Neural and behavioral effects of subordinate‐level training of novel objects across manipulations of color and spatial frequency
Bibliographic record
Abstract
Perceptual expertise is marked by subordinate-level recognition of objects in the expert domain. In this study, participants learned one family of full-color, artificial objects at the subordinate (species) level and another family at the basic (family) level. Discrimination of trained and untrained exemplars was tested before and after training across several image manipulations [full-color, grayscale, low spatial frequency (LSF) and high spatial frequency (HSF)] while event-related potentials (ERPs) were recorded. Regardless of image manipulation, discrimination (indexed by d') of trained and of untrained exemplars was enhanced after subordinate-level training, but not after basic-level training. Enhanced discrimination after subordinate-level training generalized to untrained exemplars and to grayscale images and images in which LSF or HSF information was removed. After training, the N170 and N250, recorded over occipital and occipitotemporal brain regions, were both more enhanced after subordinate-level training than after basic-level training. However, the topographic distribution of enhanced responses differed across components. The N170 latency predicted reaction time after both basic-level training and subordinate-level training, highlighting an association between behavioral and neural responses. These findings further elucidate the role of the N170 and N250 as ERP indices of subordinate-level expert object processing and demonstrate how low-level manipulations of color and spatial frequency impact behavior and the N170 and N250 components independent of training or expertise.
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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 | 0.000 | 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) | 0.001 | 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".