Expectations alter representations during object categorization
Bibliographic record
Abstract
Prior expectations influence how we recognize objects. As suggested by recent evidence, this may be done by altering internal representations. However, how expectations of complex everyday objects affect representations remains largely unknown. Such objects are composed of multiple features that may be affected differently. For example, more generic low-spatial-frequency features could be represented when there are no specific expectations about the incoming object; when there is an expectation, subjects might focus on more specific high-spatial-frequency features to try to confirm their expectation. In the present study, subjects had to perform a 4AFC object categorization task. In the expectation condition, an object name was shown prior to the object image and indicated the most likely object to appear next (with 50% validity); in the no-expectation condition, a random string of letters appeared prior to the image. We randomly sampled spatial frequencies (SFs) across 400 ms on each trial. After reverse correlating accuracy with SFs shown at each moment for each condition, we observed that low SFs (~1-25 cycles/image) throughout recognition were significantly more used to categorize objects when there were no expectations than when there were valid expectations (p < .05), indicating that subjects focus on coarser features when they have no specific expectation. We further observed that there was significant variance in the use of high SFs (~35 cycles/image) late during recognition across object expectations (p < .05), indicating that subjects alter their representation in specific ways depending on their specific prior expectation. In summary, subjects focus on generic coarse features when they have no expectation, and they use fine features differently depending on the specific expectation. These results reveal the mechanisms underlying the effects of expectations on the recognition of real-world complex objects.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".