Calibration of sensory and cognitive judgments: Two different accounts

The purpose of this study was to test predictions of two recent theories of realism of confidence. Ecological approaches to realism of confidence in one's general knowledge (Gigerenzer et al. , 1991; Juslin, in press a ; Björkman, in press) predict good calibration or, in the case of poor cognitive adjustment, overconfidence, within the cognitive domain. The subjective distance theory of confidence in sensory discriminations (Björkman et al. , 1992) predicts a pervasive underconfidence bias for sensory discriminations. Empirical data are reported showing that: (a) Calibration for sensory judgments is considerably poorer than calibration for well adapted cognitive judgements, a difference that can be entirely traced to underconfidence in the sensory domain. (b) While an initial overconfidence bias in the cognitive domain is removed by outcome feedback, the bias observed in sensory discriminations is unaffected even by a prolonged feedback session. It is suggested that the nature of confidence in sensory discriminations is different from the nature of confidence in cognitive judgments.     

Proximity bias: Interactive effect of spatial distance and outcome valence on probability judgments

Across a range of decision contexts, we provide evidence of a novel proximity bias in probability judgments, whereby spatial distance and outcome valence systematically interact in determining probability judgments. Six hypothetical and incentive-compatible experiments (combined N = 4007) show that a positive outcome is estimated as more likely to occur when near than distant, whereas a negative outcome is estimated as less likely to occur when near than distant (studies 1–6). The proximity bias is explained by wishful thinking and thus perceptions of outcome desirability (study 3), and it does not manifest when an outcome is less relevant for the self, such as the case of outcomes with little consequence for the self (studies 4 and 5) or when estimating outcomes for others who are irrelevant to the self (study 6). Overall, the proximity bias we document deepens our understanding of the antecedents of probability judgments.     

Calibration of sensory and cognitive judgments: A single model for both

In a recent issue of this journal, Winman and Juslin (34 , 135–148, 1993) present a model of the calibration of subjective probability judgments for sensory discrimination tasks. They claim that the model predicts a pervasive underconfidence bias observed in such tasks, and present evidence from a training experiment that they interpret as supporting the notion that different models are needed to describe judgment of confidence in sensory and in cognitive tasks. The model is actually part of the more comprehensive decision variable partition model of subjective probability calibration that was originally proposed in Ferrell and McGoey (Organizational Behavior and Human Performance, 26 , 32–53, 1980). The characteristics of the model are described and it is demonstrated that the model does not predict underconfidence, that it is fully compatible with the overconfidence frequently found in calibration studies with cognitive tasks, and that it well represents experimental results from such studies. It is concluded that only a single model is needed for both types of task.     

Assessing categorization performance at the individual level: a comparison of Monte Carlo simulation and probability estimate model procedures

Two analytical procedures for identifying young children as categorizers, the Monte Carlo Simulation and the Probability Estimate Model, were compared. Using a sequential touching method, children aged 12, 18, 24, and 30 months were given seven object sets representing different levels of categorical classification. From their touching performance, the probability that children were categorizing was then determined independently using Monte Carlo Simulation and the Probability Estimate Model. The two analytical procedures resulted in different percentages of children being classified as categorizers. Results using the Monte Carlo Simulation were more consistent with group-level analyses than results using the Probability Estimate Model. These findings recommend using the Monte Carlo Simulation for determining individual categorizer classification.