The capacity limit of the visual working memory of the macaque monkey
Bibliographic record
Abstract
Behavior can be guided by visual working memory as well as vision. For example, visual exploratory behavior is most efficient if subjects can accurately retain items they have previously fixated. This visual working memory capacity is limited; human studies have estimated the visual working memory capacity as 3 items on average, with values as low as 1.5 in some individuals. To date, no study has determined the capacity limit in animals and as such, no animal model has been established to investigate the neural basis of the capacity of visual working memory. We employed an adaptation of the sequential color-change detection task used in human studies to determine the visual working memory capacity in the macaque monkey (see supplementary figure 1). Each trial began with the presentation of a fixation spot on a blank screen. The monkey was required to fixate on this central fixation spot before a memory array was presented. The memory array consisted of a set of two to five highly discriminable colored stimuli, presented for 500ms. The memory array, except for the fixation spot, was removed for a retention interval of 1000ms, during which the monkey was required to maintain fixation. The test array was then presented with one of the stimuli having changed color. The monkey was required to indicate this change by making a single saccadic eye movement to its location. Consistent with the use of mnemonic processes, the performance decreased with increasing set size (see supplementary figure 2). Using the relationship between performance and set size, monkey visual working memory capacity was at least 2 memoranda, a value within the range of human capacity estimates. This similarity between the monkey and human visual working memory capacity suggests a shared common neural process, which can now be investigated with invasive techniques.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".