Effects of objective and subjective account components on forgiving

The authors explored the effects of the components of a harm-doer's account of her transgression on the victims' emotional reactions to the transgression and to the character traits that she attributes to the harm-doer. Participants were 480 people whom the authors asked to imagine an incident in which they were harmed by the careless behavior of a friend. Subsequently, the authors offered participants an account of the harm-doer. In a 2 x 2 x 2 x 2 x 2 between-subjects design, the authors manipulated 5 account components: Admitting Fault, Admitting Damage, Expressing Remorse, Asking for Pardon, and Offering Compensation. The authors measured the participants' perceptions of these components. Results indicated that (a) the subjective perception of account components occurs schematically so that components are implicitly perceived without being objectively present, (b) objective components affect victims' reactions via subjective perceptions of these components, (c) personality factors (Irreconcilability, Interpersonal Trust, and Trait Anger) affect victims' reactions directly. Finally, certain configurations of account components are more effective than others. Specifically, asking for pardon had an effect on forgiving only when it was combined with an acknowledgment of the damage and a compensation offer. This result suggests that in this situation, the victim perceives a harm-doer's asking for pardon without the other components as an insincere apology.     

A Logic For Inductive Probabilistic Reasoning

Inductive probabilistic reasoning is understood as the application of inference patterns that use statistical background information to assign (subjective) probabilities to single events. The simplest such inference pattern is direct inference: from “70% of As are Bs” and “a is an A” infer that a is a B with probability 0.7. Direct inference is generalized by Jeffrey’s rule and the principle of cross-entropy minimization. To adequately formalize inductive probabilistic reasoning is an interesting topic for artificial intelligence, as an autonomous system acting in a complex environment may have to base its actions on a probabilistic model of its environment, and the probabilities needed to form this model can often be obtained by combining statistical background information with particular observations made, i.e., by inductive probabilistic reasoning. In this paper a formal framework for inductive probabilistic reasoning is developed: syntactically it consists of an extension of the language of first-order predicate logic that allows to express statements about both statistical and subjective probabilities. Semantics for this representation language are developed that give rise to two distinct entailment relations: a relation ⊨ that models strict, probabilistically valid, inferences, and a relation that models inductive probabilistic inferences. The inductive entailment relation is obtained by implementing cross-entropy minimization in a preferred model semantics. A main objective of our approach is to ensure that for both entailment relations complete proof systems exist. This is achieved by allowing probability distributions in our semantic models that use non-standard probability values. A number of results are presented that show that in several important aspects the resulting logic behaves just like a logic based on real-valued probabilities alone.     

Top-down contingencies in peripheral cuing: The roles of color and location

According to contingent-processing accounts, peripheral cuing effects are due to the cues' inadvertent selection for processing by control settings set up for targets (e.g., C. L. Folk, R. W. Remington, & J. C. Johnston, 1992). Consequently, cues similar to targets should have stronger effects than do dissimilar cues. In the current study, this prediction is confirmed for cue-target combinations similar or dissimilar in the static features of color (Experiments 1-3) and location (Experiment 4), even when both cues and targets share the dynamic feature of abrupt onset. Perceptual priming (Experiment 2) and reallocation of attention did not account for similar-dissimilar differences (Experiments 3 and 4). The results are best explained by top-down-contingent attentional effects of the similar cues. Implications for bottom-up accounts of peripheral cuing effects are discussed. ((c) 2003 APA, all rights reserved)     

Zur Psychotherapie der Depression

Ohne Zusammenfassung     

Review

Ohne Zusammenfassung

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Review

     

Über einen methodisch geordneten Aufbau der speziellen Relativitätstheorie

About a methodically ordered reconstruction of the theory of special relativity. One of the main results of the theory of special relativity is that our basic concepts concerning space and time must be revised, because there is new experimental evidence. But on the other hand it was meant to move in a circular procedure, if the usual methods of measuring distances and temporal durations are refused on the ground of experimental results that are based on even these measuring methods. Thus a reconstruction of the theory of special relativity was demanded. It consists of a finite number of steps and basicly one may presuppose that each step may at best include the results of undertaken steps. This reconstruction will be discussed in this article. It begins with the socalled proto-physics (Protophysik) that reflects the production of measuring rods and clocks without recurring on the existence of other clocks or measuring rods. Then Newtonian mechanics, electrodynamics, the refinement of classical mechanics, the formalism of transforming according to the Lorentz Group and the construction of moved systems of reference are brought into a methodically correct order. This revised version was published online in August 2006 with corrections to the Cover Date.     

The empirical relevance of Perelman's New Rhetoric

Perelman's work has been very influential in various disciplines, among them philosophy, rhetoric and law. Especially the typology of argumentative schemes which he developed together with L. Olbrechts-Tyteca has been considered as an excellent classification of arguments in natural language. There are, however, some weaknesses of this typology which make its application to empirical research quite difficult, namely, the lack of explicitness and the absence of clear criteria of demarcation. Still, the typology is highly relevant for empirical research, if these weaknesses are removed. This is illustrated with an example: the scheme called the division of the whole into its parts by Perelman/Olbrechts-Tyteca is described explicitly and then applied to the analysis of a sample of everyday arguments (mostly taken from newspapers).