Neural network simulation of habituation and dishabituation in infant speech perception
Bibliographic record
Abstract
The habituation techniques used in infant speech perception studies are based on the fact that infants show renewed interest towards novel stimuli. Recent work has shown the possibility of using artificial neural networks to model habituation and dishabituation (e.g., Schafer and Mareschal, 2001). In our study we examine weather the self-organizing-feature-maps (SOM) (Kohonen, 1989) are appropriate for modeling short-term habituation to a repeated speech stimulus. We found that although SOMs are particularly useful for simulating categorization, they can be modified to model habituation and dishabituation, so that they can be applied to direct comparisons with behavioral data on infants’ speech discrimination abilities. In particular, we modified the SOMs to include additional parameters that control the relation of input similarity, lateral inhibition, and local and lateral activation between neurons. Preliminary results suggest that these parameters are sufficient for the network to simulate the loss of sensitivity of the auditory system due to the presentation of multiple tokens of a speech stimulus, as well as to model the recovery of sensitivity to a novel stimulus. The implications of this approach to infant speech perception research will be considered.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".