‘He's got his father's bias’: Parental influence on weight bias in young children

Our aim was to explore the role of parents in the transmission of stereotypical body size attitudes and awareness of weight loss strategies to preschool children. Participants were 279 3‐year‐old children and their parents, who provided data at baseline and 1 year later. Parents completed self‐report body size attitude and dieting measures. Child weight bias and awareness of weight loss strategies were assessed through interview. Over time, negative associations with large bodies and awareness of weight loss strategies increased. Fathers’ attitudes prospectively predicted boys’ weight bias and awareness of weight loss strategies. Among girls, parental attitudes were less predictive. Findings confirm the importance of fathers in the development of boys’ body attitudes and inform prevention programmes.     

The Embodied Penman: Effector‐Specific Motor–Language Integration During Handwriting

Several studies have illuminated how processing manual action verbs (MaVs) affects the programming or execution of concurrent hand movements. Here, to circumvent key confounds in extant designs, we conducted the first assessment of motor–language integration during handwriting—a task in which linguistic and motoric processes are co‐substantiated. Participants copied MaVs, non‐manual action verbs, and non‐action verbs as we collected measures of motor programming and motor execution. Programming latencies were similar across conditions, but execution was faster for MaVs than for the other categories, regardless of whether word meanings were accessed implicitly or explicitly. In line with the Hand‐Action‐Network Dynamic Language Embodiment (HANDLE) model, such findings suggest that effector‐congruent verbs can prime manual movements even during highly automatized tasks in which motoric and verbal processes are naturally intertwined. Our paradigm opens new avenues for fine‐grained explorations of embodied language processes.     

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.     

Reading long words embedded in sentences: informativeness of word halves affects eye movements

The possibility was explored that the informativeness of a specific region within a word can influence eye movements during reading. In Experiment 1, words containing identifying information either toward the beginning or toward the end were displayed asymmetrically around the point of fixation so that the reader was initially presented with either the informative or noninformative zone. Words were read with shorter summed initial fixation time when the reading was started from the informative zone. In Experiments 2 and 3, the target words were presented in sentences that were to be comprehended. More attention was given to the informative endings of words than to redundant endings. The latter were also skipped more often. The duration of the first fixation was not affected by information distribution within the word, whereas the second fixation duration was. The results of these experiments lend good support to the hypothesis of immediate lexical control over fixation behavior and to the notion of a convenient viewing position.     

Factor structure in groups selected on observed scores

A new method is proposed for estimating factor means and factor covariances in a group of individuals selected on their observed scores. The selection variable is, for example, the total score on an admissions test. Given a factor model for the test items based on the group of test takers, we may be interested in the factor structure for those in the top quartile. The differences in factor means and covariances between this selected group and the full group gives useful information both on successful test performance and on test validity. The new method draws on the classic Pearson-Lawley selection formulas. It avoids the fallacy of factor analysis on the selected group, which would lead to incorrect estimates. The new method is applied to a simple factor structure model for the GMAT test. Although the majority of the GMAT items test verbal skills, it is found that a quantitative factor shows the greatest change in moving from average to top quartile test takers.