Neural Correlates and Saccadic Eye Movements Involved during Letter Naming Speed Tasks
Bibliographic record
Abstract
Naming speed (NS) tasks measure how quickly and accurately subjects can name sets of highly familiar stimuli (e.g., letters) randomly presented in a visual array. NS performance has been shown to be a precursor and concurrent correlate of accurate and efficient reading. However, it is still not known what cognitive processes underlie this relationship. We used functional magnetic resonance imaging (fMRI) to investigate the neural substrates and cognitive processes underlying performance during letter and object naming speed (NS) tasks. We used three methods to evaluate task performance: (a) changing stimulus composition to emphasize phonological and/or visual aspects; (b) decomposing NS times into pause and articulation components; and (c) analyzing eye movements and brain activation involved in a NS task. 19 healthy young adults (ages 21 - 26 yrs) were recruited. We employed a block design consisting of a letter NS task and three variants with increased phonological and/or visual similarity (Compton, 2003), and an object NS task with a variant in which the object names rhymed with one another, while subjects' eye movements and articulations were recorded. We examined how these manipulations influenced performance and whether they resulted in differences in neural activation. Results indicated that letter NS manipulations were associated with specific patterns of performance which were influenced by visual rather than phonological similarity. When the task was both visually and phonologically similar, participants had significantly longer naming times and fixation durations, and made more frequent saccades and regressions. The letter NS tasks activated regions that are involved in the reading network, including the temporoparietal areas, inferior frontal cortex, and the ventral visual stream, as well as in the saccadic eye movement network. Activation in the temporoparietal areas of the reading network varied by task indicating differential neural processes that are associated with each letter NS task. Meeting abstract presented at VSS 2016
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".