A negativity bias in interpersonal evaluation

Two studies were conducted to demonstrate a bias toward negativity in evaluations of persons or their work in particular social circumstances. In Study 1, subjects evaluated materials written by peers. Those working under conditions that placed them in low status relative to the audience for their evaluations, or conditions that made their intellectual position within a group insecure, showed a clear bias toward negativity in those evaluations. Only individuals who believed their audience to be of relatively low status and at the same time believed their intellectual position to be secure did not show this bias. In Study 2, subjects viewed a videotape of a stimulus person and rated him on several intellectual and social dimensions. Again, subjects believed their audience to be of either relatively high or relatively low status. As a cross dimension, they were given instructions to focus on either the intellectual or the social abilities of the stimulus person while viewing the videotape. A strong main effect of audience status was demonstrated, but only in ratings of intellectual traits; subjects who believed their audience to be of relatively high status rated the stimulus person's intellectual qualities significantly more negatively. Moreover, this effect was independent of the instructional focus subjects had been given. The negativity bias is discussed in the context of previous demonstrations of biases toward weighting negative information more heavily than positive information, as well as previous demonstrations of seemingly pervasive positivity biases in memory and judgment.     

Foundations of risk measurement. II. Effects of gains on risk

This paper presents the second part of a two-part study on axiomatizations of perceived risk. The first part (P. C. Fishburn, Foundations of risk measurement, I. Risk as probable loss. Mimeo., Bell Laboratories, 1981) focussed on risk as probable loss. The second part considers measures of risk that include effects of gains on perceived risk. It adopts the position that increased gains can reduce the risk of fixed probable losses without completely negating this risk. Moreover, every prospect that has no chance of yielding a loss is presumed to have no risk. Several numerical measures of risk are axiomatized. The more specialized are separable in gains and losses. Further specializations isolate the effects of loss and gain probabilities.     

The advantages of mathematical modeling over traditional methods in the analysis of category clustering

A mathematical model for the analysis of category clustering is developed and testd. The model, which can be applied to categories of any size, is an extension of a two-item statistical model developed by Batchelder and Riefer (Psychological Review, 1980, 87, 375–397), and is equivalent to their model when categories consist of two items. The model is based on a current theory of clustering which postulates that the learning of a list of category items occurs on different hierarchical levels. Two category list-learning experiments are presented, and the data from these experiments are analyzed using the general statistical model. The first experiment reveals that the probabilities of storing and retrieving a cluster increase with category size, while the learning of items as singletons decreases. The effects of within-category spacing indicate that the storage of clusters decreases while cluster retrievability increases with an increase in input spacing. In the second experiment, the storage and retrieval of clusters are shown to be unaffected by whether the presentation of items is uncued or cued with the name of the category. However, the association of items decreases and the learning of items as singletons increases with uncued presentation. In the final sections, the general statistical model is compared to other methods for the measurement of category clustering. The model is shown to be superior to numerical indices of clustering, since these measures are not based on any theory of clustering, and because unitary measures cannot capture the multiprocess nature of categorized recall. The model is also argued to have certain advantages over other mathematical models that have been applied to category clustering, since these models cannot account for situations in which a portion of the items are clustered while others are learned singularly.     

Counting eliminates the repetition effect in judgments of temporal duration.

College student subjects estimated the duration of time intervals (8–54 sec) by counting or not (N=35 per group). Counting eliminated the repetition effect (a decline in the magnitude of estimations across trials), and resulted in a truncation of the psychophysical function. The former result was interpreted as contrary to Treisman's (1963) model. The latter result was attributed to the effortfulness of counting, which was established in a subsequent experiment.