“The House of Hannover”: Gezeirot Tah in Modern Jewish Historical Writing

This essay traces the development of modern Jewish historiography on the events of 1648–1649. It focuses mainly on treatments of the events in single-volume and multi-volume general histories of the Jews, and on entries in encyclopedias. Immediate historical memory, as exemplified by the Hebrew chronicles and liturgical poetry composed soon after the events, was transformed into history in the writings of the modern Jewish historians of the 19th and 20th centuries. In the process, the chronicle of Nathan Hannover dominated modern perceptions, for the most part uncritically accepted, albeit not on all counts. The major features of the modern historiography of the Gezeirot Tah ve'Tat are surveyed by discussing the causes of the attacks, their results, the number of victims, and the ways in which all three were incorporated in the larger Jewish historical scheme. Recently, some long-held approaches and conclusions have been revised, which suggests directions for future research. This revised version was published online in June 2006 with corrections to the Cover Date.     

Seek and ye shall find: test results are what you hypothesize they are

Expert clinicians were given batteries of psychodiagnostic test results (Rorschach, TAT, Draw-A-Person, Bender-Gestalt, Wechsler) to analyze. For half, a battery came along with a suggestion that the person suffers from Borderline Personality disorder, and for half, that battery was accompanied by a suggestion that he suffers from Paranoid Personality disorder. In Study 1, the suggestion was made indirectly, through a background story that preceded the test results. In Study 2, the suggestion was made directly, by the instructions given. The experts saw in the tests what they hypothesized to be there. In particular, the target diagnoses were rated higher when they were hypothesized than when they were not. Copyright © 1998 John Wiley & Sons, Ltd.     

Recalling a floor routine: the effects of skill and age on memory for order

Two studies were conducted to examine gymnasts' memory capabilities in relation to skill level (high and low) and age level (6–8 years and 14–16 years). The first experiment consisted of two trials performed 7 days apart. In the first trial, gymnasts were briefly exposed to a twelve‐element floor routine, and were then required to perform the routine in the same serial order. One week later they were again briefly presented the same sequence, but with the fifth and seventh elements missing. The task was to perform the sequence in order, including the missing elements. The second study involved three trials that were conducted on the same day. A ten‐element sequence was briefly presented on the first trial followed by an immediate test of that sequence. One hour later, participants were asked to perform the sequence again (delayed test). An hour following that, the sequence was again presented with two elements missing. The sequence had to be performed with the missing elements. The results indicate that serial position accuracy of a gymnastic floor routine improves with both age and experience, and that the older experienced gymnasts outperformed all the other gymnasts on all phases of the experiments. These results are interpreted within the Ericsson and Kintsch (1995) theory of Long‐term Working Memory. Copyright © 1999 John Wiley & Sons, Ltd.     

Maternal and infant affect at 4 months predicts performance and verbal IQ at 4 and 7 years in a diverse population

Using existing longitudinal data from 570 infants in the Maternal Lifestyle Study, we explored the predictive value of maternal and infant affect and maternal vocalizations during 2 minutes of face‐to‐face interactions at 4 months on IQ scores at 4.5 and 7 years. After controlling for demographic factors, maternal depression, and prenatal drug exposure, maternal positive affect and maternal positive vocalizations emerged as predictors of both verbal and performance IQ at 4.5 and 7 years. Although infant positive affect during the interaction with the mother was not predictive of these outcome measures, infant positive affect towards an examiner predicted verbal but not performance IQ at 4.5 years. These results suggest that maternal positive affect may index emotional engagement in interaction that facilitates both verbal and nonverbal cognitive development, while infant social positive affect is specifically related to the acquisition of verbal reasoning abilities. These findings are significant because they are based on a discrete snapshot of observable behavior in infancy (just 2 minutes of interaction), because they extend the range of maternal behaviors and characteristics known to support positive developmental outcomes, and because they are derived from high‐risk infants where prevention efforts may be beneficial. Potential mechanisms for these associations are discussed, as are the clinical implications for identifying dyads most in need of targeted interventions.     

Three ideal observer models for rule learning in simple languages

Children learning the inflections of their native language show the ability to generalize beyond the perceptual particulars of the examples they are exposed to. The phenomenon of “rule learning”—quick learning of abstract regularities from exposure to a limited set of stimuli—has become an important model system for understanding generalization in infancy. Experiments with adults and children have revealed differences in performance across domains and types of rules. To understand the representational and inferential assumptions necessary to capture this broad set of results, we introduce three ideal observer models for rule learning. Each model builds on the next, allowing us to test the consequences of individual assumptions. Model 1 learns a single rule, Model 2 learns a single rule from noisy input, and Model 3 learns multiple rules from noisy input. These models capture a wide range of experimental results—including several that have been used to argue for domain-specificity or limits on the kinds of generalizations learners can make—suggesting that these ideal observers may be a useful baseline for future work on rule learning.     

A probabilistic model of cross-categorization

Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that cross-categorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models’ predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities.     

Learning to learn causal models

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning.     

Disregarding Preliminary Information When Rating Job Applicants' Performance: Mission Impossible?1

Firms allocate considerable resources to test job applicants' skills and to ensure that hiring decisions are nondiscriminatory. Interpreting selection output in light of preliminary information may undermine the impartiality of personnel selection decisions. In this study, human resource managers were presented preliminary information about a candidate's performance and asked to rate him according to a detailed assessment‐center report of his performance. We examined several interventions for reducing reliance on preliminary information: retrieving assessment‐center information, generating a rating model, or both. Participants (N = 167) excluded preliminary information from the interventions, but relied on it when rating the candidate. The documented failure to control for the effects of preliminary information on subsequent judgments can contribute to improved selection procedures.