Investigating the effects of perceptual complexity versus conceptual meaning on the object benefit in visual working memory
Bibliographic record
Abstract
Previous research has demonstrated greater visual working memory (VWM) performance for real-world objects compared with simple features. Greater amplitudes of the contralateral delay activity (CDA)-a sustained event-related potential measured during the delay period of a VWM task-have also been noted for meaningful stimuli, despite being thought of as a neural marker of a fixed working memory capacity. The current study aimed to elucidate the factors underlying improved memory performance for real-world objects by isolating the relative contributions of perceptual complexity (i.e., number of visual features) and conceptual meaning (i.e., availability of semantic, meaningful features). Participants (N = 22) performed a lateralized VWM task to test their memory of intact real-world objects, scrambled real-world objects and colours. The CDA was measured during both encoding and WM retention intervals (600-1000 ms and 1300-1700 ms poststimulus onset, respectively), and behavioural performance was estimated by using d' (memory strength in a two-alternative forced choice task). Behavioural results revealed significantly better performance within-subjects for real-world objects relative to scrambled objects and colours, with no difference between colours and scrambled objects. The amplitude of the CDA was also largest for intact real-world objects, with no difference in magnitude for scrambled objects and colours, during working memory maintenance. However, during memory encoding, both the colours and intact real-world objects had significantly greater amplitudes than scrambled objects and were comparable in magnitude. Overall, findings suggest that conceptual meaning (semantics) supports the memory benefit for real-world objects.
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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 | 0.010 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".