Neural Mechanisms of Learning and Consolidation of Morphologically Derived Words in a Novel Language: Evidence From Hebrew Speakers
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Bibliographic record
Abstract
We examined neural mechanisms associated with the learning of novel morphologically derived words in native Hebrew speakers within the Complementary Learning Systems (CLS) framework. Across four sessions, 28 participants were trained on an artificial language, which included two types of morphologically complex words: linear (root + suffix) with a salient structure, and non-linear (root interleaved with template), with a prominent derivational structure in participants' first language (L1). A third simple monomorphemic condition, which served as baseline, was also included. On the first and fourth sessions, training was followed by testing in an fMRI scanner. Our behavioural results showed decomposition of both types of complex words, with the linear structure more easily learned than the non-linear structure. Our fMRI results showed involvement of frontal areas, associated with decomposition, only for the non-linear condition, after just the first session. We also observed training-related increases in activation in temporal areas specifically for the non-linear condition, which was correlated with participants' L1 morphological awareness. These results demonstrate that morphological decomposition of derived words occurs in the very early stages of word learning, is influenced by L1 experience, and can facilitate word learning. However, in contrast to the CLS framework, we found no support for a shift from reliance on hippocampus to reliance on cortical areas in any of our conditions. Instead, our findings align more closely with recent theories showing a positive correlation between changes in hippocampus and cortical areas, suggesting that these representations co-exist and continue to interact with one another beyond initial learning.
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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) | 0.002 | 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