Effects of Level of Processing on Implicit and Explicit Memory in Depressed Mood

Recently, several studies have addressed the question of whether depression affects priming in implicit memory tasks. The main aim of this experiment was to assess the presence of a bias for negative information in explicit memory (free recall) and implicit memory (word-stem completion) tasks among subclinically depressed subjects compared to nondepressed subjects, using the typical levels of processing manipulation. The results of this study show the existence of a mood-congruent memory bias for both implicit and explicit memory in depressed subjects. The theoretical implications of these findings for implicit and explicit memory biases associated with depressed mood are discussed.     

TheP system: A scheme for organizing Pavlovian procedures

The present paper introduces theP system as a scheme for organizing Pavlovian procedures in an orderly and comprehensive manner. The system is defined by three temporal variables and three restrictions on their possible values. It can be used to define all standard temporal variables—namely, stimulus duration, interstimulus interval, trace interval, and intertrial interval—as well as variables C and T of scalar expectancy theory. The system also permits the definition of new independent variables through combinations of the basic temporal parameters. We exemplify this possibility by defining two ratios of temporal intervals. These ratios lead to a space where traditional Pavlovian arrangements (viz., simultaneous, forward-trace, forward-delay, backward) become points on a continuum, and optimal conditions across different experimental preparations become equivalent. Finally, the system can be used to define contingency variables such asp(US/CS),p(US/～CS), and the phi coefficient (φ). In this manner, an organization of different kinds of Pavlovian procedures is achieved on the basis of a single parametric scheme. Such an organization facilitates establishing procedural and theoretical relationships between temporal and contingency variables. The paper concludes with a discussion of certain limitations of the system and other related issues     

Referenzsemantik in der Partnermodellierung

How can we explain that an assertion on something perceived can be understood in the same manner by somebody who cannot perceive that scene? This problem bases the interest in computational linguistics in how listener modeling could possibly be harmonized with reference semantics. Mental images substituting real perception appear as a way out. The architecture of the listener model has to be adapted to the creation and use of such pictorial data structures. Furthermore, the relation between the latter and a verbal (i. e., propositional) representation must be understood. The resulting architecture of a listener model with reference semantics can be employed to solve communicational problems from three general classes in a better way, as is demonstrated by an example implementation.     

A simplification of a result by zellini on the maximal rank of symmetric three-way arrays

Zellini (1979, Theorem 3.1) has shown how to decompose an arbitrary symmetric matrix of ordern ×n as a linear combination of 1/2n(n+1) fixed rank one matrices, thus constructing an explicit tensor basis for the set of symmetricn ×n matrices. Zellini's decomposition is based on properties of persymmetric matrices. In the present paper, a simplified tensor basis is given, by showing that a symmetric matrix can also be decomposed in terms of 1/2n(n+1) fixed binary matrices of rank one. The decomposition implies that ann ×n ×p array consisting ofp symmetricn ×n slabs has maximal rank 1/2n(n+1). Likewise, an unconstrained INDSCAL (symmetric CANDECOMP/PARAFAC) decomposition of such an array will yield a perfect fit in 1/2n(n+1) dimensions. When the fitting only pertains to the off-diagonal elements of the symmetric matrices, as is the case in a version of PARAFAC where communalities are involved, the maximal number of dimensions can be further reduced to 1/2n(n–1). However, when the saliences in INDSCAL are constrained to be nonnegative, the tensor basis result does not apply. In fact, it is shown that in this case the number of dimensions needed can be as large asp, the number of matrices analyzed.