Examining the neural and cognitive processes that underlie reading through naming speed tasks
Bibliographic record
Abstract
We combined fMRI with eye tracking and speech recording to examine the neural and cognitive mechanisms that underlie reading. To simplify the study of the complex processes involved during reading, we used naming speed (NS) tasks (also known as rapid automatized naming or RAN) as a focus for this study, in which average reading right-handed adults named sets of stimuli (letters or objects) as quickly and accurately as possible. Due to the possibility of spoken output during fMRI studies creating motion artifacts, we employed both an overt session and a covert session. When comparing the two sessions, there were no significant differences in behavioral performance, sensorimotor activation (except for regions involved in the motor aspects of speech production) or activation in regions within the left-hemisphere-dominant neural reading network. This established that differences found between the tasks within the reading network were not attributed to speech production motion artifacts or sensorimotor processes. Both behavioral and neuroimaging measures showed that letter naming was a more automatic and efficient task than object naming. Furthermore, specific manipulations to the NS tasks to make the stimuli more visually and/or phonologically similar differentially activated the reading network in the left hemisphere associated with phonological, orthographic and orthographic-to-phonological processing, but not articulatory/motor processing related to speech production. These findings further our understanding of the underlying neural processes that support reading by examining how activation within the reading network differs with both task performance and task characteristics.
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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 | 0.003 |
| 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 | 0.001 |
| 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".