## 4 Data Analysis  
The developed platform intends to control for several components (stimuli, modality of presentation stimuli, visual-verbal presentation of input and output, and activation processes such as recall and recognition) that are likely to be involved at various levels in reading, writing, and calculation processes. In other words, we assume that different, identifiable patterns of short-term memory (verbal, visual, item or order STM, etc.) underlie specific learning disabilities.  
First, a hierarchical cluster analysis was applied by adopting a proximity-based approach [73]. The hierarchical cluster analysis was performed using SPSS statistical software, version 29 (see [74]). and was based on a set of scores expressing achievement in the various domains of academic skills, encompassing reading speed (text, word and nonword reading), reading accuracy (text, word and nonword reading), writing/spelling (word, nonword and sentence writing accuracy), and mathematical abilities (number reading accuracy and speed, number writing accuracy, counting speed and accuracy, addition and subtraction accuracy, multiplication accuracy), all expressed as Z-scores according to age norms. The number of clusters was set to range between two and seven.  
Subsequently, we took the STM variables, which had been pre-processed (through imputation of post-interruption lists and Logit transformation to normalize their distributions, see Appendix A for details), and averaged them in order to obtain eight _overall STM variables_ (MO_VV, MI_VV, MO_AV, MI_AV, MO_VO, MI_VO, MO_AO, MI_AO). These eight variables resulted from the combination of type (item vs. order), input (visual--V vs. auditory--A) and output (oral--O vs. visual-motor--V), averaging further distinctions such as the type of stimulus (letters--Ls vs. colours--Cs) articulatory suppression (AS) (with vs. without), presentation (sequential--SEQ vs. simultaneous--SIM), and distractors--Ds (presence vs. absence). Then, the identified clusters were compared to the overall variables by means of a multivariate ANOVA with group as the between-subject factor and age as a covariate. Bonferroni correction was applied in this case, setting alpha at 0.05/8 = 0.006. For each of the overall variables that turned out to significantly differ among clusters, a repeated-measures analysis of variance (GLM) was conducted, setting all the variables that were distinctively assessed for the overall variable (e.g., sequential vs. simultaneous presentation, presence vs. absence of distractors, etc.) as within-subjects factors, and cluster as the between-subjects factor. Post hoc tests assessing differences between specific pairs of clusters were performed by applying [PERSON]'s correction. Finally, for each factor that turned out to significantly interact with the cluster variable, a further post hoc analysis was performed comparing the different levels of the variable that was significantly different among clusters. For this purpose, the other within-subject factorswere averaged each time in a single compound variable (e.g., in order to compare sequential vs. simultaneous presentation for the MO_VV variable, letters and colours, as well as with/without AS, with/without distractors were averaged into two main variables expressing all instances of sequential and simultaneous presentation).  
It should be highlighted that STM for items was computed considering only the trials including distractors (when no distractors were present, all--and only--relevant information was available at the moment of responding; therefore, errors were very unlikely to occur when identifying presented items, although they could occur when identifying their order). All the other variables (simultaneous vs. sequential presentation, presence vs. absence of articulatory suppression) were collapsed and averaged.