Parental expressed emotion toward children: prediction from early family functioning

Expressed emotion (EE), an index of family member criticism and emotional overinvolvement, predicts outcome among adults and children with mental disorders. However, limited research exists on factors contributing to EE. Aims of the current study were to (a) examine EE in mothers of young children; (b) assess relationships between demographic factors and EE in a diverse sample; and (c) investigate whether family stress and functioning, including quality of marital relationship, life events, maternal stress, and family environment, predict EE. In the current study, 276 mothers completed questionnaires when their children were between 1 and 3 years of age and a measure of EE when their children were in kindergarten. Results indicated that family expressiveness was the most consistent predictor of EE. Further research seems warranted to better delineate associations between family functioning and EE.     

Telephone conversation impairs sustained visual attention via a central bottleneck

Recent research has shown that holding telephone conversations disrupts one’s driving ability. We asked whether this effect could be attributed to a visual attention impairment. In Experiment 1, participants conversed on a telephone or listened to a narrative while engaged in multiple object tracking (MOT), a task requiring sustained visual attention. We found that MOT was disrupted in the telephone conversation condition, relative to single-task MOT performance, but that listening to a narrative had no effect. In Experiment 2, we asked which component of conversation might be interfering with MOT performance. We replicated the conversation and single-task conditions of Experiment 1 and added two conditions in which participants heard a sequence of words over a telephone. In the shadowing condition, participants simply repeated each word in the sequence. In the generation condition, participants were asked to generate a new word based on each word in the sequence. Word generation interfered with MOT performance, but shadowing did not. The data indicate that telephone conversation disrupts attention at a central stage, the act of generating verbal stimuli, rather than at a peripheral stage, such as listening or speaking.     

Human and machine perception of biological motion

More than 30 years ago, Johansson was the first to show that humans are capable of recovering information about the identity and activity of animate creatures rapidly and reliably from very sparse visual inputs – the phenomenon of biological motion. He filmed human actors in a dark setting with just a few strategic points on the body marked by lights – so-called moving light displays (MLDs). Subjects viewing the MLDs reported a vivid impression of moving human forms, and were even able to tell the activity in which the perceived humans were engaged. Subsequently, the phenomenon has been widely studied and many attempts have been made to model and to understand it. Typical questions that arise are: How precisely is the sparse low-level information integrated over space and time to produce the global percept, and how important is world knowledge (e.g., about animal form, locomotion, gravity, etc.)? In an attempt to answer such questions, we have implemented a machine-perception model of biological motion. If the computational model can replicate human data then it might offer clues as to how humans achieve the task. In particular, if it can do so with no or minimal world knowledge then this knowledge cannot be essential to the perception of biological motion. To provide human data for training and against which to assess the model, an extensive psychophysical experiment was undertaken in which 93 subjects were shown 12 categories of MLDs (e.g., normal, walking backwards, inverted, random dots, etc.) and were asked to indicate the presence or absence of natural human motion. Machine perception models were then trained on normal sequences as positive data and random sequences as negative data. Two models were used: a k-nearest neighbour (k-NN) classifier as an exemplar of ‘lazy’ learning and a back-propagation neural network as an exemplar of ‘eager’ learning. We find that the k-NN classifier is better able to model the human data but it still fails to represent aspects of knowledge about body shape (especially how relative joint positions change under rotation) that appear to be important to human judgements.     

Context processing in older adults: evidence for a theory relating cognitive control to neurobiology in healthy aging.

A theory of cognitive aging is presented in which healthy older adults are hypothesized to suffer from disturbances in the processing of context that impair cognitive control function across multiple domains, including attention, inhibition, and working memory. These cognitive disturbances are postulated to be directly related to age-related decline in the function of the dopamine (DA) system in the prefrontal cortex (PFC). A connectionist computational model is described that implements specific mechanisms for the role of DA and PFC in context processing. The behavioral predictions of the model were tested in a large sample of older (N = 81) and young (N = 175) adults performing variants of a simple cognitive control task that placed differential demands on context processing. Older adults exhibited both performance decrements and, counterintuitively, performance improvements that are in close agreement with model predictions.