Should you Sleep on it? The Effects of Overnight Sleep on Subjective Preference‐based Choice

Conventional wisdom and studies of unconscious processing suggest that sleeping on a choice may improve decision making. Although sleep has been shown to benefit several cognitive tasks, including problem solving, its impact on everyday choices remains unclear. Here we explore the effects of ‘sleeping on it’ on preference‐based decisions among multiple options. In two studies, individuals viewed several attributes describing a set of items and were asked to select their preferred item after a 12‐hour interval that either contained sleep or was spent fully awake. After an overnight period including sleep, individuals showed increases in positive perceptions of the choice set. This finding contrasts with previous research showing that sleep selectively enhances recall for negative information. In addition, this increase in positive recall did not translate into a greater desire to purchase their preferred item or into an overall benefit for choice satisfaction. Time‐of‐day controls were used to confirm that the observed effects could not be explained by circadian influences. Thus, we show that people may feel more positive about the choice options but not more confident about the choice after ‘sleeping on’ a subjective decision. We discuss how the valence of recalled choice set information may be important in understanding the effects of sleep on multi‐attribute decision making and suggest several avenues for future research. Copyright © 2015 John Wiley & Sons, Ltd.     

How Does Explicit Versus Implicit Risk Information Influence Adolescent Risk‐Taking Engagement?

Adolescents have been shown to be more likely to engage in risky behaviors in daily life. Many studies have indicated that adolescents could make advantageous choices when they receive explicit information but could fail to choose advantageously when they are not informed about risks. The current study aimed to examine the influence of explicit risk information (i.e., when risk information is directly available) versus implicit risk information (i.e., when risk information has to be learned from feedback) on risk‐taking engagement, in order to clarify whether the enhanced risk‐taking observed in decision making under ambiguity in adolescents results from either a greater exploration of ambiguous situations (i.e., a higher ambiguity tolerance) or a specific difficulty associated with learning based on previous choices' outcomes. Adolescents and young adults completed a new adaptation of the Balloon Analogue Risk Task. They were required to accumulate as many points as possible by inflating balloons associated with variable break points and avoiding explosions. This adaptation involved a manipulation of the information level with two conditions, an “informed” condition and a “noninformed” condition, in which the participants had to learn the matching of colors with balloons' resistances based on feedback. The results demonstrated that providing explicit risk information allows adolescents to be as efficient as adults at the end of the game. In contrast, adolescents failed to adjust risk‐taking to the balloon resistance in the noninformed condition. These findings critically suggest that this failure reflects a specific impairment of feedback‐based learning ability but not a global excess of risk‐taking during adolescence. Copyright © 2017 John Wiley & Sons, Ltd.     

A note on computing Louis’ observed information matrix identity for IRT and cognitive diagnostic models

Using Louis’ formula, it is possible to obtain the observed information matrix and the corresponding large-sample standard error estimates after the expectation–maximization (EM) algorithm has converged. However, Louis’ formula is commonly de-emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview that demonstrates how Louis’ formula can be applied efficiently to item response theory (IRT) models and other popular latent variable models, such as cognitive diagnostic models (CDMs). After presenting the algebraic components required for Louis’ formula, two real data analyses, with accompanying numerical illustrations, are presented. Next, a Monte Carlo simulation is presented to compare the computational efficiency of Louis’ formula with previously existing methods. Results from these presentations suggest that Louis’ formula should be adopted as a standard method when computing the observed information matrix for IRT models and CDMs fitted with the EM algorithm due to its computational efficiency and flexibility.