Connectionist perspectives on language learning, representation and processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The field of formal linguistics was founded on the premise that language is mentally represented as a deterministic symbolic grammar. While this approach has captured many important characteristics of the world's languages, it has also led to a tendency to focus theoretical questions on the correct formalization of grammatical rules while also de-emphasizing the role of learning and statistics in language development and processing. In this review we present a different approach to language research that has emerged from the parallel distributed processing or 'connectionist' enterprise. In the connectionist framework, mental operations are studied by simulating learning and processing within networks of artificial neurons. With that in mind, we discuss recent progress in connectionist models of auditory word recognition, reading, morphology, and syntactic processing. We argue that connectionist models can capture many important characteristics of how language is learned, represented, and processed, as well as providing new insights about the source of these behavioral patterns. Just as importantly, the networks naturally capture irregular (non-rule-like) patterns that are common within languages, something that has been difficult to reconcile with rule-based accounts of language without positing separate mechanisms for rules and exceptions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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