Accessing object concepts: Effects from brief exposure using anaglyphs
Bibliographic record
Abstract
We sought to investigate how concepts are accessed via object and feature recognition during two brief exposure times (50/60 or 190/200 ms). Participants performed a picture/word masked priming congruency task, whereby they had to judge whether a picture/word pair were related to each other. Participants wore blue-red anaglyph glasses, with objects presented in red in the left visual field and words presented in blue in the right visual field. Using anaglyphs allowed us to investigate the role of the early posterior visual projections during object and word recognition, by projecting the word to the visual word form area in the left hemisphere and the picture to the right temporal lobe--one of the bilateral object recognition areas. Pictures and target words were presented simultaneously with a 10 ms difference accounting for their recognition times: objects were presented for 50 or 190 ms, while words were presented for either 60 ms or 200 ms. For each picture, one of four word probes was presented for congruency decision: the basic level category label of the picture (dog), a high-prototypical (bark), a low-prototypical (fur), or a superordinate feature (pet). Response times (RTs) and accuracy to congruency decisions were analyzed through linear mixed effects models. Results showed that participants were faster and more accurate in responding to picture-word pairs when stimuli were presented for 190/200 ms rather than 50/60 ms. Furthermore, high prototypical and superordinate feature probes yielded significantly faster and more accurate responses when stimuli were presented for 190/200 ms. But crucially, basic level probes yielded significantly faster RTs and greater accuracy than all other probe types, at both presentation times. Taken together, this suggests that concept tokening relies on non-decompositional processes, and that conceptual features are processed only after concepts have been accessed.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".