Meta-analysis of the implied distribution of callous-unemotional traits across sampling methods and informant.

Callous-unemotional (CU) traits have been measured in a variety of sample-types (e.g., community or forensic) and from the perspective of different informants (e.g., self-report or parent-report) using the inventory of callous-unemotional traits total score (ICU-T). Although the positive association between CU traits and antisocial behavior is uncontroversial, the degree to which sample-types are different from each other has received little attention despite such knowledge being important for generalization and interpretation of research findings. To address this gap in the literature, we estimated the implied distribution of the ICU-T across sample-types, informants, and their interaction using meta-analytic models of sample means and variances. In unconditional models, we found that sample-type significantly moderated mean ICU-T scores but not variance, while informant significantly moderated the variance of ICU-T scores but not means. There was also a significant interaction between sample-type and informant. Mean parent-reported ICU-T scores were significantly lower than self-reported scores in community samples, but not significantly different in samples with elevated levels of antisocial behavior. Implications of our findings include improved research efficiency, the need for different ICU-T norms across informants, and greater understanding of informant biases.

When Extremists Win: Cultural Transmission Via Iterated Learning When Populations Are Heterogeneous.

How does the process of information transmission affect the cultural or linguistic products that emerge? This question is often studied experimentally and computationally via iterated learning, a procedure in which participants learn from previous participants in a chain. Iterated learning is a powerful tool because, when all participants share the same priors, the stationary distributions of the iterated learning chains reveal those priors. In many situations, however, it is unreasonable to assume that all participants share the same prior beliefs. We present four simulation studies and one experiment demonstrating that when the population of learners is heterogeneous, the behavior of an iterated learning chain can be unpredictable and is often systematically distorted by the learners with the most extreme biases. This results in group-level outcomes that reflect neither the behavior of any individuals within the population nor the overall population average. We discuss implications for the use of iterated learning as a methodological tool as well as for the processes that might have shaped cultural and linguistic evolution in the real world.

Resources masquerading as slots: Flexible allocation of visual working memory.

Whether the capacity of visual working memory is better characterized by an item-based or a resource-based account continues to be keenly debated. Here, we propose that visual working memory is a flexible resource that is sometimes deployed in a slot-like manner. We develop a computational model that can either encode all items in a memory set, or encode only a subset of those items. A fixed-capacity mnemonic resource is divided among the items in memory. When fewer items are encoded, they are each remembered with higher fidelity, but at the cost of having to rely on an explicit guessing process when probed about an item that is not in memory. We use the new model to test the prediction that participants will more often encode the entire set of items when the demands on memory are predictable.

Managing the Budget: Stock-Flow Reasoning and the CO2 Accumulation Problem.

The majority of people show persistent poor performance in reasoning about "stock-flow problems" in the laboratory. An important example is the failure to understand the relationship between the "stock" of CO2 in the atmosphere, the "inflow" via anthropogenic CO2 emissions, and the "outflow" via natural CO2 absorption. This study addresses potential causes of reasoning failures in the CO2 accumulation problem and reports two experiments involving a simple re-framing of the task as managing an analogous financial (rather than CO2 ) budget. In Experiment 1 a financial version of the task that required participants to think in terms of controlling debt demonstrated significant improvements compared to a standard CO2 accumulation problem. Experiment 2, in which participants were invited to think about managing savings, suggested that this improvement was fortuitous and coincidental rather than due to a fundamental change in understanding the stock-flow relationships. The role of graphical information in aiding or abetting stock-flow reasoning was also explored in both experiments, with the results suggesting that graphs do not always assist understanding. The potential for leveraging the kind of reasoning exhibited in such tasks in an effort to change people's willingness to reduce CO2 emissions is briefly discussed.