There is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact generative model underlying a wide class of languages, purely from observing samples of the language. We then describe a recently proposed practical framework, which quantifies natural language learnability, allowing specific learnability predictions to be made for the first time. In previous work, this framework was used to make learnability predictions for a wide variety of linguistic constructions, for which learnability has been much debated. Here, we present a new experiment which tests these learnability predictions. We find that our experimental results support the possibility that these linguistic constructions are acquired probabilistically from cognition-general principles.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.cognition.2011.02.013 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Predicting gene sequences with AI to study codon usage patterns.
Proc Natl Acad Sci U S A
January 2025
Department of Computer Science, University of Haifa, Haifa 3303221, Israel.
Selective pressure acts on the codon use, optimizing multiple, overlapping signals that are only partially understood. We trained AI models to predict codons given their amino acid sequence in the eukaryotes and and the bacteria and to study the extent to which we can learn patterns in naturally occurring codons to improve predictions. We trained our models on a subset of the proteins and evaluated their predictions on large, separate sets of proteins of varying lengths and expression levels.
View Article and Find Full Text PDFOn machine learnability of local contributions to interatomic potentials from density functional theory calculations.
Sci Rep
December 2024
Department of Physics, Shahid Beheshti University, Tehran, 1983969411, Iran.
Machine learning interatomic potentials, as a modern generation of classical force fields, take atomic environments as input and predict the corresponding atomic energies and forces. We challenge the commonly accepted assumption that the contribution of an atom can be learned from the short-range local environment of that atom. We employ density functional theory calculations to quantify the decay of the induced electron density and electrostatic potential in response to local perturbations throughout insulating, semiconducting and metallic samples of different dimensionalities.
View Article and Find Full Text PDFCMR-BENet: A confidence map refinement boundary enhancement network for left ventricular myocardium segmentation.
Comput Methods Programs Biomed
December 2024
Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China.
Background And Objective: Left ventricular myocardium segmentation is of great significance for clinical diagnosis, treatment, and prognosis. However, myocardium segmentation is challenging as the medical image quality is disturbed by various factors such as motion, artifacts, and noise. Its accuracy largely depends on the accurate identification of edges and structures.
View Article and Find Full Text PDFQuasi-Newton optimised Kolmogorov-Arnold Networks for wind farm power prediction.
Heliyon
December 2024
Department of Mechatronics, Aliko Dangote University of Science and Technology, Kano, Nigeria.
Having accurate and effective wind energy forecasting that can be easily incorporated into smart networks is important. Appropriate planning and energy generation predictions are necessary for these infrastructures. The production of wind energy is linked to instability and unpredictability.
View Article and Find Full Text PDFLearning a generalized graph transformer for protein function prediction in dissimilar sequences.
Gigascience
January 2024
School of Mathematical Sciences, Peking University, Beijing 100871, China.
Background: In the face of a growing disparity between high-throughput sequence data and low-throughput experimental studies, the emerging field of deep learning stands as a promising alternative. Generally, many data-driven approaches are capable of facilitating fast and accurate predictions of protein functions. Nevertheless, the inherent statistical nature of deep learning techniques may limit their generalization capabilities when applied to novel nonhomologous proteins that diverge significantly from existing ones.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!