The effects of age on associative and rule-based causal learning and generalization.
Bibliographic record
Abstract
We assessed how age influences associative and rule-based processes in causal learning using the Shanks and Darby (1998) concurrent patterning discrimination task. In Experiment 1, participants were divided into groups based on their learning performance after 6 blocks of training trials. High discrimination mastery young adults learned the patterning discrimination more rapidly and accurately than moderate mastery young adults. They were also more likely to induce the patterning rule and use this rule to generate predictions for novel cues, whereas moderate mastery young adults were more likely to use cue similarity as the basis for their predictions. Like moderate mastery young adults, older adults used similarity-based generalization for novel cues, but they did not achieve the same level of patterning discrimination. In Experiment 2, young and older adults were trained to the same learning criterion. Older adults again showed deficits in patterning discrimination and, in contrast to young adults, even when they reported awareness of the patterning rule, they used only similarity-based generalization in their predictions for novel cues. These findings suggest that it is important to consider how the ability to code or use cue representations interacts with the requirements of the causal learning task. In particular, age differences in causal learning seem to be greatest for tasks that require rapid coding of configural representations to control associative interference between similar cues. Configural coding may also be related to the success of rule-based processes in these types of learning tasks.
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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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".