Orienting Attention to Short-Term Memory Representations via Sensory Modality and Semantic Category Retro-Cues
Bibliographic record
Abstract
There is growing interest in characterizing the neural mechanisms underlying the interactions between attention and memory. Current theories posit that reflective attention to memory representations generally involves a fronto-parietal attentional control network. The present study aimed to test this idea by manipulating how a particular short-term memory (STM) representation is accessed, that is, based on its input sensory modality or semantic category, during functional magnetic resonance imaging (fMRI). Human participants performed a novel variant of the retro-cue paradigm, in which they were presented with both auditory and visual non-verbal stimuli followed by Modality, Semantic, or Uninformative retro-cues. Modality and, to a lesser extent, Semantic retro-cues facilitated response time relative to Uninformative retro-cues. The univariate and multivariate pattern analyses (MVPAs) of fMRI time-series revealed three key findings. First, the posterior parietal cortex (PPC), including portions of the intraparietal sulcus (IPS) and ventral angular gyrus (AG), had activation patterns that spatially overlapped for both modality-based and semantic-based reflective attention. Second, considering both the univariate and multivariate analyses, Semantic retro-cues were associated with a left-lateralized fronto-parietal network. Finally, the experimental design enabled us to examine how dividing attention cross-modally within STM modulates the brain regions involved in reflective attention. This analysis revealed that univariate activation within bilateral portions of the PPC increased when participants simultaneously attended both auditory and visual memory representations. Therefore, prefrontal and parietal regions are flexibly recruited during reflective attention, depending on the representational feature used to selectively access STM representations.
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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 | 0.001 |
| 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) | 0.001 | 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".