Testing variation of attention capacities in a complex auto-adaptive system: a Stroop task simulation
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Bibliographic record
Abstract
Abstract The Stroop task is a test commonly used in psychology to study interferences that occur in control when two cognitive processes are in competition, and where a non-habitual response, needed to reach a defined goal, competes with the habitual response. Many computer simulations already exist for this task, mostly in neural networks. This article presents a new simulation approach, the first attempt to simulate the Stroop task using the properties of complexity and auto-adaptivity in a massive multi-agent system. Our approach allows us to simulate the time effects of cognitive impairment on the task, the adaptive cognitive behaviours involved, and is a first step for the simulation of cognitive disorders. Keywords: complexityauto-adaptivitycognitive simulationStroop taskknowledge organisationemergence Acknowledgements The authors thank Nicholas Duran and anonymous reviewers for their helpful comments in revising an earlier version of this article. Notes 1. For a description of the OZ/Mozart language, see Van Roy and Haridi (Citation2004).
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it