Does Rare Error Count in Impulsivity? Difference in Error-Negativity

High impulsive individuals have problems with self - monitoring and learning from their mistakes. The aim of this study was to investigate whether error processing is impaired in high trait impulsivity, and how it is modulated by the task difficulty. Adults were classified as high ( n = 10) and low ( n = 10) impulsive participants based on the Barratt Impulsiveness Scale, and they participated in a modified flanker task. The flanker trials had three levels of task difficulty manipulated by visual degradation of the stimuli. We measured RTs and ERP components (Ne, Pe) related to erroneous responses. L ow impulsive participants responded significantly faster tha n high impulsive participants . The two groups did not differ in accuracy. The Ne amplitude was smaller in high than in low impulsivity in case of medium and high difficulty levels , but not at low difficulty level . However, the groups did not differ either in the amplitude or in the latency of Pe. We suggest that trait impulsivity is characterized by impaired error detection

Statistical learning leads to persistent memory: evidence for one-year consolidation

Statistical learning is a robust mechanism of the brain that enables the extraction of environmental patterns, which is crucial in perceptual and cognitive domains. However, the dynamical change of processes underlying long-term statistical memory formation has not been tested in an appropriately controlled design. Here we show that a memory trace acquired by statistical learning is resistant to inference as well as to forgetting after one year. Participants performed a statistical learning task and were retested one year later without further practice. The acquired statistical knowledge was resistant to interference, since after one year, participants showed similar memory performance on the previously practiced statistical structure after being tested with a new statistical structure. These results could be key to understand the stability of long-term statistical knowledge

Genome-wide Association Scan Identifies New Variants Associated with a Cognitive Predictor of Dyslexia

Developmental dyslexia (DD) is one of the most prevalent learning disorders, with high impact on school and psychosocial development and high comorbidity with conditions like attention-deficit hyperactivity disorder (ADHD), depression, and anxiety. DD is characterized by deficits in different cognitive skills, including word reading, spelling, rapid naming, and phonology. To investigate the genetic basis of DD, we conducted a genome-wide association study (GWAS) of these skills within one of the largest studies available, including nine cohorts of reading-impaired and typically developing children of European ancestry (N = 2562–3468). We observed a genome-wide significant effect (p \u3c 1 × 10−8) on rapid automatized naming of letters (RANlet) for variants on 18q12.2, within MIR924HG (micro-RNA 924 host gene; rs17663182 p = 4.73 × 10−9), and a suggestive association on 8q12.3 within NKAIN3 (encoding a cation transporter; rs16928927, p = 2.25 × 10−8). rs17663182 (18q12.2) also showed genome-wide significant multivariate associations with RAN measures (p = 1.15 × 10−8) and with all the cognitive traits tested (p = 3.07 × 10−8), suggesting (relational) pleiotropic effects of this variant. A polygenic risk score (PRS) analysis revealed significant genetic overlaps of some of the DD-related traits with educational attainment (EDUyears) and ADHD. Reading and spelling abilities were positively associated with EDUyears (p ~ [10−5–10−7]) and negatively associated with ADHD PRS (p ~ [10−8−10−17]). This corroborates a long-standing hypothesis on the partly shared genetic etiology of DD and ADHD, at the genome-wide level. Our findings suggest new candidate DD susceptibility genes and provide new insights into the genetics of dyslexia and its comorbities

Genetic analysis of dyslexia candidate genes in the European cross-linguistic NeuroDys cohort

Dyslexia is one of the most common childhood disorders with a prevalence of around 5-10% in school-age children. Although an important genetic component is known to have a role in the aetiology of dyslexia, we are far from understanding the molecular mechanisms leading to the disorder. Several candidate genes have been implicated in dyslexia, including DYX1C1, DCDC2, KIAA0319, and the MRPL19/C2ORF3 locus, each with reports of both positive and no replications. We generated a European cross-linguistic sample of school-age children - the NeuroDys cohort - that includes more than 900 individuals with dyslexia, sampled with homogenous inclusion criteria across eight European countries, and a comparable number of controls. Here, we describe association analysis of the dyslexia candidate genes/locus in the NeuroDys cohort. We performed both case-control and quantitative association analyses of single markers and haplotypes previously reported to be dyslexia-associated. Although we observed association signals in samples from single countries, we did not find any marker or haplotype that was significantly associated with either case-control status or quantitative measurements of word-reading or spelling in the meta-analysis of all eight countries combined. Like in other neurocognitive disorders, our findings underline the need for larger sample sizes to validate possibly weak genetic effects