Individual differences in arithmetic skill reflected in event-related brain potentials

The arithmetic problem size effect in children: an event-related potential study

“…The late slow wave, i.e., late positivity component (LPC), which has been put forward as a brain signature of the problem size effect, was defined as having a mean amplitude value in the 500–675 ms range. This time window was chosen because it is the window where the LPC has been described in previous ERP research in adults (Niedeggen and Rösler, 1999; Núñez-Peña et al, 2005, 2006, 2011; Núñez-Peña and Escera, 2007; Núñez-Peña, 2008) and children (Prieto-Corona et al, 2010). We only analyzed the mean amplitude of the LPC, and not the peak amplitude or peak latency of the LPC because the LPC is a slow wave component without a clear starting point, peak, and ending point.…”

“…Numerous behavioral studies have reported this problem size effect in adults and children (see for a review Zbrodoff and Logan, 2005). The electrophysiological correlates of this problem size effect have been well documented in adults (Jost et al, 2004a,b; Núñez-Peña et al, 2005, 2006, 2011; Núñez-Peña, 2008), which makes this effect an excellent paradigm to investigate mental arithmetic. To the best of our knowledge, there are no studies that have examined this problem size effect in children.…”

“…These skills have been extensively investigated with behavioral methods but more recently the use of neural measures, such as functional magnetic resonance neuroimaging or fMRI (see Arsalidou and Taylor, 2011 for a review of adult studies; see Kaufmann et al, 2011 for a review of children studies) and electrophysiology (e.g., adults: Núñez-Peña et al, 2011; children: Zhou et al, 2011), has provided evidence on the neurobiological basis of arithmetic processing. These neurobiological insights are particularly relevant for understanding the origins of atypical mathematical development or dyscalculia (Butterworth et al, 2011), an approach that has already been successful in the domain of dyslexia (Gabrieli, 2009).…”

“…First, verification tasks with sequential presentation of the arithmetic problem and solution generate two phases related to calculation (Chen et al, 2013): the production phase (between the offset of equations and the onset of potential solutions) and the verification/comparison phase (between the onset of potential solutions and the participant’s response). Some studies investigated the production phase by studying brain activity time-locked to the offset of equations (Núñez-Peña et al, 2011), whereas others investigated the verification phase by studying the brain activity time-locked to the proposed solutions of the problem (Niedeggen et al, 1999; Jost et al, 2004b; Szucs and Csepe, 2005; Luo et al, 2009; Szucs and Soltesz, 2010). Secondly, a growing number of studies have showed that mismatch processing in verification tasks based on for example the plausibility (Jost et al, 2004b; Núñez-Peña and Escera, 2007) and parity, i.e., whether the solution to a problem should be even or odd (Krueger and Hallford, 1984; Vandorpe et al, 2005), of solutions affect participants’ judgments and, consequently, the ERP waves.…”

Separating stages of arithmetic verification: An ERP study with a novel paradigm

“…Arithmetic verification paradigms have been extensively used in event-related brain potential (ERP) research on mathematical cognition (Avancini et al, 2014;Galfano et al, 2004;Galfano et al, 2009;Jasinski and Coch, 2012;Jost et al, 2004;Núñez-Peña, 2008;Núñez-Peña et al, 2011;Csépe, 2004, 2005;Szűcs and Soltész, 2010). In these paradigms participants typically encounter two operands shown simultaneously or in succession and they carry out an operation (e.g.…”