Accuracy of Inferring Self- and Other-Preferences from Spontaneous Facial Expressions

Participants?? faces were covertly recorded while they rated the attractiveness of people, the decorative appeal of paintings, and the cuteness of animals. Ratings employed a continuous scale. The same participants then returned and tried to guess ratings from 3-s videotapes of themselves and other targets. Performance was above chance in all three stimulus categories, thereby replicating the results of an earlier study (North et al. in J Exp Soc Psychol 46(6):1109?C1113, 2010) but this time using a more sensitive rating procedure. Across conditions, accuracy in reading one??s own face was not reliably better than other-accuracy. We discuss our findings in the context of ??simulation?? theories of face-based emotion recognition (Goldman in The philosophy, psychology, and neuroscience of mindreading. Oxford University Press, Oxford, 2006) and the larger body of accuracy research.     

Methods for distance-based judgment aggregation

Judgment aggregation theory, which concerns the translation of individual judgments on logical propositions into consistent group judgments, has shown that group consistency generally cannot be guaranteed if each proposition is treated independently from the others. Developing the right method of abandoning independence is thus a high-priority goal. However, little work has been done in this area outside of a few simple approaches. To fill the gap, we compare four methods based on distance metrics between judgment sets. The methods generalize the premise-based and sequential priority approaches to judgment aggregation, as well as distance-based preference aggregation. They each guarantee group consistency and implement a range of distinct functions with different properties, broadening the available tools for social choice. A central result is that only one of these methods (not previously considered in the literature) satisfies three attractive properties for all reasonable metrics.     

The Relation Between Probability and Evidence Judgment: An Extension of Support Theory

We propose a theory that relates perceived evidence to numerical probability judgment. The most successful prior account of this relation is Support Theory, advanced in Tversky and Koehler (1994). Support Theory, however, implies additive probability estimates for binary partitions. In contrast, superadditivity has been documented in Macchi, Osherson, and Krantz (1999), and both sub- and superadditivity appear in the experiments reported here. Nonadditivity suggests asymmetry in the processing of focal and nonfocal hypotheses, even within binary partitions. We extend Support Theory by revising its basic equation to allow such asymmetry, and compare the two equations' ability to predict numerical assessments of probability from scaled estimates of evidence for and against a given proposition. Both between- and within-subject experimental designs are employed for this purpose. We find that the revised equation is more accurate than the original Support Theory equation. The implications of asymmetric processing on qualitative assessments of chance are also briefly discussed.     

Extrapolating human probability judgment

We advance a model of human probability judgment and apply it to the design of an extrapolation algorithm. Such an algorithm examines a person's judgment about the likelihood of various statements and is then able to predict the same person's judgments about new statements. The algorithm is tested against judgments produced by thirty undergraduates asked to assign probabilities to statements about mammals.