More Like this

1. Adults regularize variation when linguistic cues suggest low input reliability

   https://doi.org/10.3765/plsa.v7i1.5293  

   Chen, Yiran; Schuler, Kathryn (May 2022, Proceedings of the Linguistic Society of America)

   Children regularize inconsistent probabilistic patterns in linguistic input, yet they also acquire and match probabilistic sociolinguistic variation. What factors in the language input contribute to whether children will regularize or match the probabilistic patterns they are exposed to? Here, we test the hypothesis that low input reliability facilitates regularization. As a first step, we asked adult participants to acquire a variable plural marking pattern from a written (Exp 1) and a spoken (Exp 2) artificial language under different conditions, where they were led to believe input was more, or less, reliable. In both experiments, input reliability was manipulated through both information about the speaker (e.g., whether the speaker was likely to make mistakes) and linguistic cues (e.g., typos or pronunciation errors). Results showed that adults regularized the written language more only when they were told the speaker would make mistakes and the plural variants resembled typos (Exp 1), whereas they regularized the spoken language more when the plural variants resembled pronunciation errors regardless of the speaker’s said reliability in the spoken language. We conclude that input reliability is an important factor that can modulate learners’ regularization of probabilistic linguistic input, and that linguistic cues may play a more critical role than top-down knowledge about the speaker. The current study lays down an important foundation for future work exploring whether children are able to incorporate input reliability cues when learning probabilistic linguistic variation. 

   more » « less
2. Biomechanical and Sensory Feedback Regularize the Behavior of Different Locomotor Central Pattern Generators

   https://doi.org/10.3390/biomimetics7040226  

   Deng, Kaiyu; Hunt, Alexander J.; Szczecinski, Nicholas S.; Tresch, Matthew C.; Chiel, Hillel J.; Heckman, C. J.; Quinn, Roger D. (December 2022, Biomimetics)

   This work presents an in-depth numerical investigation into a hypothesized two-layer central pattern generator (CPG) that controls mammalian walking and how different parameter choices might affect the stepping of a simulated neuromechanical model. Particular attention is paid to the functional role of features that have not received a great deal of attention in previous work: the weak cross-excitatory connectivity within the rhythm generator and the synapse strength between the two layers. Sensitivity evaluations of deafferented CPG models and the combined neuromechanical model are performed. Locomotion frequency is increased in two different ways for both models to investigate whether the model’s stability can be predicted by trends in the CPG’s phase response curves (PRCs). Our results show that the weak cross-excitatory connection can make the CPG more sensitive to perturbations and that increasing the synaptic strength between the two layers results in a trade-off between forced phase locking and the amount of phase delay that can exist between the two layers. Additionally, although the models exhibit these differences in behavior when disconnected from the biomechanical model, these differences seem to disappear with the full neuromechanical model and result in similar behavior despite a variety of parameter combinations. This indicates that the neural variables do not have to be fixed precisely for stable walking; the biomechanical entrainment and sensory feedback may cancel out the strengths of excitatory connectivity in the neural circuit and play a critical role in shaping locomotor behavior. Our results support the importance of including biomechanical models in the development of computational neuroscience models that control mammalian locomotion. 

   more » « less
3. Optimal Recovery from Inaccurate Data in Hilbert Spaces: Regularize, But What of the Parameter?

   https://doi.org/10.1007/s00365-022-09590-5  

   Foucart, Simon; Liao, Chunyang (April 2023, Constructive Approximation)
4. Optimal recovery from inaccurate data in Hilbert spaces: regularize, but what of the parameter?

   S. Foucart, C. Liao (January 2022, Constructive Approximation)
5. To grow and to defend

   https://doi.org/10.1126/science.aau9065  

   Greene, George H.; Dong, Xinnian (September 2018, Science)