Flavia H Santos, PhD

@flavinska.bsky.social

A neuroscientist at University College London 🇬🇧 @ioe.bsky.social Funded by Erasmus+, Research Ireland & Leverhulme Trust. Past Chair of #MCLS, Ad Astra Fellow! UNESCO Inclusive Policy Lab [she, her] #neuroscience #dyscalculia #MusicScience

Coming up at #MCLS2026 … Our lightning symposium on increasing diversity and inclusion in mathematical cognition research! We will discuss our lessons learnt and practical strategies and ideas for increasing inclusion and participation in research.

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Selecting an effect size for power analysis is hard. Many researchers fall back on Cohen's thresholds, but they have no empirical basis and vary wildly by field. Our new paper offers a better option: field-specific effect size distributions built from meta-analytic data doi.org/10.3758/s134...

Abstract
Effect sizes are useful for understanding the magnitude of study results and for planning new studies via power analysis.
However, despite their wide usage, effect sizes are often misinterpreted. This is mostly due to an over-reliance on general
effect size benchmarks that were not intended for broad application across diverse research fields. Inaccurate effect size
interpretations can lead to incorrect conclusions about the magnitude of study results and incorrect sample size estimates,
thereby increasing the likelihood of false-positive results. This article introduces the ESDist R package, which is designed
to calculate empirically derived effect-size benchmarks or a range of reliably detectable empirical effect sizes for a specific
research question or field of interest by computing effect size distributions (ESDs). This package can be used on data that
can be easily extracted from pre-existing meta-analyses to help researchers more accurately plan new studies or to better
understand how an individual study might relate to other studies in their field. ESDist includes a set of features that make it
easy to use in a priori power analysis. Moreover, the package includes a feature for estimating effect size benchmarks that
account for publication bias and are weighted by effect sizes' variances, which addresses existing limitations of using ESDs
for study planning or interpretation.

The Unfolding World: Causal & physical cognition in humans and other animals 🐦‍⬛ This workshop brings together experts from animal cognition, developmental psychology, computational modelling, and philosophy to investigate 24-25 April 2026 Alison Richard Building, CB3 9DP https://bit.ly/4by8cCM

The Unfolding World: Causal & physical cognition in humans and other animals.

Is mental imagery a single process? Our meta-analysis of 46 fMRI studies suggests not. We find a key divide between object imagery (visualizing an object in rich detail) and spatial imagery (mentally transforming objects). This split mirrors the ventral and dorsal streams of vision

Effects across several digital literacy outcomes. • search strategies (η² = .17) • evaluating conflicting information & source quality (η² = .14) • reasoning about credibility (η² = .10) • identifying webpage features (η² = .02) • transfer task one week later (η² = .12) doi.org/10.1080/0022...

A Design-Based Intervention to Develop Elementary Students’ Digital Literacy Skills

UNESCO identified the formation of digital literacy skills as one of the most desired outcomes of education. Using an exploratory sequential mixed methods design, we explored elementary students’ p...

doi.org