How Are Personality Judgments Made? A Cognitive Model of Reference Group Effects,Personality Scale Responses,and Behavioral Reactions

This article suggests that personality judgments are wholly relative, being the outcome of a comparison of a given individual to a reference group of others. The underlying comparison processes are the same as those used to judge psychophysical stimuli (as outlined by range frequency theory and decision by sampling accounts). Five experimental studies show that the same person's personality is rated differently depending on how his or her behavior (a) ranks within a reference group and (b) falls within the overall range of behavior shown by other reference group members. Results were invariant across stimulus type and response options (7-point Likert scale, 990-point allocation task, or dichotomous choice). Simulated occupational scenarios led participants to give different-sized bonuses and employ different people as a function of context. Future research should note that personality judgments (as in self-report personality scales) only represent perceived standing relative to others or alternatively should measure personality through behavior or biological reactivity. Personality judgments cannot be used to compare different populations when the population participants have different reference groups (as in cross-cultural research).     

Proximate and ultimate causes of punishment and strong reciprocity

While admirable, Guala's discussion of reciprocity suffers from a confusion between proximate causes (psychological mechanisms triggering behaviour) and ultimate causes (evolved function of those psychological mechanisms). Because much work on "strong reciprocity" commits this error, I clarify the difference between proximate and ultimate causes of cooperation and punishment. I also caution against hasty rejections of "wide readings" of experimental evidence.     

Bias in the Reporting of Family History: Implications for Clinical Care

Family history of cancer is critical for identifying and managing patients at risk for cancer. However, the quality of family history data is dependent on the accuracy of patient self reporting. Therefore, the validity of family history reporting is crucial to the quality of clinical care. A retrospective review of family history data collected at a community hospital between 2005 and 2009 was performed in 43,257 women presenting for screening mammography. Reported numbers of breast, colon, prostate, lung, and ovarian cancer were compared in maternal relatives vs. paternal relatives and in first vs. second degree relatives. Significant reporting differences were found between maternal and paternal family history of cancer, in addition to degree of relative. The number of paternal family histories of cancer was significantly lower than that of maternal family histories of cancer. Similarly, the percentage of grandparents' family histories of cancer was significantly lower than the percentage of parents' family histories of cancer. This trend was found in all cancers except prostate cancer. Self-reported family history in the community setting is often influenced by both bloodline of the cancer history and the degree of relative affected. This is evident by the underreporting of paternal family histories of cancer, and also, though to a lesser extent, by degree. These discrepancies in reporting family history of cancer imply we need to take more care in collecting accurate family histories and also in the clinical management of individuals in relation to hereditary risk.     

Learning to search: From weak methods to domain-specific heuristics

Learning from experience involves three distinct components—generating behavior, assigning credit, and modifying behavior. We discuss these components in the context of learning search heuristics, along with the types of learning that can occur. We then focus on SAGE, a system that improves its search strategies with practice. The program is implemented as a production system, and learns by creating and strengthening rules for proposing moves. SAGE incorporates five different heuristics for assigning credit and blame, and employs a discrimination process to direct its search through the space of rules. The system has shown its generality by learning heuristics for directing search in six different task domains. In addition to improving its search behavior on practice problems, SAGE is able to transfer its expertise to scaled-up versions of a task, and in one case, transfers its acquired search strategy to problems with different initial and goal states.