Does attention to low spatial frequencies enhance face recognition? An individual differences approach
Bibliographic record
Abstract
Faces are widely regarded as "special" due to our reliance on holistic or configural processing for their successful recognition. The processing of low spatial frequency information has been associated with holistic processing and is thought to promote a face-specific recognition advantage. There are reliable individual differences in face recognition ability, and these are related to individual differences in various holistic processing measures. There are also stable individual differences in the tendency to use high or low spatial frequency information. To date, however, there have been no investigations of potential relationships between individual differences in high/low spatial frequency use and performance on face recognition tasks. The current study investigated whether individual differences in low spatial frequency use are related to individual differences in face recognition, as well as the extent to which these relationships are face-specific. Participants completed three different face recognition tasks, two non-face recognition control tasks, and a task pitting high and low spatial frequency information against each other. As predicted, individuals who showed greater reliance on low-spatial frequency information had better face recognition ability. However, increased use of low-spatial frequency information also predicted better recognition of non-face stimuli (i.e. cars and abstract art), and the relationship between face recognition and low spatial frequency use was not significant after statistically controlling for non-face recognition performance. The results suggest that the use of low-spatial frequencies in visual processing is beneficial to recognition in general, as opposed to garnering advantages that are specific to faces. Meeting abstract presented at VSS 2014
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".