Publications

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).

Abstract

Pitch, intonation, and speech rate were analyzed in a collection of everyday speech heard by one Dutch infant between the ages of six and nine months. Components of each of these variables were measured in the speech of three adult speakers (mother, father, baby-sitter) when they addressed the infant, and when they addressed another adult. The results are in line with previously reported findings which are usually based on laboratory or prearranged settings: infant-directed speech in a natural setting exhibits more pitch variation, a larger number of simple intonation contours, and slower speech rate than does adult-directed speech.

Abstract

Van den Broeck (1977, 1980) heeft een pragmatische interpretatie voorgesteld voor de verschillende wijzen waarop leden van hogere en lagere sociaal-economische klassen hun taaluitingen syntaktisch struktureren. Ter ondersteuning hiervan presenteerde hij gegevens m.b.t. gemiddelde zinscomplexiteit bij sprekers uit twee sociaal- conomische klassen in formele en informele gesprekssituaties. Het in dit artikel gerapporteerde onderzoek slaagt er niet in van den Broeck's uitkomsten te repliceren. Waarschijnlijk berusten ze op een contaminatie van gesprekssituatie met taalkeuze (standaardtaal vs. lokaal dialekt). Mede op basis van de nieuwe gegevens wordt i.p.v. de pragmatische interpretatie een vaardigheidsinterpretatie voorgesteld. T.g.v. vooropleiding en beroep beschikken leden van verschillende sociaal- economische klassen over uiteenlopende vaardigheidsniveaus in verhalend (narrative) en verklarend (expository) taalgebruik. Dit uit zich in het gebruik van connectieven: funktiewoorden die logischsemantische relaties tussen proposities uitdrukken.

Abstract

Dutch distinguishes at least four sentence types: statements and questions, the latter type being subdivided into wh-questions (beginning with a question word), yes/no-questions (with inversion of subject and finite), and declarative questions (lexico-syntactically identical to statement). Acoustically, each of these (sub)types was found to have clearly distinct global F0-patterns, as well as a characteristic distribution of final rises [1,2]. The present paper explores the separate contribution of parameters of global downtrend and size of accent-lending pitch movements versus aspects of the terminal rise to the human identification of the four sentence (sub)types, at various positions in the time-course of the utterance. The results show that interrogativity in Dutch can be identified at an early point in the utterance. However, wh-questions are not distinct from statements.

Abstract

Jescheniak and Levelt (Jescheniak, J.-D., Levelt, W.J.M. 1994. Journal of Experimental Psychology: Learning, Memory and Cognition 20 (4), 824–843) have suggested that the speed with which native speakers of a gender-marking language retrieve the grammatical gender of a noun from their mental lexicon may depend on the recency of earlier access to that same noun's gender, as the result of a mechanism that is dedicated to facilitate gender-marked anaphoric reference to recently introduced discourse entities. This hypothesis was tested in two picture naming experiments. Recent gender access did not facilitate the production of gender-marked adjective noun phrases (Experiment 1), nor that of gender-marked definite article noun phrases (Experiment 2), even though naming times for the latter utterances were sensitive to the gender of a written distractor word superimposed on the picture to be named. This last result replicates and extends earlier gender-specific picture-word interference results (Schriefers, H. 1993. Journal of Experimental Psychology: Learning, Memory, and Cognition 19 (4), 841–850), showing that one can selectively tap into the production of grammatical gender agreement during speaking. The findings are relevant to theories of speech production and the representation of grammatical gender for that process.

Abstract

L'A. étudie les corrélations entre structure syntaxique et fonction pragmatique dans les alternances de voix en akatek, une langue maya appartenant au sous-groupe Q'anjob'ala. Les alternances pragmatiques de voix sont les mécanismes par lesquels les langues encodent les différents degrés de topicalité des deux principaux participants d'un événement sémantiquement transitif, l'agent et le patient. A l'aide d'une analyse quantitative, l'A. évalue la topicalité de ces participants et identifie les structures syntaxiques permettant d'exprimer les quatre principales fonctions de voix en akatek : active-directe, inverse, passive et antipassive