The gender gap in political psychology

IntroductionI investigated the authorship gender gap in research on political psychology. MethodsThe material comprises 1,166 articles published in the field's flagship journal Political Psychology between 1997 and 2021. These were rated for author gender, methodology, purpose, and topic. ResultsWomen were underrepresented as authors (37.1% women), single authors (33.5% women), and lead authors (35.1% women). There were disproportionately many women lead authors in papers employing interviews or qualitative methodology, and in research with an applied purpose (these were all less cited). In contrast, men were overrepresented as authors of papers employing quantitative methods. Regarding topics, women were overrepresented as authors on Gender, Identity, Culture and Language, and Religion, and men were overrepresented as authors on Neuroscience and Evolutionary Psychology. DiscussionThe (denigrated) methods, purposes, and topics of women doing research on politics correspond to the (denigrated) "feminine style" of women doing politics grounding knowledge in the concrete, lived reality of others; listening and giving voice to marginalized groups' subjective experiences; and yielding power to get things done for others.Peer reviewe

How The Internet Affects Productivity

During the past decade, there has been a lot of research focusing on the Internets effects on productivity. One of the central themes of this research has been the productivity paradox. Productivity paradox is a phenomenon in which investments in the use of information technology have not resulted in productivity improvements. The objective of this paper is to present the conclusions that this extensive research of the past few years has arrived at concerning the Internets effects on productivity. This paper also includes a practical example which illustrates how the use of the Internet might result in very high productivity improvements by redesigning the entire business model.In the literature, the research results on the issue are conflicting. Some researchers have not found any evidence that would suggest that the use of the Internet increases productivity. However, there are several success stories that imply that the Internet could be an important tool in improving productivity or overall performance of a firm. In addition, it seems that in some cases the productivity paradox has occurred because of defective or unsuitable productivity measures, or even because of conceptual confusions.The key finding of this paper is that the use of the Internet may or may not increase productivity, depending on the way it is used. From the managerial perspective, there are also many other reasons for using the Internet, such as improved customer service or competitive pressure. However, a better customer service means better value for a customer. For a firm this usually means better productivity. To conclude, it seems evident that successful investments in internet technology should lead to better productivity and the greatest productivity improvements are attainable when the Internet is used to create entirely new business models

Using a Continuous Measure of Genderedness to Assess Sex Differences in the Attitudes of the Political Elite

We investigated the attitudes of the 11,410 candidates in the Finnish 2017 municipal elections who had responded to a Voting Advice Application. Women candidates were, both in terms of economic and social attitudes, more progressive than men. Building on the gender diagnosticity approach, we used responses to the attitude items to construct a dimensional measure of political genderedness; i.e., a measure of the femininity-masculinity of the individual's political attitudes. We used this measure to investigate the magnitude of sex differences across parties and the determinants of these differences. Sex differences were larger in parties with more economically right-oriented, socially conservative, well-off, and male candidates. Moreover, these differences were caused by men in these parties being different from other candidates. A similar methodology, in which a continuous measure of genderedness is used to assess sex differences, could be used in other domains of research on political behavior.Peer reviewe

Who's in power matters : System justification and system derogation in Hungary between 2002 and 2018

The present study employed European Social Survey (ESS) data collected between 2002 and 2018 to investigate system justification versus derogation in Hungary. In all nine ESS rounds, system derogation was stronger than system justification. System justification was consistently at its strongest among those who had voted for the ruling party, be it left-wing MSZP (until 2008) or right-wing Fidesz (2010 onward). This pattern can be explained by ego and group justification motives alone, with no need to posit an autonomous system justification motive. Voters of Jobbik, who were as right-wing as Fidesz voters, but whose party was not in power, did not believe the system to be any more just than did left-wing voters. Much of the research supporting system justification theory has been conducted in stable Western democracies. Our results highlight the need for research in more politically volatile contexts.Peer reviewe

Latent Noise Segmentation: How Neural Noise Leads to the Emergence of Segmentation and Grouping

Deep Neural Networks (DNNs) that achieve human-level performance in general tasks like object segmentation typically require supervised labels. In contrast, humans are able to perform these tasks effortlessly without supervision. To accomplish this, the human visual system makes use of perceptual grouping. Understanding how perceptual grouping arises in an unsupervised manner is critical for improving both models of the visual system, and computer vision models. In this work, we propose a counterintuitive approach to unsupervised perceptual grouping and segmentation: that they arise because of neural noise, rather than in spite of it. We (1) mathematically demonstrate that under realistic assumptions, neural noise can be used to separate objects from each other, and (2) show that adding noise in a DNN enables the network to segment images even though it was never trained on any segmentation labels. Interestingly, we find that (3) segmenting objects using noise results in segmentation performance that aligns with the perceptual grouping phenomena observed in humans. We introduce the Good Gestalt (GG) datasets -- six datasets designed to specifically test perceptual grouping, and show that our DNN models reproduce many important phenomena in human perception, such as illusory contours, closure, continuity, proximity, and occlusion. Finally, we (4) demonstrate the ecological plausibility of the method by analyzing the sensitivity of the DNN to different magnitudes of noise. We find that some model variants consistently succeed with remarkably low levels of neural noise ($\sigma<0.001$), and surprisingly, that segmenting this way requires as few as a handful of samples. Together, our results suggest a novel unsupervised segmentation method requiring few assumptions, a new explanation for the formation of perceptual grouping, and a potential benefit of neural noise in the visual system

How object segmentation and perceptual grouping emerge in noisy variational autoencoders

Many animals and humans can recognize and segment objects from their backgrounds. Whether object segmentation is necessary for object recognition has long been a topic of debate. Deep neural networks (DNNs) excel at object recognition, but not at segmentation tasks - this has led to the belief that object recognition and segmentation are separate mechanisms in visual processing. Here, however, we show evidence that in variational autoencoders (VAEs), segmentation and faithful representation of data can be interlinked. VAEs are encoder-decoder models that learn to represent independent generative factors of the data as a distribution in a very small bottleneck layer; specifically, we show that VAEs can be made to segment objects without any additional finetuning or downstream training. This segmentation is achieved with a procedure that we call the latent space noise trick: by perturbing the activity of the bottleneck units with activity-independent noise, and recurrently recording and clustering decoder outputs in response to these small changes, the model is able to segment and bind separate features together. We demonstrate that VAEs can group elements in a human-like fashion, are robust to occlusions, and produce illusory contours in simple stimuli. Furthermore, the model generalizes to the naturalistic setting of faces, producing meaningful subpart and figure-ground segmentation without ever having been trained on segmentation. For the first time, we show that learning to faithfully represent stimuli can be generally extended to segmentation using the same model backbone architecture without any additional training

Crowding in humans is unlike that in convolutional neural networks

Object recognition is a primary function of the human visual system. It has recently been claimed that the highly successful ability to recognise objects in a set of emergent computer vision systems---Deep Convolutional Neural Networks (DCNNs)---can form a useful guide to recognition in humans. To test this assertion, we systematically evaluated visual crowding, a dramatic breakdown of recognition in clutter, in DCNNs and compared their performance to extant research in humans. We examined crowding in three architectures of DCNNs with the same methodology as that used among humans. We manipulated multiple stimulus factors including inter-letter spacing, letter colour, size, and flanker location to assess the extent and shape of crowding in DCNNs. We found that crowding followed a predictable pattern across architectures that was different from that in humans. Some characteristic hallmarks of human crowding, such as invariance to size, the effect of target-flanker similarity, and confusions between target and flanker identities, were completely missing, minimised or even reversed. These data show that DCNNs, while proficient in object recognition, likely achieve this competence through a set of mechanisms that are distinct from those in humans. They are not necessarily equivalent models of human or primate object recognition and caution must be exercised when inferring mechanisms derived from their operation

A comment on Guo et al. [arXiv:2206.11228]

In a recent article, Guo et al. [arXiv:2206.11228] report that adversarially trained neural representations in deep networks may already be as robust as corresponding primate IT neural representations. While we find the paper's primary experiment illuminating, we have doubts about the interpretation and phrasing of the results presented in the paper

Not only assholes drive Mercedes : Besides disagreeable men, also conscientious people drive high-status cars

In a representative sample of Finnish car owners (N = 1892) we connected the Five-Factor Model personality dimensions to driving a high-status car. Regardless of whether income was included in the logistic model, disagreeable men and conscientious people in general were particularly likely to drive high-status cars. The results regarding agreeableness are consistent with prior work that has argued for the role of narcissism in status consumption. Regarding conscientiousness, the results can be interpreted from the perspective of self-congruity theory, according to which consumers purchase brands that best reflect their actual or ideal personalities. An important implication is that the association between driving a high-status car and unethical driving behaviour may not, as is commonly argued, be due to the corruptive effects of wealth. Rather, certain personality traits, such as low agreeableness, may be associated with both unethical driving behaviour and with driving a high-status car.Peer reviewe