The Effects of Consistency among Simultaneous Forecasts on Weather-Related Decisions

People access weather forecasts from multiple sources [mobile telephone applications (“apps”), newspapers, and television] that are not always in agreement for a particular weather event. The experiment reported here investigated the effects of inconsistency among forecasts on user trust, weather-related decisions, and confidence in user decisions. In a computerized task, participants made school-closure decisions on the basis of snow forecasts from different sources and answered a series of questions about each forecast. Inconsistency among simultaneous forecasts did not significantly reduce trust, although inaccuracy did. Moreover, inconsistency may convey useful information to decision-makers. Not only do participants appear to incorporate the information provided by all forecasts into their own estimates of the outcome, but our results also suggest that inconsistency gives rise to the impression of greater uncertainty, which leads to more cautious decisions. The implications for decisions in a variety of domains are discussed.

Examining Neural Activity Related to Pitch Stimuli and Feedback at the Plate: Cognitive and Performance Implications

This study investigated the relationships among neural activity related to pitch stimuli and task feedback, self-regulatory control, and task-performance measures in expert and novice baseball players. The participants had their event-related brain potentials recorded while they completed a computerized task assessing whether thrown pitches were balls or strikes and received feedback on the accuracy of their responses following each pitch. The results indicated that college players exhibited significantly larger medial frontal negativities to pitch stimuli, as well as smaller reward positivities and larger frontocentral positivities in response to negative feedback, compared with novices. Furthermore, significant relationships were present between college players’ neural activity related to both pitches and feedback and their task performance and self-regulatory behavior. These relationships were not present for novices. These findings suggest that players efficiently associate the information received in their feedback to their self-regulatory processing of the task and, ultimately, their task performance.

Using AI Techniques in a Serious Game for Socio-Moral Reasoning Development

We present a serious game designed to help players/learners develop socio-moral reasoning (SMR) maturity. It is based on an existing computerized task that was converted into a game to improve the motivation of learners. The learner model is computed using a hybrid deep learning architecture, and adaptation rules are provided by both human experts and machine learning techniques. We conducted some experiments with two versions of the game (the initial version and the adaptive version with AI-Based learner modeling). The results show that the adaptive version provides significant better results in terms of learning gain.

Category Learning in Schoolchildren. Its Relation to Age, Academic Marks and Resolution Patterns

The aim of this research was to study the learning process using an objective and computerized task. The performance of 466 schoolchildren aged between 6 and 11 in a category learning task, the Category Learning Test (CLT), was examined. The results showed evidence of category learning throughout the trials for the whole sample, F(7, 469) = 29.979, p <.001. In addition, categorization performance improved with age, H(2) = 48.475, p <.001. However, there were old children that struggled with the task and young children that performed very well. The ability to learn the categories was related to the children’s behavior when trying to solve the task: the response speed (r = –.217, p <.01) and the organization index (r = .247, p <.01). Nevertheless, performance in the task and academic marks were not related. We discuss the impact of these findings on the promotion and improvement of learning in schools: an intervention to promote slowness and organization might help some children to learn.

Correlates of cannabinoid concentrations, real-world driving, and driving-related skills

Research on the relationship between cannabis use and safe driving has produced mixed results. Most studies have focused exclusively on the presence or concentration of delta-9-tetrahydrocannabinol (THC), the primary psychoactive ingredient in the drug. However, cannabis is a complex substance, and both toxicological research and user experience suggests that some cannabis strains—often those with at least moderate levels of cannabidiol (CBD)— produce a different, more sedating “high” than cannabis strains with no or low levels of CBD. We hypothesize that the sedating properties of some high-CBD cannabis strains has potential to impair driving and driving-related skills above and beyond the effects of THC intoxication. Three studies—one instrumented vehicle driving study and two laboratory-style epidemiological studies—examined real driving and computerized task performance as a function THC and CBD concentrations (and their interactions). In all three studies, higher CBD levels predicted greater impairment. There was relatively little evidence of impairment when CBD was zero, even at high THC levels. The results suggest that THC concentrations alone are not sufficient to predict impairment due to cannabis use. Results are interpreted in the context of drug tolerance.