Neurocognitive Processes: Measurement, Connections to Academic Achievement and Clinical Applications

Dear Colleagues,

Undoubtedly, intelligence is a significant correlate of academic achievement. Despite its acknowledged importance, there are a few issues related to intelligence that remain unresolved. First, some researchers have argued that the relationship between intelligence and academic achievement may have been confounded by the fact that popular intelligence batteries include measures of vocabulary and mathematics in the estimation of an IQ score, which are too close to the outcome measures they aim to predict, thus creating a vicious circle. To bypass this problem, Das, Naglieri, and colleagues (e.g., Das et al., 1994; Naglieri & Otero, 2017) proposed measuring intelligence in terms of neurocognitive processes like planning, attention, and simultaneous and successive processing (see the PASS theory of intelligence). Unfortunately, not many studies have examined the link between PASS processes and academic achievement in unselected samples, particularly in upper elementary grades. In addition, we do not know if academic achievement and neurocognitive processes are reciprocally related. A second issue that remains unclear is the influence of culture and race on these cognitive processes. Naglieri et al. (2005) have argued that the Cognitive Assessment System (the battery of tasks used to operationalize PASS processes) is culturally fair and allows us to measure students’ cognitive processes that are not confounded by their language skills. However, again, little work has been conducted in this area. Finally, the clinical applications of using CAS (particularly in identifying interesting profiles of students) remain understudied. Thus, the overall goal of this Special Issue is to shed light on the PASS theory of intelligence and its clinical applications.

References

Das, J. P., Naglieri, J. A., & Kirby, J. R. (1994). Assessment of Cognitive Processes: The PASS Theory of Intelligence. Allyn & Bacon.

Naglieri, J. A., & Otero, T. M. (2017). Essentials of CAS2 Assessment. Wiley.

Naglieri, J. A., Rojahn, J. R., Matto, H. C., & Aquilino, S. A. (2005). Black–white differences in intelligence: A study of the PASS theory and Cognitive Assessment System. Journal of Psychoeducational Assessment, 23, 146–160.

Prof. George K. Georgiou
Guest Editor

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• intelligence
• neurocognitive processes
• executive functions
• academic achievement
• brain-based intelligence testing
• discrepancy consistency model

Title: Putting Intelligence (not IQ) Back at the Service of Screening for Learning Disabilities: Using the Discrepancy Consistency Method with CAS-2: Brief
Authors: George K. Georgiou; Sergios C. Sergiou; Charalambos Y. Charalambous
Affiliation: University of Alberta
Abstract: The purpose of this study was to examine whether we could use the Discrepancy Consistency Method for the identification of children with specific learning disabilities (SLD) in Cyprus. Four hundred thirty-eight Grade 6 children (201 boys, 237 girls, Mage = 135.75 months, SD = 4.05 months) from Cyprus were assessed on Cognitive Assessment System-2: Brief that is used to operationalize four neurocognitive processes, namely Planning, Attention, Simultaneous and Successive (PASS) processing. They were also assessed on two measures of reading (Wordchains and CBM-Maze) and mathematics (Mathematics Achievement Test and Mathematics Reasoning Test). The results showed that 31.5% of our sample had a PASS disorder and 8% to 10% of our sample had both a PASS disorder and an academic disorder. These numbers are similar to those reported in previous studies that used DCM for the identification of SLD in North America and suggest that intelligence (operationalized in terms of neurocognitive processes) may still be relevant in the identification of SLD.

Title: Neurocognitive functioning and persistent arithmetic difficulties: Investigating how multidimensional models of domain-general and domain-specific skills assessed in Grade 2 longitudinally predict differential arithmetic performance at the end of primary education.
Authors: Valentín Iglesias-Sarmiento*; Leire Pérez*; Ángeles Conde*; Martina Ares*; Manuel Deaño*; Nuria Carriedo**
Affiliation: *University of Vigo; **National University of Education at Distance - UNED
Abstract: The most influential models in mathematical development (e.g., Cirino, 2011; Fuchs et al., 2010; LeFevre et al., 2010; Siegler & Braithwaite, 2017; von Aster & Shalev, 2007; Träff et al., 2020) are grounded on two fundamental postulates that require additional longitudinal empirical support: the hierarchical development of domain-specific skills (such as mathematical knowledge) and their integration with domain-general skills (such as intelligence, executive functions, or language skills, among others), to predict future arithmetical performance and difficulties. In this context, we conducted a longitudinal study with the aim of identifying, through Bayesian regression analyses, the best combination of longitudinal predictors for differential performance in calculation and arithmetic problem-solving. Specifically, we sought to determine which domain-general skills and which domain-specific skills assessed in Grade 2 remained predictive of the performance in calculation and problem solving of these same children four years later. Furthermore, we explored, through various logistic regression analyses, how the Bayesian models obtained in Grade 2 predict the likelihood of persistent difficulties in calculation and problem-solving in Grade 6. In pursuit of these objectives, a total of 117 Spanish students (70 girls) underwent assessments at different measurement times between Grades 2 and 6. Initially, during the first measurement time at the beginning of Grade 2, we assessed children in general domain skills using 12 neurocognitive tasks from the Cognitive Assessment System (CAS; Naglieri & Kirby, 1994; Spanish adaptation, Deaño, 2005) and 3 linguistic tasks from the Revised Battery for the Assessment of Reading Processes (PROLEC-R; Cuetos et al., 2014). Additionally, children were assesssed in 5 domain-specific tasks included in the Test for Diagnostic Assessment of Mathematical Disabilities (TEDI-MATH; Grégoire et al., 2001; Spanish adaptation, Sueiro & Pereña, 2005). At the end of Grade 2 (second measurement time), criterion variables were assessed using the calculation and arithmetic problem-solving tasks from the Evalúa-2 battery (García & González, 2002). Finally, at the end of Grade 6 (final measurement time), calculation and arithmetic problem-solving were evaluated using the Evalúa-6 battery (García & González, 2002). The results showed different combinations of predictors depending on the arithmetic task used as a criterion and the measurement time. For calculation, Bayesian models in Grade 2 included measures of resistance to distractor interference associated with the CAS attention scale (number detection), alphabetical knowledge (letter names), and verbal magnitude processing (oral number comparison). In Grade 6, Bayesian models included successive processing (sentence questions) and magnitude processing in its verbal (oral number comparison) and arabic notation (arabic number comparison), initially assessed in Grade 2. Importantly, various logistic regression analyses demonstrated the specificity of the models in classifying children with persistent difficulties in calculation between Grades 2 and 6. Specifically, successive processing and verbal magnitude processing were the variables that contributed significantly to discriminating between both performance groups. Regarding problem-solving, Bayesian models in Grade 2 included measures of resistance to distractor interference (number detection), maintenance of verbal information in working memory associated with CAS successive processing scale (sentence repetition), reading comprehension, and magnitude processing with arabic numbers (arabic number comparison). In Grade 6, the best combined Bayesian model included non-verbal intelligence associated with CAS simultaneous processing scale (non-verbal matrices), resistance to distractor interference (number detection), maintenance of verbal information in working memory (sentence repetition), and verbal magnitude processing. Moreover, logistic regression analyses showed that only the measures of resistance to distractor interference and verbal magnitude processing contributed to classifying children with persistent learning difficulties compared to their typically performing children in problem-solving between Grades 2 and 6. Specifically. Crucially, the classificatory ability of the model did not vary when the level-specific calculation performance was introduced in Grade 6. While further studies are necessary to confirm these findings, we conclude that the results of this longitudinal study suggest, foremost, the capability of multidimensional models evaluated in Grade 2 to longitudinally predict differential arithmetic performance at the end of primary education. Moreover, it appears that the changing demands of arithmetic tasks as education progresses involve shifts in the relationships between neurocognitive functioning and domain-specific skills.

Title: Improvement of math ability and cognitive processing in children with low attention: An intervention based on PASS theory
Authors: Cai Dan *; Yongjing Ge; Mingyue Wang; Ada W.S. Leung
Affiliation: Shanghai Normal University
Abstract: The study investigates the effects of math intervention among 8 to 9-year-old students with low attention ability. Fifty-six students with low attention ability were divided into 24 for the treatment and 32 for the control group. Pre-test and post-test assessments included math problem-solving, calculation fluency, and PASS cognitive processing tests for both the treatment and control participants. Children in the treatment intervention group received 3 days of intervention per week for a total of 21 days using The Children's Mathematics and Cognition Training Manual in Chinese (Das & Cai, 2017) based on the Math Modules (Das,2020; Das & Misra,2015). Results show that math intervention programs are successful in improving math problem-solving in children with low attention. Notably, the Math Module intervention enhanced the cognitive performance on CAS2 in planning, attention, and simultaneous processing tests. Finally, in conclusive remarks, we discuss the high and low roads to transfer, and Vygosky’s Zones of Proximal Development.