How prevalent is dyslexia? A definitive answer to this question has been elusive because of the continuous distribution of reading performance and predictors of dyslexia and because of the heterogeneous nature of samples of poor readers. Samples of poor readers are a mixture of individuals whose reading is consistent with or expected based on their performance in other academic areas and in language, and individuals with dyslexia whose reading is not consistent with or expected based on their other performances. In the present article, we replicate and extend a new approach for determining the prevalence of dyslexia. Using model-based meta-analysis and simulation, three main results were found. First, the prevalence of dyslexia is better represented as a distribution that varies as a function of severity as opposed to any single-point estimate. Second, samples of poor readers will contain more expected poor readers than unexpected or dyslexic readers. Third, individuals with dyslexia can be found across the reading spectrum as opposed to only at the lower tail of reading performance. These results have implications for screening and identification, and for recruiting participants for scientific studies of dyslexia.

Mathematical thinking is in high demand in the global market, but approximately 6 percent of school-age children across the globe experience math difficulties (Shalev et al., 2000). The home math environment (HME), which includes all math-related activities, attitudes, beliefs, expectations, and utterances in the home, may be associated with children’s math development. To examine the relation between the HME and children’s math abilities, a preregistered meta-analysis was conducted to estimate the average weighted correlation coefficient (r) between the HME and children’s math achievement and how potential moderators (i.e., assessment, study, and sample features) might contribute to study heterogeneity. A multilevel correlated effects model using 631 effect sizes from 64 quantitative studies comprising 68 independent samples found a positive, statistically significant average weighted correlation of r = .13 (SE = .02, p < .001). Our combined sensitivity analyses showed that the present findings were robust and that the sample of studies has evidential value. A number of assessment, study, and sample characteristics contributed to study heterogeneity, showing that no single feature of HME research was driving the large between-study differences found for the association between the HME and children’s math achievement. These findings indicate that children’s environments and interactions related to their learning are supported in the specific context of math learning. Our results also show that the HME represents a setting in which children learn about math through social interactions with their caregivers (Vygotsky, 1978) and what they learn depends on the influence of many levels of environmental input (Bronfenbrenner, 1979) and the specificity of input children receive (Bornstein, 2002).

Text-to-speech and related read-aloud tools are being widely implemented in an attempt to assist students' reading comprehension skills. Read-aloud software, including text-to-speech, is used to translate written text into spoken text, enabling one to listen to written text while reading along. It is not clear how effective text-to-speech is at improving reading comprehension. This study addresses this gap in the research by conducting a meta-analysis on the effects of text-to-speech technology and related read-aloud tools on reading comprehension for students with reading difficulties. Random effects models yielded an average weighted effect size of ([Formula: see text] = .35, with a 95% confidence interval of .14 to .56, p < .01). Moderator effects of study design were found to explain some of the variance. Taken together, this suggests that text-to-speech technologies may assist students with reading comprehension. However, more studies are needed to further explore the moderating variables of text-to-speech and read-aloud tools' effectiveness for improving reading comprehension. Implications and recommendations for future research are discussed.

The role of ␣ 1 -adrenergic receptors (␣ 1 ARs) in cognition and mood is controversial, probably as a result of past use of nonselective agents. ␣ 1A AR activation was recently shown to increase neurogenesis, which is linked to cognition and mood. We studied the effects of long-term ␣ 1A AR stimulation using transgenic mice engineered to express a constitutively active mutant (CAM) form of the ␣ 1A AR. CAM-␣ 1A AR mice showed enhancements in several behavioral models of learning and memory. In contrast, mice that have the ␣ 1A AR gene knocked out displayed poor cognitive function. Hippocampal brain slices from CAM-␣ 1A AR mice demonstrated increased basal synaptic transmission, paired-pulse facilitation, and long-term potentiation compared with wild-type (WT) mice. WT mice treated with the ␣ 1A AR-selective agonist cirazoline also showed enhanced cognitive functions. In addition, CAM-␣ 1A AR mice exhibited antidepressant and less anxious phenotypes in several behavioral tests compared with WT mice. Furthermore, the lifespan of CAM-␣ 1A AR mice was 10% longer than that of WT mice. Our results suggest that long-term ␣ 1A AR stimulation improves synaptic plasticity, cognitive function, mood, and longevity. This may afford a potential therapeutic target for counteracting the decline in cognitive function and mood associated with aging and neurological disorders.

Despite decades of research, it has been difficult to achieve consensus on a definition of common learning disabilities such as dyslexia. This lack of consensus represents a fundamental problem for the field. Our approach to addressing this issue is to use model-based meta-analyses and Bayesian models with informative priors to combine the results of a large number of studies for the purpose of yielding a more stable and well-supported conceptualization of reading disability. A prerequisite to implementing these models is establishing informative priors for dyslexia. We illustrate a new approach for doing so based on the known distribution of the difference between correlated variables, and use this distribution to determine the proportion of poor readers whose poor reading is unexpected (i.e., likely to be due to dyslexia) as opposed to expected.