Interaction in Spoken Word Recognition Models: Feedback Helps
Why this work is in the frame
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Bibliographic record
Abstract
Human perception, cognition and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis.
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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 | 0.001 |
| 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) | 0.000 | 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