Whole-Brain Task-Based BOLD Anatomical Patterns Hypothesized for the Sternberg Item Recognition Paradigm and their Task-Induced BOLD Changes
Bibliographic record
Abstract
Background: Functional magnetic resonance imaging (fMRI) investigations of manipulating information over the short term (working memory; WM) using the Sternberg delayed recognition/delayed recall task have arguably been one of the most active areas of cognitive neuroscience research for several decades. Drawing on a series of fMRI studies spanning 20 years, we have developed an anatomical and temporal account of five task-based brain networks involved in the Sternberg task and how their task-induced blood-oxygen-level-dependent (BOLD) changes are hypothesized to respond to experimental manipulations. Task: The Sternberg Item Recognition Paradigm (SIRP) task put forward for this set of hypotheses involves a string of four or six upper-case consonants being displayed for four seconds, followed by a four-second or zero-second delay. A single probe letter is then shown for two seconds. Participants are asked to respond "yes" or "no" to whether the probe letter presented had been a part of the first string of letters, using a button press with their right hand, with index finger press indicating "yes" and middle finger "no." A fixation cross can be displayed throughout the inter-trial intervals, and to minimize the disparities in basic visual perception between the 4- and 6-letter conditions, pound signs ("#") are included on each end of the 4-letter strings.Hypotheses (anatomical): The hypothesized networks for the Sternberg task can be described as the WM Big 5: Response (RESP), Focus on Visual Features (FoVF), Initiation (INIT), Maintaining Internal Attention (MAIN), and Default Mode Network (DMN). The detailed anatomical depictions of these networks are provided in Tables 1-5.Hypotheses (temporal): Broadly speaking, RESP is expected to peak latest in the trial, and to deactivate mid-trial for long delays. FoVF is expected to deactivate at particular points during the trial. INIT is predicted to be the earliest-peaking network, with a higher peak for the high-load condition. MAIN is expected to peak mid-trial, with a higher peak for the high-load condition, and to initiate activation early-to-mid-trial. The DMN is expected to be mid-trial peaking and show load-dependent deactivation, with the initiation of deactivation coinciding with the trial start. Details are presented in the text, tables and figures below.Conclusions: These anatomical and temporal hypotheses for the WM Big 5 are expected to be upheld over versions of SIRP tasks and samples. However, these networks are also part of a set of networks that are observed across many tasks (zenodo.org/record/4624418), so their functions are not SIRP-specific. Task-general fMRI networks can simplify fMRI investigations, allowing the merging of task fMRI data across multiple sites and SIRP task versions, or even different tasks assessing a range of cognitive domains.
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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 | 0.001 |
| 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) | 0.002 | 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".