With the recent proposal of the European Commission for a so-called Artificial Intelligence Act, this technology becomes a regulatory subject in its own right. While the Commission proposal primarily adopts a market-oriented approach, artificial intelligence also plays an increasingly important role in the internal decision-making of corporations. The technology promises increased efficiency, especially for business decisions that are made on the basis of extensive and complex data. Artificial intelligence makes it possible, for instance, to analyse data from customer relationships or production processes on a massive scale, and to prepare it for decision-making processes, such as in the context of algorithmic marketing, algorithmic market research or algorithmic controlling.

Although the phenomenon of stigma is global, its manifestation and impact are culturally bound. This chapter examines some of the culturally salient factors that have been investigated empirically in understanding cross-cultural differences between relatively group-oriented cultures situated in East Asia (e.g., Hong Kong, Taiwan, Japan) with cultures that are relatively more individual-oriented such as countries in Europe and North America (e.g., United States, Canada, United Kingdom). These factors include supernatural beliefs, face concern, and conformity. The internalization process of stigma is also discussed within the cultural context in order to account for possible nuances in the experience of stigma. Recent directions in stigma research such as affiliate stigma and self-stigma process are introduced to highlight the importance of emic approach in the understanding of stigma within specific cultural context.

This chapter provides an overview and delineation of the four major stigmas related to mental illness and seeking psychological help: public stigma of mental illness, public stigma of seeking help, self-stigma of mental illness, and self-stigma of seeking help. It begins with discussion of a theoretical model that distinguishes these four stigmas, outlines how they relate to each other, and asserts how they relate to mental health and help-seeking outcomes. We then discuss the evidence for the assertions of the model and the theoretical distinction of these forms of stigma. The chapter concludes with discussion of a possible new type of stigma, future directions, and cultural considerations.

Self-affirmation theory provides a sophisticated framework to understand individual differences in receptivity to health-risk communication. Health messages are often ineffective because reminders of health risks can create dissonanc, which causes people to react negatively against the perceived threat of the information. Self-affirmation interventions offer a brief and practical means of improving health communication and promoting positive change. The primary purpose of this chapter is to highlight the promise of self-affirmation in understanding and reducing mental health stigma. The chapter aims to provide a theoretical background and practical path forward for researchers and clinicians, public health professionals, mental health activists, and any persons interested in dismantling the negative stereotypes and judgments associated with mental health and seeking professional psychological help. Specifically, the chapter aims to (1) briefly summarize the relationship between mental health stigma and psychotherapy use, (2) describe self-affirmation theory and its applied intervention effects in reducing perceptions of psychological threat across levels of measurement, (3) describe a standardized method of inducing self-affirmation by reflecting on personal values, (4) examine self-affirmation’s extension to mental health stigma and professional help seeking, (5) explore potential underlying mechanisms of change, and (6) suggest future directions for research and practical application.

Almost 150 years after Kussmaul’s documentation of ‘Wortblindheit’ (word blindness) (Kussmaul 1877) the scientific community has generated a number of different theories of dyslexia and dyscalculia. While these theories are still controversially discussed, the converging findings of longitudinal developmental research now allow us to draw an increasingly clear picture of the potential origins of these learning difficulties. At the same time, the common understanding of dyslexia and dyscalculia is blurred by persistent myths, such as the notion that dyslexia causes letters to appear out of order or that dyscalculia is a sign of reduced intelligence. Moreover, families, educators, and even specialized practitioners are often not sure how a specific learning disorder is validly diagnosed and which type of support children need to cope with their difficulties. Accordingly, the purpose of this handbook is to provide a developmentally grounded perspective on these topics by integrating findings from the life sciences and social sciences.

In this chapter, we summarize the current state of research that relates the inability to mentally operate with numbers to the development of brain mechanisms. Before looking at the details about longitudinal development of the neurocognitive underpinnings in numerical disorders we would like to briefly address the neural bases of typical numerical cognition and developmental dyscalculia. Here, we refer to the clinical classification of developmental dyscalculia, which defines it as a specific learning impairment affecting numerical cognition that emerges at a very early stage of development and cannot be explained by inappropriate schooling or lack of learning opportunities. Therefore, when we talk about developmental dyscalculia, we do not include acquired numerical disorders due to loss of an established ability (e.g. after brain damage). Rather, we focus on the inability to acquire numerical concepts or procedures due to developmental issues. Based on the knowledge generated in cross-sectional studies investigating numerical cognition, we elaborate on developmental changes in brain function and structure during typical development and in developmental dyscalculia. Finally, we discuss the predictive relation between brain characteristics and numerical skills.

Reading, writing, and arithmetic skills are key cultural techniques in most societies. These academic skills must be acquired at a symbolic level – that is, enable us to produce and comprehend text and to perform more than very basic numerical operations, judgements, and calculations. These skills must be taught and learnt. During this procedural learning process (Nicolson and Fawcett 2007; Nicolson and Fawcett 2018), pre-existing functions – amongst them visual, auditory, memory, language, spatial, or quantity processing – are modified and coordinated to form novel cognitive procedures (Lachmann 2002; Lachmann 2008; Lachmann 2018; Lachmann and van Leeuwen 2014) which are then automatized after prolonged intensive training (Froyen et al. 2009; Lachmann and Van Leeuwen 2008; Barrouillet and Fayol 1998).

This chapter primarily considers anxiety associated with mathematics and (to a lesser extent) reading. An overview of relevant research in the field can be found in recent review papers on mathematics anxiety (Barroso et al. 2020; Carey, Hill et al. 2017; Chang and Beilock 2016; Dowker et al. 2016; Mammarella et al. 2019; Namkung et al. 2019; Ramirez et al. 2018; J. Zhang et al. 2019) and reading anxiety (Piccolo et al. 2017). Here the focus is on the latest results in the context of some classical research. Emphasis will be laid on large studies that provide more reliable effect size estimates than small, underpowered studies (see Szűcs and Ioannidis 2017 for a review). Specifically, the current chapter relies on international data gathered in the Program for International Student Assessment (Education at a Glance 2018: OECD Indicators, n.d.; OECD 2013), on meta-analyses collating data worldwide, and on specific data sets collected in Belgium, Colombia, Finland, Germany, Israel, India, Italy, the UK, the USA, Poland, and Switzerland.

This chapter focuses on the processes involved in the development of children’s decoding skills: the ability to translate printed words into a speech code, typically assessed by the accuracy and speed of reading aloud. If children struggle in developing this ability, they are often characterized as having dyslexia, but since decoding skills are normally distributed in the population, the cut-off for this diagnosis is somewhat arbitrary (Melby-Lervåg et al. 2012). During the first two years of school, the main aim for children is to develop accurate and fluent early reading abilities that will lay the foundation for the main goal of reading – to be able to extract meaning from text. Thus, though insufficient by itself, efficient word reading is, in turn, a necessary condition for the development of reading comprehension. Research on the foundations of learning to read has burgeoned in the last twenty-five years, with important theoretical and practical consequences.

Reading and maths ability are shaped by an interaction of genetic and environmental factors. Roughly half of the behavioural variance that is measured when assessing reading and maths performance is explained by genetic variance, while the other half is explained by environmental variance (see Chapters 6 and 7). In this context, it is often overlooked that there is a large explanatory gap between what genes do and how behavioural performance is regulated. Genes do not act directly but indirectly on reading and maths ability, most notably by playing a role in the development of the brain, the biological information processing system that makes learning to read and do maths possible. More specifically, many candidate genes for dyslexia and dyscalculia encode protein structures in developing neural circuits that ultimately form the brain systems underlying reading and maths. This explanatory pathway involving genetic variation, brain systems for reading and maths, and learning difficulties in these two domains is the key topic of the present chapter.