Unrealistic optimism and perceived control: role of personal competence

This work analyzed the effects of unrealistic optimism in the interaction between the emotional valence of future events, the perception of control over these events, and the person with whom one compares oneself. It was hypothesized that, if the person of comparison is judged as very competent, a pessimistic bias should be produced. Likelihood of four different types of events (positive and controllable, positive and uncontrollable, negative and controllable, and negative and uncontrollable) were rated by 133 university students (22 men and 111 women) for themselves, for an average student, for their best friend, and for a bright friend. A pessimistic bias was observed on the relative likelihood of the events when the comparison was made between oneself and a competent and bright friend, when events were perceived as controllable, especially positive ones. Not enough is known, however, to provide meaningful interpretation at present; that must await further data and theoretical development.     

Satisfiability Testing for Boolean Formulas Using Δ-trees

The tree-based data structure of -tree for propositional formulas is introduced in an improved and optimised form. The -trees allow a compact representation for negation normal forms as well as for a number of reduction strategies in order to consider only those occurrences of literals which are relevant for the satisfiability of the input formula. These reduction strategies are divided into two subsets (meaning- and satisfiability-preserving transformations) and can be used to decrease the size of a negation normal form A at (at most) quadratic cost. The reduction strategies are aimed at decreasing the number of required branchings and, therefore, these strategies allow to limit the size of the search space for the SAT problem.     

Action-based and vision-based selection of input: Two sources of control

In the first part of this paper we review evidence suggesting that there exists a mechanism that selects input on the basis of its similarity to the required action. This response-based input selection differs from the more established space- and object-based input selection in that it is not constrained by the structure of the input. Our evidence suggests that the two-choice Stroop effect is caused by this response-based selection mechanism. By contrast, it is known that the flanker effect is determined by the space- and object- based selection mechanisms. We explore whether the conflict resolution of the Stroop and flanker tasks is different as well by embedding these two tasks in a PRP (Psychological Refractory Period) paradigm. We show that the Stroop and the PRP effects are additive whereas the flanker and the PRP effects are underadditive, suggesting that the processes in charge of the conflict resolution in the Stroop and the flanker tasks are indeed different. We discuss possible reasons for this difference, and discuss possible ways in which the response-based mechanism can be implemented in information processing models.     

Variability and detection of invariant structure

Two experiments investigated learning of nonadjacent dependencies by adults and 18–month–olds. Each learner was exposed to three–element strings (e.g., pel–kicey–jic) produced by one of two artificial languages. Both languages contained the same adjacent dependencies, so learners could distinguish the languages only by acquiring dependencies between the first and third elements (the nonadjacent dependencies). The size of the pool from which the middle elements were drawn was systematically varied to investigate whether increasing variability (in the form of decreasing predictability between adjacent elements) would lead to better detection of nonadjacent dependencies. Infants and adults acquired nonadjacent dependencies only when adjacent dependencies were least predictable. The results point to conditions that might lead learners to focus on nonadjacent versus adjacent dependencies and are important for suggesting how learning might be dynamically guided by statistical structure.