Serge Larochelle

Lexical, sublexical, and peripheral effects in skilled typewriting ☆

Abstract It is generally accepted that expert typewriting performance is strongly affected by the sequence of letters being typed, but there is controversy about the importance of units larger than single letters, such as digraphs or words. We studied expert typists transcribing prose texts and random words. Analyses of interstroke intervals demonstrated the presence of digraph frequency, word frequency, and syllable boundary effects, in addition to the expected effects of movement difficulty. Word frequency and syllable boundary effects function primarily at the perceptual level, whereas digraph frequency and physical difficulty effects function primarily at the motor level.

Category induction in autism: Slower, perhaps different, but certainly possible

Available studies on categorization in autism indicate possibly intact category formation, performed through atypical processes. Category learning was investigated in 16 high-functioning autistic and 16 IQ-matched nonautistic participants, using a category structure that could generate a conflict between the application of a rule and exemplar memory. Same–different and matching-to-sample tasks allowed us to verify discrimination abilities for the stimuli to be used in category learning. Participants were then trained to distinguish between two categories of imaginary animals, using categorization tests early in the training and at the end (160 trials). A recognition test followed, in order to evaluate explicit exemplar memory. Similar discrimination performance was found in control tasks for both groups. For the categorization task, autistic participants did not use any identifiable strategy early in the training, but used strategies similar to those of the nonautistic participants by the end, with the same level of accuracy. Memory for the exemplars was poor in both groups. Our findings confirm that categorization may be successfully performed by autistics, but may necessitate longer exposure to material, as the top-down use of rules may be only secondary to a guessing strategy in autistics.

PASTIS: A PROGRAM FOR CURVE AND DISTRIBUTION ANALYSES

Reaction time (RT) data afford different types of analyses. One type of analysis, called “curve analysis,” can be used to characterize the evolution of performance at different moments over the course of learning. By contrast, distribution analysis aims at characterizing the spread of RTs at a specific moment. Techniques to deduce free parameters are described for both types of analyses, given an a priori choice of the curve or distribution one wants to fit, along with statistical tests of significance for distribution analysis: The log likelihood technique is used if the probability density function is given; otherwise, a root-mean-square-deviation minimization technique is used. A program—PASTIS—that searches for the optimal parameters of the following curves is presented: power law, exponential, and e-based exponential. PASTIS also searches for Weibull and the ex-Gaussian distributions. Some tests of the software are presented.

A Glossary of Terms Including a Classification of Typing Errors

A common terminology is essential when working in any area, and the study of typing is no exception. To aid ourselves and others, we have compiled a glossary of basic definitions useful in the description of the phenomena of typing. The glossary, which also contains a categorization of errors, has proved useful in several ways. Not only does it keep our terms consistent, but it has provided a framework for the description and classification of a number of typing errors. We hope this glossary will be of independent use, perhaps leading to standardization of the typing terms used throughout the typing literature.

What some effects might not be: the time to verify membership in "well-defined" categories.

Armstrong, Gleitman, and Gleitman (1983) reported shorter categorization times for members of well-defined categories judged more typical. They concluded that these effects could not originate in a graded, similarity-based category representation and consequently that the typicality effects obtained with natural categories might not be indicative of such a structure either. In this article, we re-examine this conclusion, focusing first on the performance obtained with well-defined categories of different sizes. Only the larger categories used showed variations in typicality ratings and produced typicality effects on categorization times. However, multiple regression analyses showed the effects on categorization times to be better explained by a measure of associative strength, called category dominance. The range of various predictor variables was equated in a follow-up experiment involving large, natural, and well-defined categories. Results obtained with well-defined categories showed pronounced dominance effects when typicality was controlled, but no reliable typicality effect when category dominance and instance familiarity were controlled. Results were opposite for natural categories. By showing that well-defined categories fail to produce unbiased typicality effects, our results bring added support to the hypothesis that the effects obtained with natural categories originate in a graded, similarity-based category structure.

The Origin of Exemplar Effects in Rule-Driven Categorization

S. W. Allen and L. R. Brooks (1991) have shown that exemplar memory can affect categorization even when participants are provided with a classification rule. G. Regehr and L. R. Brooks (1993) argued that stimuli must be individuated for such effects to occur. In this study, the authors further analyze the conditions that yield exemplar effects in this rule application paradigm. The results of Experiments 1-3 show that interchangeable attributes, which are not part of the rule, influence categorization only when attention is explicitly drawn on them. Experiment 4 shows that exemplar effects can occur in an incidental learning condition, whether stimulus individuation is preserved or not. The authors conclude that the influence of exemplar learning in rule-driven categorization stems from the attributes specified in the rule or in the instructions, not from the stimulus gestalts.

THE DIAGNOSIS OF USER STRATEGIES

This paper presents the architecture of an expert system for the diagnosis of users strategies on a text-editing task. The main objective of the system is to recognize inefficient strategies and to suggest more efficient ones. We demonstrate how strategies can be represented with a formalism called procedural network, and how they can be identified by a standard parsing procedure. The result of the parsing process is a hierarchy of goals, sub-goals, and observable actions at the terminal nodes, which serves as input to a knowledge assessment module. Which actions or sub-goals are chosen to accomplish a goal enables inferences to be made by the knowledge assessment module on what is presumably known or not by a particular user. Suggestions of what skills should be improved can be made on the basis of this knowledge assessment.

Plan recognition in HCI: the parsing of user actions

Plan recognition could play an important role in HCI and, in particular, for advisory systems. To assess its potential utility, we conducted a “cost-benefit” analysis. The benefit is defined with regard to a major application of plan-recognition, advising for a text editor. The proportion of text-editing methods that could correctly be recognized constitutes the measure of benefit. The cost dimension is represented by the programming and computing resources required to perform the plan recognition task. The resources grow with the complexity of the formalism used. Different types of grammatical formalisms are studied. The analysis shows that almost all efficient methods can be correctly identified with a simple formalism, lexical parsing. However, inefficient methods, which are most important for advisory systems, since they indicate that advice is needed, require more complex formalisms that take semantics into account. The objects involved in methods represent in part such semantics. Objects are handled with attribute grammars. Such grammar can recognize more than 80% of the inefficient methods. They are thus the most appropriate for the application considered because they are computationally attractive and cover most of the methods.

Learning the correlational structure of stimuli in a one-attribute classification task

In category learning experiments, participants typically do not learn within-category correlations unless the composition of the categories or the task demands compel them to do so. To determine if correlations among attributes could be learned without explicitly focusing the participants’ attention on them, a task was designed that allowed stimuli to be classified on the basis of a single perfectly predictive attribute. Each training stimulus also included attributes that were either perfectly or partly correlated with the rule attribute. Then, in a test phase, the impact of eliminating the rule attribute on classification was evaluated. The experiment showed that some of the attributes that were perfectly correlated with the rule attribute were learned. These attributes could be used to classify the test exemplars even though the rule attribute had been removed. This experiment provides evidence that within-category correlations can be learned incidentally during classification tasks.

Does training under consistent mapping conditions lead to automatic attention attraction to targets in search tasks

Schneider and Shiffrin (1977) proposed that training under consistent stimulus-response mapping (CM) leads to automatic target detection in search tasks. Other theories, such as Treisman and Gelades (1980) feature integration theory, consider target-distractor discriminability as the main determinant of search performance. The first two experiments pit these two principles against each other. The results show that CM training is neither necessary nor sufficient to achieve optimal search performance. Two other experiments examine whether CM trained targets, presented as distractors in unattended display locations, attract attention away from current targets. The results are again found to vary with target-distractor similarity. Overall, the present study strongly suggests that CM training does not invariably lead to automatic attention attraction in search tasks.