Bibliographic record
Abstract
Both Marcus (2001) and Jackendoff (2002) have emphasized the importance of finding credible explanations for the occurrence of variables within cognitive representations. Marcus, in particular, has argued that a prevailing form of connectionist modeling, eliminative connectionism, cannot adequately explain crucial forms of human generalization. Eliminative connectionism eschews the use of explicitly represented variables, and the latter, Marcus contends, play an essential role in the forms of generalization that he considers. Recently, van der Velde and de Kamps (2006) proposed a neural blackboard architecture, which they assert to have satisfied the variable representation needs that Marcus and Jackendoff identified. However, this letter argues that closely related variants of Marcus's generalization examples possess variable requirements that are incompatible with the van der Velde and de Kamps approach. Moreover, it is argued here that these newly proposed variants present a severe challenge not only for eliminative connectionism but for all network training methods that require iterative tuning of synaptic strengths. The letter focuses on generalization cases that necessitate either virtually instantaneous creation of variables or very rapid deployment of preexisting variables within highly novel contexts.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".