‘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.     

How help during pregnancy can undermine self-efficacy and increase postpartum intentions to quit

Pregnancy reflects a common experience for women in today's workforce, yet recent data suggest that some women scale back or leave the workforce following childbirth. Considering these effects on women's careers, researchers have sought to understand the underlying dynamics of these decisions. Here, we explore a paradoxical reason for weakened postpartum career attitudes: help that women receive during pregnancy. We integrate stereotype threat and benevolent sexism theories to explain how the effects of help on postpartum intentions to quit may be transmitted through reductions in work self-efficacy. In doing so, we consider the role of perceived impact—or the extent to which help interferes with versus enables women's perceived ability to continue performing their work role. Results of a weekly diary study of 105 pregnant employees suggest that work-interfering help led to decreased self-efficacy for work during the following week. Furthermore, there was an indirect effect of average help received at work during pregnancy on postpartum intentions to quit the workforce through reductions in work self-efficacy that was stronger insofar as help was work-interfering versus work-enabling. Taken together, our results highlight unintended negative consequences that occur when others provide ineffective support to women at work during pregnancy.     

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.     

Audience Design in Multiparty Conversation

How do speakers design what they say in order to communicate effectively with groups of addressees who vary in their background knowledge of the topic at hand? Prior findings indicate that when a speaker addresses a pair of listeners with discrepant knowledge, that speakers Aim Low, designing their utterances for the least knowledgeable of the two addressees. Here, we test the hypothesis that speakers will depart from an Aim Low approach in order to efficiently communicate with larger groups of interacting partners. Further, we ask whether the cognitive demands of tracking multiple conversational partners' perspectives places limitations on successful audience design. We find that speakers can successfully track information about what up to four of their partners do and do not know in conversation. When addressing groups of 3–4 addressees at once, speakers design language based on the combined knowledge of the group. These findings point to an audience design process that simultaneously represents the perspectives of multiple other individuals and combines these representations in order to design utterances that strike a balance between the different needs of the individuals within the group.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.