Bibliographic record
Abstract
Participants tend to match identical pairs of stimuli faster than different pairs. Despite many endeavours to explain this fast-same effect, there is still no theoretical consensus. A potential reason for the lack of consensus is that the cognitive architecture and capacity underlying such phenomenon is assumed and not formally tested. For example, the dual-process approach suggests that Same responses arise from a parallel treatment, whereas Different responses arise from a serial treatment. It also suggests that in both conditions, the capacity of the process is unaffected by workload (unlimited capacity). Alternative approaches argue that the fast-same effect can be explained by parallel or coactive architectures with channels working in either limited or super capacity. In this study, we formally assess the architecture (three possibilities: serial, parallel and coactive) and the capacity (three possibilities: unlimited, limited and super-capacity) of the cognitive system in a Same-Different task using Systems Factorial Technology (SFT). We recruited twenty participants to perform a double-factorial task lasting four sessions. Because of the lack of effectiveness of the blurring manipulation, we cannot draw a strong conclusion about the cognitive architecture. As for the capacity, the results show that it is mostly limited for the majority of participants. However, between 300 and 500 ms, participants tend to have a much stronger processing capacity in the Same condition compared to the Different condition. This short but strong burst of activity for identical stimuli might explain the fast-same effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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; both teacher heads agree on what is shown here.
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".