Injunctive norms, sexism, and misogyny network activation among men.

2020 ◽  
Vol 21 (1) ◽  
pp. 124-138
Author(s):  
Jennifer K. Bosson ◽  
Sophie L. Kuchynka ◽  
Dominic J. Parrott ◽  
Suzanne C. Swan ◽  
Andrew T. Schramm
Keyword(s):  
Injunctive Norms ◽  
Network Activation

An Initial Investigation of the Use of Comparative Referents to Assess Social Anxiety

2018 ◽  
Vol 34 (6) ◽  
pp. 367-375 ◽  
Author(s):  
Laura D. Seligman ◽  
Erin F. Swedish ◽  
Jason P. Rose ◽  
Jessica M. Baker
Keyword(s):  
Social Anxiety ◽  
Discriminant Validity ◽  
Self Report ◽  
Injunctive Norms ◽  
Good Reliability ◽  
Convergent And Discriminant Validity ◽  
Excellent Internal Consistency ◽  
Social Quality Of Life ◽  
Comparison Measures

Abstract. The current study examined the validity of two self-report measures of social anxiety constructed using social comparative referent points. It was hypothesized that these comparison measures would be both reliable and valid. Results indicated that two different comparative versions – one invoking injunctive norms and another invoking descriptive norms – showed good reliability, excellent internal consistency, and acceptable convergent and discriminant validity. The comparative measures also predicted positive functioning, some aspects of social quality of life, and social anxiety as measured by an independent self-report. These findings suggest that adding a comparative reference point to instructions on social anxiety measures may aid in the assessment of social anxiety.

Relationship between injunctive norms and alcohol consequences in college students

2009 ◽  
Author(s):  
Joseph W. Labrie ◽  
Justin F. Hummer ◽  
Clayton Neighbors ◽  
Mary Larimer
Keyword(s):  
College Students ◽  
Injunctive Norms

Brain Network Activation Analysis for Assessing and Managing Sport-Related Concussion

2013 ◽  
Author(s):  
R. J. Elbin ◽  
Anthony P. Kontos ◽  
Jennine Wedge ◽  
Aiobheann Cline ◽  
Scott Dakan ◽  
...  
Keyword(s):  
Activation Analysis ◽  
Brain Network ◽  
Network Activation

Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks

2019 ◽  
Vol 12 (3) ◽  
pp. 156-161 ◽  
Author(s):  
Aman Dureja ◽  
Payal Pahwa
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Primary Objective ◽  
Experimental Comparison ◽  
Activation Functions ◽  
Practical Applications ◽  
Network Activation ◽  
Non Linear ◽  
Hidden Layer

Background: In making the deep neural network, activation functions play an important role. But the choice of activation functions also affects the network in term of optimization and to retrieve the better results. Several activation functions have been introduced in machine learning for many practical applications. But which activation function should use at hidden layer of deep neural networks was not identified. Objective: The primary objective of this analysis was to describe which activation function must be used at hidden layers for deep neural networks to solve complex non-linear problems. Methods: The configuration for this comparative model was used by using the datasets of 2 classes (Cat/Dog). The number of Convolutional layer used in this network was 3 and the pooling layer was also introduced after each layer of CNN layer. The total of the dataset was divided into the two parts. The first 8000 images were mainly used for training the network and the next 2000 images were used for testing the network. Results: The experimental comparison was done by analyzing the network by taking different activation functions on each layer of CNN network. The validation error and accuracy on Cat/Dog dataset were analyzed using activation functions (ReLU, Tanh, Selu, PRelu, Elu) at number of hidden layers. Overall the Relu gave best performance with the validation loss at 25th Epoch 0.3912 and validation accuracy at 25th Epoch 0.8320. Conclusion: It is found that a CNN model with ReLU hidden layers (3 hidden layers here) gives best results and improve overall performance better in term of accuracy and speed. These advantages of ReLU in CNN at number of hidden layers are helpful to effectively and fast retrieval of images from the databases.

scholarly journals Neural mechanisms of credit card spending

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sachin Banker ◽  
Derek Dunfield ◽  
Alex Huang ◽  
Drazen Prelec
Keyword(s):  
Credit Card ◽  
Credit Cards ◽  
Purchase Behavior ◽  
Facilitation Effect ◽  
Product Price ◽  
Personal Credit ◽  
Psychological Mechanisms ◽  
Network Activation ◽  
Payment Instruments ◽  
Reward Network

AbstractCredit cards have often been blamed for consumer overspending and for the growth in household debt. Indeed, laboratory studies of purchase behavior have shown that credit cards can facilitate spending in ways that are difficult to justify on purely financial grounds. However, the psychological mechanisms behind this spending facilitation effect remain conjectural. A leading hypothesis is that credit cards reduce the pain of payment and so ‘release the brakes’ that hold expenditures in check. Alternatively, credit cards could provide a ‘step on the gas,’ increasing motivation to spend. Here we present the first evidence of differences in brain activation in the presence of real credit and cash purchase opportunities. In an fMRI shopping task, participants purchased items tailored to their interests, either by using a personal credit card or their own cash. Credit card purchases were associated with strong activation in the striatum, which coincided with onset of the credit card cue and was not related to product price. In contrast, reward network activation weakly predicted cash purchases, and only among relatively cheaper items. The presence of reward network activation differences highlights the potential neural impact of novel payment instruments in stimulating spending—these fundamental reward mechanisms could be exploited by new payment methods as we transition to a purely cashless society.

scholarly journals Challenging the Status Quo through Social Influence: Changes in Sustainable Consumption through the Influence of Social Networks

2021 ◽  
Vol 13 (10) ◽  
pp. 5513
Author(s):  
Iljana Schubert ◽  
Judith I. M. de Groot ◽  
Adrian C. Newton
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Influence ◽  
Food Consumption ◽  
Status Quo ◽  
Injunctive Norms ◽  
Sustainable Food ◽  
Consumption Choices ◽  
The Status ◽  
Consumption Practices

This study examines the influence of social network members (versus strangers) on sustainable food consumption choices to investigate how social influence can challenge the status quo in unsustainable consumption practices. We hypothesized that changes to individual consumption practices could be achieved by revealing ‘invisible’ descriptive and injunctive social norms. We further hypothesized that it matters who reveals these norms, meaning that social network members expressing their norms will have a stronger influence on other’s consumption choices than if these norms are expressed by strangers. We tested these hypotheses in a field experiment (N = 134), where participants discussed previous sustainable food consumption (revealing descriptive norms) and its importance (revealing injunctive norms) with either a stranger or social network member. We measured actual sustainable food consumption through the extent to which participants chose organic over non-organic consumables during the debrief. Findings showed that revealed injunctive norms significantly influenced food consumption, more so than revealed descriptive norms. We also found that this influence was stronger for social network members compared to strangers. Implications and further research directions in relation to how social networks can be used to evoke sustainable social change are discussed.

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