Book review of “Development of mathematical cognition: Neural substrates and genetic influences” (2016) edited by D. B. Berch, D. C. Geary, & K. M. Koepke
Bibliographic record
Abstract
Neural Substrates and Genetic Influences (2016), Berch, Geary, and Koepke introduce readers to the most current research on mathematics cognition from a neural, genetic, and behavioral perspective.With a number of new science-based technologies and methodological innovations having been developed recently, there have been significant advances in neuroscience and genetics research related to numerical cognition over the past twenty years.Given the rapid increases in knowledge generated from these advances, there is a major need to merge neural, behavioral, and genetic approaches and knowledge.This book provides readers with a strong synthesis of the most cutting-edge findings and approaches to understanding both typical and atypical mathematics development and cognition.Throughout the 12 chapters of the book, the authors, all highly acclaimed in the field, introduce us to the most current findings and debates related to various aspects of mathematical cognition.The book begins with a foreword by Butterworth, where he describes that, in order to address poor numeracy, we first need to understand how each child's unique "starter kit", or individualized cognitive, neural, and genetic components, construct and drive the development of numerical understanding across the lifespan from birth through adulthood (Piazza, 2010).He poses several key questions to readers such as "How do we understand numbers?", "How do these processes vary between individuals over across development?",and "How can we improve numerical understanding?".Next, the editors preface the book by introducing key theoretical debates and knowledge that have guided the past twenty years of research in mathematical cognition and development.In Chapter 1, they then describe how recent developments in neuroimaging and behavioral genetics can be used to inform our understanding of cognitive development both theoretically and empirically.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.033 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".