Paul C. Price

Judgmental overshadowing: Further evidence of cue interaction in contingency judgment

We investigated a phenomenon calledjudgmental overshadowing. Subjects predicted whether each of several patients had a disease on the basis of whether or not the patient had each of two symptoms. For all the subjects, the presence of the disease was moderately contingent on the presence of one ofthe symptoms (S1). In Condition 1 of our first experiment, the presence of the disease was highly contingent on the presence of the other symptom (S2). In Condition 2, the presence of the disease was independent of S2. Judgmental overshadowing occurred in that the S1-disease contingency was judged to be stronger in Condition 2 than in Condition 1. Subsequent experiments showed that judgmental overshadowing depends little on the form of the judgment, is not due to a response bias or contrast effect, and does not depend on subjects’ actively diagnosing each patient. These results are consistent with, and are generally predicted by, an associative-learning model of contingency judgment.

Good probabilistic forecasters: The 'consumer's' perspective

Abstract There is an established literature describing how probabilistic forecasts, and hence forecasters, should be evaluated. The present paper takes a different and heretofore neglected perspective on evaluation. It addresses how those who receive and use probabilistic predictions—forecast ‘consumers’—appraise these assessments. Results indicate that there are reliable and important differences between subjective and formal evaluation principles. Among the distinctive features of common subjective appraisal strategies are: (a) an emphasis on judgments being categorically ‘correct’; (b) special attention to forecast extremeness; (c) the desire for good explanations of forecasts; and (d) the sensitivity of appraisals to how pertinent information is displayed to the evaluator. Theoretical and practical implications are discussed.

A group size effect on personal risk judgments: implications for unrealistic optimism

In Experiments 1 and 2, college students (N = 32 andN = 18, respectively) read heart attack risk profiles (i.e., lists of risk factors) for each of several employees at a series of fictional companies and judged the heart attack risk of the typical employee at each company. In both experiments, subjects’ risk judgments increased as a function of the number of employees at the companies. In Experiments 3A and 3B, college students (N = 56 andN = 33, respectively) judged the heart attack risk of the typical employee at a company and also judged the risk of each individual employee. In these experiments, the typical employee was generally judged to be at higher risk than the individual employees. This group size effect might help to explain unrealistic optimism—people’s tendency to judge themselves to be at lower risk than their peers for negative life events. Furthermore, it can be modeled successfully within Fiedler’s (1996) BIAS framework.

Sample size bias in the estimation of means

The present research concerns the hypothesis that intuitive estimates of the arithmetic mean of a sample of numbers tend to increase as a function of the sample size; that is, they reflect a systematic sample size bias. A similar bias has been observed when people judge the average member of a group of people on an inferred quantity (e.g., a disease risk; see Price, 2001; Price, Smith, & Lench, 2006). Until now, however, it has been unclear whether it would be observed when the stimuli were numbers, in which case the quantity need not be inferred, and “average” can be precisely defined as the arithmetic mean. In two experiments, participants estimated the arithmetic mean of 12 samples of numbers. In the first experiment, samples of from 5 to 20 numbers were presented simultaneously and participants quickly estimated their mean. In the second experiment, the numbers in each sample were presented sequentially. The results of both experiments confirmed the existence of a systematic sample size bias.

Sample Size Bias in Judgments of Perceptual Averages

Previous research has shown that people exhibit a sample size bias when judging the average of a set of stimuli on a single dimension. The more stimuli there are in the set, the greater people judge the average to be. This effect has been demonstrated reliably for judgments of the average likelihood that groups of people will experience negative, positive, and neutral events (Price, 2001; Price, Smith, & Lench, 2006) and also for estimates of the mean of sets of numbers (Smith & Price, 2010). The present research focuses on whether this effect is observed for judgments of average on a perceptual dimension. In 5 experiments we show that peoples judgments of the average size of the squares in a set increase as the number of squares in the set increases. This effect occurs regardless of whether the squares in each set are presented simultaneously or sequentially; whether the squares in each set are different sizes or all the same size; and whether the response is a rating of size, an estimate of area, or a comparative judgment. These results are consistent with a priming account of the sample size bias, in which the sample size activates a representation of magnitude that directly biases the judgment of average.

Erratum to “Perceived Event Frequency and the Optimistic Bias: Evidence for a Two-Process Model of Personal Risk Judgments”: [Journal of Experimental Social Psychology38 (2002) 242–252]

On page 246, in Table 1, every mean in the second column (Comparative judgment, Optimism) should be one point greater than the mean actually displayed. On page 246 in Fig. 2, every open square (Comparative Judgment Condition) should be one point greater on the y axis (Mean Optimism). On page 246, left-hand column, lines 2–4 should read ‘‘In the absolute judgments condition, we coded the responses 1 (extremely low) through 7 (extremely high),’’ rather than 1 being high and 7 being low. On page 246, left-hand column, lines 12 and 13 should read: ‘‘In the comparative judgment condition, the mean judgment tended to be greater than 4,’’ rather than ‘‘less than 4.’’ Journal of Experimental Social Psychology 38 (2002) 434