Offline: The possible effects of Internet-related behavior on work values,expectations, & behavior among Ultra-Orthodox millennials

ABSTRACT

This work examines differences in the work-related values, expectations, and behaviors between millennials who are significant users of technology and social media, and those who are not. We delineate the development of millennial behavior using a unique group of millennials, those in the Ultra-Orthodox Jewish (known as “Haredi”) community in Israel. Due to religious and community norms, many Haredi millennials were shielded from digital technology, and particularly the Internet and the various technologies associated with it, such as text messaging and social media. Those who were raised and remained in Haredi communities did not encounter the Internet with any regularity as children. Many as adults are still unfamiliar with social media. Thus, this community presents a unique natural experiment, comparing Haredi millennials who are immersed in social media with those who are not.     

Attributions of Responsibility for Obesity: Narrative Communication Reduces Reactive Counterarguing Among Liberals

Informed by theory and research on attributions and narrative persuasion, we compared the effectiveness of narrative and nonnarrative messages in changing attributions of responsibility for causes and solutions related to obesity in the United States. We randomly assigned 500 adults to view one of three messages (narrative, evidence, and a hybrid of the two) emphasizing environmental causes of obesity, or a no‐exposure control condition. The narrative condition increased the belief that societal actors (government, employers) are responsible for addressing obesity, but only among liberals. This pattern of results was partially explained by the success of the narrative condition in reducing reactive counterarguing, relative to the evidence condition, among liberals. We conclude with theoretical and practical implications.     

Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology

A theoretical structure for multiattribute decision making is presented, based on a dynamical system for interactions in a neural network incorporating affective and rational variables. This enables modeling of problems that elude two prevailing economic decision theories: subjective expected utility theory and prospect theory. The network is unlike some that fit economic data by choosing optimal weights or coefficients within a predetermined mathematical framework. Rather, the framework itself is based on principles used elsewhere to model many other cognitive and behavioral data, in a manner approximating how humans perform behavioral functions. Different, interconnected modules within the network encode (a) attributes of objects among which choices are made, (b) object categories, (c) and goals of the decision maker. An example is utilized to simulate the actual consumer choice between old and new versions of Coca-Cola. Potential applications are also discussed to market decisions involving negotiations between participants, such as international petroleum traders.     

Pure agraphia and Gerstmann's syndrome as a visuospatial-language dissociation: an experimental case study

A right-handed man suffered a left parieto-occipital cerebral infarction, causing agraphia with Gerstmann's syndrome but without major aphasia, alexia, or apraxia. Oral spelling was superior to written spelling. Experiments were performed involving (1) analysis of errors in writing, (2) tasks of visual imagery, and (3) identifying letters drawn without leaving a visual trace. The results suggest that the agraphia and Gerstmann's syndrome are due to a dissociation of language skills and visuospatial skills caused by a dominant parieto-occipital lesion.     

Functional measurement approach to self-estimation in multiattribute evaluation

Multiattribute analysis depends on measurement of values and weights. Unless these measures reflect the decision maker's true values and weights, the multiattribute formula may put a less-preferred alternative in first place. To avoid such disordinality requires stringent measurement conditions: First, the values and weights must be on linear (equal interval) or ratio (known zero) scales. Second, these scales must satisfy a condition of common unit across disparate attribute dimensions. Most methods of range adjustment beg both of these measurement questions. Functional measurement theory can solve both problems and so can be useful in multiattribute analysis. Past work has established the operation of a general cognitive algebra as an empirical reality. The averaging model, in particular, makes possible the definition and estimation of weights and values as distinct psychological parameters. It can also solve the problem of common unit. Cognitive algebra thus provides a grounded theoretical foundation on which to develop self-estimation methodology, in which decision makers provide direct estimates of their values and weights. The logic is straightforward. Functional measurement can analyze global judgments to obtain validated psychological scales. These scales may then be used as validational criteria for the self-estimates. Procedures to eliminate biases in the self-estimates can thus be tested and refined in well-learned multiattribute tasks, such as judgments of meals, in which global judgments are trustworthy. Once developed, such self-estimation procedures may be used with some confidence for general multiattribute analysis. A number of studies from 20-odd years of work on the theory of information integration are summarized to show good, although not unmixed promise for self-estimation.     

The detection of surface curvatures defined by optical motion.

The detectability of surface curvatures defined by optical motion was evaluated in three experiments. Observers accurately detected very small amounts of curvature in a direction perpendicular to the direction of rotation, but they were less sensitive to curvatures along the direction of rotation. Variations in either the number of points (between 91 and 9) or the number of views (from 15 to 2) had little or no effect on discrimination accuracy. The results of this study demonstrate impressive visual sensitivity to surface curvature. Several characteristics of this sensitivity to curvature are inconsistent with many computational models for deriving three-dimensional structure from motion.