Abstract not found

The authors propose that people adopt others ’ perspectives by serially adjusting from their own. As predicted, estimates of others ’ perceptions were consistent with one’s own but differed in a manner consistent with serial adjustment (Study 1). Participants were slower to indicate that another’s perception would be different from—rather than similar to—their own (Study 2). Egocentric biases increased under time pressure (Study 2) and decreased with accuracy incentives (Study 3). Egocentric biases also increased when participants were more inclined to accept plausible values encountered early in the adjustment process than when inclined to reject them (Study 4). Finally, adjustments tend to be insufficient, in part, because people stop adjusting once a plausible estimate is reached (Study 5). We have endeavored to show... that thought in the child is egocentric, i.e., that the child thinks for himself without troubling to make himself understood nor to place himself at the other person’s point of view.... If this be the case, we must expect childish reasoning to differ very considerably from ours, to be deductive and above all less rigorous. (Piaget, 1959, p. 1) Children view their perceptions of the world as accurate reflections

This article investigates whether and how discounting and augmentation of dispositional and causal attributions differ between each other. In three experiments, the strength of a causal or dispositional attribution to a target actor (or object) was varied by manipulating the number of observations (i.e., sample size) of an alternative actor (or object). The results of Experiments 1 and 2 indicated that a greater sample size of the alternative actor (or object) resulted in greater discounting or augmentation of the target, and that this effect was alike for causal and dispositional attributions. This effect of sample size on discounting and augmentation cannot be explained by current algebraic attribution models, but is consistent with predictions from a connectionist framework. In Experiment 3, the extraction of information was made more difficult, and the effect of sample size on discounting and augmentation remained robust for causal attributions, whereas it disappeared for dispositional attributions. This failure for dispositional attributions was not predicted by any theoretical model. The discussion focuses on some potential explanations for this unexpected finding.

Two experiments examined whether dispositional attributions are sensitive to the sample size of the evidence indicating a given level of covariation between person and behavior. Participants were given high or low levels of covariation (i.e., consensus and distinctiveness), and the acquisition of dispositional attributions was monitored by requesting dispositional trait ratings at fixed intervals. The results showed that dispositional attributions were sensitive to sample size, and increased given more evidence on high person-behavior covariation while they decreased given more evidence on low person-behavior covariation. Additional analyses suggested that in making dispositional inferences (e.g., about the actor), there was a slight preference for agreement information (e.g., low distinctiveness) over difference information (e.g., low consensus). The effects of sample size are inconsistent with current statistical or probabilistic models of covariation, but are in line with connectionist networks using an error-correcting learning algorithm.