Dyslexia is one of the most common learning difficulties. Children and adults with dyslexia experience varying degrees of difficulties with reading, writing, and spelling. According to the International Dyslexia Association, key features of dyslexia are impairments in word recognition, spelling, and decoding print – difficulties that are likely to interfere with reading comprehension and vocabulary acquisition. These literacy-related difficulties can be referred to as dyslexia only if accompanied by adequate learning opportunities and intact cognitive, adaptive, and social functioning (Lyon et al. 2003, see also see Chapter 3, Skeide 2022). Dyslexia occurs with varying degrees of severity, and it is estimated that one in ten people in the United Kingdom may be affected by some form of dyslexia-related difficulties. These difficulties can range from mild impairments to a diagnosis of Specific Learning Disability (SLD) (Snowling and Hayiou-Thomas 2006). Dyslexia is included in the Neurodevelopmental Disorders category in the Fifth Edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-V) (American Psychiatric Association 2013).

There are intrinsic similarities between boys and girls in the cognitive and neural mechanisms characterizing typical reading and mathematics development, with few and small behavioural differences emerging between genders in older age groups. The similarities between the cognitive and neural processes of boys and girls in these domains are sometimes surprising because folk beliefs about differences between males and females pervade scientific discourse in psychology and even impact empirical research (Hyde and Linn 1988) and clinical practices (Shaywitz et al. 1990). The universal patterns of cognitive and neural development across gender groups are important to appreciate, not only because they are potentially surprising empirical facts, but also because they expose candidate sources of disorder in reading and mathematics. Research on typically developing children can reveal the specialized cognitive and neural mechanisms at the core of reading-specific and mathematics-specific deficits in boys and girls.

In this chapter, we provide an overview of different types of cognitive enhancement techniques that target key neural regions related to dyslexia and dyscalculia. The main emphasis is on non-invasive brain stimulation (NIBS), such as transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). The former sends a pulse of current through a coil to evoke a magnetic field that penetrates the skull and enters the brain. The direction of neuromodulation by TMS is controlled by the stimulation frequency.

(1) Neuronal migration genes: Dyslexia- and dyscalculia-related variants of the genes DCDC2, DYX1C1, KIAA0319, and ROBO1 might not properly guide young nerve cells to their target position in the cortex (neuronal migration). These associations have been most consistently found for the parietal cortex, which is related to reading (e.g., phonological short-term memory) and maths (e.g., numerosity detection).

Social and educational policy expectations regarding pre-primary education and care have changed in a fundamental way. For a long time, the main purpose of attending kindergarten was to foster the social, emotional, motor-related, and moral development of children. Nowadays, fostering children’s school-relevant skills in the domains of language, literacy, and mathematics are among the expectations of parents with regard to the educational mission of kindergartens (Roßbach and Hasselhorn 2014). As a consequence, a significant change in official guiding principles can be observed throughout the past decades (OECD 2011). Remarkably, up until the 1960s the prevailing opinion was that early learning achievement is mainly predisposed by innate skills. This view has been supplanted, however, by the idea that special support in early education can reduce children’s risk of only insufficiently acquiring basic academic skills during the primary school years.

The ‘overrepresentation’ of racially/ethnically marginalized and socioeconomically disadvantaged children in special education has been of concern to researchers, policymakers, and practitioners since at least the late 1960s (Dunn 1968; Mercer 1973; Coard 1971). The prevailing wisdom has long been that racially/ethnically and socioeconomically marginalized children are both at higher risk of disability due to cognitive and social–emotional–behavioural effects of structural inequalities, and that schools make racially/ethnically and socioeconomically biased decisions to qualify students for special education services (e.g., National Research Council 2002). Newer research has identified more nuanced and complex mechanisms in racial/ethnic inequalities in particular, in which racially/ethnically marginalized students are more likely to need services due to structural inequality, but are largely less likely to gain access to those services when they need them than their White peers (Shifrer and Fish 2020).

Dyslexia is a disorder of development. Classically, a child has shown apparently typical language acquisition and cognitive development until faced with the task of learning to read. Suddenly the child struggles: ‘In spite of laborious and persistent training, he can only with difficulty spell out words of one syllable’ (Hinshelwood 1896, p. 1378). Why this apparently specific problem with reading and writing? One hundred years later, a child with dyslexia aged 9 years wrote ‘I have blond her, Blue eys and an infeckshos smill. Pealpie tell mum haw gorgus I am and is ent she looky to have me. But under the surface I live in a tumoyl. Words look like swigles and riting storys is a disaster area because of spellings’ (I have blond hair, blue eyes and an infectious smile. People tell Mum how gorgeous I am and isn’t she lucky to have me. But under the surface I live in a turmoil. Words look like squiggles and writing stories is a disaster area because of spellings) (author’s private notes).

(1) Diagnostic criteria for specific learning disorders: (a) severity (1.5–2 standard deviations below the average grade-level (not age-level) performance); (b) persistence (a history of difficulties confirmed by repeated assessments and by response to intervention); (c) specificity (typical general cognitive development, but not an IQ discrepancy threshold); (d) unexpectedness (adequate education; sufficient language proficiency; no sensory, neural, or mental disorders; no severe psychosocial adversity); (e) manifestation (confirmed by standardized achievement tests with a high standard of objectivity, reliability, and validity). These criteria need to be tailored to the specific script/number (language) system and cultural educational setting (see Parts VI and X; Chapter 24, Nag 2022) while also taking individual circumstances into account (e.g., motivation, emotions, self-concept, noise, fatigue).

Computational modelling is a powerful tool in cognitive science to evaluate or compare existing theories and to make novel experimental predictions. In contrast to the vague formulation of traditional verbal theories (e.g., box-and-arrow models), computational models need to be formally explicit in any implementational detail and can produce accurate simulations of human performance. Computational modelling has many different flavours that reflect distinct theoretical approaches to understanding human cognition (see McClelland 2009, for a review).

1. Behavioural heterogeneity of dyslexia: Behavioural difficulties of individuals affected by dyslexia are highly heterogeneous: for example, reading accuracy, reading fluency, reading comprehension, spelling, auditory processing, language processing, phonological awareness, visual motion processing, and visual attention.

At an international conference in China, we were asked by a non-Chinese colleague: ‘Since Chinese students are already so good at mathematics, why are you studying dyscalculia?’ Advantages in the development of early mathematics ability have been generally documented in favour of Asian Pacific countries/regions including China, Japan, Korea, and Singapore (e.g., Miller et al. 2005; Leung 2014). However, this advantage does not translate to an ‘immunity’ from problems related to maths learning in children. Problems in arithmetic (termed dyscalculia) that are encountered in most western countries are found with equal prevalences in Asian Pacific countries/regions. Thus, the work we do in China along with other research is still important for understanding dyscalculia in a universal context.

(1) Quality criteria for effective intervention: There is evidence that interventions are most effective when they are individualized, adaptive, embedded into 1:1 or small group settings, focused on multiple domains, and start as early as possible. The slightly lower effect size of computer (vs. in-person interventions) should be balanced against their potentially wider applicability.