We report a new phenomenon associated with language comprehension: the action–sentence compatibility effect (ACE). Participants judged whether sentences were sensible by making a response that required moving toward or away from their bodies. When a sentence implied action in one direction (e.g., “Close the drawer ” implies action away from the body), the participants had difficulty making a sensibility judgment requiring a response in the opposite direction. The ACE was demonstrated for three sentences types: imperative sentences, sentences describing the transfer of concrete objects, and sentences describing the transfer of abstract entities, such as “Liz told you the story. ” These data are inconsistent with theories of language comprehension in which meaning is represented as a set of relations among nodes. Instead, the data support an embodied theory of meaning that relates the meaning of sentences to human action. How language conveys meaning remains an open question. The dominant approach is to treat language as a symbol manipulation system: Language conveys meaning by using abstract, amodal, and arbitrary symbols (i.e., words) combined by syntactic rules (e.g., Burgess & Lund, 1997; Chomsky, 1980; Fodor, 2000; Kintsch, 1988; Pinker, 1994). Words are abstract in that the same word, such as “chair, ” is used for big chairs and little chairs, words are amodal in that the same word is used when chairs are spoken about or written about, and words are arbitrarily related to their referents in that the phonemic and orthographic characteristics of a word bear no relationship to the physical or functional characteristics of the word’s referent. An alternative view is that linguistic meaning is

One of the most sweeping changes ever in the ecology of human cognition may be taking place today. People are beginning to learn and use very powerful and sophisticated information processing technology as a matter of daily life. From the perspective of human history, this could be a transitional point dividing a period when machines merely helped us do things from a period when machines will seriously help us think about things. But if this is so, we are indeed still very much within the transition. For most people, computers have more possibility than they have real practical utility.

Abstract. Mixed-initiative problem solving lies at the heart of knowledge-based learning environments. While learners are actively engaged in problem-solving activities, learning environments should monitor their progress and provide them with feedback in a manner that contributes to achieving the twin goals of learning effectiveness and learning efficiency. Mixed-initiative interactions are particularly critical for constructivist learning environments in which learners participate in active problem solving. We have recently begun to see the emergence of believable agents with lifelike qualities. Featured prominently in constructivist learning environments, lifelike pedagogical agents could couple key feedback functionalities with a strong visual presence by observing learners ’ progress and providing them with visually contextualized advice during mixed-initiative problem solving. For the past three years, we have been engaged in a large-scale research program on lifelike pedagogical agents and their role in constructivist learning environments. In the resulting computational framework, lifelike pedagogical agents are specified by (1) a behavior space containing animated and vocal behaviors, (2) a design-centered context model that maintains constructivist problem representations, multimodal advisory contexts, and evolving problem-solving tasks, and (3) a behavior sequencing engine that in realtime dynamically selects and assembles agents ’ actions to create pedagogically effective, lifelike behaviors. To empirically investigate this framework, it has been instantiated in a full-scale implementation of a lifelike pedagogical agent for DESIGN-A-PLANT, a learning environment developed for the domain of botanical anatomy and physiology for middle school students. Experience with focus group studies conducted with middle school students interacting with the implemented agent suggests that lifelike pedagogical agents hold much promise for mixed-initiative learning. Key words: Lifelike agents, pedagogicalagents, animated agents, knowledge-basedlearning environments, mixed-initiative interaction, intelligent tutoring systems, intelligent multimedia presentation,

Four-month-old infants sometimes can perceive the unity of a partly hidden object. In each of a series of experiments, infants were habituated to one object whose top and bottom were visible but whose center was occluded by a nearer object. They were then tested with a fully visible continuous object and with two fully visible object pieces with a gap where the occluder had been. Patterns of dishabituation suggested that infants perceive the boundaries of a partly hidden object by analyzing the movements of its surfaces: infants perceived a connected object when its ends moved in a common translation behind the occluder. Infants do not appear to perceive a connected object by analyzing the colors and forms of surfaces: they did not perceive a connected object when its visible parts were stationary, its color was homogeneous, its edges were aligned, and its shape was simple and regular. These findings do not support the thesis, from gestalt psychology, that object perception first arises as a consequence of a tendency to perceive the simplest, most regular configuration, or the Piagetian thesis that object perception depends on the prior coordination of action. Perception of objects

This paper reviews four conceptions of the nature of sensory feedback mechanisms mediating voluntary performance, including serial chaining, This report was supported in part by National Science Foundation Grant GS-1601 to the Ohio State University, administered by the author. The author is grateful to D. E. Berlyne, A. E. Goss, and A.M. Liberman for commenting on portions of an earlier draft. 2 Requests for reprints should be sent to Anthony G. Greenwald, Department of Psychology, Ohio State University, 404C West 17th Avenue, Columbus, Ohio 43210. closed-loop, and fractional anticipatory goal response mechanisms, and gives particular attention to a reformulation of the principle of ideo-motor action. At the outset, certain boundaries of the present treatment should be marked off. First, verbal mediating mechanisms and the related topics of meaning and meaningfulhess will not be given detailed coverage since it would expand this paper greatly, and perhaps unnecessarily, to attempt to do justice to the literature on verbal mediation. A number of influential writers (Goss, 1961; Luria, 1961; Miller & Dollard, 1941; Osgood, 1957; Paw lov, 1955) have assumed, as is assumed here, that verbal mediators of skilled performance differ from nonverbal mediators primarily in that the former operate at higher levels of performance organization. Accordingly, a 73 74 ANTI{ONY G. GREENWALD later section of this paper briefly treats the application to verbal behavior of principles developed herein regarding nonverbal sensory feedback mediating mechanisms. Second, since the present focus will be on mediation processes in performance of learned skills, data and theorization concerning me- diation in classical conditioning and concerning innately organized skills will not be considered...

The design of software for learners must be guided by educational theory. We present a framework for learner-centered design (LCD) that is theoretically motivated by sociocultural and constructivist theories of learning. LCD guides the design of software in order to support the unique needs of learners: growth, diversity, and motivation. To address these needs, we incorporate scaffolding into the context, tasks, tools, and interface of software learning environments. We demonstrate the application of our methodology by presenting two case studies of LCD in practice.

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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.

From the earliest ages at which infants search for hidden objects, they make the AKB error, searching perseveratively at previous rather than current hiding locations (Piaget, 1954). This paper presents a parallel distributed processing (PDP) model that instantiates an explicit set of processing mechanisms to account for a large and diverse set of data on infants’ AKB errors. The model demonstrates how basic processes – the formation of latent memory traces and their interaction with developing active memory traces – can provide a unifying framework for understanding why and when infants perseverate. Novel predictions from the model are discussed, together with its challenges for theories that posit a concept of object permanence in the first year of life.

in this article we present symmetric diffusion networks, a family of networks that instantiate the principles of continuous, stochastic, adaptive and interactive pro-pagation of information. Using methods of Markovlon diffusion theory, we for-malize the activation dynamics of these networks and then show that they can be trained to reproduce entire muitivariote probability distributions an their outputs using the contrastive Hebbian learning rule (CHL).,We show that CHL performs gradient descent on an error function that captures differences between desired and obtolned continuous multivoriate probability distributions. This allows the learning algorithm to go beyond expected values of output units and to approxi-mate complete probability distributions on continuous muitivariote activation spaces. We argue that learning continuous distributions is an important task underlying a variety of real-life situations that were beyond the scope of previous connectionist networks. Deterministic networks, like back propagation, cannot ieorn this task because they ore limited to learning average values of indepen-dent output units. Previous stochastic connectionist networks could learn pro-bobility distributions but they were limited to discrete variables. Simulations show that symmetric diffusion networks can be trained with the CHL rule to op-proximate discrete and continuous probability distributions of various types. 1.