Induction of augmented transition networks

LAS is a program that acquires augmented transition network (ATN) grammars. It requires as data sentences of the language and semantic network representatives of their meaning. In acquiring the ATN grammars, it induces the word classes of the language, the rules of formation for sentences, and the rules mapping sentences onto meaning. The induced ATN grammar can be used both for sentence generation and sentence comprehension. Critical to the performance of the program are assumptions that it makes about the relation between sentence structure and surface structure (the graph deformation condition), about when word classes may be formed and when ATN networks may be merged, and about the structure of noun phrases. These assumptions seem to be good heuristics which are largely true for natural languages although they would not be true for many nonnatural languages. Provided these assumptions are satisfied LAS seems capable of learning any context-free language.     

Self- and other regulation during the infancy period

The “other regulation” views of Lev Vygotsky and of Jerome Bruner are contrasted with Jean Piaget's self-regulatory model of infant development. Examples of adult behavior in Piaget's writings and in mother-infant interactions suggest that adults perform behaviors which are analogous to Piaget's type a and b self-regulation. Data from infant games are provided to illustrate the processes by which mothers regulate the environments for their infants and the consequences of such maternal regulation for infant development.     

Interrater agreement for reviews for Developmental Review

Reviewer reliability is examined for the first 2 years of operation of Developmental Review. Using the statistic, Finn's r, summary recommendations on publishability are shown to be higher in reliability than previously reported for other journals. Separate rating scales are shown to be less reliable than summary recommendations. Finn's r is compared with the intraclass correlation coefficient and shown to be a better method for examining reviewer agreement across different journals. Potential causes of high reviewer agreement for Developmental Review are discussed, and the importance of the distinction between reliability and validity of reviews is noted.     

Categorizing numeric information for generalization

Learning programs that generalize from real-world examples will have to deal with many different kinds of data. Continuous numeric data can cause problems for algorithms that search for examples with identical property values. These problems can be surmounted by categorizing the numeric data. However, this process has problems of its own. In this paper, we look at the need for categorizing numeric data and several methods for doing so. We concentrate on the use of generalization-based memory, a memory organization where actual examples are stored along with generalizations, which leads to a generalization-based categorization algorithm. We also consider how to use a number heuristic, looking for gaps. These methods have been implemented in the UNIMEM computer system. Examples are presented of these algorithms categorizing data about the states of the United States.     

Novel use of a virtual driving assessment to classify driver skill at the time of licensure

Motor vehicle crash rates are highest immediately after licensure, and driver error is one of the leading causes. Yet, few studies have quantified driving skills at the time of licensure, making it difficult to identify at-risk drivers before independent driving. Using data from a virtual driving assessment implemented into the licensing workflow in Ohio, this study presents the first population-level study classifying degree of skill at the time of licensure and validating these against a measure of on-road performance: license exam outcomes. Principal component and cluster analysis of 33,249 virtual driving assessments identified 20 Skill Clusters that were then grouped into 4 major summary “Driving Classes”; i) No Issues (i.e. careful and skilled drivers); ii) Minor Issues (i.e. an average new driver with minor vehicle control skill deficits); iii) Major Issues (i.e. drivers with more control issues and who take more risks); and iv) Major Issues with Aggression (i.e. drivers with even more control issues and more reckless and risk-taking behavior). Category labels were determined based on patterns of VDA skill deficits alone (i.e. agnostic of the license examination outcome). These Skill Clusters and Driving Classes had different distributions by sex and age, reflecting age-related licensing policies (i.e. those under 18 and subject to GDL and driver education and training), and were differentially associated with subsequent performance on the on-road licensing examination (showing criterion validity). The No Issues and Minor Issues classes had lower than average odds of failing, and the other two more problematic Driving Classes had higher odds of failing. Thus, this study showed that license applicants can be classified based on their driving skills at the time of licensure. Future studies will validate these Skill Cluster classes in relation to their prediction of post-licensure crash outcomes.