Publications

Abstract

This paper is an attempt to show that given the available observations on the behaviour of negation and presuppositions there is no simpler explanation than to assume that natural language has two distinct negation operators, the minimal negation which preserves presuppositions and the radical negation which does not. The three-valued logic emerging from this distinction, and especially its model-theory, are discussed in detail. It is, however, stressed that the logic itself is only epiphenomenal on the structures and processes involved in the interpretation of sentences. Horn (1985) brings new observations to bear, related with metalinguistic uses of negation, and proposes a “pragmatic” ambiguity in negation to the effect that in descriptive (or “straight”) use negation is the classical bivalent operator, whereas in metalinguistic use it is non-truthfunctional but only pragmatic. Van der Sandt (to appear) accepts Horn's observations but proposes a different solution: he proposes an ambiguity in the argument clause of the negation operator (which, for him, too, is classical and bivalent), according to whether the negation takes only the strictly asserted proposition or covers also the presuppositions, the (scalar) implicatures and other implications (in particular of style and register) of the sentence expressing that proposition. These theories are discussed at some length. The three-valued analysis is defended on the basis of partly new observations, which do not seem to fit either Horn's or Van der Sandt's solution. It is then placed in the context of incremental discourse semantics, where both negations are seen to do the job of keeping increments out of the discourse domain, though each does so in its own specific way. The metalinguistic character of the radical negation is accounted for in terms of the incremental apparatus. The metalinguistic use of negation in denials of implicatures or implications of style and register is regarded as a particular form of minimal negation, where the negation denies not the proposition itself but the appropriateness of the use of an expression in it. This appropriateness negation is truth-functional and not pragmatic, but it applies to a particular, independently motivated, analysis of the argument clause. The ambiguity of negation in natural language is different from the ordinary type of ambiguity found in the lexicon. Normally, lexical ambiguities are idiosyncratic, highly contingent, and unpredictable from language to language. In the case of negation, however, the two meanings are closely related, both truth-conditionally and incrementally. Moreover, the mechanism of discourse incrementation automatically selects the right meaning. These properties are taken to provide a sufficient basis for discarding the, otherwise valid, objection that negation is unlikely to be ambiguous because no known language makes a lexical distinction between the two readings.

Abstract

Incremental sentence generation imposes special constraints on the representation of the grammar and the design of the formulator (the module which is responsible for constructing the syntactic and morphological structure). In the model of natural speech production presented here, a formalism called Segment Grammar is used for the representation of linguistic knowledge. We give a definition of this formalism and present a formulator design which relies on it. Next, we present an object- oriented implementation of Segment Grammar. Finally, we compare Segment Grammar with other formalisms.

Abstract

Two experiments examined the nature of access, storage, and comprehension of idiomatic phrases. In both studies a Phrase Classification Task was utilized. In this, reaction times to determine whether or not word strings constituted acceptable English phrases were measured. Classification times were significantly faster to idiom than to matched control phrases. This effect held under conditions involving different categories of idioms, different transitional probabilities among words in the phrases, and different levels of awareness of the presence of idioms in the materials. The data support a Lexical Representation Hypothesis for the processing of idioms.

Abstract

This paper reports two experiments with vowels and consonants as phoneme detection targets in real words. In the first experiment, two relatively distinct vowels were compared with two confusible stop consonants. Response times to the vowels were longer than to the consonants. Response times correlated negatively with target phoneme length. In the second, two relatively distinct vowels were compared with their corresponding semivowels. This time, the vowels were detected faster than the semivowels. We conclude that response time differences between vowels and stop consonants in this task may reflect differences between phoneme categories in the variability of tokens, both in the acoustic realisation of targets and in the' representation of targets by subjects.

Abstract

Computer simulations in an instructional context can be characterized according to four aspects (themes): simulation models, learning goals, learning processes and learner activity. The present paper provides an outline of these four themes. The main classification criterion for simulation models is quantitative vs. qualitative models. For quantitative models a further subdivision can be made by classifying the independent and dependent variables as continuous or discrete. A second criterion is whether one of the independent variables is time, thus distinguishing dynamic and static models. Qualitative models on the other hand use propositions about non-quantitative properties of a system or they describe quantitative aspects in a qualitative way. Related to the underlying model is the interaction with it. When this interaction has a normative counterpart in the real world we call it a procedure. The second theme of learning with computer simulation concerns learning goals. A learning goal is principally classified along three dimensions, which specify different aspects of the knowledge involved. The first dimension, knowledge category, indicates that a learning goal can address principles, concepts and/or facts (conceptual knowledge) or procedures (performance sequences). The second dimension, knowledge representation, captures the fact that knowledge can be represented in a more declarative (articulate, explicit), or in a more compiled (implicit) format, each one having its own advantages and drawbacks. The third dimension, knowledge scope, involves the learning goal's relation with the simulation domain; knowledge can be specific to a particular domain, or generalizable over classes of domains (generic). A more or less separate type of learning goal refers to knowledge acquisition skills that are pertinent to learning in an exploratory environment. Learning processes constitute the third theme. Learning processes are defined as cognitive actions of the learner. Learning processes can be classified using a multilevel scheme. The first (highest) of these levels gives four main categories: orientation, hypothesis generation, testing and evaluation. Examples of more specific processes are model exploration and output interpretation. The fourth theme of learning with computer simulations is learner activity. Learner activity is defined as the ‘physical’ interaction of the learner with the simulations (as opposed to the mental interaction that was described in the learning processes). Five main categories of learner activity are distinguished: defining experimental settings (variables, parameters etc.), interaction process choices (deciding a next step), collecting data, choice of data presentation and metacontrol over the simulation.

Abstract

The use of computer simulations in education and training can have substantial advantages over other approaches. In comparison with alternatives such as textbooks, lectures, and tutorial courseware, a simulation-based approach offers the opportunity to learn in a relatively realistic problem-solving context, to practise task performance without stress, to systematically explore both realistic and hypothetical situations, to change the time-scale of events, and to interact with simplified versions of the process or system being simulated. However, learners are often unable to cope with the freedom offered by, and the complexity of, a simulation. As a result many of them resort to an unsystematic, unproductive mode of exploration. There is evidence that simulation-based learning can be improved if the learner is supported while working with the simulation. Constructing such an instructional environment around simulations seems to run counter to the freedom the learner is allowed to in ‘stand alone’ simulations. The present article explores instructional measures that allow for an optimal freedom for the learner. An extensive discussion of learning goals brings two main types of learning goals to the fore: conceptual knowledge and operational knowledge. A third type of learning goal refers to the knowledge acquisition (exploratory learning) process. Cognitive theory has implications for the design of instructional environments around simulations. Most of these implications are quite general, but they can also be related to the three types of learning goals. For conceptual knowledge the sequence and choice of models and problems is important, as is providing the learner with explanations and minimization of error. For operational knowledge cognitive theory recommends learning to take place in a problem solving context, the explicit tracing of the behaviour of the learner, providing immediate feedback and minimization of working memory load. For knowledge acquisition goals, it is recommended that the tutor takes the role of a model and coach, and that learning takes place together with a companion. A second source of inspiration for designing instructional environments can be found in Instructional Design Theories. Reviewing these shows that interacting with a simulation can be a part of a more comprehensive instructional strategy, in which for example also prerequisite knowledge is taught. Moreover, information present in a simulation can also be represented in a more structural or static way and these two forms of presentation provoked to perform specific learning processes and learner activities by tutor controlled variations in the simulation, and by tutor initiated prodding techniques. And finally, instructional design theories showed that complex models and procedures can be taught by starting with central and simple elements of these models and procedures and subsequently presenting more complex models and procedures. Most of the recent simulation-based intelligent tutoring systems involve troubleshooting of complex technical systems. Learners are supposed to acquire knowledge of particular system principles, of troubleshooting procedures, or of both. Commonly encountered instructional features include (a) the sequencing of increasingly complex problems to be solved, (b) the availability of a range of help information on request, (c) the presence of an expert troubleshooting module which can step in to provide criticism on learner performance, hints on the problem nature, or suggestions on how to proceed, (d) the option of having the expert module demonstrate optimal performance afterwards, and (e) the use of different ways of depicting the simulated system. A selection of findings is summarized by placing them under the four themes we think to be characteristic of learning with computer simulations (see de Jong, this volume).