Transsaccadic memory for multiple features
Bibliographic record
Abstract
Transaccadic integration refers to the integration of information across saccadic eye movements, considered to be crucial for spatial constancy. Accurate integration requires two components, 1) memorization of the object features and 2) updating of these features across eye movements, i.e. we need to remember where an object was and it’s features at that location. However, not much is known about how we remember multiple features across eye movements. Here, we tested how accurately participants remembered multiple features of an object across saccadic eye movements. We asked seven subjects to compare two bars, each varying in location (1.6° left of center to 1.6°right in 0.4° intervals on the horizontal meridian), orientation (5° counterclockwise to 5° clockwise in 2° intervals) and size (1.8° to 2.3° in 0.1° increments). Both bars were viewed peripherally and sequentially with an intervening delay during which they either remained fixated or made a saccade to the opposite side. Participants reported how the second bar was different from the first for 1) all three attributes in each trial or 2) only one attribute within a block of trials. We found that remembering three attributes increased uncertainty about each attribute for all three features (p<0.01). Participants were most uncertain about the bar location following a saccade compared to when they remained fixated (p<0.01), mostly resulting from a bias in remembering the first bar to be closer to final fixation after the saccade. The intervening saccade also degraded the certainty of the orientation of the bar (p<0.01) and induced a reduction in the remembered size of the first bar (p<0.05). Based on the findings, we conclude that updating of objects across saccades introduces uncertainty in their remembered attributes. When more attributes required memorization, uncertainty increased which points towards memory interactions between different visual features and saccadic eye movements. Meeting abstract presented at VSS 2013
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".