IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite automata, formal logic, and circuit complexity
Finite automata, formal logic, and circuit complexity
Logics For Context-Free Languages
CSL '94 Selected Papers from the 8th International Workshop on Computer Science Logic
Regular tree languages definable in FO
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Estimating strictly piecewise distributions
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Maximum likelihood estimation of feature-based distributions
SIGMORPHON '10 Proceedings of the 11th Meeting of the ACL Special Interest Group on Computational Morphology and Phonology
Finite-state methods and models in natural language processing
Natural Language Engineering
Formal and empirical grammatical inference
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Tier-based strictly local constraints for phonology
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Learning in the limit with lattice-structured hypothesis spaces
Theoretical Computer Science
| Hi-index | 0.00 |
We explore the formal foundations of recent studies comparing aural pattern recognition capabilities of populations of human and non-human animals. To date, these experiments have focused on the boundary between the Regular and Context-Free stringsets. We argue that experiments directed at distinguishing capabilities with respect to the Subregular Hierarchy, which subdivides the class of Regular stringsets, are likely to provide better evidence about the distinctions between the cognitive mechanisms of humans and those of other species. Moreover, the classes of the Subregular Hierarchy have the advantage of fully abstract descriptive (model-theoretic) characterizations in addition to characterizations in more familiar grammar- and automata-theoretic terms. Because the descriptive characterizations make no assumptions about implementation, they provide a sound basis for drawing conclusions about potential cognitive mechanisms from the experimental results. We review the Subregular Hierarchy and provide a concrete set of principles for the design and interpretation of these experiments.