, we examine relevant, current work related to modeling more complete performance, for example, on cognitive modeling of emotion, advanced techniques for testing and building models of behavior, new cognitive architectures, and agent and Belief, Desires and Intentions (BDI) technology. The report also considers the usability of these systems as an important but neglected aspect of their performance. A list of projects with high payoff for modeling human performance in synthetic environments is noted.

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This study demonstrates that listeners use lexical knowledge in perceptual learning of speech sounds. Dutch listeners first made lexical decisions on Dutch words and nonwords. The final fricative of 20 critical words had been replaced by an ambiguous sound, between [f] and [s]. One group of listeners heard ambiguous [f]-final words (e.g., [WI WItlo?], from witlof, chicory) and unambiguous [s]-final words (e.g., naaldbos, pine forest). Another group heard the reverse (e.g., ambiguous [na:ldbo?], unambiguous witlof). Listeners who had heard [?] in [f]-final words were subsequently more likely to categorize ambiguous sounds on an [f]–[s] continuum as [f] than those who heard [?] in [s]-final words. Control conditions ruled out alternative explanations based on selective adaptation and contrast. Lexical information can thus be used to train categorization of speech. This use of lexical information differs from the on-line lexical feedback embodied in interactive models of speech perception. In contrast to online feedback, lexical feedback for learning is of benefit to spoken word recognition (e.g., in

The task of tracking a small number (about 4 or 5) visual targets within a larger set of identical items, each of which moves randomly and independently, has been used extensively to study object-based attention. Analysis of this multiple-object tracking task shows that it logically entails solving the correspondence problem for each target over time, and thus that the individuality of each of the targets must be tracked. This suggests that when successfully tracking objects, observers must also keep track of them as unique individuals. Yet in the present studies we show that observers are poor at recalling the identity of successfully tracked objects (as specified by a unique identifier associated with each target, such as a number or starting location). Studies also show that the identity of targets tends to be lost when they come close together and that this tendency is greater between pairs of targets than between targets and nontargets. The significance of this finding in relation to the multiple-object tracking paradigm is discussed.

We describe the initial steps in developing an agentbased cognitive architecture designed to support psychologically plausible human variability. The new architecture, COJACK, is based on JACK, a BDI-based agent language. It will constrain the agents to reason and act in a psychologically plausible manner. Their information processing will be adjusted by a set of parameters that moderate the agent's reasoning and actions, combined with a set of guidelines for developing plans and beliefs for the agents. This set of parameters will also support varying the agents' performance, both in terms of differences across agents as well as differences that arise within an individual due to internal and external factors. We conclude that other architectures will want to include a similar set, including representing a body, its interaction with the environment, and the passage of time.

The computation-as-cognition metaphor requires that all cognitive objects are constructed from a fixed set of basic primitives; prominent models of cognition and perception try to provide that fixed set. Despite this effort, however, there are no extant computational models that can actually generate complex concepts and processes from simple and generic basic sets, and there are good reasons to wonder whether such models may be forthcoming. We suggest that one can have the benefits of computationalism without a commitment to fixed feature sets, by postulating processes that slowly develop special-purpose feature languages, from which knowledge is constructed. This provides an alternative to the fixed-model conception without radical anti-representationlism. Substantial evidence suggests that such feature development adaptation actually occurs in the perceptual learning that accompanies category learning. Given the existence of robust methods for novel feature creation, the assumption of a fixed basis set of primitives as psychologically necessary is at best

We simulated organisms with an arm terminating with a hand composed by two fingers, a thumb and an index, each composed by two segments, whose behavior was guided by a nervous system simulated through an artificial network. The organisms, which evolved through a genetic algorithm, lived in a bidimensional environment containing four objects, either large or small, either grey or black. In a baseline simulation the organisms had to learn to grasp small objects with a precision grip and large objects with a power grip. In Simulation 1 the organisms learned to perform two tasks: in Task 1 they continued to grasp objects according to their size, in Task 2 they had to decide the objects ' color by using a precision or a power grip. Learning occured earlier when the grip required to respond to the object and to decide the color was the same than when it was not, even if object size was irrelevant to the task. The simulation replicates the result of an experiment by Tucker & Ellis (2001) suggesting that seeing objects automatically activates motor information on how to grasp them.

There is nothing in this world constant, but inconstancy.