Non-Linguistic Working Memory Challenges in Dyslexia in The Context of Applied Consumer Psychology
Bibliographic record
Abstract
In our daily lives, we frequently encounter non-linguistic visual content that we must remember in a spatial context. Individuals with dyslexia are known to experience difficulty with linguistic content and show impairments in cognitive functioning. Recently researchers have highlighted that vision and executive functioning may play a role in explaining dyslexia symptoms, but the specific role of visuo-spatial working memory skills in dyslexia is poorly understood. In the present study, we used non-linguistic everyday brand logos from consumer psychology to investigate visuo-spatial working memory performance in relation to dyslexia. We used an adapted online version of the Sternberg task, where 51 participants with dyslexia and 129 participants without a dyslexia diagnosis recalled content from the same stimulus category presented in either three (low-working memory load condition) or six (high-working memory load condition) specific locations – analogous to recalling a particular branded item on a grocery shelf. Every trial included one of three types of non-linguistic content novel to participants, such as fictious brand logos without their brand name, symbols, or numbers. Bayesian modelling provided evidence for lower and slower working memory performance across all stimulus types for the dyslexia group compared to the non-dyslexia group. However, individuals in both groups showed similar decreases in performance when the working memory load was increased. These findings speak to the role of dyslexia as a multifaceted learning disorder that stretches beyond its linguistic definition and may exert wider reaching effects that also touch upon aspects of daily living that do not involve language.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".