Visual Attention and Visual Memory in Struggling Readers: Are Anomalies Revealed in ERP N2pc and SPCN?
Bibliographic record
Abstract
Children with reading difficulties often show deficits in selective visual attention (SVA), and in encoding visual information into visual working memory (VWM). We analyzed the ERP N2pc wave component, representing SVA, and the SPCN component, representing successful VWM storage, in good- and poor-reading children while they performed an object-substitution masking (OSM) task. During OSM an illusory object elicited by a mask replaces the target stimulus, particularly with delayed mask offset. Thus, we predicted that storage of the target into VWM would be hindered under delayed mask offset. Because poor readers tend to show anomalous encoding into VWM, we expected to see a reduced SPCN in these individuals. Previous research indicates that N2pc is found for both incorrect and correct trials with delayed but not with co-termination offsets. We therefore expected to find N2pc in delayed-offset trials, and that the amplitude would be reduced in poor readers due to SVA anomalies. In support of our hypothesis, results showed that N2pc was reliably elicited in children in delayed offset conditions, and suggested greater load on SVA resources in the delayed conditions in poor readers. Visual analysis of the wave components indicated SPCN in correct trials, representing successful storage of the target into VWM. Results on reading group differences are discussed in relation to current conceptualizations of SVA anomalies in poor readers. Meeting abstract presented at VSS 2017
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".