Publications

Abstract

A t(X;1)(p11.2;q21.2) has been reported in cases of papillary renal cell tumors arising in males. In this study two cell lines derived from this tumor type have been used to indicate the breakpoint region on the X chromosome. Both cell lines have the translocation in addition to other rearrangements and one is derived from the first female case to be reported with the t(X;1)(p11.2;q21.2). Fluorescence in situ hybridization (FISH) has been used to position YACs belonging to contigs in the Xp11.2 region relative to the breakpoint. When considered together with detailed mapping information from the Xp11.2 region the position of the breakpoint in both cell lines was suggested as follows: Xpter-->Xp11.23-OATL1-GATA1-WAS-TFE3-SY P-t(X;1)-DXS255-CLCN5-DXS146-OATL2- Xp11.22-->Xcen. The breakpoint was determined to lie in an uncloned region between SYP and a YAC called FTDM/1 which extends 1 Mb distal to DXS255. These results are contrary to the conclusion from previous FISH studies that the breakpoint was near the OATL2 locus, but are consistent with, and considerably refine, the position that had been established by molecular analysis.

Abstract

An introductory study of the perception of stochastically specified events is reported. The initial problem was to determine whether the perceiver can split visual input data of this kind into random and determined components. The inability of subjects to do so with the stimulus material used (a filmlike sequence of dot patterns), led to the more general question of how subjects code this kind of visual material. To meet the difficulty of defining the subjects' responses, two experiments were designed. In both, patterns were presented as a rapid sequence of dots on a screen. The patterns were more or less disturbed by “noise,” i.e. the dots did not appear exactly at their proper places. In the first experiment the response was a rating on a semantic scale, in the second an identification from among a set of alternative patterns. The results of these experiments give some insight in the coding systems adopted by the subjects. First, noise appears to be detrimental to pattern recognition, especially to patterns with little spread. Second, this shows connections with the factors obtained from analysis of the semantic ratings, e.g. easily disturbed patterns show a large drop in the semantic regularity factor, when only a little noise is added.

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

Three experiments examined the time course of phonological encoding in speech production. A new methodology is introduced in which subjects are required to monitor their internal speech production for prespecified target segments and syllables. Experiment 1 demonstrated that word initial target segments are monitored significantly faster than second syllable initial target segments. The addition of a concurrent articulation task (Experiment 1b) had a limited effect on performance, excluding the possibility that subjects are monitoring a subvocal articulation of the carrier word. Moreover, no relationship was observed between the pattern of monitoring latencies and the timing of the targets in subjects′ overt speech. Subjects are not, therefore, monitoring an internal phonetic representation of the carrier word. Experiment 2 used the production monitoring task to replicate the syllable monitoring effect observed in speech perception experiments: responses to targets were faster when they corresponded to the initial syllable of the carrier word than when they did not. We conclude that subjects are monitoring their internal generation of a syllabified phonological representation. Experiment 3 provides more detailed evidence concerning the time course of the generation of this representation by comparing monitoring latencies to targets within, as well as between, syllables. Some amendments to current models of phonological encoding are suggested in light of these results.

Abstract

The purpose of this paper is to question some of the basic assumpiions concerning motion verbs. In particular, it examines the assumption that "come" and "go" are lexical universals which manifest a universal deictic Opposition. Against the background offive working hypotheses about the nature of'come" and ''go", this study presents a comparative investigation of t wo unrelated languages—Mparntwe Arrernte (Pama-Nyungan, Australian) and Longgu (Oceanic, Austronesian). Although the pragmatic and deictic "suppositional" complexity of"come" and "go" expressions has long been recognized, we argue that in any given language the analysis of these expressions is much more semantically and systemically complex than has been assumed in the literature. Languages vary at the lexical semantic level äs t o what is entailed by these expressions, äs well äs differing äs t o what constitutes the prototype and categorial structure for such expressions. The data also strongly suggest that, ifthere is a lexical universal "go", then this cannof be an inherently deictic expression. However, due to systemic Opposition with "come", non-deictic "go" expressions often take on a deictic Interpretation through pragmatic attribution. Thus, this crosslinguistic investigation of "come" and "go" highlights the need to consider semantics and pragmatics äs modularly separate.