Modelling the Accuracy Rates of Spatial Relational Reasoning Problems: An Analysis Facilitating ACT-R and PRISM Theory
Bibliographic record
Abstract
Relational reasoning involves evaluating relations between representations.Spatial relational reasoning problems have long been used in psychology to study deductive inference abilities.Research incorporating such tasks has resulted in findings of a variety of effects and reasons as to why some problems are more difficult than others.Computational accounts of the relational reasoning of spatial information offer valuable insights, such as how individuals may construct a mental model to infer conclusions and why some cognitive strategies might be preferred over others.However, many of these accounts either fail to incorporate or remain general to the impact of other factors affecting the difficulty of these reasoning problems, such as the effects of working memory errors.This thesis aims to investigate how the different types of memory errors of omission and commission may be computationally modelled to provide a theoretically unifying account of qualitatively different cognitive reasoning processes and quantitatively different accuracy rates on experimental measurements of spatial relational reasoning problems.Our models demonstrate that modelling memory errors of omission and commission in Python ACT-R based on PRISM theory produces a similar negative relationship of a decrease in accuracy rates with each increase of premise and dimensionality complexity per question as found in the relational reasoning experimental literature.Our results highlight the need for future modelling to consider individual differences in participant micro-strategy preferences, how reasoning processes may be affected by different memory errors, and how future measures may be constructed to better address raised concerns.
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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 | 0.016 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".