No abstract

The present research investigated whether the attribution process through which people explain self-disclosures differs in text-based computer-mediated interactions versus face to face, and whether differences in causal attributions account for the increased intimacy frequently observed in mediated communication. In the experiment participants were randomly assigned to a face-to-face or computer-mediated interaction with a confederate who made either high-or low-intimacy self-disclosures. Results indicated that computer-mediated interactions intensified the association between disclosure and intimacy relative to face-to-face interactions, and this intensification effect was fully mediated by increased interpersonal (relationship) attributions observed in the computer-mediated condition. The article presents an attributional extension of the hyperpersonal model (Walther, 1996) by demonstrating the role of causal attributions in interpersonal intensification processes in text-based computer-mediated interactions.

Crowdsourcing contests are contests by which organizations tap into the wisdom of crowds by outsourcing tasks to large groups of people on the Internet. In an online environment often characterized by anonymity and lack of trust, there are inherent uncertainties for participants of such contests. This study focuses on crowdsourcing contests with winner-take-all prizes. During these contests, submissions are made sequentially and contest hosts can provide public in-process feedback to the submissions as soon as they are received. Drawing on the uncertainty literature, we examine how the use of prize guarantees (guaranteeing that a winner will be picked and paid) and in-process feedback (numeric ratings to individual designs and public textual comments during the contest) can help reduce the various uncertainties faced by the contestant, thereby attracting more submissions. We find that guaranteeing the prize increases submissions. The volume of in-process feedback (both numeric reviews and textual comments) has a positive effect on the number of submissions, and such an effect is bigger in contests without prize guarantees. In addition, providing highly positive or extremely negative feedback discourage overall future submissions, and the negative effect of highly positive feedback is mitigated in guaranteed contests.

This study presented a model specifying the relationship of unit-level safety climate and perceived colleagues' safety knowledge/behavior (PCSK/B) to safety behavior (safety compliance and safety participation), as well as safety performance (injuries and near misses). PCSK/B, a measure of descriptive norms, was taken as a new individual-level predictor. Hierarchical linear modeling analyses indicated the significant cross-level interaction effects of unit-level safety climate and PCSK/B on safety behavior, i.e., the more positive the safety climate, the stronger effects PCSK/B has on safety behavior. The effect of PCSK/B on injuries was mediated by safety behavior. Implications for management and safety climate research were discussed.

Objectives-To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG).Methods-Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. NIH Public AccessResults-The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention.Conclusions-Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training.Significance-Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.