Expectations alter representations during object categorization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.003 | 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 it