There are several tests used in clinical practice and research worldwide that have been devised to assess the functions subsumed by the frontal lobes of the brain. Anatomical localisation has revealed that the frontal lobes can be divided into sub-regions with different functional domains. As a result, a number of authors working in the frontal lobe literature have made a case for patients with frontal lobe damage to be considered in their distinct subgroups, rather than considered together in one unitary group. As a result, it is important for clinicians and researchers to be made aware of the functions assessed by individual frontal tests and understand which frontal regions might be impaired in their patient groups, as patients with damage to one of these regions will perform poorly on tasks tapping that region yet may perform well on tasks tapping the unaffected regions within the frontal lobes. The 'Handbook of Frontal Lobe Assessment' provides a critical review and appraisal of both the neuropsychological and experimental tests that have been devised to assess frontal lobe functions. It includes many tests that have not been included in previously published neuropsychological compendia. Throughout, the book discusses the available frontal tests in relation to patient and lesion data, neuroimaging data and aging data in order to offer clinicians and researchers the opportunity to choose the best assessment instrument for their purpose.

Educational attainment (EA) is influenced by characteristics other than cognitive ability, but little is known about the genetic architecture of these "non-cognitive" contributions to EA. Here, we use Genomic Structural Equation Modelling and prior genome-wide association studies (GWASs) of EA (N = 1,131,881) and cognitive test performance (N = 257,841) to estimate SNP associations with EA variation that is independent of cognitive ability. We identified 157 genome-wide significant loci and a polygenic architecture accounting for 57% of genetic variance in EA. Non-cognitive genetics were as strongly related to socioeconomic success and longevity as genetic variants associated with cognitive performance. Noncognitive genetics were further related to openness to experience and other personality traits, less risky behavior, and increased risk for psychiatric disorders. Non-cognitive genetics were enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with gray-matter brain volumes. By conducting a GWAS of a phenotype that was not directly measured, we offer a first view of genetic architecture of non-cognitive skills influencing educational success.