PROBabilities from EXemplars (PROBEX): a “lazy” algorithm for probabilistic inference from generic knowledge

PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgment based on generic knowledge is presented. Its properties are that: (a) it provides an exemplar model satisfying bounded rationality; (b) it is a “lazy” algorithm that presumes no pre‐computed abstractions; (c) it implements a hybrid‐representation, similarity‐graded probability. We investigate the ecological rationality of PROBEX and find that it compares favorably with Take‐The‐Best and multiple regression (Gigerenzer, Todd, & the ABC Research Group, 1999). PROBEX is fitted to the point estimates, decisions, and probability assessments by human participants. The best fit is obtained for a version that weights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implements speed and frugality in a psychologically plausible way.     

Medically Documented Suicide Ideation Among U.S. Army Soldiers

We used administrative data to examine predictors of medically documented suicide ideation (SI) among Regular Army soldiers from 2006 through 2009 (N = 10,466 ideators, 124,959 control person‐months). Enlisted ideators (97.8% of all cases) were more likely than controls to be female, younger, older when entering service, less educated, never or previously deployed, and have a recent mental health diagnosis. Officer ideators were more likely than controls to be female, younger, younger when entering service, never married, and have a recent mental health diagnosis. Risk among enlisted soldiers peaked in the second month of service and declined steadily, whereas risk among officers remained relatively stable over time. Risk of SI is highest among enlisted soldiers early in Army service, females, and those with a recent mental health diagnosis.     

The Causal Organisation of Emotional Knowledge: A Developmental Study

Abstract

Using a theory of emotional understanding, the basis for distinguishing among happiness, anger, and sadness was investigated. Three and six-year-old children and adults predicted and explained people's emotional responses to different types of events. The events varied as to whether a person's goal was to attain or to avoid a state, whether the goal was achieved or not, who or what was responsible for success or failure, and whether the outcome was intentional or accidental. For all groups, the attainment and maintenance of goals was the primary focus of explanations for emotions and for the plans that followed emotions. A distinct set of features was used to infer and explain happiness as opposed to anger and sadness. Happiness was elicited by goal success and was followed by plans to maintain or enjoy current goal states. Anger and sadness were elicited by goal failure and were followed by plans to reinstate, replace, or forfeit goals. Anger occurred more frequently than sadness when an aversive rather than a loss state occurred, when an animate agent rather than a natural event caused a negative outcome, and when attention was focused on the cause rather than the consequence of goal failure. Two dimensions associated with anger changed as a function of age. First grade children, and adults were more likely than preschool children to predict anger in response to intentional harm, and their explanations for anger were more likely to refer to the agent or cause of goal failure. For all age groups, however, the majority of subjects responded to aversive situations with anger responses, independent of the causal conditions that produced the aversive state. The results therefore indicate that anger can be produced without intentional harm, but that intentional harm becomes an important dimension in attributing anger, especially as a function of development.