The New Muslim Religious Brokers in European Cities and Politics of Muslim Citizenship

On the basis of the research into active social citizenship amongst the new Muslim religious brokers in Brussels and London, this paper explores the transition from the politics of Muslim identity to the politics of Muslim citizenship, a major change in the public mobilisation of Islam in Belgium and Britain. It argues that this move has been closely linked with the development of civic consciousness among certain segments of the Muslim populations in Europe and the construction of a new type of identity – ‘Muslim civicness’ - which is characterised by strong support for the national projects, activism beyond Muslim symbolic boundaries, emphasis on the similar rights to other citizens and obligations vis-à-vis all the citizens regardless of their religious adherence

Do what you love, an opera in one act.

Do What You Love is an opera in one act, based on the American culture idea of doing what you love in life, following your passions or living the American dream. The libretto, which was generated by AI, presents the story of four main characters and one episodic character, each representing the worst outcome or highlighting vicious drives of following the motto Do What You Love. The opera is also a cliché version of historic opera, uniting typical baroque opera characters and styles with quasi AI Generated, cliché, stereotypical music. Synopsis: The Artist is preparing for a huge audition that he wants to participate in. It’s a huge chance for his career. The accompanist helps him to prepare for it, even though he doesn’t believe in the Artist’s talent. He himself was pursuing a goal of being a great virtuoso, but failed to do so and he is an accompanist for untalented, rich, spoiled kids. During rehearsal, the vicious Servant comes to the chamber, she throws some sarcastic comments around about the Artist’s performance. The Richman, father of the Artist and a man who has achieved everything in life and feel s quite lost about what to do next, enters the chamber. During his contemplation he comes to the conclusion that what he wants from life is satisfying his lust. At last to the chamber comes Manipulator, who is supposed to be judge in the Artist’s audition. He is treacherous person, seeking to know desires of each character to use and exploit them. He applauds the Artist and offers great career. For Servant he offer a lot of wealth and gold, for the Richman he propose ways to satisfy his lust. In exchange he asks each character for their souls, that they will sell in the Grand Waltz

Prior knowledge about events depicted in scenes decreases oculomotor exploration.

The visual input that the eyes receive usually contains temporally continuous information about unfolding events. Therefore, humans can accumulate knowledge about their current environment. Typical studies on scene perception, however, involve presenting multiple unrelated images and thereby render this accumulation unnecessary. Our study, instead, facilitated it and explored its effects. Specifically, we investigated how recently-accumulated prior knowledge affects gaze behavior. Participants viewed sequences of static film frames that contained several 'context frames' followed by a 'critical frame'. The context frames showed either events from which the situation depicted in the critical frame naturally followed, or events unrelated to this situation. Therefore, participants viewed identical critical frames while possessing prior knowledge that was either relevant or irrelevant to the frames' content. In the former case, participants' gaze behavior was slightly more exploratory, as revealed by seven gaze characteristics we analyzed. This result demonstrates that recently-gained prior knowledge reduces exploratory eye movements

Investigating the role of image meaning and prior knowledge in human eye movements control

Humans sample visual information by making eye movements towards different parts of their surroundings. Understanding what guides this sampling process is an important goal of vision science, and the present thesis is a contribution to this endeavour. Chapter One provides an overview of factors influencing human eye movements, which are typically divided into bottom-up (stimulus-dependent) and top-down (observer-dependent) processes. One of the challenges in studying these factors stem from the fact that they are often difficult to operationalize in a precise, unambiguous way. This is particularly problematic for semantic information contained in visual scenes (‘image meaning’), a top-down factor which is the backbone of the recently proposed framework for understanding human eye movements: the meaning maps approach. Chapter Two evaluates this approach and demonstrates that meaning maps – a crowd-sourced method designed to quantify the distribution of meaning in natural scenes – might be sensitive to complex visual features, rather than meaning. Chapter Three builds on that finding and shows that contextualized meaning maps, the most recent variant of the original meaning maps, share the limitations of their predecessors. Chapter Four adopts a novel perspective on eye-movement control and focuses on the interactions between image features (a bottom-up factor) and prior object-knowledge possessed by an observer (a top-down factor). Specifically, it shows that the same stimuli – black and white, Mooney-style two-tone images – are looked at differently depending on whether the observer possesses object-knowledge that enables them to bind images into coherent percepts of objects. The final chapter summarizes the thesis and maps the future directions for studies on eye movements. Taken together, findings reported here indicate that while top-down factors such as prior object-knowledge play a crucial role in guiding human gaze, the tools to study them offered by the meaning maps approach still need to be improve

Influence of prior knowledge on eye movements to scenes as revealed by hidden Markov models.

Human visual experience usually provides ample opportunity to accumulate knowledge about events unfolding in the environment. In typical scene perception experiments, however, participants view images that are unrelated to each other and, therefore, they cannot accumulate knowledge relevant to the upcoming visual input. Consequently, the influence of such knowledge on how this input is processed remains underexplored. Here, we investigated this influence in the context of gaze control. We used sequences of static film frames arranged in a way that allowed us to compare eye movements to identical frames between two groups: a group that accumulated prior knowledge relevant to the situations depicted in these frames and a group that did not. We used a machine learning approach based on hidden Markov models fitted to individual scanpaths to demonstrate that the gaze patterns from the two groups differed systematically and, thereby, showed that recently accumulated prior knowledge contributes to gaze control. Next, we leveraged the interpretability of hidden Markov models to characterize these differences. Additionally, we report two unexpected and interesting caveats of our approach. Overall, our results highlight the importance of recently acquired prior knowledge for oculomotor control and the potential of hidden Markov models as a tool for investigating it

Saving and fearing Muslim women in ‘post-communist’ Poland : troubling Catholic and secular Islamophobia

Sexual politics play a key role in anti-Muslim narratives. This has been observed by scholarship problematising liberal feminist approaches towards ‘non-Western’ subjects focusing on countries such as France, the USA and the Netherlands. Yet interrogations into how these debates play out in European national contexts that are located outside of the European ‘West’ have attracted significantly less scholarly attention. Drawing on qualitative data collected in Poland this article aims to begin to fill this gap by analysing the centrality of feminist discourses within Islamophobic agendas in Poland. The article asks how discourses around women’s rights are mobilised simultaneously, and paradoxically, by both secular and Catholic groups in ‘post-communist’ Poland. By showcasing how feminist sentiments are employed by ideologically opposing groups, we sketch out some of the complexities in the ways Islamophobia operates in a Central and Eastern European context

Knowledge-driven perceptual organization reshapes information sampling via eye movements

Humans constantly move their eyes to explore the environment. However, how image-computable features and object representations contribute to eye-movement control is an ongoing debate. Recent developments in object perception indicate a complex relationship between features and object representations, where image-independent object knowledge generates objecthood by reconfiguring how feature space is carved up. Here, we adopt this emerging perspective, asking whether object-oriented eye movements result from gaze being guided by image-computable features, or by the fact that these features are bound into an object representation. We recorded eye movements in response to stimuli that initially appear as meaningless patches but are experienced as coherent objects once relevant object knowledge has been acquired. We demonstrate that fixations on identical images are more object-centered, less dispersed, and more consistent across observers once these images are organized into objects. Gaze guidance also showed a shift from exploratory information sampling to exploitation of object-related image areas. These effects were evident from the first fixations onwards. Importantly, eye movements were not fully determined by knowledge-dependent object representations but were best explained by the integration of these representations with image-computable features. Overall, the results show how information sampling via eye movements is guided by a dynamic interaction between image-computable features and knowledge-driven perceptual organization

Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations

Eye movements are vital for human vision, and it is therefore important to understand how observers decide where to look. Meaning maps (MMs), a technique to capture the distribution of semantic information across an image, have recently been proposed to support the hypothesis that meaning rather than image features guides human gaze. MMs have the potential to be an important tool far beyond eye-movements research. Here, we examine central assumptions underlying MMs. First, we compared the performance of MMs in predicting fixations to saliency models, showing that DeepGaze II – a deep neural network trained to predict fixations based on high-level features rather than meaning – outperforms MMs. Second, we show that whereas human observers respond to changes in meaning induced by manipulating object-context relationships, MMs and DeepGaze II do not. Together, these findings challenge central assumptions underlying the use of MMs to measure the distribution of meaning in images