Adjustment to Acute Leukemia: The Impact of Social Support and Marital Satisfaction on Distress and Quality of Life Among Newly Diagnosed Patients and Their Caregivers

Little is known about the specific patterns of adjustment among newly diagnosed acute leukemia patients and their caregivers. This study examined the trajectories of patient and caregiver distress over time as well as the extent to which marital satisfaction and social support moderated these trajectories among those with significant-other caregivers. Forty six patient-caregiver dyads provided ratings at four time points: within 1 week of diagnosis (T1), 2 week follow-up (T2), 6 week follow-up (T3) and 12 week follow-up (T4). As anticipated, patients and caregivers reported higher levels of distress around the time of diagnosis than they did during subsequent time points. Marital satisfaction was a significant predictor of distress among patients, whereas among caregivers, social support predicted distress and quality of life. Results support the inclusion of relational variables such as social support and relationship satisfaction in the assessment of newly diagnosed patients and families in order to best identify those at risk for distress over time.     

Association between brain activation (fMRI), cognition and school performance in extremely preterm and term born children

The purpose of the study was to investigate a possible association between brain activation in functional magnetic resonance imaging scans, cognition and school performance in extremely preterm children and term born controls. Twenty eight preterm and 28 term born children were scanned while performing a working memory/selective attention task, and school results from national standardized tests were collected. Brain activation maps reflected difference in cognitive skills but not in school performance. Differences in brain activation were found between children born preterm and at term, and between high and low performers in cognitive tests. However, the differences were located in different brain areas. The implication may be that lack of cognitive skills does not alone explain low performance due to prematurity.     

Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor

Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over languages to lose the influence of a common ancestor and converge to a form that is determined by constraints on language learning. We show that modeling language learning as Bayesian inference of n binary parameters or the ordering of n constraints results in convergence in a number of generations that is on the order of n log n. We relax some of the simplifying assumptions of this model to explore how different assumptions about language evolution affect predictions about the time to convergence; in general, convergence time increases as the model becomes more realistic. This allows us to characterize the assumptions about language learning (given the models that we consider) that are sufficient for convergence to have taken place on a timescale that is consistent with the origin of human languages. These results clearly identify the consequences of a set of simple models of language evolution and show how analysis of convergence rates provides a tool that can be used to explore questions about the relationship between accounts of language learning and the origins of similarities across languages.     

The role of structural characteristics in video-game play motivation: a Q-methodology study

Until recently, there has been very little naturalistic study of what gaming experiences are like, and how gaming fits into people's lives. Using a recently developed structural characteristic taxonomy of video games, this study examined the psycho-structural elements of computer games that motivate gamers to play them. Using Q-Sort methodology, 40 gamers participated in an online Q-sort task. Results identified six distinct types of gamers based on the factors generated: (a) story-driven solo gamers; (b) social gamers; (c) solo limited gamers; (d) hardcore online gamers; (e) solo control/identity gamers; and (f?) casual gamers. These gaming types are discussed, and a brief evaluation of similar and unique elements of the different types of gamer is also offered. The current study shows Q-methodology to be a relevant and applicable method in the psychological research of gaming.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

Speeded induction under uncertainty: The influence of multiple categories and feature conjunctions

When people are uncertain about the category membership of an item (e.g., Is it a dog or a dingo?), research shows that they tend to rely only on the dominant or most likely category when making inductions (e.g., How likely is it to befriend me?). An exception has been reported using speeded induction judgments where participants appeared to use information from multiple categories to make inductions (Verde, Murphy, &; Ross, 2005). In two speeded induction studies, we found that participants tended to rely on the frequency with which features co-occurred when making feature predictions, independently of category membership. This pattern held whether categories were considered implicitly (Experiment 1) or explicitly (Experiment 2) prior to feature induction. The results converge with other recent work suggesting that people often rely on feature conjunction information, rather than category boundaries, when making inductions under uncertainty.     

A probabilistic model of theory formation

Concept learning is challenging in part because the meanings of many concepts depend on their relationships to other concepts. Learning these concepts in isolation can be difficult, but we present a model that discovers entire systems of related concepts. These systems can be viewed as simple theories that specify the concepts that exist in a domain, and the laws or principles that relate these concepts. We apply our model to several real-world problems, including learning the structure of kinship systems and learning ontologies. We also compare its predictions to data collected in two behavioral experiments. Experiment 1 shows that our model helps to explain how simple theories are acquired and used for inductive inference. Experiment 2 suggests that our model provides a better account of theory discovery than a more traditional alternative that focuses on features rather than relations.     

A rational analysis of the effects of memory biases on serial reproduction

Many human interactions involve pieces of information being passed from one person to another, raising the question of how this process of information transmission is affected by the cognitive capacities of the agents involved. Bartlett (1932) explored the influence of memory biases on the “serial reproduction” of information, in which one person’s reconstruction of a stimulus from memory becomes the stimulus seen by the next person. These experiments were done using relatively uncontrolled stimuli, but suggested that serial reproduction could transform information in a way that reflected the biases inherent in memory. We formally analyze serial reproduction using a Bayesian model of reconstruction from memory, giving a general result characterizing the effect of memory biases on information transmission. We then test the predictions of this account in four experiments using simple one-dimensional stimuli. Our results provide theoretical and empirical justification for the idea that serial reproduction reflects memory biases.