2023 |
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Julia Ericson, Torkel Klingberg A dual-process model for cognitive training Journal Article Science of Learning, 8 (1), pp. 12, 2023. @article{Ericson2023, title = {A dual-process model for cognitive training}, author = {Julia Ericson, Torkel Klingberg}, url = {https://www.nature.com/articles/s41539-023-00161-2}, doi = {https://doi.org/10.1038/s41539-023-00161-2}, year = {2023}, date = {2023-05-06}, journal = {Science of Learning}, volume = {8}, number = {1}, pages = {12}, abstract = {A key goal in cognitive training research is understanding whether cognitive training enhances general cognitive capacity or provides only task-specific improvements. Here, we developed a quantitative model for describing the temporal dynamics of these two processes. We analyzed data from 1300 children enrolled in an 8 week working memory training program that included 5 transfer test sessions. Factor analyses suggested two separate processes: an early task-specific improvement, accounting for 44% of the total increase, and a slower capacity improvement. A hidden Markov model was then applied to individual training data, revealing that the task-specific improvement plateaued on the third day of training on average. Thus, training is not only task specific or transferable but a combination of the two. The models provide methods for quantifying and separating these processes, which is crucial for studying the effects of cognitive training and relating these effects to neural correlates.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A key goal in cognitive training research is understanding whether cognitive training enhances general cognitive capacity or provides only task-specific improvements. Here, we developed a quantitative model for describing the temporal dynamics of these two processes. We analyzed data from 1300 children enrolled in an 8 week working memory training program that included 5 transfer test sessions. Factor analyses suggested two separate processes: an early task-specific improvement, accounting for 44% of the total increase, and a slower capacity improvement. A hidden Markov model was then applied to individual training data, revealing that the task-specific improvement plateaued on the third day of training on average. Thus, training is not only task specific or transferable but a combination of the two. The models provide methods for quantifying and separating these processes, which is crucial for studying the effects of cognitive training and relating these effects to neural correlates. | |
2022 |
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Richard Scruggs, Jalal Nouri, Torkel Klingberg Using Knowledge Tracing to Predict Students’ Performance in Cognitive Training and Math Proceeding Springer International Publishing, 2022. @proceedings{Scruggs2022, title = {Using Knowledge Tracing to Predict Students’ Performance in Cognitive Training and Math}, author = {Richard Scruggs, Jalal Nouri, Torkel Klingberg}, url = {https://link.springer.com/chapter/10.1007/978-3-031-11647-6_81}, doi = {https://doi.org/10.1007/978-3-031-11647-6_81}, year = {2022}, date = {2022-07-06}, booktitle = {Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium: 23rd International Conference}, pages = {410-413}, publisher = {Springer International Publishing}, abstract = {Cognitive training aims to improve skills such as working memory capacity and spatial ability, which have been linked to math skills. In this study, we fit Deep Knowledge Tracing with Transformers (DKTT), Dynamic Key-Value Memory Networks (DKVMN), and Knowledge Tracing Machines (KTM) to a large dataset from a cognitive training system. DKVMN achieved the highest AUC (0.739) of the algorithms. To explore connections between math skills and cognitive skills, the data was split into cognitive and math items. DKVMN’s AUC on the math items was higher (0.745) than on the cognitive (0.706). Notably, the split model AUCs did not differ from skill-level AUCs produced by a model trained on the entire dataset, suggesting that math performance did not improve DKVMN’s cognitive predictions and vice versa.}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Cognitive training aims to improve skills such as working memory capacity and spatial ability, which have been linked to math skills. In this study, we fit Deep Knowledge Tracing with Transformers (DKTT), Dynamic Key-Value Memory Networks (DKVMN), and Knowledge Tracing Machines (KTM) to a large dataset from a cognitive training system. DKVMN achieved the highest AUC (0.739) of the algorithms. To explore connections between math skills and cognitive skills, the data was split into cognitive and math items. DKVMN’s AUC on the math items was higher (0.745) than on the cognitive (0.706). Notably, the split model AUCs did not differ from skill-level AUCs produced by a model trained on the entire dataset, suggesting that math performance did not improve DKVMN’s cognitive predictions and vice versa. | |
2021 |
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Nicholas Judd, Torkel Klingberg, Douglas Sjöwall Working memory capacity, variability, and response to intervention at age 6 and its association to inattention and mathematics age 9 Journal Article Cognitive Development, 58 (12), pp. 101013, 2021. @article{Judd2021, title = {Working memory capacity, variability, and response to intervention at age 6 and its association to inattention and mathematics age 9}, author = {Nicholas Judd, Torkel Klingberg, Douglas Sjöwall}, url = {https://www.sciencedirect.com/science/article/pii/S0885201421000083}, doi = {https://doi.org/10.1016/j.cogdev.2021.101013}, year = {2021}, date = {2021-04-01}, journal = {Cognitive Development}, volume = {58}, number = {12}, pages = {101013}, abstract = {Classically, neuropsychological evaluation only estimates an individual’s performance at one time point. For example, working memory (WM) capacity is commonly determined in a single test session. However, recent research in WM plasticity and variability has suggested performance over several sessions/days might aid in evaluating children. Here, we explored four temporal properties of WM: WM measured once, as a mean over three days (multiple-session-baseline performance), variability over 8 weeks, and performance improvement over an 8-week WM training program. To examine independence we controlled for a single-session, multiple task WM assessment while predicting inattention and mathematics three years later (n = 178, mean age 80 months at training, 49 % boys). Our results showed improved prediction for mathematics from WM training improvement and variability, yet this was not the case for inattention. While the additional variance added was not substantial, our results indicate clinically relevant information present in these alternative WM measures.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Classically, neuropsychological evaluation only estimates an individual’s performance at one time point. For example, working memory (WM) capacity is commonly determined in a single test session. However, recent research in WM plasticity and variability has suggested performance over several sessions/days might aid in evaluating children. Here, we explored four temporal properties of WM: WM measured once, as a mean over three days (multiple-session-baseline performance), variability over 8 weeks, and performance improvement over an 8-week WM training program. To examine independence we controlled for a single-session, multiple task WM assessment while predicting inattention and mathematics three years later (n = 178, mean age 80 months at training, 49 % boys). Our results showed improved prediction for mathematics from WM training improvement and variability, yet this was not the case for inattention. While the additional variance added was not substantial, our results indicate clinically relevant information present in these alternative WM measures. | |
2020 |
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Carlos Guillermo Bozzoli, Maria Luz Gonzalez-Gadea, Maria Julia Hermida, Lucía Navarro, Tomás Olego, Torkel Klingberg Digital, mathematical and cognitive training: Evidence from a randomized trial Journal Article PsyArXiv, 2020. @article{Bozzoli2020b, title = {Digital, mathematical and cognitive training: Evidence from a randomized trial}, author = {Carlos Guillermo Bozzoli, Maria Luz Gonzalez-Gadea, Maria Julia Hermida, Lucía Navarro, Tomás Olego, Torkel Klingberg}, url = {https://psyarxiv.com/24ej7/}, doi = {10.31234/osf.io/24ej7}, year = {2020}, date = {2020-11-02}, journal = {PsyArXiv}, abstract = {In this paper, we experimentally evaluate a cognitive training tool that aims to improve children’s mathematical ability through technology in rural primary schools in Argentina. We conducted a large cluster-randomized trial: schools in the treatment group used an app to train mathematical skills, while schools in the control group received a literacy book. We tested the math skills of 1,304 children in the 2nd through 6th grades from 80 rural schools and applied three cognitive tests: digit-span (working memory), face-perception (attention to objects), and block design (visuospatial reasoning), directly before and after the 10-week intervention period. In schools that received the treatment, we found no improvement in the digit-span or face-perception tests, but significant and positive effects in visuospatial reasoning and mathematical abilities. The improvement among students from treatment schools was 54 percentage points higher in math skills and 42 percentage points higher in visuospatial abilities than the gains by students in control schools. This study suggests this intervention is a feasible and effective way of enhancing the mathematical and cognitive abilities of children in rural areas.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this paper, we experimentally evaluate a cognitive training tool that aims to improve children’s mathematical ability through technology in rural primary schools in Argentina. We conducted a large cluster-randomized trial: schools in the treatment group used an app to train mathematical skills, while schools in the control group received a literacy book. We tested the math skills of 1,304 children in the 2nd through 6th grades from 80 rural schools and applied three cognitive tests: digit-span (working memory), face-perception (attention to objects), and block design (visuospatial reasoning), directly before and after the 10-week intervention period. In schools that received the treatment, we found no improvement in the digit-span or face-perception tests, but significant and positive effects in visuospatial reasoning and mathematical abilities. The improvement among students from treatment schools was 54 percentage points higher in math skills and 42 percentage points higher in visuospatial abilities than the gains by students in control schools. This study suggests this intervention is a feasible and effective way of enhancing the mathematical and cognitive abilities of children in rural areas. | |
2016 |
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Margot A Schel, Torkel Klingberg Specialization of the Right Intraparietal Sulcus for Processing Mathematics During Development Journal Article Cerebral Cortex, 27 (9), pp. 4436–4446, 2016, ISSN: 1047-3211. @article{Schel2017, title = {Specialization of the Right Intraparietal Sulcus for Processing Mathematics During Development}, author = {Margot A Schel and Torkel Klingberg}, url = {http://cercor.oxfordjournals.org/cgi/doi/10.1093/cercor/bhw246}, doi = {10.1093/cercor/bhw246}, issn = {1047-3211}, year = {2016}, date = {2016-08-01}, journal = {Cerebral Cortex}, volume = {27}, number = {9}, pages = {4436--4446}, abstract = {Mathematical ability, especially perception of numbers and performance of arithmetics, is known to rely on the activation of intraparietal sulcus (IPS). However, reasoning ability and working memory, 2 highly associated abilities also activate partly overlapping regions. Most studies aimed at localizing mathematical function have used group averages, where individual variability is averaged out, thus confounding the anatomical specificity when localizing cognitive functions. Here, we analyze the functional anatomy of the intraparietal cortex by using individual analysis of subregions of IPS based on how they are structurally connected to frontal, parietal, and occipital cortex. Analysis of cortical thickness showed that the right anterior IPS, defined by its connections to the frontal lobe, was associated with both visuospatial working memory, and mathematics in 6-year-old children. This region specialized during development to be specifically related to mathematics, but not visuospatial working memory in adolescents and adults. This could be an example of interactive specialization, where interacting with the environment in combination with interactions between cortical regions leads from a more general role of right anterior IPS in spatial processing, to a specialization of this region for mathematics.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Mathematical ability, especially perception of numbers and performance of arithmetics, is known to rely on the activation of intraparietal sulcus (IPS). However, reasoning ability and working memory, 2 highly associated abilities also activate partly overlapping regions. Most studies aimed at localizing mathematical function have used group averages, where individual variability is averaged out, thus confounding the anatomical specificity when localizing cognitive functions. Here, we analyze the functional anatomy of the intraparietal cortex by using individual analysis of subregions of IPS based on how they are structurally connected to frontal, parietal, and occipital cortex. Analysis of cortical thickness showed that the right anterior IPS, defined by its connections to the frontal lobe, was associated with both visuospatial working memory, and mathematics in 6-year-old children. This region specialized during development to be specifically related to mathematics, but not visuospatial working memory in adolescents and adults. This could be an example of interactive specialization, where interacting with the environment in combination with interactions between cortical regions leads from a more general role of right anterior IPS in spatial processing, to a specialization of this region for mathematics. | |
2015 |
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Henrik Ullman, Megan Spencer-Smith, Deanne K Thompson, Lex W Doyle, Terrie E Inder, Peter J Anderson, Torkel Klingberg Neonatal MRI is associated with future cognition and academic achievement in preterm children Journal Article Brain, 138 (11), pp. 3251–3262, 2015, ISSN: 14602156. @article{Ullman2015, title = {Neonatal MRI is associated with future cognition and academic achievement in preterm children}, author = {Henrik Ullman and Megan Spencer-Smith and Deanne K Thompson and Lex W Doyle and Terrie E Inder and Peter J Anderson and Torkel Klingberg}, doi = {10.1093/brain/awv244}, issn = {14602156}, year = {2015}, date = {2015-01-01}, journal = {Brain}, volume = {138}, number = {11}, pages = {3251--3262}, abstract = {textcopyright 2015 The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com. School-age children born preterm are particularly at risk for low mathematical achievement, associated with reduced working memory and number skills. Early identification of preterm children at risk for future impairments using brain markers might assist in referral for early intervention. This study aimed to examine the use of neonatal magnetic resonance imaging measures derived from automated methods (Jacobian maps from deformation-based morphometry; fractional anisotropy maps from diffusion tensor images) to predict skills important for mathematical achievement (working memory, early mathematical skills) at 5 and 7 years in a cohort of preterm children using both univariable (general linear model) and multivariable models (support vector regression). Participants were preterm children born textless30 weeks' gestational age and healthy control children born ≥37 weeks' gestational age at the Royal Women's Hospital in Melbourne, Australia between July 2001 and December 2003 and recruited into a prospective longitudinal cohort study. At term-equivalent age (±2 weeks) 224 preterm and 46 control infants were recruited for magnetic resonance imaging. Working memory and early mathematics skills were assessed at 5 years (n = 195 preterm; n = 40 controls) and 7 years (n = 197 preterm; n = 43 controls). In the preterm group, results identified localized regions around the insula and putamen in the neonatal Jacobian map that were positively associated with early mathematics at 5 and 7 years (both P textless 0.05), even after covarying for important perinatal clinical factors using general linear model but not support vector regression. The neonatal Jacobian map showed the same trend for association with working memory at 7 years (models ranging from P = 0.07 to P = 0.05). Neonatal fractional anisotropy was positively associated with working memory and early mathematics at 5 years (both P textless 0.001) even after covarying for clinical factors using support vector regression but not general linear model. These significant relationships were not observed in the control group. In summary, we identified, in the preterm brain, regions around the insula and putamen using neonatal deformation-based morphometry, and brain microstructural organization using neonatal diffusion tensor imaging, associated with skills important for childhood mathematical achievement. Results contribute to the growing evidence for the clinical utility of neonatal magnetic resonance imaging for early identification of preterm infants at risk for childhood cognitive and academic impairment.}, keywords = {}, pubstate = {published}, tppubtype = {article} } textcopyright 2015 The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com. School-age children born preterm are particularly at risk for low mathematical achievement, associated with reduced working memory and number skills. Early identification of preterm children at risk for future impairments using brain markers might assist in referral for early intervention. This study aimed to examine the use of neonatal magnetic resonance imaging measures derived from automated methods (Jacobian maps from deformation-based morphometry; fractional anisotropy maps from diffusion tensor images) to predict skills important for mathematical achievement (working memory, early mathematical skills) at 5 and 7 years in a cohort of preterm children using both univariable (general linear model) and multivariable models (support vector regression). Participants were preterm children born textless30 weeks' gestational age and healthy control children born ≥37 weeks' gestational age at the Royal Women's Hospital in Melbourne, Australia between July 2001 and December 2003 and recruited into a prospective longitudinal cohort study. At term-equivalent age (±2 weeks) 224 preterm and 46 control infants were recruited for magnetic resonance imaging. Working memory and early mathematics skills were assessed at 5 years (n = 195 preterm; n = 40 controls) and 7 years (n = 197 preterm; n = 43 controls). In the preterm group, results identified localized regions around the insula and putamen in the neonatal Jacobian map that were positively associated with early mathematics at 5 and 7 years (both P textless 0.05), even after covarying for important perinatal clinical factors using general linear model but not support vector regression. The neonatal Jacobian map showed the same trend for association with working memory at 7 years (models ranging from P = 0.07 to P = 0.05). Neonatal fractional anisotropy was positively associated with working memory and early mathematics at 5 years (both P textless 0.001) even after covarying for clinical factors using support vector regression but not general linear model. These significant relationships were not observed in the control group. In summary, we identified, in the preterm brain, regions around the insula and putamen using neonatal deformation-based morphometry, and brain microstructural organization using neonatal diffusion tensor imaging, associated with skills important for childhood mathematical achievement. Results contribute to the growing evidence for the clinical utility of neonatal magnetic resonance imaging for early identification of preterm infants at risk for childhood cognitive and academic impairment. | |
2012 |
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Iroise Dumontheil, Torkel Klingberg Brain activity during a visuospatial working memory task predicts arithmetical performance 2 years later Journal Article Cerebral Cortex, 22 (5), pp. 1078–1085, 2012, ISSN: 10473211. @article{Dumontheil2012, title = {Brain activity during a visuospatial working memory task predicts arithmetical performance 2 years later}, author = {Iroise Dumontheil and Torkel Klingberg}, doi = {10.1093/cercor/bhr175}, issn = {10473211}, year = {2012}, date = {2012-01-01}, journal = {Cerebral Cortex}, volume = {22}, number = {5}, pages = {1078--1085}, abstract = {Visuospatial working memory (WM) capacity is highly correlated with mathematical reasoning abilities and can predict future development of arithmetical performance. Activity in the intraparietal sulcus (IPS) during visuospatial WM tasks correlates with interindividual differences in WM capacity. This region has also been implicated in numerical representation, and its structure and activity reflect arithmetical performance impairments (e.g., dyscalculia). We collected behavioral (N = 246) and neuroimaging data (N = 46) in a longitudinal sample to test whether IPS activity during a visuospatial WM task could provide more information than psychological testing alone and predict arithmetical performance 2 years later in healthy participants aged 6-16 years. Nonverbal reasoning and verbal and visuospatial WM measures were found to be independent predictors of arithmetical outcome. In addition, WM activation in the left IPS predicted arithmetical outcome independently of behavioral measures. A logistic model including both behavioral and imaging data showed improved sensitivity by correctly classifying more than twice as many children as poor arithmetical performers after 2 years than a model with behavioral measures only. These results demonstrate that neuroimaging data can provide useful information in addition to behavioral assessments and be used to improve the identification of individuals at risk of future low academic performance.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Visuospatial working memory (WM) capacity is highly correlated with mathematical reasoning abilities and can predict future development of arithmetical performance. Activity in the intraparietal sulcus (IPS) during visuospatial WM tasks correlates with interindividual differences in WM capacity. This region has also been implicated in numerical representation, and its structure and activity reflect arithmetical performance impairments (e.g., dyscalculia). We collected behavioral (N = 246) and neuroimaging data (N = 46) in a longitudinal sample to test whether IPS activity during a visuospatial WM task could provide more information than psychological testing alone and predict arithmetical performance 2 years later in healthy participants aged 6-16 years. Nonverbal reasoning and verbal and visuospatial WM measures were found to be independent predictors of arithmetical outcome. In addition, WM activation in the left IPS predicted arithmetical outcome independently of behavioral measures. A logistic model including both behavioral and imaging data showed improved sensitivity by correctly classifying more than twice as many children as poor arithmetical performers after 2 years than a model with behavioral measures only. These results demonstrate that neuroimaging data can provide useful information in addition to behavioral assessments and be used to improve the identification of individuals at risk of future low academic performance. | |
