EEG-based decoding of shapes and their categories in visual working memory
Bibliographic record
Abstract
Visual working memory (VWM) allows us to store information in a highly accessible format for an upcoming task. Traditionally, VWM studies require participants to keep a precise copy of a stimulus in mind. But in the real world, we might need to store the same information for different types of tasks, such as recognition or categorisation judgements. For example, when deciding if a bike is the exact model you want, or the same brand. In this study, we asked how categorisation modulates VWM representations. Participants first learned to group unfamiliar shapes from the Validated Circular Shape (VCS) space (Li et al., 2020) into two categories based on their visual features. They then completed a shape VWM task that either required delayed match-to-sample or delayed match-to-category judgements on different blocks while we collected electroencephalography (EEG) data. We tracked the emergence of stimulus-, category- and task level information with high temporal resolution using multivariate pattern analyses of EEG. The neural activity pattern over posterior electrodes contained information about the memorised shape for about one second following VWM encoding. Initially, the stimulus code overlapped across the two tasks, but quickly separated according to task. Later in the delay, stimulus coding persisted only for the match-to-category task and was accompanied by a neural category signal, indicating that categorisation may require an active stimulus representation. To our knowledge, this is the first illustration that the VCS space is decodable from EEG, preserving the circular similarity structure. This provides a fruitful avenue for researchers looking to characterise neural representations of unfamiliar visual stimuli with high temporal resolution. The results of this study will help elucidate the neural mechanisms supporting VWM under different task demands.
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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".