The role of relevant information in functional counterfactual thinking

Although counterfactual thinking is typically activated by a negative outcome, it can have positive effects by helping to regulate and improve future behavior. Known as the content-specific pathway, these counterfactual ruminations use relevant information (i.e., information that is directly related to the problem at hand) to elicit insights about the problem, create a connection between the counterfactual and the desired behavior, and strengthen relevant behavioral intentions. The current research examines how changing the type of relevant information provided (i.e., so that it is either concrete and detailed or general and abstract) influences the relationship between counterfactual thinking and behavioral intentions. Experiments 1 and 2 found that counterfactual thinking facilitated relevant intentions when these statements involved detailed information (Experiment 1) or specific behaviors (Experiment 2) compared to general information (Experiment 1), categories of behavior, or traits (Experiment 2). Experiment 3 found that counterfactuals containing a category of behavior facilitated specific behavioral intentions, relative to counterfactuals focusing on a trait. However, counterfactuals only facilitated intentions that included specific behaviors, but not when intentions focused on categories of behaviors or traits (Experiment 4). Finally, this effect generalized to other relevant specific behaviors; a counterfactual based on one relevant specific behavior facilitated an intention based on another relevant specific behavior (Experiment 5). Together, these studies further clarify our understanding of the content-specific pathway and provide a more comprehensive understanding of functional counterfactual thinking.

A path-based framework for analyzing large markov models

Reliability and dependability modeling can be employed during many stages of analysis of a computing system to gain insights into its critical behaviors. To provide useful results, realistic models of systems are often necessarily large and complex. Numerical analysis of these models presents a formidable challenge because the sizes of their state-space descriptions grow exponentially in proportion to the sizes of the models. On the other hand, simulation of the models requires analysis of many trajectories in order to compute statistically correct solutions. This dissertation presents a novel framework for performing both numerical analysis and simulation. The new numerical approach computes bounds on the solutions of transient measures in large continuous-time Markov chains (CTMCs). It extends existing path-based and uniformization-based methods by identifying sets of paths that are equivalent with respect to a reward measure and related to one another via a simple structural relationship. This relationship makes it possible for the approach to explore multiple paths at the same time,· thus significantly increasing the number of paths that can be explored in a given amount of time. Furthermore, the use of a structured representation for the state space and the direct computation of the desired reward measure (without ever storing the solution vector) allow it to analyze very large models using a very small amount of storage. Often, path-based techniques must compute many paths to obtain tight bounds. In addition to presenting the basic path-based approach, we also present algorithms for computing more paths and tighter bounds quickly. One resulting approach is based on the concept of path composition whereby precomputed subpaths are composed to compute the whole paths efficiently. Another approach is based on selecting important paths (among a set of many paths) for evaluation. Many path-based techniques suffer from having to evaluate many (unimportant) paths. Evaluating the important ones helps to compute tight bounds efficiently and quickly.

Hand detection on images based on deformable part models and additional features

Hand detection on images has important applications on person activities recognition. This thesis focuses on PASCAL Visual Object Classes (VOC) system for hand detection. VOC has become a popular system for object detection, based on twenty common objects, and has been released with a successful deformable parts model in VOC2007. A hand detection on an image is made when the system gets a bounding box which overlaps with at least 50% of any ground truth bounding box for a hand on the image. The initial average precision of this detector is around 0.215 compared with a state-of-art of 0.104; however, color and frequency features for detected bounding boxes contain important information for re-scoring, and the average precision can be improved to 0.218 with these features. Results show that these features help on getting higher precision for low recall, even though the average precision is similar.