The Role of Attention and Response Based Learning in the Visual Hebb Supra-span Sequence Learning Task: Investigating Age-related Learning Deficits
Bibliographic record
Abstract
Using Hebb’s (1961) paradigm, it has been shown that older adults (OAs) fail to learn recurrent visuospatial supra-span sequence information (Turcotte, Gagnon, & Poirier, 2005); a deficit which has not been demonstrated on verbal versions of the same task or in younger adults (YAs). Since the Hebb paradigm is thought to rely on working memory and thus attention (Conway & Engle, 1996), one interpretation concerns an OA’s capacity to allocate the necessary attentional resources to carry out the various components of the task. Five studies investigated this proposal. The first three (Article 1) examined attention in a general manner by reducing the amount of attentional resources that a YA could devote to carrying out the visuospatial Hebb supra-span sequence learning task through the implementation of a verbal dual task (DT) procedure. The fourth (Article 2) further investigated the role of attention by using a DT induced at retrieval that overlapped extensively with the requirements (spatial and response features) of the visuospatial Hebb task. The final study (Article 3) aimed to use our previous findings to demonstrate learning among OAs in a visuospatial Hebb learning paradigm in which the motor response was replaced by a verbal response. Our findings confirm that attentional resources employed at the retrieval phase of the task appear to be particularly important for the demonstration of visuospatial sequence learning. The inclusion of a spatial and motor based DT at recall eliminated learning of the repeated sequence in YAs. Interestingly, the learning deficit of OAs was partially eliminated when the motor and spatial requirements at retrieval were reduced. Our findings offer strong support to the contention that supra-span learning of the Hebb type is not altered by the effect of age. However, learning deficits can be observed among OAs when the retrieval component of the task overly taxes attention-related processes. In the case of the visuospatial sequences, the basis of the deficit likely concerns an individual’s capacity to discriminate between responses made to previously presented sequences versus those that need to be made in reaction to the just seen sequence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".