As these examples make clear, our understanding of cognitive evolution would be seriously incomplete if we focused exclusively on comparisons of humans with other primates (a narrow comparative approach). It is unfortunate that such limited comparisons were the primary source of comparative data concerning language evolution for most of the 20th century, despite a few dissenting voices (e.g., Nottebohm, , ). Fortunately, the genomic revolution has led to a widespread recognition of the fundamental conservatism of gene function in very disparate species (e.g., sponges, flies, and humans; Coutinho, Fonseca, Mansurea, & Borojevic, ) and there is a rising awareness that distant relatives like birds may have as much, or more, to tell us about the biology and evolution of human traits as comparisons with other primates (Emery & Clayton, ).
Every week I contribute and participate in meetings with the educational team to discuss each child's progress using Cottage Acquisition Scales of Speech, Language and Listening (CASSLLS).
Part 4, “Empirical data,” provides a more comprehensive overview of the data that are relevant to testing models of language evolution. I subdivide these into four broad classes: comparative biological data, fossil/archaeological data, neural data, and genetic data. The sheer abundance and diversity of these data is problematic, because few if any scientists are fully competent to evaluate them all. This means that many “facts” that are accepted and repeated frequently in the secondary literature do not stand up to serious scrutiny by the standards of their specific fields, so that the outsider may be left with the feeling that everything is contested (and therefore nothing can be taken seriously). While skepticism is certainly necessary when evaluating all data, I try to separate the wheat from the chaff and focus on results that seem most solid. Of these four classes of data, the most exciting are genetic data, and particularly paleo-DNA from extinct hominins, which offer the tantalizing hope of explicitly testing and rejecting predictions of current models of language evolution.
Language evolution offers an ideal arena for strong inference because decades of speculation have led to many plausible hypotheses about how specific DCLs evolved, and in some cases detailed arguments about the order in which they appeared in human phylogeny. Similarly abundant models exist when we consider the cognitive and neural bases of language and their relationship to traits found in other species. This plethora of existing models (each of which at least one scholar deemed plausible enough to publish) means that we have quite a full roster of explanations and predictions concerning incoming data. Many of these models can be falsified by new data, especially when their predictions contrast with those from other hypotheses. And, as I will document in detail below, there is plenty of relevant data, and more coming in every day. The main problem for this approach is not with data or hypotheses, but sociological: There is no well-developed tradition of scholars in language evolution taking each other’s models seriously. Instead the tradition has been one where others’ models are ridiculed or (worse) ignored. Many of the articles in this issue illustrate that scientists are increasingly taking into account each other’s models, and a wide variety of data from many disciplines, when proffering their own hypotheses. And that represents real progress.
The present article will attempt to concisely summarize this progress and to provide a snapshot of language evolution research as it stood in late 2016. It will also serve as an overview of the current special issue. The article has five main parts. First, I provide a theoretical overview of the conceptual playing field, stressing the importance of a multicomponent approach to language, of strong inference over multiple plausible hypotheses, and of a comparative approach using behavioral, neural, and genetic data from a broad range of living species to inform our understanding of the mechanisms underlying language. These are general points that apply to any problem in cognitive evolution. I then turn in “What evolved?” to language specifically, focusing on three derived components of linguistic cognition that are not shared with chimpanzees, our nearest living cousins (vocal control, hierarchical syntax, and complex semantics/pragmatics). Part 3 “Models of language evolution” gives a brief overview of some of the debates and models currently dominating the conceptual landscape.
The American Speech-Language-Hearing Foundation released the following press release acknowledging Sarah for this accomplishment. Congratulations, Sarah!
The Graduate Student Scholarship program supports master’s and doctoral students in the field of speech-language pathology or audiology who demonstrate outstanding academic achievement.
Drs. Erika Skoe, Jennifer Tufts, and Christine Hare have been awarded a research grant from the American Hearing Research Foundation () to study the early warning signs of noise-induced hearing loss in college musicians. This multi-lab collaboration, which received support from the UConn Speech and Hearing Fund in 2014-2015, seeks to identify biomarkers of hearing loss before the loss becomes clinically significant. This project unites research and clinical faculty in the department of Speech, Language and Hearing Sciences as part of a larger effort by the department and the UConn Speech and Hearing Clinic to provide hearing screenings as well as hearing conservation education and services to music students at UConn. For more information about the project and how you can get involved, please email:
The ASHFoundation is a charitable organization that promotes a better quality of life for children and adults with communication disorders. The ASHFoundation is affiliated with ASHA and is part of the Association’s annual convention ─ the most comprehensive development conference for speech-language pathologists, audiologists, and speech, language and hearing scientists.
The national professional, scientific, and credentialing association for more than 182,000 audiologists, speech-language pathologists, and speech, language, and hearing scientists. Audiologists specialize in preventing and assessing hearing and balance disorders as well as providing audiologic treatment, including hearing aids. Speech-language pathologists identify, assess, and treat speech and language problems, including swallowing disorders.
Rhythm plays an important role in language; therefore, employing singing, spoken music and pitched percussive Orff instruments enhances speech and language awareness in Children with ASD....
The study of language evolution, and human cognitive evolution more generally, has often been ridiculed as unscientific, but in fact it differs little from many other disciplines that investigate past events, such as geology or cosmology. Well-crafted models of language evolution make numerous testable hypotheses, and if the principles of strong inference (simultaneous testing of multiple plausible hypotheses) are adopted, there is an increasing amount of relevant data allowing empirical evaluation of such models. The articles in this special issue provide a concise overview of current models of language evolution, emphasizing the testable predictions that they make, along with overviews of the many sources of data available to test them (emphasizing comparative, neural, and genetic data). The key challenge facing the study of language evolution is not a lack of data, but rather a weak commitment to hypothesis-testing approaches and strong inference, exacerbated by the broad and highly interdisciplinary nature of the relevant data. This introduction offers an overview of the field, and a summary of what needed to evolve to provide our species with language-ready brains. It then briefly discusses different contemporary models of language evolution, followed by an overview of different sources of data to test these models. I conclude with my own multistage model of how different components of language could have evolved.
Despite the nonexistence of time machines, and the oft-mentioned fact that language does not fossilize, there is no reason in principle that models of language evolution need be any more speculative or untestable than those of other scientific disciplines that deal with singular chains of events in the distant past. Cosmologists interested in the Big Bang, or geologists studying continental drift and plate tectonics, are quite familiar with such difficulties. They proceed in their study of the past by examining present-day phenomena empirically, and using these data to evaluate explicit contesting models of what might have happened when, and why. Assuming that certain principles of physics, chemistry, or geology remain unchanged, this enables practitioners in these fields to triangulate on adequate models and further refine them by generating further testable predictions. This process has led, for example, to plate tectonics going from a speculative hypothesis, often ridiculed, to something universally accepted in modern geology (Gohau, ). There is little preventing the same general scientific process from being effective in the study of language evolution. We have a relatively clear endpoint of the process in the present, and can reconstruct the starting point (our last common ancestor with chimpanzees) in detail using the comparative method with existing species. Making the reasonable assumption that many of the biological principles underlying genetic, neural, cognitive, and behavioral traits have remained constant during the intervening 6 million years, and fed by the fragmentary but important data of the fossil record (and now “fossil” DNA), we enjoy essentially the same preconditions to progress as 20th-century geologists evaluating plate tectonics. This leads to the real possibility of fundamental advances in understanding language evolution in the coming years, building on the progress of the last few decades.