Expectations modulate the time course of information use during object recognition
Bibliographic record
Abstract
Prior expectations have been shown to affect object recognition. However, it is unknown whether the expectation of a specific object modulates how information is sampled across time during object recognition. Coarse information (low spatial frequencies, SFs) is typically sampled before more detailed information (high SFs). Some authors have suggested that low SFs processed early activate expectations about the object’s identity and that high SFs processed afterwards allow the confirmation and refinement of this hypothesis; an existent expectation could therefore reduce the need for confirmatory high SF information. In this study, we verified whether expectations influenced how SFs are used across time to recognize objects. On each trial, one object was randomly chosen among eighty, and all its SFs were randomly sampled across 333 ms. In half the trials (expectation condition), an object name was shown before the object; in the other half (no-expectation condition), it was shown after. Subjects had to indicate whether the name matched the object; it did so on 50% of trials. We first observed, after reverse correlating accuracy with SFs shown at each moment, that the early use of low SFs (1–30 cycles/image) was increased in the expectation condition. We then found that the late use of high SFs (~35 cycles/image), although not visible in the average results, was correlated with general recognition ability in the no-expectation condition, more so than in the expectation condition. Finally, we found that the early use of high SFs (~35 cycles/image) was affected differently by different expectations (i.e. different object names). Together, these results reveal how the processing of sensory information sampled across time during object recognition is modulated by expectations and they support the hypothesis that low and high SFs are affected differently by this modulation.
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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 | 0.006 |
| 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 | 0.000 | 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 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".