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Sloppy Models, Renormalization Group Realism, and the Success of Science
Get PDFThe “sloppy models” program originated in systems biology, but has seen applications across a range of fields. Sloppy models are dependent on a large number of parameters, but highly insensitive to the vast majority of parameter combinations. Sloppy models proponents claim that the program may explain the success of science. I argue that the sloppy models program can at best provide a very partial explanation. Drawing a parallel with renormalization group realism, I argue that it would only give us grounds for a minimal kind of scientific realism. Nonetheless, the program can offer certain epistemic virtues
Uncertainty in digital history
No full textThis paper introduces a model to understand uncertainty in digital history. The model seeks to reduce the noise without losing multivocality in historical data. Digital humanities tend to lean towards a neo-positivist direction approaching research problems from their quantifiable side. However, even if digital, history is unpredictable, and dealing with it is embedded in the historian’s practice.
History also acknowledges its limits in interpreting the sources. The discipline has reflected its interpretative approach several times in its course, starting from the question of narration (Hyden White 1980 or Natalie Zeamon Davis 1990) to multivocal interpretation of the sources (Jean-Claude Schmitt 2010). These approaches tend to agree that the interpretation provided by historians - despite making the most effort to stay true to the sources and their context - is a chosen narrative from the many.
When digital methods come into the picture, the researcher has the urge to leave the inherent uncertainty of the discipline behind, which can lead to finishing on less probable results which are further from being true to the source. The aim is to find a balance between what MacEachren called precision and accuracy (1992) or what Earl Babbie named reliability and validity (1975). Both taxonomies differentiate between those two qualities of research, which decide if the study runs methodologically correctly and/or reflects reality. Digital methods and automation tend to create an imbalance between precision (reliability) and accuracy (validity) at the latter's expense.
I propose to discover this critical equilibrium of working with historical data with the help of the case study of the Operation War Diary project
Wax Wars - Reshaping the material culture of WWI in Hungary.
No full text“If the gloves don‘t fit” - you must sew new ones. This paper analyses how the Hungarian Centenary WWI exhibition, A New World Was Born, mimicked the use of material culture to redefine the historical narrative of WWI. It also examines the impact of using pseudo-material culture on shaping public perceptions of the War.
This curatorial practice helped express the four main narrative pillars of the exhibition. The first theme evokes the image of a pre-WWI Carpathian Basin without territorial claims or ethnic conflicts, where peace was broken only by the
outbreak of the War. The second one elaborates on how the Allied powers triggered WWI and considers the Central Powers as victims of the Western aggressors. The third theme relativises WWI by reducing it to a Fraternal War, and the final one blames the tragedy of the conflict on left-wing governments.
These themes are twisting historical objectivity by providing grounding soil for myths beneficial for contemporary political discourses. This paper discusses how these themes are expressed with the help of pseudo-material culture in the exhibition space through installations and storytelling. It also analyses how the exhibited themes nurture the mnemonic conflict between the strengthening Hungarian nationalism and the representation of the European Union in a populist political climate.
Overall, this paper provides a case study which gives a thorough insight into how the museological choice of using commissioned installations at the expense of authentic material culture promoted the stakeholders‘ interpretations
of the past in a populist setting by rewriting the traditional reference points of history and bringing new memory constellations to life
Pupil responses to colorfulness are selectively reduced in healthy older adults
Get PDFThe alignment between visual pathway signaling and pupil dynamics offers a promising non-invasive method to further illuminate the mechanisms of human color perception. However, only limited research has been done in this area and the effects of healthy aging on pupil responses to the different color components have not been studied yet. Here we aim to address this by modelling the effects of color lightness and chroma (colorfulness) on pupil responses in young and older adults, in a closely controlled passive viewing experiment with 26 broad-spectrum digital color fields. We show that pupil responses to color lightness and chroma are independent from each other in both young and older adults. Pupil responses to color lightness levels are unaffected by healthy aging, when correcting for smaller baseline pupil sizes in older adults. Older adults exhibit weaker pupil responses to chroma increases, predominantly along the Green–Magenta axis, while relatively sparing the Blue–Yellow axis. Our findings complement behavioral studies in providing physiological evidence that colors fade with age, with implications for color-based applications and interventions both in healthy aging and later-life neurodegenerative disorders
Frankenstein
Get PDFThis chapter explores the notion of ‘the possible’ in Mary Shelley’s novel Frankenstein (1818; revised in 1831). ‘The possible’ is an emerging area within psychology and the social sciences concerned with the relationship between present realities and the potential for their transformation, and in the context of this encyclopedia, the chapter therefore makes new connections between literary study and these fields. One of the earliest literary examples of science fiction, Frankenstein has often been read as a cautionary tale about the dangers of scientific development; it has had a long history of being adapted for the stage and film, which have rendered Frankenstein and his creature cultural icons. Reading the novel in this context, and in the context of critical debates surrounding the novel, the chapter argues that Frankenstein helps us apprehend the significance of the imagination and storytelling for our understanding of how we relate to one another, and for what we wish to be possible in the future
Convolutional Neural Networks for Vision Neuroscience: Significance, Developments, and Outstanding Issues
Get PDFConvolutional Neural Networks (CNN) are a class of machine learning models predominately used in computer vision tasks and can achieve human-like performance through learning from experience. Their striking similarities to the structural and functional principles of the primate visual system allow for comparisons between these artificial networks and their biological counterparts, enabling exploration of how visual functions and neural representations may emerge in the real brain from a limited set of computational principles. After considering the basic features of CNNs, we discuss the opportunities and challenges of endorsing CNNs as in silico models of the primate visual system. Specifically, we highlight several emerging notions about the anatomical and physiological properties of the visual system that still need to be systematically integrated into current CNN models. These tenets include the implementation of parallel processing pathways from the early stages of retinal input and the reconsideration of several assumptions concerning the serial progression of information flow. We suggest design choices and architectural constraints that could facilitate a closer alignment with biology provide causal evidence of the predictive link between the artificial and biological visual systems. Adopting this principled perspective could potentially lead to new research questions and applications of CNNs beyond modeling object recognition
Higher-order organization of multivariate time series
Get PDFTime series analysis has proven to be a powerful method to characterize several phenomena in biology, neuroscience and economics, and to understand some of their underlying dynamical features. Several methods have been proposed for the analysis of multivariate time series, yet most of them neglect the effect of non-pairwise interactions on the emerging dynamics. Here, we propose a framework to characterize the temporal evolution of higher-order dependencies within multivariate time series. Using network analysis and topology, we show that our framework robustly differentiates various spatiotemporal regimes of coupled chaotic maps. This includes chaotic dynamical phases and various types of synchronization. Hence, using the higher-order co-fluctuation patterns in simulated dynamical processes as a guide, we highlight and quantify signatures of higher-order patterns in data from brain functional activity, financial markets and epidemics. Overall, our approach sheds light on the higher-order organization of multivariate time series, allowing a better characterization of dynamical group dependencies inherent to real-world data
The effect of value chain importance on regional economic recovery
Get PDFThis paper investigates the link between value chain importance and economic growth for the EU24 regions between 2008-2018. It finds that relying more on GVCs worsened regional growth during the financial crisis, but led to higher growth in the long run. The results contribute to the literature on regional resilience and the public debate on the impact of shocks and the desirability of GVCs. Furthermore, by separately analysing the importance of global and regional value chains it contributes to the discussion on the effect of regionalisation and provides insights on how the re-configuring of value chains may affect regional growth
