Two ways of learning associations

How people learn chunks or associations between adjacent items in sequences was modelled. Two previously successful models of how people learn artificial grammars were contrasted: the CCN, a network version of the competitive chunker of Servan‐Schreiber and Anderson [J. Exp. Psychol.: Learn. Mem. Cogn. 16 (1990) 592], which produces local and compositionally‐structured chunk representations acquired incrementally; and the simple recurrent network (SRN) of Elman [Cogn. Sci. 14 (1990) 179], which acquires distributed representations through error correction. The models' susceptibility to two types of interference was determined: prediction conflicts, in which a given letter can predict two other letters that appear next with an unequal frequency; and retroactive interference, in which the prediction made by a letter changes in the second half of training. The predictions of the models were determined by exploring parameter space and seeing howdensely different regions of the space of possible experimental outcomes were populated by model outcomes. For both types of interference, human data fell squarely in regions characteristic of CCN performance but not characteristic of SRN performance.     

Brain weights correlate with behavioral parameters in individual inbred mice housed in a common and enriched environment

Fifty 8-week-old Balb/c mice were individually identified and housed together in a large and enriched environment for 5 months. Maze and open field exploration, response to an aversive noise, swimming, and induced grooming tests were applied to each mouse in an initial search for possible relationships between brain morphology and spontaneous behavior in isogenic individuals living in a complex social and physical environment. The tasks generated 39 quantitative behavioral indices which include locomotion, rearing, still, and grooming bout frequencies, latencies, total, and mean bout durations. At the end of the tests, the 7-month-old mice were sacrificed and the fresh weights of their whole brain, cerebellum, brain stem, diencephalon, telencephalon, and prosencephalon were rapidly obtained. Behavioral data have wide variations and do not adjust to normal population distributions. Means of the same parameter differ between tests. A Spearman correlation matrix of all data yielded many significant correlations between indices of the same task which can be interpreted in terms of time budget and sequence probability. Significant correlations between indices of different tests suggest diverse emotionalities, exploratory strategies, and motor skills. The correlations between body and brain weights and among separate brain regions were not significant. There were several low but significant correlations between brain weights and behavioral indices. Such correlations, the resulting factors, and significant behavioral differences between mice with large and small brains suggest that mice displaying low motor activity in novel environments have larger brains and forebrain/hindbrain ratios than mice with high activity, and that animals with high scores of some specific behaviors have larger brain areas physiologically related to such behaviors.(ABSTRACT TRUNCATED AT 250 WORDS)     

Using item-specific instructional information in achievement modeling

The problem of detecting instructional sensitivity (item basis) in test items is considered. An illustration is given which shows that for tests with many biased items, traditional item bias detection schemes give a very poor assessment of bias. A new method is proposed instead. This method extends item response theory (IRT) by including item-specific auxiliary measurement information related to opportunity-to-learn. Item-specific variation in measurement relations across students with varying opportunity-to-learn is allowed for.This paper was presented at the 1987 AERA meeting in Washington, DC. This research was supported by grant OERI-G-86-003 from the Office of Educational Research and Improvement, Department of Education. The author thanks Michael Hollis and Chih-fen Kao for valuable research assistance, and appreciates valuable comments made by an anonymous reviewer.     

The psychological cost of waiting

The study of any problem involving waiting situations invariably assumes that people dislike having to wait. The analysis of queueing problems usually prescribes that some costs should be associated with the time people spend waiting to be served. These are usually considered to be of an economic nature, though the existence of other costs of a psychological nature is accepted. It is generally accepted that after having to wait for a certain amount of time, anxiety and stress start to build up in an individual, due both to the sense of waste and the uncertainty involved in a waiting situation. This work provides a theoretical basis for analysing this building up process as it occurs during the waiting period. It is proved that, under very general conditions, the resulting psychological stress accumulated during the process is a marginal increasing function of the waiting time, and some strategies to minimise it are developed. The results obtained give theoretical support to the usual assumptions underlying the development of systems to provide “real time” information to people in waiting situations. This is especially important in the operation of public transportation systems. The results also provide theoretical support to the “convex” treatment usually given to the waiting cost function in queueing problems.     

Lay conceptions of personality: Eliciting tiers of a scientific conception of personality

Two studies are reported which examine the availability of scientific propositions of personality in lay conceptions of personality. It is argued from a social constructivist perspective that models of personality must derive from and refer to lay conceptions of persons. Eysenck's trait-type model of introversion-extraversion, containing specific propositions about phenotypic and genotypic differences between extraverts and introverts, was utilized as the scientific model of personality and its availability in lay conceptions of personality was examined in two studies. In the first study, subjects were presented with a genotypic characterization of either an introvert or an extravert target person and asked to infer corresponding phenotypic differences. In the second study, the inference process was reversed with subjects being asked to infer genotypic characteristics of introverts versus extraverts on the basis of phenotypic target person desecrations of the two types. Results from both studies show a high degree of accuracy in subjects' inferences, suggesting that laypersons have well-formed conceptions about personality containing ‘higher-order’ psychogenetic propositions corresponding to Eysenck's trait-type model. The implications of the findings for theory construction are discussed.     

Social structure features in three inbred strains of mice, C57Bl/6J, Balb/cj, and NIH: a comparative study

Some features of social living were investigated in three inbred strains of mice: C57Bl/6j, Balb/cj, and NIH. Five social behaviors--Aggression, Submission, Allogrooming, Social Rest and Rest Alone, as well as Wheel Running Activity--were recorded 2 h daily for a week, in 26 groups of seven male mice living in an enriched environment. Dominants and subordinates among strains shared some behavioral characteristics, but strain contributions upon social structure were also detected. Similarities among strains included the appearance of a single dominant mouse per group rated as the most aggressive, besides from being the animal presented with most Submissions. Allogrooming was more frequent among subordinates than among dominants and subordinates. Differences among strains were that agonistic behavior among subordinates was higher in the Balb/cj and NIH groups than in the C57Bl/6j mice. NIH mice were recorded in Rest Alone more often than mice from the other strains. C57Bl/6j dominants showed higher rates of Wheel Running Activity than subordinates.     

On structural equation modeling with data that are not missing completely at random

A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a prerequisite for unbiased estimation in large samples, as when using the traditional listwise or pairwise present data approaches. The theory is connected with old and new results in the area of selection and factorial invariance. It is pointed out that in many applications, maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software, such as LISREL and LISCOMP. Several sets of artifical data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior.The research of the first author was supported by grant No. SES-8312583 from the National Science Foundation and by a Spencer Foundation grant. We wish to thank Chuen-Rong Chan for drawing the path diagram.     

Three-dimensional perspective drawing of two-dimensional functions with microcomputers

In this paper, we present a BASIC program for Apple II series computers that allows the user to obtain perspective drawings of two-dimensional functions of known analytical expression. Also, we outline a procedure for obtaining perspective representations of two-dimensional arrays of data using the same program. The program should run on any computer with graphics mode with only slight modifications in the graphics commands.