Mode effect: an issue of perspective? Writing mode differences in a spelling assessment in German children with and without developmental dyslexia

Digital technology has an increasing influence on writing processes. In this context, the question arises whether changes in writing mode (i.e., handwriting vs. computer‐keyboard typing) also require changes in writing assessments. However, data directly comparing writing mode influences in children with and without developmental writing deficits are scarce. This study investigated the influence of writing mode in German‐speaking, typically developing children and children with developmental dyslexia (DD) from two different levels. Results showed on a general level that writing mode influenced overall spelling accuracy, writing time, and self‐corrections comparably in children with and without DD. On a rule‐specific level, outcomes for writing time and self‐corrections substantiated these findings. However, as regards spelling accuracy, a mode effect was only apparent for capitalization, whereas other spelling rules were resistant to writing mode influences. Present findings suggest that a mode effect is present only for typing specific aspects (e.g., capitalization) rather than reflecting a general influence on orthographic principles (e.g., grapheme–phoneme assignment, morphologic principles). These mode‐specific aspects seem to comparably affect the writing performance of typically developing children and children with DD. We recommend writing assessments to consider that different writing modes may influence individual spelling rules differently

Editorial: Neuro-cognitive architecture of numerical cognition and its development

No description supplie

Results for more difficult mental arithmetic (e.g., 54+38 = ?).

<p>Panel A reflects cortical regions showing increase of fMRI signal due to <i>increasing</i> values of the conjunction of unit sum as well as decade sum (uncorrected p-value of<.005, cluster size k = 10 voxels). Activated areas for magnitude-related processing include IPS, pIPS, BA 44, BA 45, BA 47, SMA, FEF, and the visual number form (VNF), most of them bilaterally. Panel B depicts identified pathways in a 3D volume rendering with PIBI values>0.0148. It can be observed that for more difficult mental arithmetic a system of both dorsal (SLF) and ventral (EC/emC) connections is recruited (depicted in red). Panel C gives a detailed view on the course of the fiber tracts in axial orientation (depicted in red), demonstrating a dorsal vs. ventral fiber pathway profile, encompassing the SLF system and the EC/EmC system. Abbreviations: BA = Brodmann area; EC = external capsule; EmC = extreme capsule; FEF = frontal eye fields; IPS = intraparietal sulcus; pIPS = posterior intraparietal sulcus; SLF = superior longitudinal fascicle; SMA = supplementary motor area.</p

S1 Appendix -

(DOCX)</p

Seed regions and connections included. Seed regions were centered at the MNI coordinates given in parentheses.

<p>AG: angular gyrus; BA: Brodmann Area; FEF: frontal eye fields; IFG: inferior frontal gyrus; IPS: intraparietal sulcus; LH: left hemisphere; medial FG: medial frontal gyrus; MTG: middle temporal gyrus; pIPS: posterior intraparietal sulcus; PSPL: posterior superior parietal lobule; SMA: supplementary motor area; SMG: supramarginal gyrus; STG; superior temporal gyrus; RC: retrosplenial cortex; RH: right hemisphere; VNF: visual number form area.</p>*<p>Seed points proposed but not exactly specified by the TCM are given in italics. Second, the seed points not proposed by the TCM but nevertheless theoretically motivated are given in italics and marked by an asterisk.</p>**<p>Please note that the fiber pathways involved travel from the FEF first via the LH IPS before arching in association fibres to the pIPS (thereby making the connection between FEF and pIPS almost identical to the connection between FEF and IPS).</p><p>Number of individual tracts accounting for mean tract:</p><p>Evaluation of how consistently the mean-courses of the tracts are reflected by the individual tracts of the participants. This way, it is possible to indicate, in how many participants their individual tracts are more or less identical to the mean tracts. However, for those participants whose individual tracts does not correspond to the mean course of the respective tract, it is important to note that the respective PIBI value can nevertheless be <i>large</i> (simply reflecting that the algorithm can tell for sure that the respective tract passes via a different pathway in these participants).</p><p>Nevertheless, this means that the mean tracts identified by our probabilistic fibertracking accounted for at least 28 out of 33 individual tracts.</p

Correlations between fraction processing, basic numerical skills, general cognitive ability, as well as sex and grade level.

Correlations between fraction processing, basic numerical skills, general cognitive ability, as well as sex and grade level.</p

Descriptive statistics and obtained range of all basic numerical measures (N = 939).

Descriptive statistics and obtained range of all basic numerical measures (N = 939).</p

Multiple regression and relative weight results.

Multiple regression and relative weight results.</p

Cortical regions activated significantly more due to either the conjunction of increasing or decreasing unit sum and decade sum.

<p>p<.005, uncorrected; cluster size = 10 voxels; MNI: Montreal Neurological Institute coordinates.</p

Hypothetical model capturing the interplay between magnitude-related processing and arithmetic fact retrieval.

<p>The figure depicts an interaction model: Basic fact retrieval related processing is assumed to be involved even in difficult arithmetic, for instance to retrieve partial results from long term memory. However, in easier tasks a larger variable component of fact retrieval may be recruited, whereas in more difficult tasks more and more magnitude-related processing is assumed.</p