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Record W4312167719 · doi:10.5539/ijps.v15n1p1

Activation of the Anterior Prefrontal Cortex by Abacus Activity in Children: A Case Study on the Effect of Moderate Load Training on Working Memory

2022· article· en· W4312167719 on OpenAlexvenueno aff
Nobuki Watanabe

Bibliographic record

VenueInternational Journal of Psychological Studies · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsWorking memoryMemory spanPrefrontal cortexPsychologyAbacus (architecture)Wechsler Adult Intelligence ScaleWorking memory trainingShort-term memoryCognitive psychologyAudiologyCognitionNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Much attention has been paid to the enhancement of working memory, which can improve children’s lives. Part of the value of working memory training is that it activates the prefrontal cortex. Therefore, it is important to determine the parts of the prefrontal cortex that are activated by working memory training. While there is much evidence that mental abacus effectively trains working memory, few studies have assessed whether the abacus (Soroban) in Japan should be considered an effective training approach for working memory and if it activates the prefrontal cortex. Therefore, in this case study, a 16-channel functional near-infrared spectroscopy device (OEG-16H, Spectratech, Japan) was used to compare brain activation during the abacus task using the Wechsler Intelligence Scale for Children Working Memory Index tasks (i.e., digit span (forward), digit span (backward), letter–number sequencing, and picture span tasks). First-grade boys and fifth-grade girls participated in this study. The results revealed that the anterior part of the prefrontal cortex was specifically activated by the abacus task. These findings support the possibility that the abacus activity is an effective training approach for working memory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.201
GPT teacher head0.430
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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