Abstract

Executive functions are a class of cognitive processes critical for purposeful goal-directed behavior. Cognitive training is the adequate stimulation of executive functions and has been extensively studied and applied for more than 20 years. However, there is still a lack of solid consensus in the scientific community about its potential to elicit consistent improvements in untrained domains. Individual differences are considered one of the most important factors of inconsistent reports on cognitive training benefits, as differences in cognitive functioning are both genetic and context-dependent, and might be affected by age and socioeconomic status. We here present a proof of concept based on the hypothesis that baseline individual differences among subjects would provide valuable information to predict the individual effectiveness of a cognitive training intervention. With a dataset from an investigation in which 73 6-year-olds trained their executive functions using an online software with a fixed protocol, freely available at www.matemarote.org.ar, we trained a support vector classifier that successfully predicted (average accuracy = 0.67, AUC = 0.707) whether a child would improve, or not, after the cognitive stimulation, using baseline individual differences as features. We also performed a permutation feature importance analysis that suggested that all features contribute equally to the model's performance. In the long term, this results might allow us to design better training strategies for those players who are less likely to benefit from the current training protocols in order to maximize the stimulation for each child.

Highlights

  • If you encounter an add claiming “Do you want to improve your memory? With these brain exercises you will see changes in less than x time! Scientifically tested method!”, what would you think? to date, there is still not a well described and thoroughly tested method that consistently improves cognitive processes (Dorbath et al, 2011; Au et al, 2014; Buttelmann and Karbach, 2017)

  • Following previous studies which show that some people benefit from a given training protocol more than others (Titz and Karbach, 2014; Karbach et al, 2017), we wanted to know if a training gain could be predicted based solely on previous cognitive traits

  • With a small dataset (N = 73) from a past intervention performed with a free cognitive training software designed by our group (Goldin et al, 2013, 2014), we aimed to train a set of binary classifiers to predict the outcome of a particular stimulation protocol

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Summary

Introduction

If you encounter an add claiming “Do you want to improve your memory? With these brain exercises you will see changes in less than x time! Scientifically tested method!”, what would you think? to date, there is still not a well described and thoroughly tested method that consistently improves cognitive processes (Dorbath et al, 2011; Au et al, 2014; Buttelmann and Karbach, 2017). Even though over the last 25 years many cognitive or brain training protocols have been put to the test and shown positive outcomes (Goldin et al, 2014; Hsu et al, 2014; Diamond and Ling, 2016; Klingberg, 2016; Buttelmann and Karbach, 2017), many other show the opposite results (Melby-lervåg et al, 2016; Simons et al, 2016; Sala et al, 2019). EF mature with the great variety of stimulus and experiences that we undergo from birth and continue to develop throughout life (Colé et al, 2014; Delalande et al, 2020; Johann and Karbach, 2020) While this implies that some aspects of life might act as negative modulators of EF development, such as early vulnerability and prenatal malnutrition (McDermott et al, 2012; McGaughy et al, 2014; Deater-Deckard et al, 2019; Howard et al, 2020), it signifies that proper life experiences can have positive effects in EF and, improve academic performance and other general life outcomes. One of the most frequent cognitive training strategies is to and progressively challenge EF through games, and it has proven to be a powerful positive modulator relevant during childhood, when behavioral and neural plasticity are intense (Sigman et al, 2014; Steinbeis and McCrory, 2020)

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