Biology subjects

Csepe, V.

Publications and source records attributed to Csepe, V..

3 recordsLinked to original sources

bioRxiv2018

Word stress representations are language specific: evidence from event-related brain potentials

Understanding speech at the basic levels entails the simultaneous and independent processing of phonemic and prosodic features. While it is well-established that phoneme perception relies on language-specific long-term traces, it is unclear if the processing of prosodic features similarly involves language-specific representations. In the present study, we investigated the processing of a specific prosodic feature, word stress, using the method of event-related brain potentials (ERPs) employing a cross-linguistic approach. Hungarian participants heard disyllabic pseudowords stressed either on the first (legal stress) or on the second (illegal stress) syllable, pronounced either by a Hungarian or a German speaker. Results obtained using a data-driven ERP analysis methodology showed that all pseudowords in the deviant position elicited an Early Differentiating Negativity (EDN) and a Mismatch Negativity (MMN) component, except for the Hungarian pseudowords stressed on the first syllable. This suggests that Hungarian listeners did not process the native legal stress pattern as deviant, but the same stress pattern with a non-native accent was processed as deviant. This implies that the processing of word stress was based on language-specific long-term memory traces.

neuroscience↗

bioRxiv2018

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 among children and is characterized by deficits in different cognitive skills, including reading, spelling, short term memory and others. To help unravel the genetic basis of these skills, we conducted a Genome Wide Association Study (GWAS), including nine cohorts of reading-impaired and typically developing children of European ancestry, recruited across different countries (N=2,562-3,468).\n\nWe observed a genome-wide significant effect (p<1x10-8) on rapid automatized naming of letters (RANlet) for variants on 18q12.2 within MIR924HG (micro-RNA 924 host gene; p = 4.73x10-9), and a suggestive association on 8q12.3 within NKAIN3 (encoding a cation transporter; p = 2.25 x10-8). RAN represents one of the best universal predictors of reading fluency across orthographies and linkage to RAN has been previously reported within CELF4 (18q12.2), a gene highly expressed in the fetal brain which is co-expressed with NKAIN3 and predicted to be a target of MIR924. These findings suggest new candidate DD susceptibility genes and provide insights into the genetics and neurobiology of dyslexia.

genomics↗

bioRxiv2017

Sensitivity to sequential regularities in risky decision making

Probabilistic sequence learning involves a set of robust mechanisms that enable the extraction of statistical patterns embedded in the environment. It contributes to different perceptual and cognitive processes as well as to effective behavior adaptation, which is a crucial aspect of decision making. Although previous research attempted to model reinforcement learning and reward sensitivity in different risky decision-making paradigms, the basic mechanism of the sensitivity to statistical regularities has not been anchored to external tasks. Therefore, the present study aimed to investigate the statistical learning mechanism underlying individual differences in risky decision making. To reach this goal, we tested whether implicit probabilistic sequence learning and risky decision making share common variance. To have a more complex characterization of individual differences in risky decision making, hierarchical cluster analysis was conducted on performance data obtained in the Balloon Analogue Risk Task (BART) in a large sample of healthy young adults. Implicit probabilistic sequence learning was measured by the Alternating Serial Reaction Time (ASRT) task. According to the results, a four-cluster structure was identified involving average risk-taking, slowly responding, risk-taker, and risk-averse groups of participants, respectively. While the entire sample showed significant learning on the ASRT task, we found greater sensitivity to statistical regularities in the risk-taker and risk-averse groups than in participants with average risk-taking. These findings revealed common mechanisms in risky decision making and implicit probabilistic sequence learning and an adaptive aspect of higher risk taking on the BART. Our results could help to clarify the neurocognitive complexity of decision making and its individual differences.

neuroscience↗