Implicit updating of object representation via temporal regularities
Bibliographic record
Abstract
An adaptive function of the visual system is that it can flexibly update existing representations of objects upon changes in the environment. Moreover, these changes can alter the representations of other associated objects that are not directly visible. For example, the increasing size of headlights at night signals an approaching car, although the car may not be visible. What mechanism supports such inference? We propose that statistical learning provides a channel through which new information about one object can be transferred to related objects. Observers viewed a continuous sequence of circles grouped into color pairs (e.g., red always appeared before blue). Afterwards, the first circle in each pair increased or decreased in size. Observers recalled either the size of the second circle in the pair, or the size of a random circle that never followed the first one. We found that the size of the second circle was judged to be larger (or smaller) than the random circle if the first circle increased (or decreased) in size (Experiment 1). This suggests that changes in one object are automatically transferred to the object that previously reliably followed. This transfer may be facilitated by the fact that the first circle predicted the second circle, or the mere association between the two circles. To tease these ideas apart, in Experiment 2 the second circle increased or decreased in size, and observers recalled the size of the first circle, or a random circle. We found no difference between the judged size of the first circle and the random circle, suggesting that changes in one object are transferred to the predicted object, but not vice versa. No observer was explicitly aware of the color pairs. Thus, statistical learning implicitly and automatically updates the representation of objects upon changes to other objects via temporal prediction. Meeting abstract presented at VSS 2016
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".