Does power corrupt? The moderating effect of status

Findings on the effect of power on corruption are mixed. To make sense of these mixed results, three studies were conducted to examine the moderating role of status on this effect. In Study 1, corrupt intent was measured using a corruption scenario that contained manipulations of power and status. In Study 2, corrupt behaviour was measured in a corruption game that contained manipulations of power and status. Study 3 was conducted in real organisational settings, and aimed to expand the external validity of Studies 1 and 2. The results of all three studies consistently indicated that the effect of power was moderated by status. Specifically, power increased corruption when status was low, whereas this effect disappeared when status was high. The implications of reducing the facilitating effect of power on corruption by considering status from the perspective of social hierarchy are discussed.     

Parallel distributed processing and neuropsychology: A neural network model of Wisconsin card sorting and verbal fluency

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.     

Connectionist models of conditioning: A tutorial

Models containing networks of neuron-like units have become increasingly prominent in the study of both cognitive psychology and artificial intelligence. This article describes the basic features of connectionist models and provides an illustrative application to compound-stimulus effects in respondent conditioning. Connectionist models designed specifically for operant conditioning are not yet widely available, but some current learning algorithms for machine learning indicate that such models are feasible. Conversely, designers for machine learning appear to have recognized the value of behavioral principles in producing adaptive behavior in their creations.     

An Ecologically Differentiated, Multifactor Model of Adolescent Network Orientation

The paper presents a test of an ecologically differentiated model of social network orientation for adolescents that distinguished between different social network reference groups (family, peers, and nonfamily adults). The model was tested in two consecutive studies. Study 1 describes initial model development (N = 120). Study 2 presents a confirmatory factor analysis with a second sample (N = 430) to replicate the factor structure developed in Study 1. Results supported a three-factor model of network orientation that differentiated between network reference groups. Analyses of concurrent and predictive validity indicated that orientation to network reference groups was differentially related to the perceived quality and frequency of support from members of respective social network groups. Group differences (gender, race) regarding network orientation to different network reference groups were consistent with studies of other social network processes. Implications for the study of the network orientation and the study of social networks more generally are discussed.     

Single-Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty

The disambiguation of a syntactically ambiguous sentence in favor of a less preferred parse can lead to slower reading at the disambiguation point. This phenomenon, referred to as a garden-path effect, has motivated models in which readers initially maintain only a subset of the possible parses of the sentence, and subsequently require time-consuming reanalysis to reconstruct a discarded parse. A more recent proposal argues that the garden-path effect can be reduced to surprisal arising in a fully parallel parser: words consistent with the initially dispreferred but ultimately correct parse are simply less predictable than those consistent with the incorrect parse. Since predictability has pervasive effects in reading far beyond garden-path sentences, this account, which dispenses with reanalysis mechanisms, is more parsimonious. Crucially, it predicts a linear effect of surprisal: the garden-path effect is expected to be proportional to the difference in word surprisal between the ultimately correct and ultimately incorrect interpretations. To test this prediction, we used recurrent neural network language models to estimate word-by-word surprisal for three temporarily ambiguous constructions. We then estimated the slowdown attributed to each bit of surprisal from human self-paced reading times, and used that quantity to predict syntactic disambiguation difficulty. Surprisal successfully predicted the existence of garden-path effects, but drastically underpredicted their magnitude, and failed to predict their relative severity across constructions. We conclude that a full explanation of syntactic disambiguation difficulty may require recovery mechanisms beyond predictability.     

Cognitive computing models for estimation of reference evapotranspiration: A review

Irrigation practices can be advanced by the aid of cognitive computing models. Repeated droughts, population expansion and the impact of global warming collectively impose rigorous restrictions over irrigation practices. Reference evapotranspiration (ET₀) is a vital factor to predict the crop water requirements based on climate data. There are many techniques available for the prediction of ET₀. An efficient ET₀ prediction model plays an important role in irrigation system to increase water productivity. In the present study, a review has been carried out over cognitive computing models used for the estimation of ET₀. Review exhibits that artificial neural network (ANN) approach outperforms support vector machine (SVM) and genetic programming (GP). Second order neural network (SONN) is the most promising approach among ANN models.     

Crafting Mosaics: Person-Centered Religious Influence and Selection in Adolescent Friendships

This research addresses the intersection of two key domains of adolescents’ lives: religion and peer networks. Religion scholars argue that religion is multi-faceted and better understood by focusing on combinations of indicators (i.e., mosaics), versus a variable-centered approach. We adopt this framework and investigate the interplay between religion and peer networks, both in how religious mosaics are shaped by friends and how religious profiles affect friend selection dynamics. With data from two schools in the National Longitudinal Study of Adolescent Health, we estimate religious mosaics using latent class analysis (LCA) to identify profiles consisting of combinations of commonly available survey-based measures of religious attitudes, behaviors, and identities. Finding evidence of theoretically expected profiles, we then use stochastic actor–based models (SABMs) to investigate network dynamics for these LCA-based religious profiles. We demonstrate how the profile data can be integrated within the SABM framework to evaluate processes of friend selection and influence. Results show evidence of adolescents influencing one another's religious mosaics, but not selecting friends on that basis.     

Assessing Representations of Close Relationships Among Chinese and Japanese Adolescents and Young Adults: Commonalities and Differences in the Two Confucian Cultures

This article proposed an affective relationships model with its assessment instrument and examined how Chinese and Japanese adolescents and young adults construct mental representations of their close relationships, and the influence of Confucian cultural beliefs on these representations. The participants were 1,565 students aged 14 to 24 years living in China or Japan. The students were asked about their relationships with four figures: mother, father, closest friend, and romantic partner. We found that: (a) adolescents and young adults in both cultures constructed constellations of relationships containing multiple figures, and they articulated reasons for each figure's significance; and (b) there were between-group differences in the relationships with the father and the romantic partner for Chinese versus Japanese adolescents and young adults. These differences were partly explained by the influence of Confucianism on the Chinese participants’ cultural beliefs that retained the patriarchal values.     

Shivering for Status: When Cold Temperatures Increase Product Evaluation

While anecdotal evidence suggests that consumers maintain an association between high‐status products and cold temperature, no research has empirically examined this linkage. We propose and demonstrate that physical cold can indeed increase consumers’ perceptions of a product's status signaling and luxuriousness. We demonstrate this consequence can stem from tactile or visually induced temperatures and ultimately increase consumers’ overall evaluations of products. Finally, we highlight boundary conditions for when the effect is likely to manifest. Our findings offer theoretical contributions along with several practical implications for retailing, product design, brand management, and marketing communications.