Recent advances in genetics and neurobiology have greatly increased the degree of variation that one finds in what is taken to provide the biological foundations of our species-specific linguistic capacities. In particular, this variation seems to cast doubt on the purportedly homogeneous nature of the language faculty traditionally captured by the concept of “Universal Grammar.” In this article we discuss what this new source of diversity reveals about the biological reality underlying Universal Grammar. Our discussion leads us to support (1) (...) certain hypotheses advanced in evolutionary developmental biology that argue for the existence of robust biological mechanisms capable of canalizing variation at different levels, and (2) a bottom-up perspective on comparative cognition. We conclude by sketching future directions for what we call “comparative biolinguistics,” specifying which experimental directions may help us succeed in this new research avenue. (shrink)

Whether pattern-parsing mechanisms are specific to language or apply across multiple cognitive domains remains unresolved. Formal language theory provides a mathematical framework for classifying pattern-generating rule sets (or “grammars”) according to complexity. This framework applies to patterns at any level of complexity, stretching from simple sequences, to highly complex tree-like or net-like structures, to any Turing-computable set of strings. Here, we explored human pattern-processing capabilities in the visual domain by generating abstract visual sequences made up of abstract tiles differing in (...) form and color. We constructed different sets of sequences, using artificial “grammars” (rule sets) at three key complexity levels. Because human linguistic syntax is classed as “mildly context-sensitive,” we specifically included a visual grammar at this complexity level. Acquisition of these three grammars was tested in an artificial grammar-learning paradigm: after exposure to a set of well-formed strings, participants were asked to discriminate novel grammatical patterns from non-grammatical patterns. Participants successfully acquired all three grammars after only minutes of exposure, correctly generalizing to novel stimuli and to novel stimulus lengths. A Bayesian analysis excluded multiple alternative hypotheses and shows that the success in rule acquisition applies both at the group level and for most participants analyzed individually. These experimental results demonstrate rapid pattern learning for abstract visual patterns, extending to the mildly context-sensitive level characterizing language. We suggest that a formal equivalence of processing at the mildly context sensitive level in the visual and linguistic domains implies that cognitive mechanisms with the computational power to process linguistic syntax are not specific to the domain of language, but extend to abstract visual patterns with no meaning. (shrink)

A number of recent studies have revealed correspondences between song- and language-related neural structures, pathways, and gene expression in humans and songbirds. Analyses of vocal learning, song structure, and the distribution of song elements have similarly revealed a remarkable number of shared characteristics with human speech. This article reviews recent developments in the understanding of these issues with reference to the phonological phenomena observed in human language. This investigation suggests that birds possess a host of abilities necessary for human phonological (...) computation, as evidenced by behavioral, neuroanatomical, and molecular genetic studies. Vocal-learning birds therefore present an excellent model for studying some areas of human phonology, though differences in the primitives of song and language as well as the absence of a human-like morphosyntax make human phonology differ from birdsong phonology in crucial ways. (shrink)

Is formal simplicity a guide to learning in humans, as simplicity is said to be a guide to the acceptability of theories in science? Does simplicity determine the difficulty of various learning tasks? I argue that, similarly to how scientists sometimes preferred complex theories when this facilitated calculations, results from perception, learning and reasoning suggest that formal complexity is generally unrelated to what is easy to learn and process by humans, and depends on assumptions about available representational and processing primitives. (...) “Simpler” hypotheses are preferred only when they are also easier to process. Historically, “simpler”, easier‐to‐process, scientific theories might also be preferred if they are transmitted preferentially. Empirically viable complexity measures should build on the representational and processing primitives of actual learners, even if explanations of their behaviour become formally more complex. (shrink)